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WINTER HABITAT SELECTION BY WHITE-TAILED DEER IN THE PEND D’OREILLE VALLEY, SOUTHEASTERN BRITISH COLUMBIA PREPARED BY John G. Boulanger, Kim G. Poole, John Gwilliam, Guy P. Woods, John Krebs, and Ian Parfitt FOR Columbia Basin Fish & Wildlife Compensation Program September 2000 COLUMBIA BASIN FISH & WILDLIFE COMPENSATION PROGRAM Ministry of Environment, Lands & Parks BC Fisheries
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WINTER HABITAT SELECTION BYWHITE-TAILED DEER IN THE PEND

D’OREILLE VALLEY, SOUTHEASTERNBRITISH COLUMBIA

PREPARED BYJohn G. Boulanger, Kim G. Poole, John Gwilliam,

Guy P. Woods, John Krebs, and Ian Parfitt

FORColumbia Basin Fish & Wildlife Compensation Program

September 2000

COLUMBIA BASIN

FISH & WILDLIFECOMPENSATION

PROGRAM

Ministry of Environment,Lands & ParksBC Fisheries

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Columbia BasinFish & WildlifeCompensation Program

103 - 333 Victoria Street, Nelson, British Columbia V1L 4K3Phone: (250) 352-6874 Fax: (250) 352-6178

WINTER HABITAT SELECTION BY WHITE-TAILED

DEER IN THE PEND D’OREILLE VALLEY,

SOUTHEASTERN BRITISH COLUMBIA

John G. Boulanger, Integrated Ecological Research, 924 Innes St.,Nelson BC V1L 5T2

Kim G. Poole,1 Timberland Consultants Ltd., P.O. Box 171, Nelson BCV1L 5P9

John Gwilliam, Columbia Basin Fish and Wildlife CompensationProgram, 103-333 Victoria St., Nelson BC V1L 4K3

Guy P. Woods, BC Ministry of Environment, Lands and Parks, 401-333Victoria St., Nelson BC V1L 4K3

John Krebs, Columbia Basin Fish and Wildlife Compensation Program,103-333 Victoria St., Nelson BC V1L 4K3

Ian Parfitt, Columbia Basin Fish and Wildlife Compensation Program,103-333 Victoria St., Nelson BC V1L 4K3

1 Present address: Aurora Wildlife Research, RR 1, Site 21, Comp 22,Nelson BC V1L 5P4

September 2000

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Pend d’Oreille white-tailed deer winter range habitat use iii

TABLE OF CONTENTS

TABLE OF CONTENTS.............................................................................................................. III

LIST OF FIGURES ....................................................................................................................... V

LIST OF TABLES........................................................................................................................ VI

EXECUTIVE SUMMARY ............................................................................................................ 1

INTRODUCTION .......................................................................................................................... 3

STUDY AREA ............................................................................................................................... 4

METHODS ..................................................................................................................................... 6

Field Techniques......................................................................................................................... 6Plot habitat classification ........................................................................................................ 7

Predictor variables ...................................................................................................................... 7Winter severity........................................................................................................................ 7Days on range, pellet group counts, deer density ................................................................... 8Aspect, slope, elevation .......................................................................................................... 8Habitat classes and forest cover attributes .............................................................................. 9

Response variables.................................................................................................................... 10Data Screening and tests of general analysis assumptions ....................................................... 10

Pellet plots as an index of overall habitat abundance ........................................................... 10Distribution of habitat classes in relation to topographic variables ...................................... 11

Analysis of temporal changes in habitat use and availability ................................................... 11Change in habitat availability over time ............................................................................... 11Change in proportional use of habitat over time................................................................... 11Statistical methods for habitat selection analysis.................................................................. 11Base additive model to define resource selection functions ................................................. 12AIC methods used to select optimal interaction models....................................................... 12Modeling of winter severity.................................................................................................. 13Further refinement of habitat classes using forest cover data............................................... 13

RESULTS ..................................................................................................................................... 14

Predictor variables .................................................................................................................... 14Winter severity...................................................................................................................... 14Population change over time................................................................................................. 15Pellet group distribution........................................................................................................ 16

Data screening and tests of analysis assumptions..................................................................... 17Pellet plots as an index of overall habitat availability .......................................................... 17Distribution of habitat classes in relation to topographic variables ...................................... 17

Analysis of temporal change in habitat use and availability..................................................... 18Change in habitat availability over time ............................................................................... 18Proportional use of habitat classes over time........................................................................ 18

Statistical habitat selection analysis using the pooled 20 year data set .................................... 21Base additive model.............................................................................................................. 21AIC model selection of models with interaction terms ........................................................ 22

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Pend d’Oreille white-tailed deer winter range habitat use iv

Evaluation of AIC model fit ................................................................................................. 23Analysis with bad and good winter data only....................................................................... 26Forest cover based analyses .................................................................................................. 29

DISCUSSION............................................................................................................................... 31

Winter habitat selection patterns and winter severity ............................................................... 33Forest cover versus pre-defined habitat class models............................................................... 34Topographic variables and sample size issues.......................................................................... 34Influence of population on deer distribution............................................................................. 34Shapes of response curves ........................................................................................................ 35Limitations to findings.............................................................................................................. 35Maps of predicted proportional use .......................................................................................... 35Recommendation for future analyses........................................................................................ 38

WINTER RANGE MANAGEMENT GUIDELINES.................................................................. 39

MAINTENANCE AND ENHANCEMENT OF PEND D’OREILLE WINTER RANGE ......... 39

ACKNOWLEDGEMENTS.......................................................................................................... 41

LITERATURE CITED ................................................................................................................. 41

APPENDIX 1 – STATISTICAL DETAILS................................................................................. 44

Shapes of response curves ........................................................................................................ 44The Information Theoretic (AIC) approach to model selection ............................................... 44The use of Generalized Estimating Equations for longitudinal data ........................................ 45Fit of model to a binomial distribution ..................................................................................... 45Sensitivity of predictions to unequal plot spacing .................................................................... 45Sensitivity of predictions to uncorrected forest data................................................................. 46

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Pend d’Oreille white-tailed deer winter range habitat use v

LIST OF FIGURES

Figure 1. Pend d’Oreille white-tailed deer winter range study. ..................................................... 5Figure 2. Winters with good, bad, and average ratings from Grand Forks snowfall data. The * in

graphs was the mean and the middle bar was the median. The large rectangles representquartiles of the distribution. The bars represent the range of observations. Triangles andsmall boxes represent outlier observations which are farther than 1.5 times the inter-quartilerange from the quartiles. ....................................................................................................... 14

Figure 3. Trends in white-tailed deer population size wintering in the Pend d’Oreille Valley asreflected by mean pellet group counts and spotlight counts. ................................................ 15

Figure 4. The distribution of deer pellet groups per plot for the entire 20 year Pend d'Oreille dataset (n = 30,341). .................................................................................................................... 16

Figure 5. Distribution of habitat classes as estimated by GIS and percentage of plots. .............. 17Figure 6. Yearly availability of habitat classes (forage excluded) as a function of aspect and

elevation. Data reflects availability of plots in 1990. .......................................................... 18Figure 7. Changes in the frequency of habitat classes (forage classes excluded (A) and included

(B)) from 1978 to 1997, as indicated by frequencies of pellet plots in the Pend d'Oreillestudy area. ............................................................................................................................. 19

Figure 8. Use of cover habitat classes from 1978 to 1997 as estimated by proportion of plotswith deer pellets, Pend d’Oreille........................................................................................... 20

Figure 9. Use of non-cover habitat classes from 1978 to 1997 as estimated by proportion of plotswith deer pellets, Pend d’Oreille........................................................................................... 20

Figure 10. Contour plots for the interactions of aspect, slope (%), and elevation (m).Proportional use for each contour is listed in the graphs. The response was standardized forthe forage habitat class.......................................................................................................... 24

Figure 11. Observed and predicted proportional use of habitat classes for years with a goodwinter and low population (1981), good winter and large population (1990), and bad winterand moderate population size (1997). Note the large difference in scale in proportional use(y-axis), which was mainly a function of population size. ................................................... 25

Figure 12. Predicted proportional use of habitats as a function of winter severity, slope (%), andelevation (m). Proportional use for each contour is listed in the graphs. The elevation plotswere standardized for forage habitat class, moderate population size and south /westaspects. .................................................................................................................................. 27

Figure 13. Predicted proportional use of habitat classes as a function of winter severity. Resultswere standardized for a south and west aspect, moderate slope (30%), moderate elevation(790 m) and moderate population levels............................................................................... 28

Figure 14. Distribution of crown closure class and age class for tree/habitat species associationgroups, Pend d’Oreille. Sample sizes reflect plots available in 1990. ................................. 29

Figure 15. The interaction of crown closure and tree species groups. The grand fir-cedar anddeciduous-larch curves should be interpreted cautiously due to non-significance ofinteraction slope parameters. Crown closure values are offset to ease interpretation.Confidence interval bars are given for each prediction. ....................................................... 31

Figure 16. Predicted proportional use of habitat in the Pend d’Oreille winter range under low(minvalue), average (meanvalue) and high (maxvalue) population levels. .......................... 36

Figure 17. Predicted proportional use of habitat in the Pend d’Oreille winter range under good(low snow; goodwinter) and bad (deep snow; badwinter) winters. ...................................... 37

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Pend d’Oreille white-tailed deer winter range habitat use vi

LIST OF TABLES

Table 1. Main predictor variables used in winter deer habitat selection analysis, Pend d’OreilleValley. ..................................................................................................................................... 8

Table 2. Preliminary habitat classes derived from forest cover data, Pend d’Oreille Valley. ....... 9Table 3. Forest cover attributes used in winter habitat analysis, Pend d’Oreille valley. ............. 10Table 4. Interactions among predictor variables considered ....................................................... 13Table 5. AIC model selection results for 20-year deer pellet group study, Pend d’Oreille......... 21Table 6. Results of GEE analysis of AIC model, Pend d’Oreille. A parameter was considered

significant if at least 1 of its categorical slope (β) estimates was significant. ...................... 22Table 7. AIC results for analysis of winter interaction terms, Pend d’Oreille............................. 26Table 8. Interactions between habitat class (habitat) and winter severity (winter). .................... 28Table 9. AIC model selection results for forest cover-based models. ......................................... 30Table 10. Summary of results, white-tailed deer winter habitat use in the Pend d’Oreille valley.

............................................................................................................................................... 32Table 11. Summary of input parameters for GIS maps (Figs. 16 and 17) displaying predicted

proportional use of the Pend d’Oreille winter range............................................................. 35Table 12: Results of GEE analysis of base model, Pend d’Oreille with uncorrected forest cover

based habitat classes. A parameter was considered significant if at least 1 of its categoricalslope (β) estimates was significant. ...................................................................................... 47

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Pend d’Oreille white-tailed deer winter range habitat use 1

EXECUTIVE SUMMARYMaintenance of ungulate winter range has been assigned a high priority in forest development

planning in the Kootenay of southeastern British Columbia (B.C.). White-tailed deer (Odocoileusvirginianus) habitat use has been poorly studied in western North America, and most studies have been ofrelatively short duration. Here we report on white-tailed deer winter habitat use and selection in the Pendd’Oreille valley in the West Kootenay, using a 20-year data set of pellet group transects. We examineddeer selection of cover types at different population levels and among winters of different overall snowdepth.

The study was conducted from 1978 to 1997, within approximately 4,950 ha of white-tailed deerwinter range as designated in the late 1970s. The winter range was on southeast to southwest facingslopes along the Pend d’Oreille River. Habitats within the study area were in the Interior Cedar-Hemlockxeric, warm (ICHxw), dry, warm (ICHdw), and moist, warm (ICHmw2) biogeoclimatic subzones.Habitat impacts within the valley have been relatively extensive, a result of flooding behind 2 dams,forest harvesting, power transmission line right-of-way clearing, cattle grazing, and road construction, aswell as suppression of wildfire. Elk (Cervus elaphus) and mule deer (O. hemionus) were present on thewinter range in low numbers. Both population estimates of deer on the study area, derived from pelletgroups, and spring spotlight counts varied 3-fold over the study, and all winters were classified accordingto overall snow depth as good, average, and bad.

Pellet groups were counted and cleared annually on 76-77 permanent plots distributed over 20transects, totaling 1,534 plots. The pellet plots were originally laid out to provide annual populationestimates of deer on the winter range, thus plot spacing on each transects ranged from 18-77 m. Thecircular plots were 100 feet2 (9.3 m2) in size (5.64 foot radius; 1.72 m). We determined topographic(elevation, slope, and aspect) and forest cover (tree species and percentage, age class, crown closure)attributes for each plot. We also assigned each plot into 1 of 6 habitat classes based on speciescomposition, age class and crown closure: cover (optimal, good, marginal, recruitment), forage, and other.We rated habitats with Douglas-fir (Pseudotsuga menziesii) – leading stands of age class ≥6 and crownclosure class ≥5 as optimal cover. Where field checks of the plots differed from the digital data, weadded corrected habitat attributes for each type of error in the database, and recorded the year of changefor human-induced disturbances.

We examined a number of variables that could potentially predict deer habitat selection: winterseverity, mean pellet group count, yearly days on winter range, estimated deer density, aspect, slope,elevation, and pre-defined habitat classes. We also conducted habitat analyses using individual forestcover attributes to determine if we could improve the fit of the models generated. We usedpresence/absence of deer pellets as our response variable, since 69.3% of plots had no pellets and only asmall (5%) percentage of “outlier” plots exhibiting counts >3 pellet groups/plot. We used InformationTheoretic Methods and the accompanying Akaike Information Criterion (AIC) to evaluate model fit.

Forage was the dominant habitat class within the study area, accounting for about 61% of plots.The distribution of sample sizes among habitat classes differed by aspect and elevation, confoundingmodeling and limiting the applicability of this analyses to other areas. The abundance of habitat classeschanged relatively little during the study; the most significant changes were in the optimal and good coverclasses which were reduced by approximately 15%, primarily within the first 5 years of the study.Proportion of use of all habitat classes increased and then slightly decreased between 1978 and 1997,correlated with overall population change. Overall, optimal and good cover classes were selected for,whereas forage, recruitment cover, and marginal cover were not selected for. The habitat class mostselected for was optimal cover.

We compared habitat selection between good and bad winters to examine which habitats deertend towards during periods of higher snow depths. Selection for steeper slopes and moderate elevationincreases in bad winters, and we observed the strongest positive selection towards optimal cover and good

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Pend d’Oreille white-tailed deer winter range habitat use 2

cover, and negative selection towards forage. In terms of proportional use the optimal and good coverhabitat classes were generally utilized the most in bad winters as opposed to good winters, and therecruitment cover and forage classes were utilized the least.

Modeling of leading tree species, age class and crown closure class was problematic due touneven sample sizes for combinations of these variables, and the fact that crown closure and age classwere correlated. The AIC model selection results suggest that models with the interaction of leadingspecies and crown closure were most supported by the data. Models that pooled crown closure weremuch less supported by the data, suggesting that there were species and crown closure interactions. Therewas strong selection for Douglas-fir stands with lower and higher crown closure classes.

Our modeling suggested that the main driving force in terms of winter habitat selection by white-tailed deer in the Pend d’Oreille valley was local topography (slope, aspect, and elevation), with habitatclass or habitat structural attributes selected on an additive basis. Over-riding all selection was changes inthe size of the deer population on the study area; this was the single strongest determinant of pellet groupdistribution. Our results also suggest that selection of habitat classes was mainly towards the “best” 2 ofthe pre-defined cover classes, optimal and good, with greatest selection for the optimal cover and duringwinters with the worst snow accumulation. Optimal cover had the highest age class and crown closurestands within the study area, and would provide the greatest snow interception and thermal cover.Douglas-fir stands also provide forage.

The pellet group technique can provide an index of total habitat use among broad areas.However, caution must be used in trying to interpret pellet data at too fine a habitat scale, primarilybecause of differences in defecation rates during resting and traveling. In addition, habitat selection bydeer differs among periods of the winter, and since pellet group counts provide an index to annual use ofhabitat (or at least use averaged over an entire winter), the technique cannot readily highlight habitatsused during the mid-winter “critical” period. We used winters with high average snow accumulation as asurrogate to identify periods of greatest stress and habitat selection pressure.

Since the study was initially designed to estimate deer population size on the winter range and thedistribution of predictor variables was not a representative sample of all white-tailed deer habitat used inall seasons, wide-scale extrapolations to other areas should be done cautiously. Statistical limitationswere encountered primarily because of the uneven distribution of predictor variables within the studyarea.

Our models suggested that Douglas-fir stands with crown closure greater than 6 are most selectedfor by white-tailed deer. The crown closure requirements for ungulate winter range guidelines pertainingto this area could be increased to reflect these higher crown closures.

Our analysis suggests that although higher crown closure stands on steeper slopes are utilized to agreater degree during winters with higher snow accumulation, stands that produce forage are alsoextensively used. We suggest that timber management should emphasize retention of Douglas-fir standsof the highest possible crown closure in juxtaposition with forage stands. Selective logging may enhanceforage production within stands, but will reduce the value of the stand for mid-winter cover. Thus, use ofsmall clear-cuts while retaining un-touched stands of high crown closure may provide the greatest benefitfor deer. Silviculture prescriptions that involve commercial thinning of conifer canopies or reduction inunderstory conifer density are not desirable on mid-winter ranges.

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Pend d’Oreille white-tailed deer winter range habitat use 3

INTRODUCTIONWhite-tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) populations in many

areas of their distribution in western North America are limited by the extent and quality of their winterrange (Thomas 1979, Mackie et al. 1998). Winter range can be defined as areas that provide theresources deer need in all but the mildest winters (Armleder et al. 1986). These areas often contain accessto both forage and thermal or security cover, and have been characterized as areas of southerly to westerlyaspects, exposed ridges, steeper slopes including rock outcrops, lower elevations, and in forested standsthat provide high crown closure for thermal cover and snow interception (Thomas 1979, Nyberg and Janz1990, Pauley et al. 1993, Mackie et al. 1998). Mackie et al. (1998) referred to these areas as “wintermaintenance habitats” which provide all resources necessary for adult survival, but not necessarilyrecruitment of young. Most native forages available in winter are too low in nutritional value to meet themaintenance needs of deer (Wallmo et al. 1977), thus deer survive by supplementing energy reservesaccumulated prior to winter with energy intake from winter diets and adopting an energy conservationmode of behaviour (Moen 1978). Some authors refer to habitats used during periods of deep snow coverand extreme cold temperatures as “critical” winter range, often containing high thermal cover and snowinterception but little forage (Gilbert et al. 1970, Pauley et al. 1993, Secord 1994). Whether these habitatsare critical is debatable (Harestad 1985), but it is clear that winter range use is heavily influenced by snowdepth (Gilbert et al. 1970, Woods 1984, Pauley et al. 1993, Armleder et al. 1994), primarily because ofincreased energetic costs of locomotion (Parker et al. 1984).

Fecal pellet group counts (Bennett et al. 1940, Neff 1968) are a widely used index for monitoringungulate abundance and relative distribution. Although some studies suggest that pellet-group depositionis a poor measure of ungulate distribution relative to topography and broad habitat classes (Welles andWelles 1961 [cited in Edge and Marcum 1989], Collins and Urness 1981), others have found that they canbe used to compare broad areas of use (Leopold et al. 1984, Loft and Kie 1988, Edge and Marcum 1989),especially in representing habitat use during a seasonal time period (Loft and Kie 1988). Studies thatsupported the use of pellet group counts concluded that the technique can be used to estimate ungulatedistribution relative to topographic factors (Edge and Marcum 1989) and to rank relative use of habitats(Loft and Kie 1988). Edge and Marcum (1989) divided each topographic variable into 3-8 intervalclasses and counted pellets on permanent belt transects over 2-month summer seasons. Loft and Kie(1989) used temporary plots over 3-month summer seasons. Both studies concluded that pellet groupcounts were appropriate for ranking use when there was a clear disparity in percent use among habitats,and for differentiating high-, medium-, and low-use habitats at a coarse-grained level. However, theseresearchers suggest that caution must be used in trying to interpret pellet data at too fine a habitat scale,since the relative deposition of pellets at any 1 plot may not be indicative of relative use or importance ofthat plot within the study area. Collins and Urness (1981) suggested that 30% of pellet deposition occurswhile deer are traveling, an activity which the animals spend only 4% of the day. Loft and Kie (1989)found that pellet group counts underestimated deer use of habitats used primarily for resting comparedwith radio-telemetry, again because the deer were not active and were not defecating.

White-tailed deer habitat use has been described in northwestern United States (Keay and Peak1980, Owens 1981, Dusek et al. 1989, Pauley et al. 1993, Mackie et al. 1998), and to a limited extent inwestern Canada (Woods 1984). Many of these studies describe habitat use over relatively short periodsof time, and few attempt to develop predictive models of winter habitat selection (but see Pauley et al.1993). Here we present a 20-year data set monitoring white-tailed deer abundance and distribution in thePend d’Oreille valley in southeastern British Columbia (B.C.) using fecal pellet group plots. Due tofavorable climate, soil, and vegetation patterns, the lower levels of the south-facing slopes along the Pendd’Oreille River valley contain the highest capability for white-tailed deer winter range in the WestKootenay region (Vold et al. 1980).

We examined pellet group distribution relative to available habitats, habitat change over time (viasuccession and land use practices), and winter severity. We examined a number of topographic and

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Pend d’Oreille white-tailed deer winter range habitat use 4

habitat attributes, and developed a model of deer winter habitat selection applicable to this area and otherareas of similar habitat and topography. We initially consider a model that uses pre-defined habitatclasses that represent current assumptions about deer habitat selection (ungulate winter range guidelinesprovided in the Kootenay-Boundary Land Use Plan [KBLUP]; Kootenay Inter-Agency ManagementCommittee 1997). We use this model to assess change in deer distributions caused by winter severity andother factors. Second, we further refined habitat classification by developing a model that used forestcover attributes instead of pre-defined habitat classes. Finally, we assessed the application of currentwhite-tailed deer winter range guidelines provided in the KBLUP (Kootenay Inter-Agency ManagementCommittee 1997), and provide recommendations for maintenance and enhancement of winter range in thePend d’Oreille valley.

STUDY AREAThe study area encompassed approximately 4,950 ha on the north side of the Pend d’Oreille River

in the West Kootenay, adjacent to the border with the United States (Fig. 1). The study area delineated allwhite-tailed deer winter range as designated in the late 1970’s using local knowledge and radio-collaredanimals (Woods 1984). White-tailed deer was the primary ungulate species wintering in valley; elk(Cervus elaphus) were present in much lower numbers (Woods 1983), and mule deer winter on arelatively small proportion of the designated winter range above the confluence of the Salmo and Pendd’Oreille rivers (J. Gwilliam, Columbia Basin Fish and Wildlife Compensation Program [CBFWCP]personal communication). White-tailed deer wintering in the study area utilized summer range coveringover 2,500 km2, primarily east, north and west, but also south of the study area (Woods 1983, 1984, J.Gwilliam, CBFWCP unpublished data).

The study area encompassed southern aspects and relatively steep slopes on the north side of thePend d’Oreille River. Elevations in the study area ranged from 470 m along the Pend d’Oreille River to1,410 m at the upper extent of designated winter range; upslope peaks extended to 1,850 m. The area waswithin the Interior Cedar Hemlock (ICH) biogeoclimatic zone, including the xeric, warm (xm) subzone inthe valley bottom, dw (dry, warm) subzone on lower to mid-elevation slopes, and mw (moist, warm)subzone at midslope (Meidinger and Pojar 1991). Douglas-fir (Pseudotsuga menziesii) commonlydominate southern exposures, much of it even-aged stands that resulted from a major fire in the 1890’s(Vold et al. 1980, Woods 1984). Shrub or grass communities with open Douglas-fir or Ponderosa pine(Pinus ponderosa) stands occupy steep south aspects influenced by fire. Western redcedar (Thujaplicata) and grand fir (Abies grandis) prevail throughout lower and mid-elevation moist sites. Lodgepolepine (Pinus contorta), western white pine (Pinus monticola) and western larch (Larix occidentalis) arefound on some sites, and deciduous species include white birch (Betula papyrifera) and trembling aspen(Populus tremuloides). Cougars (Felis concolor) were the primary predators on ungulates wintering inthe study area, and coyotes (Canis latrans) were common. Human harvest of the white-tailed deerpopulation averaged roughly 200-350 deer harvested annually between the mid-1980's to mid-1990's inManagement Unit 4-08, which covers about 90% of the summer range used by the white-tailed deer thatwinter in the Pend d’Oreille valley (B.C. Environment, Lands and Parks harvest statistics).

The climate of the area is transitional between wetter temperate coastal and drier continentalweather patterns. Mean July and January temperatures for Waneta, located in the valley bottom at thewest end of the study area, are 19.7 and –4.8 C, respectively (Vold et al. 1980). Annual precipitation atWaneta averages 630 mm, with 180 cm falling as snow; total precipitation within the valley increasesfrom west to east and with increasing elevation (Vold et al. 1980). Snow often persists on the valley floorfrom early or mid-December to mid-March, but during mild winters low elevation south-facing slopesmay be snow-free for periods during mid-winter.

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Pend d’Oreille white-tailed deer winter range habitat use 5

Figure 1. Pend d’Oreille white-tailed deer winter range study.

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Pend d’Oreille white-tailed deer winter range habitat use 6

In addition to natural succession, the Pend d’Oreille valley has been influenced by a number ofdisturbances. Construction of the Waneta Dam near the mouth of the Pend d’Oreille River in the mid-1950’s flooded approximately 7 km of river and 175 ha of valley bottom (Vold et al. 1980). The SevenMile Dam 15 km up stream flooded a further 14 km of river and 212 ha after construction in 1979 andagain in 1988 when the reservoir level was raised 5 m. There are approximately 54 km of transmissionlines within the study area, directly affecting roughly 250 ha of habitat. Harvesting (primarily Douglas-fir) of forests has occurred over portions of the area. Wildfire, historically the most important naturaldisturbance, has been suppressed over much of the past half century, such that few natural fires haveoccurred since the 1930s (Woods 1984). Forest management has occurred in the valley, including a fewprescribed burns up to 70 ha in size, shrub-cutting to rejuvenate decadent shrubs and promote growth ofyoung Douglas-fir and ponderosa pine, some planting of Douglas-fir seedlings (J. Gwilliam, CBFWCPunpublished data).

METHODS

Field TechniquesWe established 20 permanent pellet group transects, designed to provide an estimate of the

number of deer wintering in the Pend d’Oreille winter range, following sampling design in Smith et al.(1969: Method 1). A map of the winter range was overlain with a grid of lines angled at approximately321º. One transect was randomly chosen as the starting point, and 19 additional transects weresystematically chosen based on a pre-determined spacing interval (based on cumulative transect length)from this first transect; thus the chance of a transect being selected was proportional to its length.Transect length averaged 3,070 ± 300.8 m (± SE; range 1421-5718 m; Fig. 1). We placed 77 plots oneach selected transect, such that plot spacing varied from 18-77 m and the total number of plots was1,540. Since -transect spacing was based on the cumulative grid length (Smith et al. 1969), each plotrepresented the same area of the winter range (approximately 3.2 ha). Plot center of circular plots 100feet2 (9.3 m2) in size (5.64 foot radius; 1.72 m) were permanently marked. We estimated average deerdensity on the wintering grounds based on a formulae calculated from mean pellet counts, length of herdoccupancy, and assumed defecation rates (Smith et al. 1969).

Plots were established and cleared in summer 1977. Between 1 May and 15 June from 1978 to1997, deer and elk pellet groups were counted and cleared off the plots. We used 2 persons to searchplots in a circular fashion using a plot cord anchored to the plot center. Pellet groups (�10 pellets) werecounted if more than half the pellets were within the plot. We checked each plot each year, however afew plots were missed in some years because of habitat disturbance (i.e., logging), missing plot centers, ordisagreement with landowners. The lowest plot on 6 transects were flooded in 1988 due to rising waterfrom Seven Mile Dam; these plots were removed from the analyses.

The number of days on winter range by the white-tailed deer herd using the area was subjectivelydetermined by field personnel familiar with the area from 1977 to 1991, and using radio-collared deerfrom 1991 to 1997 (J. Gwilliam, CBFWCP unpublished data). Start and end occupancy dates werestandardized to when approximately half of the herd or collared animals arrived on or left the range.

To provide another index to deer numbers, we conducted spotlight counts (Progulske and Duerre1964) on the lower sections of the wintering grounds between late March and early May from 1981 to1997. Deer were counted along a 19-km stretch of road through the valley bottom adjacent to the river.We used the maximum number of deer observed on 2 passes for each count (out and back), and selectedthe peak number from among the 8-12 counts conducted each spring.

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Pend d’Oreille white-tailed deer winter range habitat use 7

Plot habitat classification

Plot locations were accurately (±2-5 m) determined using a differentially corrected GlobalPositioning System (GPS; ProXRS, Trimble Navigation Ltd., Sunnyvale, California, USA). We obtaineddigital 1:20,000 scale topographic (Terrain Resource Information Mapping) and forest cover (B.C.Ministry of Forests, Forest Inventory Program) files of the study area, and determined a number ofattributes for each plot location. We obtained elevation from a digital elevation model (DEM) andcalculated aspect and slope by creating a triangulated irregular network (tin) developed from the DEM,which averaged these parameters over each tin; tins were generally 60-100 m on each side of the triangle.B.C. forest cover maps delineate relatively homogeneous forest stands or forest cover classes based on theinterpretation of aerial photographs and ground truthing information collected in field surveys. Thesemaps include information on tree species, projected age and height class, site index, and tree density(stocking level), and are widely used in B.C. for operational forest development and reforestationplanning. Areas not supporting commercial forests are generally described as non-productive, meaningthe area was not capable of supporting commercial forests (e.g., alpine, alpine forest, brush, clay banks, orrock), or non-forest, meaning that the area was not currently forested but was capable of supportingcommercial forests (e.g., a logged area that was not sufficiently restocked). We examined a number offorest cover attributes for each stand, included leading tree species, percent species composition, crownclosure, stand age, and non-productive and non forest descriptors. The smallest mapped forest coverpolygon size was 0.7 ha.

Habitat attributes (leading tree species, age class, canopy closure) were examined in the field foreach plot location and were compared to the digital forest cover data. Differences in habitat classificationbetween field observations and forest cover data were observed at a number of sites, and were attributedto 2 classes of factors. Habitat attributes observed at 320 plots (20.9%) differed from the digital forestcover data, primarily because of scale, incorrect/inaccurate forest cover descriptions, and inaccuratemapping of forest cover polygon boundaries. Changes due to timber harvesting, power line right-of-wayclearing, farming and other human-related factors affected another 164 plots (10.7%). We addedcorrected habitat attributes for each type of error in the database, and recorded the year of change forhuman-induced disturbances. This created a matrix with a time series of habitat attributes for each plotfor each year of the study.

Predictor variablesEight variables that potentially predict habitat selection were considered in the initial habitat

analysis (Table 1).

Winter severity

We constructed a history of winter severity using B.C. Ministry of Transportation and Highwayssnow depth data from Grand Forks, B.C., located 65 km west of the study area. We selected Grand Forksbecause it was the closest snow depth data set from an area of similar elevation to the Pend d’Oreille,although with lower overall precipitation. We compared the daily snow depth between November 15 andMarch 15 of each winter using the mean, median, and distribution using box plots. Biologists from theCBFWCP used these snow depth records and field notes to assign 3 categories of winter snowaccumulation (good: low snow; average; and bad: high snow). Hereafter, we refer to each winter by itspost 1 January year, such that the winter of 1977-78 was winter 1978.

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Pend d’Oreille white-tailed deer winter range habitat use 8

Table 1. Main predictor variables used in winter deer habitat selection analysis, Pendd’Oreille Valley.Factor Class Comments

1. Winter severity Categorical Determined from data from Grand Forks, B.C.

2. Mean pellet groupcount

Continuous

3. Yearly days onrange

Continuous From CBFWCP biologist notes and radio telemetrydata

4. Estimated deerdensity

Continuous Used mean pellet group count and yearly days onrange for calculation.

5. Aspect Categorical N and E, or S and W

6. Slope Continuous/categorical

Definition of variable dependent on model fit

7. Elevation Continuous/categorical

Definition of variable dependent on model fit

8. Habitat class Categorical From CBFWCP biologists/KBLUPUsed Forest cover data, plot checks to defineRevised yearly if disturbance occurred

Days on range, pellet group counts, deer density

The habitat selection model can be conceptualized as the analysis of factors that determine deerdistribution above and beyond simple increases and decreases in yearly deer density. It was obvious thatmore deer will take up more space and distribution will change, therefore, deer density in this study was acovariate parameter. We therefore included population-based predictor variables in habitat selectionmodeling to account for the variance in deer distribution caused by changes in population size. Thecombination of population based predictors which accounted for the most variation in the data (asdetermined by relative model fit) was used in subsequent analysis.

Aspect, slope, elevation

Aspect: Vegetation Resource Inventory (VRI; Ministry of Forests 1999) categories were used todefine aspects in the analysis (north [cold]: 286-59°; east [cool]: 60-135°; south [hot]: 136-240°; west[warm]: 241-285°). Sixty two percent of plots were on south aspects, whereas 27%, 4.2%, and 6.3% ofplots were on east, north, and west aspects, respectively (n = 1,534). Therefore, we pooled south andwest, and north and east aspects for the analysis. This allowed a contrast of warm and hot aspects, withcold and cool aspects.

Slope: The mean slope from all the plots was 33.9 ± 15.4% (n = 1,534). Because we believedthat the relationship between slope and pellet group distribution was non-linear, we added higher orderpolynomial terms (i.e. slope2) to test for non-linear trends between pellet group distribution and slope(discussed later).

Elevation: The mean elevation of all plots was 802.1 ± 174.04 m (range 472-1397 m, n = 1,534).We therefore added higher order polynomial terms (i.e. elevation2) to test for non-linear trends betweenpellet group distribution and elevation (discussed later).

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Pend d’Oreille white-tailed deer winter range habitat use 9

Slope and elevation values were standardized by mean and standard deviation to aid in modelconvergence (White et al. 1999).

Habitat classes and forest cover attributes

We conducted initial habitat selection analyses using preliminary habitat classes derived from theexisting KBLUP winter range guidelines (Table 2). Habitat classes were divided into 3 broad coverclasses, a recruitment cover class, and a forage class, based primarily on leading tree species, age classand crown closure. Aspect was not considered in assigning habitat classes. For all analysis the correctedforest cover data were used. As a test of model robustness, we ran analyses with original (uncorrected)and corrected forest cover data to determine the influence of error in forest cover designation on results(Appendix 1).

Table 2. Preliminary habitat classes derived from forest cover data, Pend d’Oreille Valley.

Habitat class Leading species Age class Crown closure Comments

Optimal cover Douglas-fir, grand fir >6 >5

Good cover Douglas-fir, grand fir >6 3-4

Marginal cover Douglas-fir, grand fir 5 >3

Recruitment cover Douglas-fir, grand fir <5 >3

Forage Deciduous, non-forested, coniferousspp if crown closure<3

<3

Other Primarily cedar if crown closure = 3or age class <3; larch

Leftovers

We also conducted habitat analyses using individual attributes obtained from the forest cover datato determine if we could improve the fit of the models generated (Table 3). Initially, we considered bothleading and secondary species associations. However, further analysis revealed that sample sizes werevery low for stand associations in which the secondary species was greater than 30%. Therefore, asimplified composite "species" category was derived from the leading species composition within thestand (based on relative gross volume for older stands and number of stems/ha for younger stands). Theleading species categories were further pooled on likely associations (i.e. grand fir-cedar, larch-deciduous) to accommodate low species-specific sample sizes.

In some cases a plot was located in a forest cover polygon that had no tree species present. Mostof these polygons were described as non-forest (not sufficiently restocked) and non productive forest (nonproductive brush, non productive, clearings); therefore, a separate forage class was created toaccommodate these plots.

We compared the fit of the models with combinations of these attributes to models that used thepre-defined habitat classes to determine which attributes influenced model fit the greatest. However,attributes were highly non-independent for crown closure, age classes and species (older trees had highercrown closure; forage plots obviously had no plots of older age class and higher crown closure). Inaddition, there was extreme non-evenness of the species category and distributions of crown closure andage class, and low sample sizes for combinations of age class, crown closure, and species. To mitigatethis problem we treated age class and crown closure variables as continuous variables, with the midpoint

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Pend d’Oreille white-tailed deer winter range habitat use 10

of each class being used as the continuous variable value. For example, crown closure class 1corresponds to 6-15% crown closure; therefore a continuous value of 10% was used to describe this class.This approach allows the pooling of ordinal classes, therefore partially mitigating sparse data for anycrown closure or age class. It also carries the assumption that the midpoint value is an adequaterepresentation of any class.

Table 3. Forest cover attributes used in winter habitat analysis, Pend d’Oreille valley.Attribute Describes Categorized to:

Crown closure class1 Continuous (based on class midpoints)

Age class2 Continuous (based on class midpoints)

Species Categorical composite based on pooling ofrelated leading species:-Leading species-Forage if none of the above

Douglas firGrand fir-cedarDeciduous-larchForage/other

1 Crown closure class 0: �5% crown closure (midpoint used: 2.5%); class 1: 6-15% (midpoint used:10%), etc.2 Early seral: age classes 1: 1-20 yrs (midpoint used; 10); class 2: 21-40 yrs (midpoint used: 30), etc.

Response variablesThe basic response variable at plots could be considered as either deer pellet count, or occurrence

of deer pellets (presence/absence) as an index of habitat use. We initially considered modeling deer pelletcount as a Poisson variate under the assumption that the number of pellet groups on a plot was directlyproportional to habitat use. This method has the advantage that areas of very high use will be factoredinto the analysis. However, it was difficult to convert the results of this analysis into the probability ofselection (or proportional use), and therefore many authors use presence or absence of pellets on plots(and accompanying binomial distribution-based logistic regression analysis), which can be more easilyinterpreted (Manly et al. 1993). Presence and absence data can be described in terms of proportional use,which is simply the plots used divided by plots available. This measure scales the plots used by the plotsavailable and is therefore a convenient way to interpret use and availability data.

We based our final decision on whether to model the data as presence/absence (binomialdistribution) or raw count data (Poisson distribution) on the following criteria. First, we conducted ascreening correlation analysis to determine if the observed proportion of plots used each year (calculatedfrom presence/absence) for each habitat class scores were correlated with yearly mean pellet count foreach habitat class. If they were highly correlated, then it would suggest that logistic regression ofpresence/absence data could be used with minimal loss of information or test power. Second, weconsidered the fit of the binomial and Poisson distribution to the pellet group data as determined bygoodness of fit tests to regression models used in the analysis (McCullough and Nelder 1989) (seeAppendix 1). The distribution that best fit the data was then used in subsequent analyses.

Data Screening and tests of general analysis assumptions

Pellet plots as an index of overall habitat abundance

A fundamental assumption of the analysis was the distribution of pellet group plots was indicativeof the overall distribution of habitat classes. This assumption was tested by comparison of the frequency

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Pend d’Oreille white-tailed deer winter range habitat use 11

of plots in each habitat class with the proportion of each habitat class in the study area as estimated usingGIS.

Distribution of habitat classes in relation to topographic variables

Because the study area was predetermined as the wintering range of white-tailed deer in the Pendd’Oreille valley, the sampling design was not balanced; for example, unequal numbers of forage habitatsoccurred on north versus south aspects. Therefore, we screened the data to determine if the relativedistribution of habitat classes was similar across elevation, slope and aspect, or if certain habitat classeswere only found in specific combinations of topographic variables that would affect the tests ofinteraction. We also modeled predictor variables as a continuous rather than a categorical variable wherepossible to minimize the number of categorical combinations of predictor variables in the analysis.Finally, we structured the model to account for the effects of local topography on habitat selection tominimize problems with uneven distributions of habitat classes as a function of topography.

Analysis of temporal changes in habitat use and availability

Change in habitat availability over time

One objective of the analysis was to document changes in habitat availability over time. Weconducted a summary analysis in which the proportion of plots in each habitat class for each year wascalculated. Graphical analysis of this data set therefore allowed us to determine if the degree of habitatchange due to anthropogenic factors and forest succession was relatively large. The results of thisanalysis were used to determine the viability of documenting change in proportional use by deer as aresult of habitat change.

Change in proportional use of habitat over time

The proportional use or presence/absence data of pellet groups was used to determine if deershowed a large degree of variation in habitat use for each of the 20 years of the study. This analysis wasuseful in that it allowed a preliminary evaluation of the feasibility of pooling data for statistical habitatselection analysis. If deer showed a large degree of variability in relative habitat use, then it wouldsuggest that year-specific factors played a large role in habitat selection. If variance was low, then thesame general factors probably influenced habitat selection, and pooling was a reasonable strategy. Theassumption of year-specific effects was further tested statistically by comparison of model fit betweenpooled and year-specific models.

Statistical methods for habitat selection analysis

The main statistical method used in this analysis was logistic regression. Logistic regressiondirectly estimates probabilities from binomial (presence/absence) count data and is best suited for analysisof pellet group data (McCullough and Nelder 1989, Agresti 1990, SAS Institute 1997). Habitat selectionstudies determine which habitat class or attribute will exhibit the highest probability of selection if offeredon equal basis to others. Evaluation of the significance of slope terms (also called resource selectionfunctions) allows inference into the importance of each predictor variable in determining deer pellet group

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Pend d’Oreille white-tailed deer winter range habitat use 12

distribution. Resource selection function estimates are not given for the last category for any givencategorical parameter because the last category is not independent from the others, and therefore it cannotbe estimated. Given this restriction, a parameter was considered significant if at least 1 of its categoricalslope (β) estimates was significant (Stokes et al. 1997). The actual interpretation of selection should beconsidered by both resource selection estimates as well as in terms of predicted proportional use. Thedisplay of results in terms of proportional use (in later sections) allows a better assessment of thebiological significance of resource selection (Manly et al. 1993).

The main objective of this analysis was the description of pellet group distribution as a functionof habitat variables, topographic variables, winter severity, and population size. The large size of the dataset and large number of potential analysis issues made it impossible to use traditional habitat selectionanalysis methods. Instead we formulated a sequential set of analysis steps that allowed objectiveevaluation of model results for each analysis objective.

Base additive model to define resource selection functions

All of the model variables outlined in Table 1 have been shown to be important factors indetermining deer winter habitat selection (summarized in Mowat 1999). Therefore, the first process inmodel fitting was to determine if all factors were significant predictors of deer habitat selection within thePend d'Oreille data set. An initial full additive model that considered aspect, elevation, slope, days onrange, mean pellets/year, and deer density was tested. Higher order polynomial terms were also tested inthis step to detect and account for non-linear relationships. All significant terms were kept in this model,which would form the base model in the subsequent steps.

Fixed plots in the Pend d’Oreille study were checked on a yearly basis. This constitutespseudoreplication, which could potentially bias resource selection function estimates if yearly data arepooled to estimate the functions. The end result of pseudoreplicated data is a higher type 1 error rate thanthe α level specified (i.e., predictor variables will appear significant when they are not; Hurlbert 1984,Manly et al. 1993). Therefore, we used a generalized estimating equation (GEE) approach, which directlymodeled the correlation for deer use for each transect and nested plot over the 20 years it was monitored.The GEE method corrects the slope parameter estimates, variance estimates, and P-values for eachpredictor variable (Liang and Zeger 1986). More details on the GEE method and other statisticalchallenges with the Pend d'Orielle data set are presented in Appendix 1.

AIC methods used to select optimal interaction models

It was likely that topographic factors interact in the prediction of deer habitat use. For example,deer may select habitat on south faces at higher elevations but at lower elevations selection of aspectswould be more general. Thus, we assumed that there would be interactions among many of the predictorvariables (Table 4).

The increased complexity of models that considered interactions made it impossible to usetraditional significance test model selection methods. Instead, we used Information Theoretic Methodsand the accompanying Akaike Information Criterion (AIC) to evaluate model fit (Burnham and Anderson1998). Our general approach was to use the AIC method to select the most parsimonious model, asindicated by the model with the lowest AIC score. See Appendix 1 for more details on the AIC approach.

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Pend d’Oreille white-tailed deer winter range habitat use 13

Modeling of winter severity

Winter severity was not considered to be an initial base model term because severity of winterwas a dynamic process that most likely interacts with habitat class, topographic factors, and otherpredictors. Therefore, a better approach was to assess the relative relationship between winter severityand predictors through the analysis of interactions. The model that best fit the data for the entire 20-yeardata set was used as a starting point for this analysis. Models with interaction terms, such as winterseverity*habitat class and winter severity*elevation, were modeled to determine the significance of deerresponse of use for each predictor as a function of winter severity. Model fit was evaluated using AICmethods and resource selection coefficient significance tests.

Table 4. Interactions among predictor variables considered

Interaction Biological justification/hypotheses

Slope*aspect Snow depth will vary by both slope and aspect which will affect pellet

distribution

Slope*habitat Certain habitat classes (i.e. forage) may be more selected at different slopes

Elevation*slope Steeper slopes may be avoided or selected dependant on elevation

Elevation*habitat Certain habitat classes (i.e. forage) may be more selected at different

elevations

Elevation*aspect Certain elevation and aspects may be snow free therefore influencing deer

distribution

Elevation*aspect*

slope

Deer select very specific combinations of aspect and slope as a function of

elevation.

Winter*aspect Winter with high snow may lead to deer selecting south aspects only

Winter*slope Winter with high snow may lead to deer selecting steeper slopes only

Winter*elevation Winter with high snow may lead to deer selecting lower elevations

Winter*habitat Cover habitat classes may be selected in bad winters

Further refinement of habitat classes using forest cover data

The pre-defined habitat classes represent 1 attempt at defining likely habitat classes that deerassociate with. These categories provide a useful heuristic model in assessing the response of deerdistribution to factors such as winter severity. However, the actual categories are predetermined asopposed to estimated from the data, which will affect model fit. Therefore, models were built whichconsidered elements of forest cover to determine which forest cover attributes (i.e. species, crown closure,age class) that deer associate with the most. The fit of these models was then compared to models basedon habitat classes using the AIC method.

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Pend d’Oreille white-tailed deer winter range habitat use 14

RESULTS

Predictor variables

Winter severity

In general, winters in which cumulative snow depth at Grand Forks averaged <10 cm, 10-20 cm and >20cm were rated as good, average and bad, respectively (Fig. 2). Five winters had generally low snowdepths and were rated as good, 9 were average, and 6 had high overall snow depths and were rated as bad

0

10

20

30

40

50

60

Year81 87 90 92 95

Snow D

epth (cm)

Good Winter

Snow D

epth (cm)

0

10

20

30

40

50

60

Year78 82 84 85 86 97

Snow D

epth (cm)

Bad Winter

0

10

20

30

40

50

60

Year

79 80 83 88 89 91 93 94 96

Snow D

epth (cm)

Average Winter

Figure 2. Winters with good, bad, and average ratings from Grand Forks snowfall data.The * in graphs was the mean and the middle bar was the median. The large rectanglesrepresent quartiles of the distribution. The bars represent the range of observations.Triangles and small boxes represent outlier observations which are farther than 1.5 timesthe inter-quartile range from the quartiles.

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Pend d’Oreille white-tailed deer winter range habitat use 15

(Fig. 2). Winter 1997 in the West Kootenay was especially severe, with 50-year record snowfall andaccumulation.

Population change over time

As noted above, the primary objective of using the population-based variables in the models wasto sponge up as much variation in the data, which should increase the power to detect selection of deertowards habitat and topographic variables. The estimated deer population size was correlated with meanpellets per year (Pearson r = 0.947, P = 0.0001, n = 20), but not with days on range (r = -0.339, 19 df, P =0.15). Days on range was not correlated with mean pellets per year (r = 0.022, 19 df, P = 0.93). Wecompared the logistic model fit with combinations of estimated density, mean pellets per year, and dayson range. The combination of parameters that explained the most variation in the data was used insubsequent model runs. Results of preliminary AIC analysis with combinations of these parameterssuggested that mean pellets per year and days on range provided the best fit to the data and therefore theseparameters were used in future model building.

Deer population estimates derived from pellet plot data and spring spotlight counts bothdemonstrated a change in population size between 1978 and 1997, with a peak between 1989 and 1993 or1994 (Fig. 3).

0

50

100

150

200

250

300

350

400

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Year

Spot

light

cou

nt

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500

1000

1500

2000

2500

3000

3500

Popu

latio

n es

timat

e

Spotlight counts Population estimate

Figure 3. Trends in white-tailed deer population size wintering in the Pend d’OreilleValley as reflected by mean pellet group counts and spotlight counts.

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Pend d’Oreille white-tailed deer winter range habitat use 16

Response variables

Pellet group distribution

Of the pooled pellet group data from 1978-1997, 69.3% of plots had no pellets and 15.8% hadscores of 1 pellet (Fig. 4). The remaining 14.9% had scores >1, with most of the 30,341 plots (98%)exhibiting scores under 5 pellet groups/plot. These results indicate that the data set was reallypresence/absence with a small (5%) percentage of “outlier” plots exhibiting counts >3 pellet groups/plot.Proportion of use was highly correlated (P < 0.00001) with pellet groups counted on each plot.Therefore, we concluded that proportional use (presence/absence of pellet groups) can be used as aresponse variable with minimal loss of information.

We also evaluated the fit of the raw count data to the Poisson distribution and the fit of thepresence/absence data to the binomial distribution. The fit of the raw count data to the Poissondistribution-based regression models was poor (as determined by Pearson χ2 goodness of fit tests;McCullough and Nelder 1989). In contrast, Pearson χ2 tests suggested a much better fit of thepresence/absence data to binomial distribution-based logistic regression models (See Appendix 1). Wetherefore used logistic regression of presence/absence data as the principal analysis technique.

Percent

0

10

20

30

40

50

60

70

No. pellet groups per plot

0 1 2 3 4 5 6 7 8 9 10

Figure 4. The distribution of deer pellet groups per plot for the entire 20 year Pendd'Oreille data set (n = 30,341).

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Pend d’Oreille white-tailed deer winter range habitat use 17

Data screening and tests of analysis assumptions

Pellet plots as an index of overall habitat availability

Comparison of relative frequencies of habitat classes estimated from pellet plots with theproportion of habitat classes within the study area suggested that the plots were a representative sample ofavailable habitat classes (Fig. 5). The forage class was the dominant habitat class within the study area.

0

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70

O ptimal Cover G ood Cover M arginalCo ver

RecruitmentCo ver

Forage O ther

H abitat class

Prop

ortio

n of

tota

l are

a/pl

ots

G IS-en tire area Percentage plots

Figure 5. Distribution of habitat classes as estimated by GIS and percentage of

plots.

Distribution of habitat classes in relation to topographic variables

The distribution of sample sizes among habitat classes differed by aspect and elevation (Fig. 6).Most habitat classes had few plots on north aspects, especially at lower elevations, because there werefew north-facing slopes within the study area. The overall sample size (i.e., 20 years of 1,534 plots/year)was large so cells of <5 observations were a rare occurrence. However, the sample sizes for somecombinations of parameters examined within a year, such as many of the northern and eastern aspects,were low and therefore inference in terms of habitat selection for these aspects will be compromised.This limits the applicability of this model to other areas that may show different distributions of habitatclasses (Fig. 6). As discussed later, the modeling of elevation as a continuous variable, and the structureof the statistical model in the analysis partially accounts for uneven sample sizes by considering thetopographic position of each plot in the estimation of proportional use.

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Pend d’Oreille white-tailed deer winter range habitat use 18

Frequency

Habitat type Good Cover Marginal CoverOptimal Cover Recruitment CoverOther

0

20

40

60

80

100

120

140

160

Aspect

Elevation<700m 700-900m >900m

N E S W N E S W N E S W

Figure 6. Yearly availability of habitat classes (forage excluded) as a function ofaspect and elevation. Data reflects availability of plots in 1990.

Analysis of temporal change in habitat use and availability

Change in habitat availability over time

The abundance of habitat classes changed relatively little during the study (Fig. 7). The mostsignificant changes were in the optimal and good cover classes which were reduced by approximately15%, mostly within the first 5 years of the study. The small changes in the abundance of habitat classesover the study period precluded rigorous analysis of deer response to habitat change. A more site-specificapproach to change in habitat usage may be the best strategy to qualitatively determine deer response toforest harvest and other activities.

Proportional use of habitat classes over time

Proportion of use of all habitat classes increased and then slightly decreased between 1978 and1997 (Figs. 8 and 9), correlated with overall population change (Fig. 3). In addition, the proportional useof habitat classes did not vary to a significant degree between adjacent years for many of the habitatclasses. Notable exceptions were the increased use of the optimal cover classes in 1978, 1983 to 1985and 1997, which was potentially due to increased snow accumulation during these winters (Fig. 8).

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Pend d’Oreille white-tailed deer winter range habitat use 19

A)

Habitat class Good Cover Marginal CoverOptimal Cover OtherRecruitment Cover

020406080

100120140160180200220

Year

76 78 80 82 84 86 88 90 92 94 96 98

Frequency

B)

Habitat class Forage Good CoverMarginal Cover Optimal CoverOther Recruitment Cover

0100200300400500600700800900

1000

Year

76 78 80 82 84 86 88 90 92 94 96 98Frequency

Figure 7. Changes in the frequency of habitat classes (forage classes excluded (A) andincluded (B)) from 1978 to 1997, as indicated by frequencies of pellet plots in the Pendd'Oreille study area.

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Pend d’Oreille white-tailed deer winter range habitat use 20

Habitat class Marginal Cover Good CoverOptimal Cover

0.00

0.10

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0.50

Year

76 78 80 82 84 86 88 90 92 94 96 98

Proportional Use

Figure 8. Use of cover habitat classes from 1978 to 1997 as estimated by proportionof plots with deer pellets, Pend d’Oreille.

Habitat class Forage OtherRecruitment Cover

0.00

0.10

0.20

0.30

0.40

0.50

Year

76 78 80 82 84 86 88 90 92 94 96 98

Proportional Use

Figure 9. Use of non-cover habitat classes from 1978 to 1997 as estimated byproportion of plots with deer pellets, Pend d’Oreille.

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Pend d’Oreille white-tailed deer winter range habitat use 21

Statistical habitat selection analysis using the pooled 20 year data set

Base additive model

Most of the base model terms (slope, aspect, elevation, mean pellets/year, and days on range) inthe initial base additive model analysis were significant at α = 0.05 (Table 5). Exceptions to this werelinear slope and elevation factors. Quadratic and cubic elevation terms were significant suggesting a non-linear relationship between elevation and pellet group distribution. The non-significance of slope andlinear elevation terms was treated cautiously given the simplistic structure of the base additive model.These terms were kept in the model and were re-assessed using AIC methods.

Table 5. AIC model selection results for 20-year deer pellet group study, Pend d’Oreille.

Additive terms(+ terms below1) Interaction terms

Number ofparameters AIC

DeltaAIC

habitat, slope2 aspect*slope, elevation*aspect 20 35850.69 0.00

habitat, (no elevation3) aspect*slope, elevation*aspect 19 35859.63 8.93

habitat aspect*slope, elevation*aspect 19 35899.02 48.32

habitat slope*aspect 17 35944.85 94.16

habitat habitat*elevation 21 35947.60 96.90

habitat aspect*habitat 27 35961.17 110.48

habitat slope*habitat 21 35963.67 112.98

habitat elevation*aspect 17 35995.56 144.86

habitat winter*slope 18 36010.06 159.37

habitat aspect*elevation*slope 17 36028.04 177.34

habitat winter*elevation 17 36028.04 177.34

habitat winter*habitat 33 36028.15 177.46

habitat habitat only 15 36029.23 178.54

habitat slope*elevation 16 36030.61 179.92

habitat winter*aspect 21 36038.17 187.47

Habitat habitat*yr 155 36065.21 214.51

no habitat 9 36132.03 281.33

1 All models included the following parameters: aspect, elevation, elevation2, elevation3, slope, days onrange, and mean pellets/year.

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Pend d’Oreille white-tailed deer winter range habitat use 22

AIC model selection of models with interaction terms

AIC model selection results suggested that many of the models that included interaction terms fitthe data better than the simpler non-interaction models (Table 5). Models that considered the interactionsamong slope, aspect, and elevation in addition to the additive effect of habitat class were most supportedby the data. Habitat and topographic interaction models (i.e. habitat*aspect, habitat*slope terms) wereless supported, a result that was verified by non-significant resource selection function estimates. Ingeneral, models that included winter severity did not show better fit to the data when compared to thebase additive model with no interactions.

GEE resource selection coefficient estimates for the optimal AIC model are provided in Table 6.The significant resource selection coefficients suggested that the optimal and good cover classes were

Table 6. Results of GEE analysis of AIC model, Pend d’Oreille. A parameter wasconsidered significant if at least 1 of its categorical slope (ββββ) estimates was significant.

Parameter CategorySelection

coefficient (β) SE (β) Z P (β=0)Aspect South and west 0.248 0.075 3.31 0.0009

North and east 0.000 0.000 0.00 0.0000

Slope -0.122 0.065 -1.87 0.0619

Slope2 -0.082 0.029 -2.84 0.0044

Elevation -0.190 0.082 -2.32 0.0204

Elevation2 -0.138 0.037 -3.75 0.0002

Elevation3 0.022 0.016 1.39 0.1659

Habitat Optimal cover 0.588 0.235 2.50 0.0123

Good cover 0.477 0.236 2.02 0.0430

Marginal cover 0.338 0.248 1.36 0.1729

Recruitment

cover

0.083 0.253 0.33 0.7425

Forage 0.247 0.218 1.14 0.2562

Other 0.000 0.000 0.00 0.0000

Slope*aspect South and west 0.340 0.079 4.30 0.0000

North and east

Elevation*aspect South and west 0.270 0.088 3.08 0.0021

North and east

Mean pellets/year 2.074 0.067 30.85 0.0000

Days on range -0.004 0.001 -4.66 0.0000

Intercept -1.959 0.252 -7.78 0.0000

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Pend d’Oreille white-tailed deer winter range habitat use 23

selected for, whereas forage, recruitment cover, and marginal cover were not selected for. The habitatclass most selected for was optimal cover, as indexed by the largest selection coefficient. The significantresource selection coefficients also support the inclusion of the slope*aspect and elevation*aspect terms.The sign of the elevation and slope (β) selection coefficients should not be interpreted literally due to thefact that they are based on standardized values. In addition, the actual contribution of slope and elevationto predicted proportional use was based upon both the single additive terms and the interaction terms (i.e.slope*aspect and elevation*aspect), and therefore evaluating the relative importance of slope andelevation terms based upon β value alone is difficult. Graphical analysis methods should be used tointerpret the relative magnitude of the selection towards elevation and slope parameters.

A contour plot, which displays predicted proportional use for combinations of elevation andslope, illustrates the strong interaction of elevation and slope (Fig. 10). The predicted proportional usefrom combinations of aspect, elevation, and slope suggested that south and west aspects, with slopes>40% in mid elevations were the most selected for. North and east aspects were only selected for in lowelevations and on moderate slopes. In general, north and east aspects were less selected for in mostcombinations of elevation and aspects than southern and western aspects. The general pattern of thecontour plot was non-linear, supporting the inclusion of higher order polynomial terms for both slope andelevation. In terms of habitat selection, this model suggested that the most dominant factors influencingdistribution of deer were topographical, and that habitat selection was additive on top of these factors. Interms of this model, each habitat class would show a similar contour surface, however the proportionaluse for each contour would be influenced by the overall selection of the particular habitat.

Evaluation of AIC model fit

Analysis of chi-square residuals suggested that most models exhibited adequate fit, including thebase additive model, although the fit of the interaction models were better, especially in terms of theresponse variables. Adequate model fit in this case was based on the fact that most observations werewithin a chi-square residual value of 2 (Stokes et al. 1997). In general, the AIC model (Table 6) was bestat predicting the absence rather than presence of deer on plots, as indicated by the symmetry of residualsaround the 0 chi square origin, a reasonable result since most of the data in the Pend d'Oreille data setpertained to absence rather than presence.

Observed and predicted proportional use (averaged across all plots) were compared for 3examples of yearly data representing differences in estimated population size and winter severity (Fig.11). Proportional use differed among years, attributed to differences in population size. The years of1981 and 1990 both had good winters, however in 1990 the estimated population size was 5-6 times aslarge. In contrast, 1997 was a very bad winter with a moderate population size. In general, the fit of themodel was adequate from the data of all winters, with the exception of the “other” habitat class. Themodel did not explicitly estimate the other habitat class, and the proportional use of this category wasestimated as 1 minus the proportional use of all the other habitat categories combined. Therefore, it wasexpected that this category would exhibit the poorest fit.

The fit of the model to the 1981 data was poorer than the other 2 years presented. However, thisyear had the lowest estimated population levels and low levels of proportional use, as reflected by thescale of the y-axis (Fig. 11). Given this, the actual deviation between predicted and observed proportionaluse was small (i.e. <0.05 in most cases). In general, the fit of the model was better with years with higherpopulation size because the model had more presence/absence data, which allowed more precise andaccurate estimates. The model overestimated the proportional use of cover classes in the good winters(1981 and 1990) and underestimated the use of cover classes in the bad winter (1997), primarily becausewinter severity was not included in the model and the model was predicting "average" yearly use.

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Pend d’Oreille white-tailed deer winter range habitat use 24

Slope

Aspect= South and West

0

20

40

60

80

Elevation

400 650 900 1150 1400

0.35

0.30

0.250.200.15

Aspect=North and East

0

20

40

60

80

Elevation

400 650 900 1150 1400

Slope0.30

0.25

0.20

0.15

0.15

Figure 10. Contour plots for the interactions of aspect, slope (%), and elevation (m).Proportional use for each contour is listed in the graphs. The response was standardizedfor the forage habitat class.

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Pend d’Oreille white-tailed deer winter range habitat use 25

1981-Good Winter, Low Population

0.00

0.05

0.10

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0.25

OptimalCover

Good Cover MarginalCover

RecruitmentCover

Forage Other

Predicted Observed

1990-Good Winter, High Population

0.00

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0.60

Good Cover MarginalCover

RecruitmentCover

Forage Other

Predicted Observed

1997-Bad Winter, Moderate Population

0.00

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0.50

OptimalCover

Good Cover MarginalCover

RecruitmentCover

Forage Other

Predicted Observed

Figure 11. Observed and predicted proportional use of habitat classes for years with a good winter and low population (1981),good winter and large population (1990), and bad winter and moderate population size (1997). Note the large difference inscale in proportional use (y-axis), which was mainly a function of population size.

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Pend d’Oreille white-tailed deer winter range habitat use 26

Analysis with bad and good winter data only

We took a subset of the 20-year data set so that only "bad" and "good" winters were included.The AIC method was used to determine the fit of models that considered the interactions of winter withslope, elevation, aspect, and habitat class. The optimal model defined for the 20-year data set with pre-defined habitat classes was used as the base model in this analysis. A model which includedwinter*slope, winter*elevation, and winter*habitat interaction terms was most supported by the data(Table 7). Proportional use for elevations and slope as predicted from the AIC model changed for southand west aspects (Fig. 12).

Table 7. AIC results for analysis of winter interaction terms, Pend d’Oreille.

Winter interacts with (+ base model)1 AICDeltaAIC

Number ofparameters

Slope, elevation, habitat 19395.81 0.00 36

Slope, habitat 19398.14 2.33 34

Slope, elevation 19399.12 3.31 24

Slope 19400.86 5.04 22

Slope*aspect, elevation*aspect, habitat 19401.41 5.59 40

Slope, elevation, habitat, aspect 19401.75 5.93 40

Slope, slope*aspect 19403.14 7.33 30

Slope*aspect, elevation*aspect 19403.58 7.77 28

Slope, elevation, aspect 19404.06 8.24 28

Slope 19418.21 22.40 22

Habitat 19419.53 23.71 32

Elevation*aspect 19421.61 25.79 22

Winter additive 19426.68 30.87 22

Aspect 19430.13 34.31 24

1All models include the following parameters: aspect, days on range, elevation, mean pellets/year, habitat,slope, slope2, elevation, elevation2, elevation3, slope*aspect, aspect*elevation.

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Pend d’Oreille white-tailed deer winter range habitat use 27

Bad Winter

0

20

40

60

80

Elevation

400 650 900 1150 1400

0.35

Slope

0.30

0.25

0.20

0.15

0.10

Good Winter

0

20

40

60

80

Elevation

400 650 900 1150 1400

Slope

0.35

0.30

0.25

0.200.15

Figure 12. Predicted proportional use of habitats as a function of winter severity, slope(%), and elevation (m). Proportional use for each contour is listed in the graphs. Theelevation plots were standardized for forage habitat class, moderate population size andsouth /west aspects.

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Pend d’Oreille white-tailed deer winter range habitat use 28

The shapes of plot contours suggest that selection for steeper slopes and low to moderateelevation increased in bad winters (Fig. 12). In contrast, deer selected similarly for mid-steepness slopesacross mid to higher elevations in good winters. Analysis of GEE estimates for the winter*habitatinteraction term for the AIC selected model also suggested significant selection for cover classes in badwinters (Table 8). These results corresponded to the direction of selection in bad winters compared togood winters. Resource selection coefficient estimates showed strongest positive selection towardsoptimal cover and good cover, and negative selection towards forage. In terms of proportional use, thecover habitat classes were generally utilized more in bad winters as opposed to good winters (Fig. 13). Incontrast, the recruitment cover and forage classes were utilized less in bad winters.

Table 8. Interactions between habitat class (habitat) and winter severity (winter).

Interaction Habitat classResource Selection

Function (β) Z-score P-valueHabitat*winter Optimal cover 0.312 3.698 0.000

Habitat*winter Good cover 0.197 2.133 0.033

Habitat*winter Marginal cover 0.083 0.763 0.445

Habitat*winter Recruitment cover -0.151 -1.106 0.269

Habitat*winter Forage -0.139 -3.212 0.001

0.15

0.25

0.35

0.45

0.55

OptimalCover

Good Cover MarginalCover

RecruitmentCover

Forage

Habitat type

Prop

ortio

nal u

se

Bad winter Good winter

Figure 13. Predicted proportional use of habitat classes as a function ofwinter severity. Results were standardized for a south and west aspect,moderate slope (30%), moderate elevation (790 m) and moderate populationlevels.

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Pend d’Oreille white-tailed deer winter range habitat use 29

Forest cover based analyses

The AIC model selected in the previous section was further developed by using forest coverattributes in place of the pre-defined habitat classes. The general development of this model wasproblematic due to uneven sample sizes for combinations of species, crown closure, and age class.Modeling age class and crown closure as a continuous variable partially mitigated this problem, however,the degree of inference for stands that did not have similar age class and crown closure distributions waslimited.

The distribution of crown closure was more even than age class for Douglas-fir and other treespecies, which made the use of this variable less problematic (Fig. 14). Crown closure and age class werecorrelated (r = 0.74, P < 0.0001, n = 1,534) suggesting that the age class potentially determines the crownclosure of a stand (and vice versa).

One potential modeling method to confront the problem of non-even crown closure (or age class)for each species was to pool crown closure for all species, therefore circumventing sample size issues forany particular species. This method had the restrictive assumption that the response of deer pelletdistribution was similar for each tree species (which is biologically unlikely). Models that explicitlyconsidered the relationship between crown closure and individual species (i.e. crown closure*speciesinteraction) were compared with models that pooled crown closure (and age class) to determine if poolingof age class or crown closure was a valid strategy. Note that models that consider species alone (and notcrown closure or age class) would not be valid for this study due to the species-specific distributions ofage class and crown closure.

Species Deciduous-Larch Douglas FirForage/other Grand Fir-Cedar

0

100

200

300

400

500

600

Crown closure

0 1 2 3 4 5 6 7 8

Frequency

Species Deciduous-Larch Douglas FirForage/other Grand Fir-Cedar

0

100

200

300

400

500

600

Age class

0 1 2 3 4 5 6 7 8

Frequency

Figure 14. Distribution of crown closure class and age class for tree/habitat speciesassociation groups, Pend d’Oreille. Sample sizes reflect plots available in 1990.

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Pend d’Oreille white-tailed deer winter range habitat use 30

Table 9. AIC model selection results for forest cover-based models.

Additive terms1,2 Interactions AIC Delta AICNumber ofparameters

Species Species*crown closure 1,2 35374.00 0.00 26

Species Species*crown closure 1,2,3 35378.19 4.20 30

Crown closure 1,2 35428.52 54.52 20

Species, crown closure 1,2,3 35444.91 70.92 20

Species Species*ageclass 1,2,3 35478.40 104.40 30

Species, crown closure, age class Crown closure*age class 35481.02 107.03 21

Species, crown closure, age class 35483.31 109.32 20

Species, age class 35491.33 117.33 19

Species, age class 1,2,3 35492.20 118.21 21

Species Species*age class 35493.90 119.90 22

Species, crown closure 35578.46 204.46 19

Species Species*crown closure 35581.89 207.89 22

Age class 35709.25 335.25 15

Pre-defined habitat 35850.69 476.70 20

Crown closure 35866.20 492.21 15

1Topographic model includes the following parameters: aspect, days on range, elevation, meanpellets/year, and slope, slope2, elevation, elevation2, elevation3, slope*aspect, aspect*elevation.2Superscripts refer to the powers in which a parameter was modeled (i.e. crown closure1,2)=crownclosure1, crown closure2

AIC model selection results suggest that models with the interaction of leading species and crownclosure were most supported by the data (Table 9). Models that pooled crown closure were much lesssupported by the data, suggesting that there were species and crown closure interactions.

Proportional use of deer pellet groups as a function of leading species and crown closure variedamong tree species (Fig. 15). The interaction curves for deciduous-larch and grand fir-cedar were notsignificant (α = 0.05), which may have been due to low sample sizes. The Douglas-fir interaction termwas significant, and suggested selection for lower and higher crown closure classes. Note that in generalthe interactions of the Douglas-fir and grand fir-cedar species with crown closure appeared to be similarat lower crown closures, but diverged at higher crown closures (Fig. 15); however, this finding waslimited by the small range of crown closure values for grand fir-cedar stands. The deciduous-larchshowed a different response, but again the actual response curve should be interpreted cautiously due toinsignificant slope estimates and low sample sizes. However, in general a species-specific crown closure(interaction) model was more supported by the data than a pooled crown closure model, suggestingspecies-specific habitat selection (Table 9).

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Pend d’Oreille white-tailed deer winter range habitat use 31

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 1 2 3 4 5 6 7 8 9

Crown closure

Prop

ortio

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Douglas Fir Deciduous Larch Grand Fir-Cedar Forage

Figure 15. The interaction of crown closure and tree species groups. The grand fir-cedarand deciduous-larch curves should be interpreted cautiously due to non-significance ofinteraction slope parameters. Crown closure values are offset to ease interpretation.Confidence interval bars are given for each prediction.

AIC results suggested that a forest cover species and crown closure-based model was moresupported by the data than the model using pre-defined habitat classes. The limitations of this result arediscussed below. Recommendations for future work to further develop forest cover-based models aregiven later in the report.

DISCUSSIONAnalysis of this 20-year pellet count data set suggests that the main driving force in terms of

winter habitat selection by white-tailed deer in the Pend d’Oreille valley was local topography (slope,aspect, and elevation), with habitat class or habitat attributes selected on an additive basis (Table 10).Over-riding all selection was changes in the size of the deer population on the study area as indexed bymean pellet group counts; this was the single strongest determinant of pellet group distribution.Essentially this means that within the confines of changing population size, habitat selection occurred onthe subset of areas that tend to accumulate low snow depths as determined by topographic constraints.Our results also suggest that selection of habitat classes was mainly towards the “best” 2 of the pre-defined cover classes, optimal and good, with greatest selection for the optimal cover and during winterswith the highest snow accumulation (Table 10). Optimal cover (defined as Douglas-fir or grand fir −leading stands with age class ≥6 and crown closure ≥5) had the highest age and crown closure within thestudy area, and would provide the greatest snow interception and thermal cover. Douglas-fir stands arealso important as a source of forage (Dawson et al. 1990). We found significant selection for both lowerand higher crown closure classes for Douglas-fir.

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Pend d’Oreille white-tailed deer winter range habitat use 32

Table 10. Summary of results, white-tailed deer winter habitat use in the Pend d’Oreillevalley.Predictor variable Summary of results

1) Aspect • South and west aspects selected at most elevations and slopes (Fig. 10)• North and east aspects only selected at lower elevations and less steep

slopes (where aspect is of little importance; Fig. 10)• Interacts with elevation and slope (Table 6)

2) Slope • Interacts with aspect: moderate to steeper slopes selected on south and westaspects (Table 6 and Fig. 10)

• Lower angles slopes selected on north and east aspects at lower elevations• Interacts strongly with winter severity: steeper slopes (40-60%) selected for

in bad winters (Fig. 12)3) Elevation • Mid-elevations selected for on south and west aspects. Lower elevations

selected for on north and west aspects (Fig. 10)• Interacts with winter severity: selection for lower to middle elevations (450-

1000 m.) and steeper slopes in bad winters (Fig. 12)4) Habitat class Primary selection determined by topographical variables (above). Habitat level

selection was additive. Basically, habitat selection occurs on the subset of areasthat have reduced snow as determined by topographical constraints (factorslisted above)

a) Optimal cover • The most selected habitat class in terms of resource selection coefficients(Table 6) and proportional use (Fig. 13)

• Interacts with winter severity: higher selection in bad wintersb) Good cover • The second most selected habitat class in terms of resource selection (Table

6) and proportional use (Fig. 13)c) Marginal cover • Not selected for in terms of resource selection (Table 6) and proportional

use (Fig. 13)d) Recruitmentcover

• Not selected for in terms of resource selection (Table 6) and proportionaluse (Fig. 13)

e) Forage • Marginally selected for in terms of resource selection (Table 6) andproportional use (Fig. 13). Forage was the most abundant habitat class anduse was substantial, and therefore greater selection occurs for the morelimiting optimal and good cover habitat classes

5) Crown closure • Only Douglas-fir has suitable range of crown closure values to fullyevaluate crown closure effects (Fig. 14)

• Interacts significantly with Douglas-fir forest species; selection for higherand lower crown closure classes of Douglas-fir (Fig. 15)

• Interacts with winter severity: selection for higher crown closure in badwinters

• Highly correlated with age class6) Forest coverspecies

Pooled to 3 categories due to sample size constraints

a) Douglas-fir • Significant selection for lower and higher crown class categories forDouglas-fir (Fig. 15)

b) Deciduous-larch • Mild selection for lower crown closure categories (Fig. 15)• Small sample size precludes full investigation of selection for this species

group

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Pend d’Oreille white-tailed deer winter range habitat use 33

Table 10. Continued.c) Grand fir-cedar • No selection for this species, however this may be due to low sample sizes

(Fig. 15)7) Mean pellet groupcount

• The strongest determinant of pellet group distribution as documented by avery large resource selection coefficient (Table 6)

8) Yearly days onrange

• A significant predictor of pellet group distribution suggesting that timespent on the winter range also determines pellet group distribution (Table 6)

Winter habitat selection patterns and winter severityWe modeled winters with overall low and high average snow accumulation (“good” versus “bad”

winters) in an attempt to elucidate selection of topographic and habitat attributes during periods of higheststress (deepest snows), and presumably greatest winter habitat selection pressure. We found selection forsteeper slopes and lower elevation increased in bad winters, which suggests that these variables are notsimple correlates of habitat quality. We also found strong positive selection towards optimal cover andgood cover (generally higher proportions of higher crown closure Douglas-fir) during bad winters, andnegative selection towards forage. Proportional use of optimal and good cover habitat classes was greaterand use of recruitment cover and forage classes less in bad winters as opposed to good winters, inagreement with how the animals should react when adopting an energy conservation mode of behaviour(Moen 1978) with increasing snow accumulation (Parker et al. 1984). We did not find significant use ofgrand fir-cedar stands with higher crown closure during severe winters, in agreement with Woods (1984),although low sample size compromised the power of this analysis.

Using radiotelemetry techniques, other studies in northwestern North American have founddifferent habitat selection by white-tailed deer during different periods of the winter. Habitat selectionduring snow depths of <30 cm (Pauley et al. 1993) to <40 cm (Woods 1984; early and late winter)appears to be non-selective (Woods 1984) or directed towards stands that furnish little canopy cover orsnow interception relative to mature forest stands, but provide the greatest abundance of preferred forage(Pauley et al. 1993). During these seasons of low snow accumulation, white-tailed deer should choosehabitats somewhat irrespective of terrain and canopy cover, except to the extent that these factors affectforage availability (Pauley et al. 1993). During mid-winter and the greatest snow accumulation, deerselect advanced forest age classes that provided the most optimum snow conditions, primarily throughhigher canopy closure, but may provide little available forage (Pauley et al. 1993, Secord 1994).

Within the confines of our methodology our results followed this general pattern, with less use ofthe more closed-canopy stands during winters of lower snow accumulation, and more use of high crownclosure during winters of high snow accumulation. A potential drawback to the pellet group method ofdetermining habitat use is that pellet plots provide an index of average use of a given habitat over anentire year, or at least an entire winter. We have assumed that there is little use of our study area duringsummer and fall (Woods 1984). Still, the pellet plots provide an indication of use over the entire winter,which generally runs from arrival on the winter range during low and accumulating snow depths, throughthe deepest snows (and presumably the highest habitat selection pressure) in mid-winter, to springdeparture from the winter range after much of the snow has melted from lower and mid-elevation sites.As noted, habitat selection pressures and habitat use differ among periods of the winter, primarily relatedto snow depth (i.e., Woods 1984, Pauley et al 1993, Mackie et al. 1998). Therefore, pellet group countscannot readily highlight habitats used during the mid-winter “critical” period. Identification of habitatsselected during a particular period of the winter may be easier using techniques that provide a moreinstantaneous index to habitat selection, such as radio-telemetry (Woods 1984, Pauley et al. 1993, Mackieet al. 1998, Poole et al. 2000) or track counts (Waterhouse et al. 1990, D’Eon 2000). We used

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Pend d’Oreille white-tailed deer winter range habitat use 34

comparisons of habitat selection between winters of low and high overall snow accumulation as asurrogate for having data to identify periods of greatest stress and selection pressure during each winter.

Forest cover versus pre-defined habitat class modelsThe pre-defined habitat classes and forest cover-based vegetation structure variables displayed

similar predictions in terms of habitat use. Results from the pre-defined habitat class model (Table 6, andFigs. 11 and 13) suggest weak selection for forage classes (with no crown closure), no selection for midcrown closure habitat classes (recruitment and marginal cover) and increased selection for cover classeswith greater crown closure (good and optimal cover). A similar trend is seen with the AIC model usingseparate forest cover structural attributes (Fig. 15). Unlike the pre-defined habitat class model, therelationship between crown closure and proportional use was statistically estimated with the forest cover-based model. In addition, species such as larch and grand fir were modeled as a separate class instead ofbeing pooled (within the pre-defined cover classes). All of these differences result in a comparativelybetter fit of the forest cover-based model to the data (Table 9). However, the general applicability of theforest cover-based crown closure and species interaction model is limited due to the limited range ofcrown closure classes for the leading combinations of species. The interaction terms of crown closureand species will only be applicable to the narrow range of crown closure classes for each species used inestimating model parameters. For example, this model could not be used in an area that had grand fir-cedar stands with crown closure class >6 since there were no such stands in the Pend d'Oreille whichcould be used to estimate parameters for greater crown closures. However, it does highlight some of thestronger selection components (i.e., selection towards greater crown closure in Douglas-fir) that wouldmost likely drive habitat selection by deer in areas of similar biogeoclimatic subzones.

Topographic variables and sample size issuesOne of the main findings of this analysis was the strong interaction of deer habitat selection

among slope, aspect, and elevation (Figs. 10 and 12). This interaction was likely because only certaincombinations of these topographic strata shed snow and accumulate low levels of snow, and thereforeonly these areas were available to deer in all but the lowest snow accumulation winters. Given this, oneof the main challenges in interpreting the results was the adequate modeling of habitat classes and treespecies that do not occur evenly across these topographic strata. The direct modeling interactionsbetween slope, aspect, and elevation partially accounted for this by modeling the topographic location ofeach plot. However, yearly sample sizes between some combinations of strata were low (i.e., northaspects at lower elevations).

Influence of population on deer distributionThe large influence of deer population size on distribution of deer was documented by the large

value of model slope parameters for mean pellets per year. This result suggests that deer will expand orcontract areas of use based on population size. Therefore, managers and researchers should consider therelative population size of deer in study areas when generalizing the results of studies for any given year,an aspect of ungulate habitat use that is rarely considered in habitat use studies.

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Pend d’Oreille white-tailed deer winter range habitat use 35

Shapes of response curvesMany of the predictor variables were modeled as higher order polynomial terms to relax the

restrictive assumption of a logistic relationship between response and predictor variables. This allowednon-uniform response surfaces (e.g., Fig. 12). It should be obvious that non-linear responses should beconsidered in all habitat studies. Many of the published studies of habitat selection blindly use logisticregression with little attention to non-linear relationships and other restrictive assumptions of thistechnique.

Limitations to findingsThis study was not designed as a controlled study of winter habitat selection by white-tailed deer.

The main objective of the pellet group transects was to estimate population size on the winter range(using the methods of Smith et al. 1969), and therefore the distribution of predictor variables was not arepresentative sample of all white-tailed deer habitat in the area. Therefore, the results of this study mayonly apply to winter range selection within the Pend d’Oreille study area. Wide scale extrapolations toother areas should be done cautiously, and include rigorous comparisons of the ranges of predictorvariables in the Pend d’Oreille study with those found in other areas. For example, it would not be validto use the model derived in this exercise for an area that has a large percentage of north-facing aspects,nor would it be applicable to use this model for areas that have vastly different classes and distributions oftree species.

Maps of predicted proportional useWe generated maps that display predicted proportional use of the Pend d'Oreille study area under

a variety of scenarios (Figs. 16 and 17; see Appendix 2 for detailed map). The scenarios, the models usedto generate the proportional use, and the input parameters are described in Table 11. Other inputparameters (i.e. topographic and habitat) were taken from TRIM and forest cover data.

Table 11. Summary of input parameters for GIS maps (Figs. 16 and 17) displayingpredicted proportional use of the Pend d’Oreille winter range.Map title Model used to generate predictions Population parameters1 Winter severityMinvalue AIC model from pooled 20 year data set

(Table 6)Minimum observed Average

Meanvalue AIC model from pooled 20 year data set(Table 6)

Average observed Average

Maxvalue AIC model from pooled 20 year data set(Table 6)

Maximum observed Average

Goodwinter AIC Good and bad winter only model(Table 7)

Average observed Good

Badwinter AIC Good and bad winter only model(Table 7)

Average observed Bad

1Population parameters are mean pellets/year and days on range.

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Pend d’Oreille white-tailed deer winter range habitat use 36

Figure 16. Predicted proportional use of habitat in the Pend d’Oreille winter range underlow (minvalue), average (meanvalue) and high (maxvalue) population levels.

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Pend d’Oreille white-tailed deer winter range habitat use 37

Figure 17. Predicted proportional use of habitat in the Pend d’Oreille winter range undergood (low snow; goodwinter) and bad (deep snow; badwinter) winters.

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Pend d’Oreille white-tailed deer winter range habitat use 38

The minvalue, meanvalue, and maxvalue maps (Fig. 16) display how the expected range ofproportional use predicted in a winter range area is highly dependent on the population of deer present onthe winter range and the amount of time spent on range. These maps are based upon the same logisticmodel, and therefore similar areas are highlighted on the maps. However, the range of proportional usevaries as a function of population size on the winter range area. For example, in the case of the minvaluemap (low population size), so few deer were present that the actual range of proportional use seen wassmall because of the small number of pellets deposited during the winter. In this case, not enough pelletswere deposited to highlight the ranges of proportional use.

The good and bad winter maps (Fig. 17) display how the predicted winter range proportional usedepended on winter severity. In the case of the bad winter (deep snow), key areas (in green) are usedmore than other areas. In a good winter (low snow), selection does not occur for these areas, andtherefore only general selection for larger areas occurs.

These maps highlight some important findings and recommendations from this study. First, theactual proportional use of winter range by deer as indexed by pellet plots is a function of the number ofdeer present, the amount of time they spend on the winter range, and the severity of the winter.Therefore, observations of deer use for any given year may not be indicative of long-term winter rangeuse. As a result, researchers should attempt to get an approximate idea of relative population size andwinter severity when attempting to extrapolate yearly results of selection studies to long-term winterrange use. For example, a study conducted in a mild winter with low population size will probably notdetect key habitat attributes that would be detected in a bad winter with large population size. Moredirect methods of assessing habitat selection, such as radio telemetry, may allow a finer picture of habitatuse in critical winter areas when compared to pellet plot indices for short-term studies. Second, managersshould determine the key attributes in which winter range should be managed for. For example, therelative importance of winter severity and population size should be considered when interpreting studiesand drawing up management guidelines.

Recommendation for future analysesOur analysis presents a baseline exploration of the dominant factors influencing winter habitat

selection by deer in the Pend d’Oreille. The magnitude of the data set alone allowed a large array ofpotential analysis strategies to be employed. The model used to analyze the fit of species and crownclosure interactions was somewhat crude due to sample size constraints (i.e. 3 tree species-based classes).It may be possible to further develop this model into more species-based categories. However, to do thiswill require a more intensive study of the distribution of tree species topographical distribution. Forexample, it may be possible to split the Douglas-fir “species” into components based on secondaryspecies.

There was some disparity between models that used corrected and uncorrected forest cover datain terms of selection of habitat types (Appendix 1). It is difficult to determine the exact reason for thedifferences because both the corrected and uncorrected data are models or approximations of the truedistribution of habitat types. Therefore, we can only ascertain that the models give different results ratherthan make the conclusion that one model is "right" or "wrong". One potential explanation for thedisparity in habitat selection is that corrected forest cover data is centered around a plot based scale,whereas the forest cover data extends to a landscape scale in terms of the measurement of habitatavailability. Even though the forest cover data often is erroneous at a plot scale, it may be a betterdescriptor of landscape scale availability in certain cases. Therefore, models that use corrected anduncorrected data may be really assessing selection at different scales. The issue of scale can be exploredby defining habitat types at different distances from plots using a moving window approach (Apps andKinley 1998). In this case we would define habitat availability at a plot by multiple habitat types (i.e.

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Pend d’Oreille white-tailed deer winter range habitat use 39

20% forage, 80% optimal cover), so that there would be multiple habitat-based predictor variables in theanalysis. This type of analysis would also provide more inference in terms of optimal interspersion ofhabitat types.

The best way to test this model would be to compare the selection of habitat based on analysis ofradio-collared points with predicted use from the pellet group-based model. It is important to rememberthat the model has averaged selection across the 20-year data set. The best comparison would be to runthe model for the approximate winter severity in which the radio-collared deer were monitored (ifsufficient data are available), as well as the population levels (as reflected by mean pellets per year) andthe days on range estimated for the year of study.

WINTER RANGE MANAGEMENT GUIDELINESCurrent KBLUP ungulate winter range guidelines for white-tailed deer in the ICHxw

biogeoclimatic subzone call for a minimum of 30% forest cover retention of >100 year old (≥ class 6)trees with an average of 50% crown closure (class 5) in units >20 ha in size every 250 ha (KootenayInter-Agency Management Committee 1997). On slopes >50% the minimum amount of mature forestcover is reduced to 15%. In the ICHdw subzone these requirements are increased to a minimum of 40%forest cover with an average crown closure of 60%, with no allowance for slope. These habitatmanagement objectives are to: maintain suitable security cover, snow interception cover and connectivityhabitat value; maintain mature forest cover at an optimum distance to forage sites; to maintain high forageto cover differentiation; and to particularly maintain mature Douglas-fir stands (Kootenay Inter-AgencyManagement Committee 1997).

Our pellet plot analysis of the Pend d’Oreille winter range did not allow examination of spatiallyexplicit habitat prescriptions. We observed the strongest selection for Douglas-fir – leading stands,supporting KBLUP guidelines and highlighting the importance of this species. Age class 6 (101-120years) stands were most prevalent forested stands within the study area, most of which were Douglas-fir-leading stands (Fig. 14). We were unable to successfully model age class within our data set because ofuneven sample sizes by species, but the KBLUP age class recommendation appears reasonable sinceapproximately two-thirds of crown closure class 6 and greater stands within the study area werecomposed of age class 6-8 Douglas-fir – leading stands. Age class was highly correlated with crownclosure, and our model demonstrated significant selection for lower and higher crown closure classes forDouglas-fir (Fig. 15). AIC model selection results support the model with the interaction of leadingspecies and crown closure as best supporting the data (Table 9). Our models suggest that Douglas-firstands with crown closure equal or greater than 6 are most selected for by white-tailed deer. We did notdifferentiate our analysis by biogeoclimatic subzone; however, our analyses suggest that the value of acover stand increases with increasing crown closure. Whether crown closure class 5 stands are“adequate” for deer during periods of high snow accumulation is difficult to ascertain; there wasincreased proportional use of crown closure class 5 stands compared to lower crown closure stands.Because crown closure class 6-8 stands were most selected for during bad winters, the crown closurerequirements for ungulate winter range guidelines pertaining to this area could be increased to reflectthese higher crown closures.

MAINTENANCE AND ENHANCEMENT OF PEND D’OREILLE WINTER RANGEOur analysis suggests that although higher crown closure stands on steeper slopes are utilized to a

greater degree during winters with higher snow accumulation, stands that produce forage are alsoextensively used. This use did not translate into selection, but given the great proportion of forage standsavailable within the study area, access to forage is of obvious importance. This suggests support for an

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Pend d’Oreille white-tailed deer winter range habitat use 40

interspersion of multi-aged stands, which provide juxtaposition of snow interception cover and forage(Woods 1984).

We found that stand age was not as useful a variable as crown closure in describing deer winterhabitat selection, and that stands of high crown closure (primarily Douglas-fir) were always usedregardless of age. Our results suggest strong selection for high crown closure stands; even though standsof Douglas-fir with crown closure classes 7 and 8 were poorly represented within the study area (Fig. 14),proportional use of these stands was high relative to lower crown closure classes (Fig. 15). Mackie et al.(1998) reported that white-tailed deer consistently preferred overstory canopy coverages ≥50% duringsevere weather conditions. Thus, we suggest that timber management should emphasize retention ofDouglas-fir stands of the highest possible crown closure; reduction of crown closure is not desirable inany age class stand.

Our analysis showed preference for certain combinations of topography, with increased selectionfor moderate to steep slopes on south and west-facing aspects at lower to mid-elevations. Logging ofmature Douglas-fir – leading stands on these slopes should be avoided or minimized. Logging directed athigher elevation sites and on north and east aspects would minimize the reduction in preferred deer winterrange.

KBLUP ungulate winter range guidelines for white-tailed deer call for 30-40% mature forestcover within winter range, depending upon biogeoclimatic subzone, with an allowance down to 15%forest cover on >50% slopes (Kootenay Inter-Agency Management Committee 1997). Using correctedforest cover data for the Pend d’Oreille winter range, it appears that currently about 16% of the range iscomprised of stands with ≥40% Douglas-fir of crown closure class ≥5. This suggests that these mature,Douglas-fir – leading stands with the recommended crown closure are well below the minimum amountrecommended by KBLUP for slopes <50%, and that further harvest of these stands should be curtaileduntil more crown closure class ≥5 stands become available through forest maturation. Approximately12% of the study area is currently comprised of Douglas-fir – leading stands of crown closure class 4.

This simple analysis makes the assumption that forest cover mapping accurately depicts crownclosure of mapped polygons. Forest cover provides the average crown closure for a stand, but in realitythe same crown closure may look very different on the ground. Deer may view a stand that is composedof an even distribution of 40% crown closure less favorably than one that averages 40% crown closurebut is composed of clumps of mature trees with 70% crown closure interspersed with more open areaswith 10% crown closure. Finer-scale mapping than forest cover may be required to explore thesedifferences.

Examination of the spatial distribution of habitats was not possible with the pellet plot data set,but it seems prudent to develop a juxtaposition of forage with high crown closure stands within the winterrange. Approximately two-thirds of crown closure class 6 and greater stands within the study area werecomposed of age class 6-8 Douglas-fir – leading stands, thus retention of these age class stands should beencouraged. Selective logging may enhance forage production within stands, but will reduce the value ofthe stand for mid-winter cover. Thus, use of small clear-cuts while retaining un-touched stands of highcrown closure may provide the greatest benefit for deer. Enhancement of juxtaposition of cover andforage on mid-winter deer habitat could involve cut-blocks of small size (0.5-1.0 ha [Woods 1984], or0.1-0.6 ha [Mackie et al. 1998]). Silviculture prescriptions that involve commercial thinning of conifercanopies or reduction in understory conifer density are not desirable on mid-winter ranges (Mackie et al.1998).

Given the limitations of forest cover data in identifying forage characteristics of areas selected,we are reluctant to propose management considerations for forage enhancement. Prescribed burning andslashing of decadent shrubs can increase forage production that can benefit ungulates during winter andearly spring green-up (Woods 1984, Mackie et al. 1998).

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Pend d’Oreille white-tailed deer winter range habitat use 41

ACKNOWLEDGEMENTSPend d’Oreille/Seven Mile Fish and Wildlife Compensation Program, B.C. Ministry of

Environment, Lands and Parks, B.C. Hydro and Power Authority, and the Columbia Basin Fish andWildlife Compensation Program provided funding for this study. This study would have not beenpossible without the assistance of numerous field workers. In particular, we would like to thank D.Houghton, D. Ross, R. Clarke, and E. Parton for their diligent assistance in the field and office. We thankG. Mowat for reviewing drafts of this manuscript.

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WATERHOUSE, M. J., H. M. DAWSON, AND H. M. ARMLEDER. 1990. The effects of juvenilespacing on wildlife habitat use during winter in the interior Douglas-fir zone of British Columbia.B.C. Ministry of Forests Research Report 89003-CA, Victoria, B.C., Canada.

WELLES, R. W., AND F. B. WELLES. 1961. The bighorn of Death Valley. U.S. National Park ServiceFauna Series 6.

WHITE, G. C., K. P. BURNHAM, AND D. R. ANDERSON. 1999. Advanced features of programMARK. Department of Fishery and Wildlife Biology. Colorado State Univ. Ft. Collins, Colorado,USA.

WOODS, G. P. 1983. Pend d’Oreille wildlife management plan. Fish and Wildlife Branch, B.C.Ministry of Environment, Nelson, B.C., USA.

WOODS, G. P. 1984. Habitat selection of white-tailed deer in the Pend d’Oreille Valley, BritishColumbia. M.S. Thesis, University of Idaho, Moscow, Idaho, USA.

ZIEGLER, A., AND G. ULRIKE. 1998. The generalised estimating equations: A comparison ofprocedures available in commercial statistical software packages. Biometrical Journal 40:245-260.

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Pend d’Oreille white-tailed deer winter range habitat use 44

APPENDIX 1 – STATISTICAL DETAILS

Shapes of response curvesWhen logistic regression is used for habitat selection analysis it is assumed that the relationship

between pellet group distribution (the response variable) and the continuous predictor variables (elevationand slope) is logistic. The logistic curve can accommodate near-linear, exponential, and sigmoidalrelationships between predictor and response variables. However, it cannot accommodate non-linearrelationships between variables. We therefore introduced higher order polynomial terms (i.e. elevation2,elevation3) to test for and account for non-linear relationships (Trexler and Travis 1993). Both slope andelevation were standardized by mean and standard deviation to aid in model convergence with the higherorder polynomial terms. AIC methods and significance tests using GEE methods were used to evaluatethe fit of higher order polynomial terms. However, the detail in terms of response shapes was still limitedgiven that each polynomial term could only accommodate a restricted range of shapes. An alternative tothe use of logistic regression was generalized additive modeling, which allows the fitting of splinefunctions and other less restricted response curve shapes (Bio et al. 1998). Unfortunately, this procedurewas still in the theoretical realm for SAS (SAS Institute 1997), and other mainstream statistical softwaredo not support this type of analysis. In the future, it may be productive to reassess the findings of thisstudy using this newer technique.

The Information Theoretic (AIC) approach to model selectionTraditional hypothesis testing methods, such as ANOVA techniques and stepwise model selection

procedures, become limited as model complexity increases (Flack and Chang 1987). In addition, thetheoretical basis for a hypothesis testing approach becomes muddled with ecological data in which avariety of hypotheses can explain the outcome of a data set (Johnson 1999). Therefore, we usedinformation theoretic approaches to model selection as an alternative to traditional approaches. Theinformation theoretic approach and associated Akaike Information Criterion (AIC) model selectionmethod has been shown to be the best method for selection of models from complex data sets (Burnhamand Anderson 1998). Much of the application of the AIC approach has been for mark-recapture analysis;however, the general approach was applicable to any analysis that utilizes generalized linear models(Burnham and Anderson 1998). The information theoretic approach was based upon the logic that theoptimal models for a set of data are the ones that explain the most variation using the least number ofparameters (the most parsimonious model as indexed by AIC scores). This philosophy acknowledges thefact that a "true model" would contain infinite parameters and was not obtainable or desirable forstatistical inference. Given this constraint, a set of candidate models based on the biology of the questionof interest should be proposed, and from these appropriate model(s) should be selected based on samplesize and other factors that affect model fit.

The information theoretic method was superior to traditional stepwise model selection proceduresfor many reasons. First, it was not affected by multiple tests, so no adjustment of scores was needed(such as the Bonferroni adjustment to P-values when multiple tests are done on the same data set).Second, it was less affected by correlation among variables, given that full models are compared, asopposed to individual predictor variables within a model (Burnham and Anderson 1998). Third, it wasmore robust to "noise" in data sets, which causes erroneous variable selection in stepwise regressionprocedures (Flack and Chang 1987).

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Pend d’Oreille white-tailed deer winter range habitat use 45

The use of Generalized Estimating Equations for longitudinal dataThe GEE method utilizes a "working correlation matrix", which has many potential structures

dependant on the class of data being analyzed. There has been some controversy over the best forms ofworking correlation matrices to use with longitudinal data or repeated measure data (Lipsitz et al. 1994,Crowder 1995). An unstructured working correlation structure as implemented in SAS PROC GENMOD(SAS Institute 1997) was used for the GEE model. The unstructured correlation matrix made the leastnumber of assumptions about the correlation structure in the data and therefore should produce the mostrobust estimates(Ziegler and Ulrike 1998).

Fit of model to a binomial distributionLogistic regression analysis assumes that the pellet group data fit a binomial distribution. We

used analysis of deviance to evaluate the fit of models to the data using the Pearson chi-square test todetermine if the general assumption of model fit could be met (McCullough and Nelder 1989). Onecommon reason for reduced model fit was non-independence of observations. The degree of non-independence in observations was modeled by a dispersion parameter ( c ) estimated by the Pearson chi-square statistic divided by the degrees of freedom in the analysis. If c was >1.5 then QAICc values wereused to evaluate model fit rather than AICc values.

Sensitivity of predictions to unequal plot spacingSpacing between plots varied between 18 to 77 m. Theoretically, plots with close spacing may

exhibit increased spatial autocorrelation when compared to plots with greater spacing. Increasedautocorrelation with closely spaced plots will result in non-independence of observations, which couldlead to negatively biased variance estimates and a higher probability of class 1 errors in the statisticalanalysis. To investigate this we calculated the degree of autocorrelation between plots using both theSpearman and Pearson correlation coefficients and the formulas of Brown and Rothery (1993). Deerabundance was modeled in terms of a binomial or presence/absence variate. This presented a range ofdata with varying levels of mean deer abundance.

We also conduced a generalized linear model analysis to determine if the degree of correlationbetween plots was associated with plot spacing and the mean abundance of deer in any given year. Theresponse variables for this analysis were the Pearson correlation coefficient calculated between each plotand then summarized for each transect. The predictor variables were the mean plot spacing and meanabundance for each year. The correlation coefficients were modeled as a normal variate. Thesignificance tests were corrected for pseudoreplication using the GEE method and an unstructuredcorrelation matrix similar to the method discussed above.

Results of the generalized linear model (GLM) analysis suggested that the distance between plotsdid not affect the degree of autocorrelation between plots (GEE Z = -0.505, P = 0.96). The meanabundance of deer did affect the degree of correlation between all plots, regardless of spacing (Z = 2.205,P = 0.027). However, this effect appears to be independent of plot spacing, as indicated by a non-significant abundance*plot spacing interaction term (Z = -1.553, P = 0.12). This result makes intuitivesense in that when deer abundance was low most plots did not show presence of deer, and therefore thecorrelation was minimal. As deer abundance increased the probability of use of adjacent plots alsoincreased. However, this affect occured across all plots with minimal difference in terms of plot spacing.

In general, all tests suggested that there was little correlation between repeated (annual)observations on plots (as reflected by the values of the GEE scale parameter being close to 1 for all

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Pend d’Oreille white-tailed deer winter range habitat use 46

analysis). In addition, there was also minimal correlation between adjacent plots. There are severalpossible reasons for this result. First, the majority of plots in the study area (69.3%) recorded no deerpellet groups. Given this, no correlation would be found between these plots in terms of yearlycorrelation between repeated measures on individual plots, or spatial autocorrelation between adjacentplots. If the abundance of deer was higher and more plots were used, then correlation may have beendetected. The results of the adjacent plot analysis suggested that correlation does increase between plotsas density of deer increases; however, in no cases was the degree of correlation high. A second reasonwas that the model used does not have enough structure to adequately model the correlation between plotsover space and time. However, this was doubtful since the GEE analysis explicitly models yearlyobservations on individual plots. If this was the case then the GEE method would have a tendency toexhibit type 1 errors at a greater than 5% rate. In addition, the AIC method would have a tendency tooverfit models.

Sensitivity of predictions to uncorrected forest dataThe AIC selected model (Table 6) was run with uncorrected habitat classes to test the sensitivity

of model predictions to uncorrected forest cover data (Table 12). In terms of non-habitat parameters thefit of the model was similar to the model run with the corrected habitat class data. However, 1 differenceis that all habitat classes show significant selection, whereas only good and optimal cover showedsignificant selection with the model run with corrected forest data.

It is difficult to determine which model (corrected and uncorrected forest cover) is the bestdescriptor of the distribution of habitat classes over the entire duration of the study. This is because thetrue distribution of habitat types is unknown and both forest cover and corrected plot data will only beapproximations to this distribution. One likely reason for the disparity is the fact that the corrected forestcover data is mainly based upon the plot scale whereas forest cover data is based upon a landscape scale.Even though the forest cover data is often erroneous at a plot scale it may be a better descriptor oflandscape scale availability in certain cases. More discussion of this, including strategies to investigatethe scale issue, are given in the discussion section.

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Pend d’Oreille white-tailed deer winter range habitat use 47

Table 12: Results of GEE analysis of base model, Pend d’Oreille with uncorrected forestcover based habitat classes. A parameter was considered significant if at least 1 of itscategorical slope (ββββ) estimates was significant.

Parameter CategorySelection

coefficient (β) SE (β) Z P (β=0)Aspect South and west 0.17 0.07 2.32 0.0204

North and east 0.00 0.00 0.00 0.0000

Slope -0.16 0.06 -2.53 0.0114

Slope2 -0.08 0.03 -2.86 0.0042

Elevation -0.15 0.08 -1.84 0.0665

Elevation2 -0.14 0.03 -4.04 0.0001

Elevation3 0.02 0.01 1.64 0.1003

Habitat Optimal cover 0.59 0.19 3.13 0.0018

Good cover 0.66 0.19 3.52 0.0004

Marginal cover 0.66 0.20 3.29 0.0010

Recruitmentcover

0.40 0.22 1.83 0.0672

Forage 0.49 0.17 2.91 0.0036

Other 0.00 0.00 0.00 0.0000

Slope*aspect South and west 0.35 0.08 4.67 0.0000

North and east 0.00 0.00 0.00 0.0000

Elevation*aspect South and west 0.22 0.09 2.56 0.0103

North and east 0.00 0.00 0.00 0.0000

Mean pellets/year 2.07 0.06 32.10 0.0000

Days on range 0.00 0.00 -4.04 0.0001

Intercept -2.26 0.21 -10.80 0.0000

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Pend d’Oreille white-tailed deer winter range habitat use 48

APPENDIX 2 – DETAILED GIS MAP OF THE PEND D’OREILLE WINTER RANGE

UNDER AVERAGE POPULATION LEVELS.

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Columbia BasinFish & WildlifeCompensation Program

103 - 333 Victoria Street, Nelson, British Columbia V1L 4K3Phone: (250) 352-6874 Fax: (250) 352-6178

WINTER HABITAT SELECTION BY WHITE-TAILED

DEER IN THE PEND D’OREILLE VALLEY,

SOUTHEASTERN BRITISH COLUMBIA

John G. Boulanger, Integrated Ecological Research, 924 Innes St.,Nelson BC V1L 5T2

Kim G. Poole,1 Timberland Consultants Ltd., P.O. Box 171, Nelson BCV1L 5P9

John Gwilliam, Columbia Basin Fish and Wildlife CompensationProgram, 103-333 Victoria St., Nelson BC V1L 4K3

Guy P. Woods, BC Ministry of Environment, Lands and Parks, 401-333Victoria St., Nelson BC V1L 4K3

John Krebs, Columbia Basin Fish and Wildlife Compensation Program,103-333 Victoria St., Nelson BC V1L 4K3

Ian Parfitt, Columbia Basin Fish and Wildlife Compensation Program,103-333 Victoria St., Nelson BC V1L 4K3

1 Present address: Aurora Wildlife Research, RR 1, Site 21, Comp 22,Nelson BC V1L 5P4

September 2000

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Pend d’Oreille white-tailed deer winter range habitat use iii

TABLE OF CONTENTS

TABLE OF CONTENTS.............................................................................................................. III

LIST OF FIGURES........................................................................................................................ V

LIST OF TABLES ........................................................................................................................VI

EXECUTIVE SUMMARY............................................................................................................. 1

INTRODUCTION........................................................................................................................... 3

STUDY AREA................................................................................................................................ 4

METHODS...................................................................................................................................... 6

Field Techniques ......................................................................................................................... 6Plot habitat classification ........................................................................................................ 7

Predictor variables....................................................................................................................... 7Winter severity ........................................................................................................................ 7Days on range, pellet group counts, deer density.................................................................... 8Aspect, slope, elevation........................................................................................................... 8Habitat classes and forest cover attributes .............................................................................. 9

Response variables .................................................................................................................... 10Data Screening and tests of general analysis assumptions........................................................ 10

Pellet plots as an index of overall habitat abundance............................................................ 10Distribution of habitat classes in relation to topographic variables ...................................... 11

Analysis of temporal changes in habitat use and availability ................................................... 11Change in habitat availability over time ............................................................................... 11Change in proportional use of habitat over time ................................................................... 11Statistical methods for habitat selection analysis.................................................................. 11Base additive model to define resource selection functions ................................................. 12AIC methods used to select optimal interaction models ....................................................... 12Modeling of winter severity .................................................................................................. 13Further refinement of habitat classes using forest cover data ............................................... 13

RESULTS...................................................................................................................................... 14

Predictor variables..................................................................................................................... 14Winter severity ...................................................................................................................... 14Population change over time ................................................................................................. 15Pellet group distribution ........................................................................................................ 16

Data screening and tests of analysis assumptions ..................................................................... 17Pellet plots as an index of overall habitat availability........................................................... 17Distribution of habitat classes in relation to topographic variables ...................................... 17

Analysis of temporal change in habitat use and availability..................................................... 18Change in habitat availability over time ............................................................................... 18Proportional use of habitat classes over time ........................................................................ 18

Statistical habitat selection analysis using the pooled 20 year data set..................................... 21Base additive model .............................................................................................................. 21AIC model selection of models with interaction terms......................................................... 22

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Pend d’Oreille white-tailed deer winter range habitat use iv

Evaluation of AIC model fit.................................................................................................. 23Analysis with bad and good winter data only ....................................................................... 26Forest cover based analyses .................................................................................................. 29

DISCUSSION ............................................................................................................................... 31

Winter habitat selection patterns and winter severity ............................................................... 33Forest cover versus pre-defined habitat class models ............................................................... 34Topographic variables and sample size issues .......................................................................... 34Influence of population on deer distribution ............................................................................. 34Shapes of response curves......................................................................................................... 35Limitations to findings .............................................................................................................. 35Maps of predicted proportional use........................................................................................... 35Recommendation for future analyses........................................................................................ 38

WINTER RANGE MANAGEMENT GUIDELINES.................................................................. 39

MAINTENANCE AND ENHANCEMENT OF PEND D�OREILLE WINTER RANGE.......... 39

ACKNOWLEDGEMENTS .......................................................................................................... 41

LITERATURE CITED ................................................................................................................. 41

APPENDIX 1 � STATISTICAL DETAILS ................................................................................. 44

Shapes of response curves......................................................................................................... 44The Information Theoretic (AIC) approach to model selection................................................ 44The use of Generalized Estimating Equations for longitudinal data......................................... 45Fit of model to a binomial distribution ..................................................................................... 45Sensitivity of predictions to unequal plot spacing .................................................................... 45Sensitivity of predictions to uncorrected forest data................................................................. 46

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Pend d’Oreille white-tailed deer winter range habitat use v

LIST OF FIGURES

Figure 1. Pend d�Oreille white-tailed deer winter range study. ..................................................... 5Figure 2. Winters with good, bad, and average ratings from Grand Forks snowfall data. The * in

graphs was the mean and the middle bar was the median. The large rectangles representquartiles of the distribution. The bars represent the range of observations. Triangles andsmall boxes represent outlier observations which are farther than 1.5 times the inter-quartilerange from the quartiles......................................................................................................... 14

Figure 3. Trends in white-tailed deer population size wintering in the Pend d�Oreille Valley asreflected by mean pellet group counts and spotlight counts. ................................................ 15

Figure 4. The distribution of deer pellet groups per plot for the entire 20 year Pend d'Oreille dataset (n = 30,341)...................................................................................................................... 16

Figure 5. Distribution of habitat classes as estimated by GIS and percentage of plots. .............. 17Figure 6. Yearly availability of habitat classes (forage excluded) as a function of aspect and

elevation. Data reflects availability of plots in 1990............................................................ 18Figure 7. Changes in the frequency of habitat classes (forage classes excluded (A) and included

(B)) from 1978 to 1997, as indicated by frequencies of pellet plots in the Pend d'Oreillestudy area............................................................................................................................... 19

Figure 8. Use of cover habitat classes from 1978 to 1997 as estimated by proportion of plotswith deer pellets, Pend d�Oreille. .......................................................................................... 20

Figure 9. Use of non-cover habitat classes from 1978 to 1997 as estimated by proportion of plotswith deer pellets, Pend d�Oreille. .......................................................................................... 20

Figure 10. Contour plots for the interactions of aspect, slope (%), and elevation (m).Proportional use for each contour is listed in the graphs. The response was standardized forthe forage habitat class. ......................................................................................................... 24

Figure 11. Observed and predicted proportional use of habitat classes for years with a goodwinter and low population (1981), good winter and large population (1990), and bad winterand moderate population size (1997). Note the large difference in scale in proportional use(y-axis), which was mainly a function of population size..................................................... 25

Figure 12. Predicted proportional use of habitats as a function of winter severity, slope (%), andelevation (m). Proportional use for each contour is listed in the graphs. The elevation plotswere standardized for forage habitat class, moderate population size and south /westaspects. .................................................................................................................................. 27

Figure 13. Predicted proportional use of habitat classes as a function of winter severity. Resultswere standardized for a south and west aspect, moderate slope (30%), moderate elevation(790 m) and moderate population levels. .............................................................................. 28

Figure 14. Distribution of crown closure class and age class for tree/habitat species associationgroups, Pend d�Oreille. Sample sizes reflect plots available in 1990. ................................. 29

Figure 15. The interaction of crown closure and tree species groups. The grand fir-cedar anddeciduous-larch curves should be interpreted cautiously due to non-significance ofinteraction slope parameters. Crown closure values are offset to ease interpretation.Confidence interval bars are given for each prediction......................................................... 31

Figure 16. Predicted proportional use of habitat in the Pend d�Oreille winter range under low(minvalue), average (meanvalue) and high (maxvalue) population levels. .......................... 36

Figure 17. Predicted proportional use of habitat in the Pend d�Oreille winter range under good(low snow; goodwinter) and bad (deep snow; badwinter) winters. ...................................... 37

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Pend d’Oreille white-tailed deer winter range habitat use vi

LIST OF TABLES

Table 1. Main predictor variables used in winter deer habitat selection analysis, Pend d�OreilleValley. ..................................................................................................................................... 8

Table 2. Preliminary habitat classes derived from forest cover data, Pend d�Oreille Valley. ....... 9Table 3. Forest cover attributes used in winter habitat analysis, Pend d�Oreille valley. ............. 10Table 4. Interactions among predictor variables considered........................................................ 13Table 5. AIC model selection results for 20-year deer pellet group study, Pend d�Oreille. ........ 21Table 6. Results of GEE analysis of AIC model, Pend d�Oreille. A parameter was considered

significant if at least 1 of its categorical slope (β) estimates was significant. ...................... 22Table 7. AIC results for analysis of winter interaction terms, Pend d�Oreille. ............................ 26Table 8. Interactions between habitat class (habitat) and winter severity (winter)...................... 28Table 9. AIC model selection results for forest cover-based models........................................... 30Table 10. Summary of results, white-tailed deer winter habitat use in the Pend d�Oreille valley.

............................................................................................................................................... 32Table 11. Summary of input parameters for GIS maps (Figs. 16 and 17) displaying predicted

proportional use of the Pend d�Oreille winter range. ............................................................ 35Table 12: Results of GEE analysis of base model, Pend d�Oreille with uncorrected forest cover

based habitat classes. A parameter was considered significant if at least 1 of its categoricalslope (β) estimates was significant........................................................................................ 47

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Pend d’Oreille white-tailed deer winter range habitat use 1

EXECUTIVE SUMMARYMaintenance of ungulate winter range has been assigned a high priority in forest development

planning in the Kootenay of southeastern British Columbia (B.C.). White-tailed deer (Odocoileusvirginianus) habitat use has been poorly studied in western North America, and most studies have been ofrelatively short duration. Here we report on white-tailed deer winter habitat use and selection in the Pendd�Oreille valley in the West Kootenay, using a 20-year data set of pellet group transects. We examineddeer selection of cover types at different population levels and among winters of different overall snowdepth.

The study was conducted from 1978 to 1997, within approximately 4,950 ha of white-tailed deerwinter range as designated in the late 1970s. The winter range was on southeast to southwest facingslopes along the Pend d�Oreille River. Habitats within the study area were in the Interior Cedar-Hemlockxeric, warm (ICHxw), dry, warm (ICHdw), and moist, warm (ICHmw2) biogeoclimatic subzones.Habitat impacts within the valley have been relatively extensive, a result of flooding behind 2 dams,forest harvesting, power transmission line right-of-way clearing, cattle grazing, and road construction, aswell as suppression of wildfire. Elk (Cervus elaphus) and mule deer (O. hemionus) were present on thewinter range in low numbers. Both population estimates of deer on the study area, derived from pelletgroups, and spring spotlight counts varied 3-fold over the study, and all winters were classified accordingto overall snow depth as good, average, and bad.

Pellet groups were counted and cleared annually on 76-77 permanent plots distributed over 20transects, totaling 1,534 plots. The pellet plots were originally laid out to provide annual populationestimates of deer on the winter range, thus plot spacing on each transects ranged from 18-77 m. Thecircular plots were 100 feet2 (9.3 m2) in size (5.64 foot radius; 1.72 m). We determined topographic(elevation, slope, and aspect) and forest cover (tree species and percentage, age class, crown closure)attributes for each plot. We also assigned each plot into 1 of 6 habitat classes based on speciescomposition, age class and crown closure: cover (optimal, good, marginal, recruitment), forage, and other.We rated habitats with Douglas-fir (Pseudotsuga menziesii) � leading stands of age class ≥6 and crownclosure class ≥5 as optimal cover. Where field checks of the plots differed from the digital data, weadded corrected habitat attributes for each type of error in the database, and recorded the year of changefor human-induced disturbances.

We examined a number of variables that could potentially predict deer habitat selection: winterseverity, mean pellet group count, yearly days on winter range, estimated deer density, aspect, slope,elevation, and pre-defined habitat classes. We also conducted habitat analyses using individual forestcover attributes to determine if we could improve the fit of the models generated. We usedpresence/absence of deer pellets as our response variable, since 69.3% of plots had no pellets and only asmall (5%) percentage of �outlier� plots exhibiting counts >3 pellet groups/plot. We used InformationTheoretic Methods and the accompanying Akaike Information Criterion (AIC) to evaluate model fit.

Forage was the dominant habitat class within the study area, accounting for about 61% of plots.The distribution of sample sizes among habitat classes differed by aspect and elevation, confoundingmodeling and limiting the applicability of this analyses to other areas. The abundance of habitat classeschanged relatively little during the study; the most significant changes were in the optimal and good coverclasses which were reduced by approximately 15%, primarily within the first 5 years of the study.Proportion of use of all habitat classes increased and then slightly decreased between 1978 and 1997,correlated with overall population change. Overall, optimal and good cover classes were selected for,whereas forage, recruitment cover, and marginal cover were not selected for. The habitat class mostselected for was optimal cover.

We compared habitat selection between good and bad winters to examine which habitats deertend towards during periods of higher snow depths. Selection for steeper slopes and moderate elevationincreases in bad winters, and we observed the strongest positive selection towards optimal cover and good

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Pend d’Oreille white-tailed deer winter range habitat use 2

cover, and negative selection towards forage. In terms of proportional use the optimal and good coverhabitat classes were generally utilized the most in bad winters as opposed to good winters, and therecruitment cover and forage classes were utilized the least.

Modeling of leading tree species, age class and crown closure class was problematic due touneven sample sizes for combinations of these variables, and the fact that crown closure and age classwere correlated. The AIC model selection results suggest that models with the interaction of leadingspecies and crown closure were most supported by the data. Models that pooled crown closure weremuch less supported by the data, suggesting that there were species and crown closure interactions. Therewas strong selection for Douglas-fir stands with lower and higher crown closure classes.

Our modeling suggested that the main driving force in terms of winter habitat selection by white-tailed deer in the Pend d�Oreille valley was local topography (slope, aspect, and elevation), with habitatclass or habitat structural attributes selected on an additive basis. Over-riding all selection was changes inthe size of the deer population on the study area; this was the single strongest determinant of pellet groupdistribution. Our results also suggest that selection of habitat classes was mainly towards the �best� 2 ofthe pre-defined cover classes, optimal and good, with greatest selection for the optimal cover and duringwinters with the worst snow accumulation. Optimal cover had the highest age class and crown closurestands within the study area, and would provide the greatest snow interception and thermal cover.Douglas-fir stands also provide forage.

The pellet group technique can provide an index of total habitat use among broad areas.However, caution must be used in trying to interpret pellet data at too fine a habitat scale, primarilybecause of differences in defecation rates during resting and traveling. In addition, habitat selection bydeer differs among periods of the winter, and since pellet group counts provide an index to annual use ofhabitat (or at least use averaged over an entire winter), the technique cannot readily highlight habitatsused during the mid-winter �critical� period. We used winters with high average snow accumulation as asurrogate to identify periods of greatest stress and habitat selection pressure.

Since the study was initially designed to estimate deer population size on the winter range and thedistribution of predictor variables was not a representative sample of all white-tailed deer habitat used inall seasons, wide-scale extrapolations to other areas should be done cautiously. Statistical limitationswere encountered primarily because of the uneven distribution of predictor variables within the studyarea.

Our models suggested that Douglas-fir stands with crown closure greater than 6 are most selectedfor by white-tailed deer. The crown closure requirements for ungulate winter range guidelines pertainingto this area could be increased to reflect these higher crown closures.

Our analysis suggests that although higher crown closure stands on steeper slopes are utilized to agreater degree during winters with higher snow accumulation, stands that produce forage are alsoextensively used. We suggest that timber management should emphasize retention of Douglas-fir standsof the highest possible crown closure in juxtaposition with forage stands. Selective logging may enhanceforage production within stands, but will reduce the value of the stand for mid-winter cover. Thus, use ofsmall clear-cuts while retaining un-touched stands of high crown closure may provide the greatest benefitfor deer. Silviculture prescriptions that involve commercial thinning of conifer canopies or reduction inunderstory conifer density are not desirable on mid-winter ranges.

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Pend d’Oreille white-tailed deer winter range habitat use 3

INTRODUCTIONWhite-tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) populations in many

areas of their distribution in western North America are limited by the extent and quality of their winterrange (Thomas 1979, Mackie et al. 1998). Winter range can be defined as areas that provide theresources deer need in all but the mildest winters (Armleder et al. 1986). These areas often contain accessto both forage and thermal or security cover, and have been characterized as areas of southerly to westerlyaspects, exposed ridges, steeper slopes including rock outcrops, lower elevations, and in forested standsthat provide high crown closure for thermal cover and snow interception (Thomas 1979, Nyberg and Janz1990, Pauley et al. 1993, Mackie et al. 1998). Mackie et al. (1998) referred to these areas as �wintermaintenance habitats� which provide all resources necessary for adult survival, but not necessarilyrecruitment of young. Most native forages available in winter are too low in nutritional value to meet themaintenance needs of deer (Wallmo et al. 1977), thus deer survive by supplementing energy reservesaccumulated prior to winter with energy intake from winter diets and adopting an energy conservationmode of behaviour (Moen 1978). Some authors refer to habitats used during periods of deep snow coverand extreme cold temperatures as �critical� winter range, often containing high thermal cover and snowinterception but little forage (Gilbert et al. 1970, Pauley et al. 1993, Secord 1994). Whether these habitatsare critical is debatable (Harestad 1985), but it is clear that winter range use is heavily influenced by snowdepth (Gilbert et al. 1970, Woods 1984, Pauley et al. 1993, Armleder et al. 1994), primarily because ofincreased energetic costs of locomotion (Parker et al. 1984).

Fecal pellet group counts (Bennett et al. 1940, Neff 1968) are a widely used index for monitoringungulate abundance and relative distribution. Although some studies suggest that pellet-group depositionis a poor measure of ungulate distribution relative to topography and broad habitat classes (Welles andWelles 1961 [cited in Edge and Marcum 1989], Collins and Urness 1981), others have found that they canbe used to compare broad areas of use (Leopold et al. 1984, Loft and Kie 1988, Edge and Marcum 1989),especially in representing habitat use during a seasonal time period (Loft and Kie 1988). Studies thatsupported the use of pellet group counts concluded that the technique can be used to estimate ungulatedistribution relative to topographic factors (Edge and Marcum 1989) and to rank relative use of habitats(Loft and Kie 1988). Edge and Marcum (1989) divided each topographic variable into 3-8 intervalclasses and counted pellets on permanent belt transects over 2-month summer seasons. Loft and Kie(1989) used temporary plots over 3-month summer seasons. Both studies concluded that pellet groupcounts were appropriate for ranking use when there was a clear disparity in percent use among habitats,and for differentiating high-, medium-, and low-use habitats at a coarse-grained level. However, theseresearchers suggest that caution must be used in trying to interpret pellet data at too fine a habitat scale,since the relative deposition of pellets at any 1 plot may not be indicative of relative use or importance ofthat plot within the study area. Collins and Urness (1981) suggested that 30% of pellet deposition occurswhile deer are traveling, an activity which the animals spend only 4% of the day. Loft and Kie (1989)found that pellet group counts underestimated deer use of habitats used primarily for resting comparedwith radio-telemetry, again because the deer were not active and were not defecating.

White-tailed deer habitat use has been described in northwestern United States (Keay and Peak1980, Owens 1981, Dusek et al. 1989, Pauley et al. 1993, Mackie et al. 1998), and to a limited extent inwestern Canada (Woods 1984). Many of these studies describe habitat use over relatively short periodsof time, and few attempt to develop predictive models of winter habitat selection (but see Pauley et al.1993). Here we present a 20-year data set monitoring white-tailed deer abundance and distribution in thePend d�Oreille valley in southeastern British Columbia (B.C.) using fecal pellet group plots. Due tofavorable climate, soil, and vegetation patterns, the lower levels of the south-facing slopes along the Pendd�Oreille River valley contain the highest capability for white-tailed deer winter range in the WestKootenay region (Vold et al. 1980).

We examined pellet group distribution relative to available habitats, habitat change over time (viasuccession and land use practices), and winter severity. We examined a number of topographic and

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Pend d’Oreille white-tailed deer winter range habitat use 4

habitat attributes, and developed a model of deer winter habitat selection applicable to this area and otherareas of similar habitat and topography. We initially consider a model that uses pre-defined habitatclasses that represent current assumptions about deer habitat selection (ungulate winter range guidelinesprovided in the Kootenay-Boundary Land Use Plan [KBLUP]; Kootenay Inter-Agency ManagementCommittee 1997). We use this model to assess change in deer distributions caused by winter severity andother factors. Second, we further refined habitat classification by developing a model that used forestcover attributes instead of pre-defined habitat classes. Finally, we assessed the application of currentwhite-tailed deer winter range guidelines provided in the KBLUP (Kootenay Inter-Agency ManagementCommittee 1997), and provide recommendations for maintenance and enhancement of winter range in thePend d�Oreille valley.

STUDY AREAThe study area encompassed approximately 4,950 ha on the north side of the Pend d�Oreille River

in the West Kootenay, adjacent to the border with the United States (Fig. 1). The study area delineated allwhite-tailed deer winter range as designated in the late 1970�s using local knowledge and radio-collaredanimals (Woods 1984). White-tailed deer was the primary ungulate species wintering in valley; elk(Cervus elaphus) were present in much lower numbers (Woods 1983), and mule deer winter on arelatively small proportion of the designated winter range above the confluence of the Salmo and Pendd�Oreille rivers (J. Gwilliam, Columbia Basin Fish and Wildlife Compensation Program [CBFWCP]personal communication). White-tailed deer wintering in the study area utilized summer range coveringover 2,500 km2, primarily east, north and west, but also south of the study area (Woods 1983, 1984, J.Gwilliam, CBFWCP unpublished data).

The study area encompassed southern aspects and relatively steep slopes on the north side of thePend d�Oreille River. Elevations in the study area ranged from 470 m along the Pend d�Oreille River to1,410 m at the upper extent of designated winter range; upslope peaks extended to 1,850 m. The area waswithin the Interior Cedar Hemlock (ICH) biogeoclimatic zone, including the xeric, warm (xm) subzone inthe valley bottom, dw (dry, warm) subzone on lower to mid-elevation slopes, and mw (moist, warm)subzone at midslope (Meidinger and Pojar 1991). Douglas-fir (Pseudotsuga menziesii) commonlydominate southern exposures, much of it even-aged stands that resulted from a major fire in the 1890�s(Vold et al. 1980, Woods 1984). Shrub or grass communities with open Douglas-fir or Ponderosa pine(Pinus ponderosa) stands occupy steep south aspects influenced by fire. Western redcedar (Thujaplicata) and grand fir (Abies grandis) prevail throughout lower and mid-elevation moist sites. Lodgepolepine (Pinus contorta), western white pine (Pinus monticola) and western larch (Larix occidentalis) arefound on some sites, and deciduous species include white birch (Betula papyrifera) and trembling aspen(Populus tremuloides). Cougars (Felis concolor) were the primary predators on ungulates wintering inthe study area, and coyotes (Canis latrans) were common. Human harvest of the white-tailed deerpopulation averaged roughly 200-350 deer harvested annually between the mid-1980's to mid-1990's inManagement Unit 4-08, which covers about 90% of the summer range used by the white-tailed deer thatwinter in the Pend d�Oreille valley (B.C. Environment, Lands and Parks harvest statistics).

The climate of the area is transitional between wetter temperate coastal and drier continentalweather patterns. Mean July and January temperatures for Waneta, located in the valley bottom at thewest end of the study area, are 19.7 and �4.8 C, respectively (Vold et al. 1980). Annual precipitation atWaneta averages 630 mm, with 180 cm falling as snow; total precipitation within the valley increasesfrom west to east and with increasing elevation (Vold et al. 1980). Snow often persists on the valley floorfrom early or mid-December to mid-March, but during mild winters low elevation south-facing slopesmay be snow-free for periods during mid-winter.

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Pend d'Oreille white-tailed deer winter range habitat use 5

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Figure 1: Pend d'Oreille white-tailed deer winter range study

Kootenay Region Ungulate Winter Range

Study Area

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Pend d’Oreille white-tailed deer winter range habitat use 6

In addition to natural succession, the Pend d�Oreille valley has been influenced by a number ofdisturbances. Construction of the Waneta Dam near the mouth of the Pend d�Oreille River in the mid-1950�s flooded approximately 7 km of river and 175 ha of valley bottom (Vold et al. 1980). The SevenMile Dam 15 km up stream flooded a further 14 km of river and 212 ha after construction in 1979 andagain in 1988 when the reservoir level was raised 5 m. There are approximately 54 km of transmissionlines within the study area, directly affecting roughly 250 ha of habitat. Harvesting (primarily Douglas-fir) of forests has occurred over portions of the area. Wildfire, historically the most important naturaldisturbance, has been suppressed over much of the past half century, such that few natural fires haveoccurred since the 1930s (Woods 1984). Forest management has occurred in the valley, including a fewprescribed burns up to 70 ha in size, shrub-cutting to rejuvenate decadent shrubs and promote growth ofyoung Douglas-fir and ponderosa pine, some planting of Douglas-fir seedlings (J. Gwilliam, CBFWCPunpublished data).

METHODS

Field TechniquesWe established 20 permanent pellet group transects, designed to provide an estimate of the

number of deer wintering in the Pend d�Oreille winter range, following sampling design in Smith et al.(1969: Method 1). A map of the winter range was overlain with a grid of lines angled at approximately321º. One transect was randomly chosen as the starting point, and 19 additional transects weresystematically chosen based on a pre-determined spacing interval (based on cumulative transect length)from this first transect; thus the chance of a transect being selected was proportional to its length.Transect length averaged 3,070 ± 300.8 m (± SE; range 1421-5718 m; Fig. 1). We placed 77 plots oneach selected transect, such that plot spacing varied from 18-77 m and the total number of plots was1,540. Since -transect spacing was based on the cumulative grid length (Smith et al. 1969), each plotrepresented the same area of the winter range (approximately 3.2 ha). Plot center of circular plots 100feet2 (9.3 m2) in size (5.64 foot radius; 1.72 m) were permanently marked. We estimated average deerdensity on the wintering grounds based on a formulae calculated from mean pellet counts, length of herdoccupancy, and assumed defecation rates (Smith et al. 1969).

Plots were established and cleared in summer 1977. Between 1 May and 15 June from 1978 to1997, deer and elk pellet groups were counted and cleared off the plots. We used 2 persons to searchplots in a circular fashion using a plot cord anchored to the plot center. Pellet groups (≥10 pellets) werecounted if more than half the pellets were within the plot. We checked each plot each year, however afew plots were missed in some years because of habitat disturbance (i.e., logging), missing plot centers, ordisagreement with landowners. The lowest plot on 6 transects were flooded in 1988 due to rising waterfrom Seven Mile Dam; these plots were removed from the analyses.

The number of days on winter range by the white-tailed deer herd using the area was subjectivelydetermined by field personnel familiar with the area from 1977 to 1991, and using radio-collared deerfrom 1991 to 1997 (J. Gwilliam, CBFWCP unpublished data). Start and end occupancy dates werestandardized to when approximately half of the herd or collared animals arrived on or left the range.

To provide another index to deer numbers, we conducted spotlight counts (Progulske and Duerre1964) on the lower sections of the wintering grounds between late March and early May from 1981 to1997. Deer were counted along a 19-km stretch of road through the valley bottom adjacent to the river.We used the maximum number of deer observed on 2 passes for each count (out and back), and selectedthe peak number from among the 8-12 counts conducted each spring.

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Pend d’Oreille white-tailed deer winter range habitat use 7

Plot habitat classification

Plot locations were accurately (±2-5 m) determined using a differentially corrected GlobalPositioning System (GPS; ProXRS, Trimble Navigation Ltd., Sunnyvale, California, USA). We obtaineddigital 1:20,000 scale topographic (Terrain Resource Information Mapping) and forest cover (B.C.Ministry of Forests, Forest Inventory Program) files of the study area, and determined a number ofattributes for each plot location. We obtained elevation from a digital elevation model (DEM) andcalculated aspect and slope by creating a triangulated irregular network (tin) developed from the DEM,which averaged these parameters over each tin; tins were generally 60-100 m on each side of the triangle.B.C. forest cover maps delineate relatively homogeneous forest stands or forest cover classes based on theinterpretation of aerial photographs and ground truthing information collected in field surveys. Thesemaps include information on tree species, projected age and height class, site index, and tree density(stocking level), and are widely used in B.C. for operational forest development and reforestationplanning. Areas not supporting commercial forests are generally described as non-productive, meaningthe area was not capable of supporting commercial forests (e.g., alpine, alpine forest, brush, clay banks, orrock), or non-forest, meaning that the area was not currently forested but was capable of supportingcommercial forests (e.g., a logged area that was not sufficiently restocked). We examined a number offorest cover attributes for each stand, included leading tree species, percent species composition, crownclosure, stand age, and non-productive and non forest descriptors. The smallest mapped forest coverpolygon size was 0.7 ha.

Habitat attributes (leading tree species, age class, canopy closure) were examined in the field foreach plot location and were compared to the digital forest cover data. Differences in habitat classificationbetween field observations and forest cover data were observed at a number of sites, and were attributedto 2 classes of factors. Habitat attributes observed at 320 plots (20.9%) differed from the digital forestcover data, primarily because of scale, incorrect/inaccurate forest cover descriptions, and inaccuratemapping of forest cover polygon boundaries. Changes due to timber harvesting, power line right-of-wayclearing, farming and other human-related factors affected another 164 plots (10.7%). We addedcorrected habitat attributes for each type of error in the database, and recorded the year of change forhuman-induced disturbances. This created a matrix with a time series of habitat attributes for each plotfor each year of the study.

Predictor variablesEight variables that potentially predict habitat selection were considered in the initial habitat

analysis (Table 1).

Winter severity

We constructed a history of winter severity using B.C. Ministry of Transportation and Highwayssnow depth data from Grand Forks, B.C., located 65 km west of the study area. We selected Grand Forksbecause it was the closest snow depth data set from an area of similar elevation to the Pend d�Oreille,although with lower overall precipitation. We compared the daily snow depth between November 15 andMarch 15 of each winter using the mean, median, and distribution using box plots. Biologists from theCBFWCP used these snow depth records and field notes to assign 3 categories of winter snowaccumulation (good: low snow; average; and bad: high snow). Hereafter, we refer to each winter by itspost 1 January year, such that the winter of 1977-78 was winter 1978.

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Pend d’Oreille white-tailed deer winter range habitat use 8

Table 1. Main predictor variables used in winter deer habitat selection analysis, Pendd’Oreille Valley.Factor Class Comments

1. Winter severity Categorical Determined from data from Grand Forks, B.C.

2. Mean pellet groupcount

Continuous

3. Yearly days onrange

Continuous From CBFWCP biologist notes and radio telemetrydata

4. Estimated deerdensity

Continuous Used mean pellet group count and yearly days onrange for calculation.

5. Aspect Categorical N and E, or S and W

6. Slope Continuous/categorical

Definition of variable dependent on model fit

7. Elevation Continuous/categorical

Definition of variable dependent on model fit

8. Habitat class Categorical From CBFWCP biologists/KBLUPUsed Forest cover data, plot checks to defineRevised yearly if disturbance occurred

Days on range, pellet group counts, deer density

The habitat selection model can be conceptualized as the analysis of factors that determine deerdistribution above and beyond simple increases and decreases in yearly deer density. It was obvious thatmore deer will take up more space and distribution will change, therefore, deer density in this study was acovariate parameter. We therefore included population-based predictor variables in habitat selectionmodeling to account for the variance in deer distribution caused by changes in population size. Thecombination of population based predictors which accounted for the most variation in the data (asdetermined by relative model fit) was used in subsequent analysis.

Aspect, slope, elevation

Aspect: Vegetation Resource Inventory (VRI; Ministry of Forests 1999) categories were used todefine aspects in the analysis (north [cold]: 286-59°; east [cool]: 60-135°; south [hot]: 136-240°; west[warm]: 241-285°). Sixty two percent of plots were on south aspects, whereas 27%, 4.2%, and 6.3% ofplots were on east, north, and west aspects, respectively (n = 1,534). Therefore, we pooled south andwest, and north and east aspects for the analysis. This allowed a contrast of warm and hot aspects, withcold and cool aspects.

Slope: The mean slope from all the plots was 33.9 ± 15.4% (n = 1,534). Because we believedthat the relationship between slope and pellet group distribution was non-linear, we added higher orderpolynomial terms (i.e. slope2) to test for non-linear trends between pellet group distribution and slope(discussed later).

Elevation: The mean elevation of all plots was 802.1 ± 174.04 m (range 472-1397 m, n = 1,534).We therefore added higher order polynomial terms (i.e. elevation2) to test for non-linear trends betweenpellet group distribution and elevation (discussed later).

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Pend d’Oreille white-tailed deer winter range habitat use 9

Slope and elevation values were standardized by mean and standard deviation to aid in modelconvergence (White et al. 1999).

Habitat classes and forest cover attributes

We conducted initial habitat selection analyses using preliminary habitat classes derived from theexisting KBLUP winter range guidelines (Table 2). Habitat classes were divided into 3 broad coverclasses, a recruitment cover class, and a forage class, based primarily on leading tree species, age classand crown closure. Aspect was not considered in assigning habitat classes. For all analysis the correctedforest cover data were used. As a test of model robustness, we ran analyses with original (uncorrected)and corrected forest cover data to determine the influence of error in forest cover designation on results(Appendix 1).

Table 2. Preliminary habitat classes derived from forest cover data, Pend d’Oreille Valley.

Habitat class Leading species Age class Crown closure Comments

Optimal cover Douglas-fir, grand fir >6 >5

Good cover Douglas-fir, grand fir >6 3-4

Marginal cover Douglas-fir, grand fir 5 >3

Recruitment cover Douglas-fir, grand fir <5 >3

Forage Deciduous, non-forested, coniferousspp if crown closure<3

<3

Other Primarily cedar if crown closure = 3or age class <3; larch

Leftovers

We also conducted habitat analyses using individual attributes obtained from the forest cover datato determine if we could improve the fit of the models generated (Table 3). Initially, we considered bothleading and secondary species associations. However, further analysis revealed that sample sizes werevery low for stand associations in which the secondary species was greater than 30%. Therefore, asimplified composite "species" category was derived from the leading species composition within thestand (based on relative gross volume for older stands and number of stems/ha for younger stands). Theleading species categories were further pooled on likely associations (i.e. grand fir-cedar, larch-deciduous) to accommodate low species-specific sample sizes.

In some cases a plot was located in a forest cover polygon that had no tree species present. Mostof these polygons were described as non-forest (not sufficiently restocked) and non productive forest (nonproductive brush, non productive, clearings); therefore, a separate forage class was created toaccommodate these plots.

We compared the fit of the models with combinations of these attributes to models that used thepre-defined habitat classes to determine which attributes influenced model fit the greatest. However,attributes were highly non-independent for crown closure, age classes and species (older trees had highercrown closure; forage plots obviously had no plots of older age class and higher crown closure). Inaddition, there was extreme non-evenness of the species category and distributions of crown closure andage class, and low sample sizes for combinations of age class, crown closure, and species. To mitigatethis problem we treated age class and crown closure variables as continuous variables, with the midpoint

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Pend d’Oreille white-tailed deer winter range habitat use 10

of each class being used as the continuous variable value. For example, crown closure class 1corresponds to 6-15% crown closure; therefore a continuous value of 10% was used to describe this class.This approach allows the pooling of ordinal classes, therefore partially mitigating sparse data for anycrown closure or age class. It also carries the assumption that the midpoint value is an adequaterepresentation of any class.

Table 3. Forest cover attributes used in winter habitat analysis, Pend d’Oreille valley.Attribute Describes Categorized to:

Crown closure class1 Continuous (based on class midpoints)

Age class2 Continuous (based on class midpoints)

Species Categorical composite based on pooling ofrelated leading species:-Leading species-Forage if none of the above

Douglas firGrand fir-cedarDeciduous-larchForage/other

1 Crown closure class 0: ≤5% crown closure (midpoint used: 2.5%); class 1: 6-15% (midpoint used:10%), etc.2 Early seral: age classes 1: 1-20 yrs (midpoint used; 10); class 2: 21-40 yrs (midpoint used: 30), etc.

Response variablesThe basic response variable at plots could be considered as either deer pellet count, or occurrence

of deer pellets (presence/absence) as an index of habitat use. We initially considered modeling deer pelletcount as a Poisson variate under the assumption that the number of pellet groups on a plot was directlyproportional to habitat use. This method has the advantage that areas of very high use will be factoredinto the analysis. However, it was difficult to convert the results of this analysis into the probability ofselection (or proportional use), and therefore many authors use presence or absence of pellets on plots(and accompanying binomial distribution-based logistic regression analysis), which can be more easilyinterpreted (Manly et al. 1993). Presence and absence data can be described in terms of proportional use,which is simply the plots used divided by plots available. This measure scales the plots used by the plotsavailable and is therefore a convenient way to interpret use and availability data.

We based our final decision on whether to model the data as presence/absence (binomialdistribution) or raw count data (Poisson distribution) on the following criteria. First, we conducted ascreening correlation analysis to determine if the observed proportion of plots used each year (calculatedfrom presence/absence) for each habitat class scores were correlated with yearly mean pellet count foreach habitat class. If they were highly correlated, then it would suggest that logistic regression ofpresence/absence data could be used with minimal loss of information or test power. Second, weconsidered the fit of the binomial and Poisson distribution to the pellet group data as determined bygoodness of fit tests to regression models used in the analysis (McCullough and Nelder 1989) (seeAppendix 1). The distribution that best fit the data was then used in subsequent analyses.

Data Screening and tests of general analysis assumptions

Pellet plots as an index of overall habitat abundance

A fundamental assumption of the analysis was the distribution of pellet group plots was indicativeof the overall distribution of habitat classes. This assumption was tested by comparison of the frequency

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Pend d’Oreille white-tailed deer winter range habitat use 11

of plots in each habitat class with the proportion of each habitat class in the study area as estimated usingGIS.

Distribution of habitat classes in relation to topographic variables

Because the study area was predetermined as the wintering range of white-tailed deer in the Pendd�Oreille valley, the sampling design was not balanced; for example, unequal numbers of forage habitatsoccurred on north versus south aspects. Therefore, we screened the data to determine if the relativedistribution of habitat classes was similar across elevation, slope and aspect, or if certain habitat classeswere only found in specific combinations of topographic variables that would affect the tests ofinteraction. We also modeled predictor variables as a continuous rather than a categorical variable wherepossible to minimize the number of categorical combinations of predictor variables in the analysis.Finally, we structured the model to account for the effects of local topography on habitat selection tominimize problems with uneven distributions of habitat classes as a function of topography.

Analysis of temporal changes in habitat use and availability

Change in habitat availability over time

One objective of the analysis was to document changes in habitat availability over time. Weconducted a summary analysis in which the proportion of plots in each habitat class for each year wascalculated. Graphical analysis of this data set therefore allowed us to determine if the degree of habitatchange due to anthropogenic factors and forest succession was relatively large. The results of thisanalysis were used to determine the viability of documenting change in proportional use by deer as aresult of habitat change.

Change in proportional use of habitat over time

The proportional use or presence/absence data of pellet groups was used to determine if deershowed a large degree of variation in habitat use for each of the 20 years of the study. This analysis wasuseful in that it allowed a preliminary evaluation of the feasibility of pooling data for statistical habitatselection analysis. If deer showed a large degree of variability in relative habitat use, then it wouldsuggest that year-specific factors played a large role in habitat selection. If variance was low, then thesame general factors probably influenced habitat selection, and pooling was a reasonable strategy. Theassumption of year-specific effects was further tested statistically by comparison of model fit betweenpooled and year-specific models.

Statistical methods for habitat selection analysis

The main statistical method used in this analysis was logistic regression. Logistic regressiondirectly estimates probabilities from binomial (presence/absence) count data and is best suited for analysisof pellet group data (McCullough and Nelder 1989, Agresti 1990, SAS Institute 1997). Habitat selectionstudies determine which habitat class or attribute will exhibit the highest probability of selection if offeredon equal basis to others. Evaluation of the significance of slope terms (also called resource selectionfunctions) allows inference into the importance of each predictor variable in determining deer pellet group

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Pend d’Oreille white-tailed deer winter range habitat use 12

distribution. Resource selection function estimates are not given for the last category for any givencategorical parameter because the last category is not independent from the others, and therefore it cannotbe estimated. Given this restriction, a parameter was considered significant if at least 1 of its categoricalslope (β) estimates was significant (Stokes et al. 1997). The actual interpretation of selection should beconsidered by both resource selection estimates as well as in terms of predicted proportional use. Thedisplay of results in terms of proportional use (in later sections) allows a better assessment of thebiological significance of resource selection (Manly et al. 1993).

The main objective of this analysis was the description of pellet group distribution as a functionof habitat variables, topographic variables, winter severity, and population size. The large size of the dataset and large number of potential analysis issues made it impossible to use traditional habitat selectionanalysis methods. Instead we formulated a sequential set of analysis steps that allowed objectiveevaluation of model results for each analysis objective.

Base additive model to define resource selection functions

All of the model variables outlined in Table 1 have been shown to be important factors indetermining deer winter habitat selection (summarized in Mowat 1999). Therefore, the first process inmodel fitting was to determine if all factors were significant predictors of deer habitat selection within thePend d'Oreille data set. An initial full additive model that considered aspect, elevation, slope, days onrange, mean pellets/year, and deer density was tested. Higher order polynomial terms were also tested inthis step to detect and account for non-linear relationships. All significant terms were kept in this model,which would form the base model in the subsequent steps.

Fixed plots in the Pend d�Oreille study were checked on a yearly basis. This constitutespseudoreplication, which could potentially bias resource selection function estimates if yearly data arepooled to estimate the functions. The end result of pseudoreplicated data is a higher type 1 error rate thanthe α level specified (i.e., predictor variables will appear significant when they are not; Hurlbert 1984,Manly et al. 1993). Therefore, we used a generalized estimating equation (GEE) approach, which directlymodeled the correlation for deer use for each transect and nested plot over the 20 years it was monitored.The GEE method corrects the slope parameter estimates, variance estimates, and P-values for eachpredictor variable (Liang and Zeger 1986). More details on the GEE method and other statisticalchallenges with the Pend d'Orielle data set are presented in Appendix 1.

AIC methods used to select optimal interaction models

It was likely that topographic factors interact in the prediction of deer habitat use. For example,deer may select habitat on south faces at higher elevations but at lower elevations selection of aspectswould be more general. Thus, we assumed that there would be interactions among many of the predictorvariables (Table 4).

The increased complexity of models that considered interactions made it impossible to usetraditional significance test model selection methods. Instead, we used Information Theoretic Methodsand the accompanying Akaike Information Criterion (AIC) to evaluate model fit (Burnham and Anderson1998). Our general approach was to use the AIC method to select the most parsimonious model, asindicated by the model with the lowest AIC score. See Appendix 1 for more details on the AIC approach.

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Pend d’Oreille white-tailed deer winter range habitat use 13

Modeling of winter severity

Winter severity was not considered to be an initial base model term because severity of winterwas a dynamic process that most likely interacts with habitat class, topographic factors, and otherpredictors. Therefore, a better approach was to assess the relative relationship between winter severityand predictors through the analysis of interactions. The model that best fit the data for the entire 20-yeardata set was used as a starting point for this analysis. Models with interaction terms, such as winterseverity*habitat class and winter severity*elevation, were modeled to determine the significance of deerresponse of use for each predictor as a function of winter severity. Model fit was evaluated using AICmethods and resource selection coefficient significance tests.

Table 4. Interactions among predictor variables considered

Interaction Biological justification/hypotheses

Slope*aspect Snow depth will vary by both slope and aspect which will affect pellet

distribution

Slope*habitat Certain habitat classes (i.e. forage) may be more selected at different slopes

Elevation*slope Steeper slopes may be avoided or selected dependant on elevation

Elevation*habitat Certain habitat classes (i.e. forage) may be more selected at different

elevations

Elevation*aspect Certain elevation and aspects may be snow free therefore influencing deer

distribution

Elevation*aspect*

slope

Deer select very specific combinations of aspect and slope as a function of

elevation.

Winter*aspect Winter with high snow may lead to deer selecting south aspects only

Winter*slope Winter with high snow may lead to deer selecting steeper slopes only

Winter*elevation Winter with high snow may lead to deer selecting lower elevations

Winter*habitat Cover habitat classes may be selected in bad winters

Further refinement of habitat classes using forest cover data

The pre-defined habitat classes represent 1 attempt at defining likely habitat classes that deerassociate with. These categories provide a useful heuristic model in assessing the response of deerdistribution to factors such as winter severity. However, the actual categories are predetermined asopposed to estimated from the data, which will affect model fit. Therefore, models were built whichconsidered elements of forest cover to determine which forest cover attributes (i.e. species, crown closure,age class) that deer associate with the most. The fit of these models was then compared to models basedon habitat classes using the AIC method.

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Pend d’Oreille white-tailed deer winter range habitat use 14

RESULTS

Predictor variables

Winter severity

In general, winters in which cumulative snow depth at Grand Forks averaged <10 cm, 10-20 cm and >20cm were rated as good, average and bad, respectively (Fig. 2). Five winters had generally low snowdepths and were rated as good, 9 were average, and 6 had high overall snow depths and were rated as bad

0

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Year81 87 90 92 95

Snow D

epth (cm)

Good Winter

Snow D

epth (cm)

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Year78 82 84 85 86 97

Snow D

epth (cm)

Bad Winter

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Year

79 80 83 88 89 91 93 94 96

Snow D

epth (cm)

Average Winter

Figure 2. Winters with good, bad, and average ratings from Grand Forks snowfall data.The * in graphs was the mean and the middle bar was the median. The large rectanglesrepresent quartiles of the distribution. The bars represent the range of observations.Triangles and small boxes represent outlier observations which are farther than 1.5 timesthe inter-quartile range from the quartiles.

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Pend d’Oreille white-tailed deer winter range habitat use 15

(Fig. 2). Winter 1997 in the West Kootenay was especially severe, with 50-year record snowfall andaccumulation.

Population change over time

As noted above, the primary objective of using the population-based variables in the models wasto sponge up as much variation in the data, which should increase the power to detect selection of deertowards habitat and topographic variables. The estimated deer population size was correlated with meanpellets per year (Pearson r = 0.947, P = 0.0001, n = 20), but not with days on range (r = -0.339, 19 df, P =0.15). Days on range was not correlated with mean pellets per year (r = 0.022, 19 df, P = 0.93). Wecompared the logistic model fit with combinations of estimated density, mean pellets per year, and dayson range. The combination of parameters that explained the most variation in the data was used insubsequent model runs. Results of preliminary AIC analysis with combinations of these parameterssuggested that mean pellets per year and days on range provided the best fit to the data and therefore theseparameters were used in future model building.

Deer population estimates derived from pellet plot data and spring spotlight counts bothdemonstrated a change in population size between 1978 and 1997, with a peak between 1989 and 1993 or1994 (Fig. 3).

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78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Year

Spot

light

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Popu

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Spotlight counts Population estimate

Figure 3. Trends in white-tailed deer population size wintering in the Pend d’OreilleValley as reflected by mean pellet group counts and spotlight counts.

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Pend d’Oreille white-tailed deer winter range habitat use 16

Response variables

Pellet group distribution

Of the pooled pellet group data from 1978-1997, 69.3% of plots had no pellets and 15.8% hadscores of 1 pellet (Fig. 4). The remaining 14.9% had scores >1, with most of the 30,341 plots (98%)exhibiting scores under 5 pellet groups/plot. These results indicate that the data set was reallypresence/absence with a small (5%) percentage of �outlier� plots exhibiting counts >3 pellet groups/plot.Proportion of use was highly correlated (P < 0.00001) with pellet groups counted on each plot.Therefore, we concluded that proportional use (presence/absence of pellet groups) can be used as aresponse variable with minimal loss of information.

We also evaluated the fit of the raw count data to the Poisson distribution and the fit of thepresence/absence data to the binomial distribution. The fit of the raw count data to the Poissondistribution-based regression models was poor (as determined by Pearson χ2 goodness of fit tests;McCullough and Nelder 1989). In contrast, Pearson χ2 tests suggested a much better fit of thepresence/absence data to binomial distribution-based logistic regression models (See Appendix 1). Wetherefore used logistic regression of presence/absence data as the principal analysis technique.

Percent

0

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70

No. pellet groups per plot

0 1 2 3 4 5 6 7 8 9 10

Figure 4. The distribution of deer pellet groups per plot for the entire 20 year Pendd'Oreille data set (n = 30,341).

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Pend d’Oreille white-tailed deer winter range habitat use 17

Data screening and tests of analysis assumptions

Pellet plots as an index of overall habitat availability

Comparison of relative frequencies of habitat classes estimated from pellet plots with theproportion of habitat classes within the study area suggested that the plots were a representative sample ofavailable habitat classes (Fig. 5). The forage class was the dominant habitat class within the study area.

0

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O ptimal Cover G ood C over M arginalCo ver

RecruitmentCover

Forage O ther

H abitat class

Prop

ortio

n of

tota

l are

a/pl

ots

G IS-en tire area Percen tage p lots

Figure 5. Distribution of habitat classes as estimated by GIS and percentage of

plots.

Distribution of habitat classes in relation to topographic variables

The distribution of sample sizes among habitat classes differed by aspect and elevation (Fig. 6).Most habitat classes had few plots on north aspects, especially at lower elevations, because there werefew north-facing slopes within the study area. The overall sample size (i.e., 20 years of 1,534 plots/year)was large so cells of <5 observations were a rare occurrence. However, the sample sizes for somecombinations of parameters examined within a year, such as many of the northern and eastern aspects,were low and therefore inference in terms of habitat selection for these aspects will be compromised.This limits the applicability of this model to other areas that may show different distributions of habitatclasses (Fig. 6). As discussed later, the modeling of elevation as a continuous variable, and the structureof the statistical model in the analysis partially accounts for uneven sample sizes by considering thetopographic position of each plot in the estimation of proportional use.

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Pend d’Oreille white-tailed deer winter range habitat use 18

Frequency

Habitat type Good Cover Marginal CoverOptimal Cover Recruitment CoverOther

0

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60

80

100

120

140

160

Aspect

Elevation<700m 700-900m >900m

N E S W N E S W N E S W

Figure 6. Yearly availability of habitat classes (forage excluded) as a function ofaspect and elevation. Data reflects availability of plots in 1990.

Analysis of temporal change in habitat use and availability

Change in habitat availability over time

The abundance of habitat classes changed relatively little during the study (Fig. 7). The mostsignificant changes were in the optimal and good cover classes which were reduced by approximately15%, mostly within the first 5 years of the study. The small changes in the abundance of habitat classesover the study period precluded rigorous analysis of deer response to habitat change. A more site-specificapproach to change in habitat usage may be the best strategy to qualitatively determine deer response toforest harvest and other activities.

Proportional use of habitat classes over time

Proportion of use of all habitat classes increased and then slightly decreased between 1978 and1997 (Figs. 8 and 9), correlated with overall population change (Fig. 3). In addition, the proportional useof habitat classes did not vary to a significant degree between adjacent years for many of the habitatclasses. Notable exceptions were the increased use of the optimal cover classes in 1978, 1983 to 1985and 1997, which was potentially due to increased snow accumulation during these winters (Fig. 8).

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Pend d’Oreille white-tailed deer winter range habitat use 19

A)

Habitat class Good Cover Marginal CoverOptimal Cover OtherRecruitment Cover

020406080

100120140160180200220

Year

76 78 80 82 84 86 88 90 92 94 96 98

Frequency

B)

Habitat class Forage Good CoverMarginal Cover Optimal CoverOther Recruitment Cover

0100200300400500600700800900

1000

Year

76 78 80 82 84 86 88 90 92 94 96 98Frequency

Figure 7. Changes in the frequency of habitat classes (forage classes excluded (A) andincluded (B)) from 1978 to 1997, as indicated by frequencies of pellet plots in the Pendd'Oreille study area.

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Pend d’Oreille white-tailed deer winter range habitat use 20

Habitat class Marginal Cover Good CoverOptimal Cover

0.00

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0.50

Year

76 78 80 82 84 86 88 90 92 94 96 98

Proportional Use

Figure 8. Use of cover habitat classes from 1978 to 1997 as estimated by proportionof plots with deer pellets, Pend d’Oreille.

Habitat class Forage OtherRecruitment Cover

0.00

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0.40

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Year

76 78 80 82 84 86 88 90 92 94 96 98

Proportional Use

Figure 9. Use of non-cover habitat classes from 1978 to 1997 as estimated byproportion of plots with deer pellets, Pend d’Oreille.

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Pend d’Oreille white-tailed deer winter range habitat use 21

Statistical habitat selection analysis using the pooled 20 year data set

Base additive model

Most of the base model terms (slope, aspect, elevation, mean pellets/year, and days on range) inthe initial base additive model analysis were significant at α = 0.05 (Table 5). Exceptions to this werelinear slope and elevation factors. Quadratic and cubic elevation terms were significant suggesting a non-linear relationship between elevation and pellet group distribution. The non-significance of slope andlinear elevation terms was treated cautiously given the simplistic structure of the base additive model.These terms were kept in the model and were re-assessed using AIC methods.

Table 5. AIC model selection results for 20-year deer pellet group study, Pend d’Oreille.

Additive terms(+ terms below1) Interaction terms

Number ofparameters AIC

DeltaAIC

habitat, slope2 aspect*slope, elevation*aspect 20 35850.69 0.00

habitat, (no elevation3) aspect*slope, elevation*aspect 19 35859.63 8.93

habitat aspect*slope, elevation*aspect 19 35899.02 48.32

habitat slope*aspect 17 35944.85 94.16

habitat habitat*elevation 21 35947.60 96.90

habitat aspect*habitat 27 35961.17 110.48

habitat slope*habitat 21 35963.67 112.98

habitat elevation*aspect 17 35995.56 144.86

habitat winter*slope 18 36010.06 159.37

habitat aspect*elevation*slope 17 36028.04 177.34

habitat winter*elevation 17 36028.04 177.34

habitat winter*habitat 33 36028.15 177.46

habitat habitat only 15 36029.23 178.54

habitat slope*elevation 16 36030.61 179.92

habitat winter*aspect 21 36038.17 187.47

Habitat habitat*yr 155 36065.21 214.51

no habitat 9 36132.03 281.331 All models included the following parameters: aspect, elevation, elevation2, elevation3, slope, days onrange, and mean pellets/year.

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Pend d’Oreille white-tailed deer winter range habitat use 22

AIC model selection of models with interaction terms

AIC model selection results suggested that many of the models that included interaction terms fitthe data better than the simpler non-interaction models (Table 5). Models that considered the interactionsamong slope, aspect, and elevation in addition to the additive effect of habitat class were most supportedby the data. Habitat and topographic interaction models (i.e. habitat*aspect, habitat*slope terms) wereless supported, a result that was verified by non-significant resource selection function estimates. Ingeneral, models that included winter severity did not show better fit to the data when compared to thebase additive model with no interactions.

GEE resource selection coefficient estimates for the optimal AIC model are provided in Table 6.The significant resource selection coefficients suggested that the optimal and good cover classes were

Table 6. Results of GEE analysis of AIC model, Pend d’Oreille. A parameter wasconsidered significant if at least 1 of its categorical slope (ββββ) estimates was significant.

Parameter CategorySelection

coefficient (β) SE (β) Z P (β=0)Aspect South and west 0.248 0.075 3.31 0.0009

North and east 0.000 0.000 0.00 0.0000

Slope -0.122 0.065 -1.87 0.0619

Slope2 -0.082 0.029 -2.84 0.0044

Elevation -0.190 0.082 -2.32 0.0204

Elevation2 -0.138 0.037 -3.75 0.0002

Elevation3 0.022 0.016 1.39 0.1659

Habitat Optimal cover 0.588 0.235 2.50 0.0123

Good cover 0.477 0.236 2.02 0.0430

Marginal cover 0.338 0.248 1.36 0.1729

Recruitment

cover

0.083 0.253 0.33 0.7425

Forage 0.247 0.218 1.14 0.2562

Other 0.000 0.000 0.00 0.0000

Slope*aspect South and west 0.340 0.079 4.30 0.0000

North and east

Elevation*aspect South and west 0.270 0.088 3.08 0.0021

North and east

Mean pellets/year 2.074 0.067 30.85 0.0000

Days on range -0.004 0.001 -4.66 0.0000

Intercept -1.959 0.252 -7.78 0.0000

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Pend d’Oreille white-tailed deer winter range habitat use 23

selected for, whereas forage, recruitment cover, and marginal cover were not selected for. The habitatclass most selected for was optimal cover, as indexed by the largest selection coefficient. The significantresource selection coefficients also support the inclusion of the slope*aspect and elevation*aspect terms.The sign of the elevation and slope (β) selection coefficients should not be interpreted literally due to thefact that they are based on standardized values. In addition, the actual contribution of slope and elevationto predicted proportional use was based upon both the single additive terms and the interaction terms (i.e.slope*aspect and elevation*aspect), and therefore evaluating the relative importance of slope andelevation terms based upon β value alone is difficult. Graphical analysis methods should be used tointerpret the relative magnitude of the selection towards elevation and slope parameters.

A contour plot, which displays predicted proportional use for combinations of elevation andslope, illustrates the strong interaction of elevation and slope (Fig. 10). The predicted proportional usefrom combinations of aspect, elevation, and slope suggested that south and west aspects, with slopes>40% in mid elevations were the most selected for. North and east aspects were only selected for in lowelevations and on moderate slopes. In general, north and east aspects were less selected for in mostcombinations of elevation and aspects than southern and western aspects. The general pattern of thecontour plot was non-linear, supporting the inclusion of higher order polynomial terms for both slope andelevation. In terms of habitat selection, this model suggested that the most dominant factors influencingdistribution of deer were topographical, and that habitat selection was additive on top of these factors. Interms of this model, each habitat class would show a similar contour surface, however the proportionaluse for each contour would be influenced by the overall selection of the particular habitat.

Evaluation of AIC model fit

Analysis of chi-square residuals suggested that most models exhibited adequate fit, including thebase additive model, although the fit of the interaction models were better, especially in terms of theresponse variables. Adequate model fit in this case was based on the fact that most observations werewithin a chi-square residual value of 2 (Stokes et al. 1997). In general, the AIC model (Table 6) was bestat predicting the absence rather than presence of deer on plots, as indicated by the symmetry of residualsaround the 0 chi square origin, a reasonable result since most of the data in the Pend d'Oreille data setpertained to absence rather than presence.

Observed and predicted proportional use (averaged across all plots) were compared for 3examples of yearly data representing differences in estimated population size and winter severity (Fig.11). Proportional use differed among years, attributed to differences in population size. The years of1981 and 1990 both had good winters, however in 1990 the estimated population size was 5-6 times aslarge. In contrast, 1997 was a very bad winter with a moderate population size. In general, the fit of themodel was adequate from the data of all winters, with the exception of the �other� habitat class. Themodel did not explicitly estimate the other habitat class, and the proportional use of this category wasestimated as 1 minus the proportional use of all the other habitat categories combined. Therefore, it wasexpected that this category would exhibit the poorest fit.

The fit of the model to the 1981 data was poorer than the other 2 years presented. However, thisyear had the lowest estimated population levels and low levels of proportional use, as reflected by thescale of the y-axis (Fig. 11). Given this, the actual deviation between predicted and observed proportionaluse was small (i.e. <0.05 in most cases). In general, the fit of the model was better with years with higherpopulation size because the model had more presence/absence data, which allowed more precise andaccurate estimates. The model overestimated the proportional use of cover classes in the good winters(1981 and 1990) and underestimated the use of cover classes in the bad winter (1997), primarily becausewinter severity was not included in the model and the model was predicting "average" yearly use.

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Pend d’Oreille white-tailed deer winter range habitat use 24

Slope

Aspect= South and West

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Aspect=North and East

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Figure 10. Contour plots for the interactions of aspect, slope (%), and elevation (m).Proportional use for each contour is listed in the graphs. The response was standardizedfor the forage habitat class.

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Pend d’Oreille white-tailed deer winter range habitat use 25

1981-Good Winter, Low Population

0.00

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OptimalCover

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Predicted Observed

1990-Good Winter, High Population

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1997-Bad Winter, Moderate Population

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OptimalCover

Good Cover MarginalCover

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Predicted Observed

Figure 11. Observed and predicted proportional use of habitat classes for years with a good winter and low population (1981),good winter and large population (1990), and bad winter and moderate population size (1997). Note the large difference inscale in proportional use (y-axis), which was mainly a function of population size.

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Pend d’Oreille white-tailed deer winter range habitat use 26

Analysis with bad and good winter data only

We took a subset of the 20-year data set so that only "bad" and "good" winters were included.The AIC method was used to determine the fit of models that considered the interactions of winter withslope, elevation, aspect, and habitat class. The optimal model defined for the 20-year data set with pre-defined habitat classes was used as the base model in this analysis. A model which includedwinter*slope, winter*elevation, and winter*habitat interaction terms was most supported by the data(Table 7). Proportional use for elevations and slope as predicted from the AIC model changed for southand west aspects (Fig. 12).

Table 7. AIC results for analysis of winter interaction terms, Pend d’Oreille.

Winter interacts with (+ base model)1 AICDeltaAIC

Number ofparameters

Slope, elevation, habitat 19395.81 0.00 36

Slope, habitat 19398.14 2.33 34

Slope, elevation 19399.12 3.31 24

Slope 19400.86 5.04 22

Slope*aspect, elevation*aspect, habitat 19401.41 5.59 40

Slope, elevation, habitat, aspect 19401.75 5.93 40

Slope, slope*aspect 19403.14 7.33 30

Slope*aspect, elevation*aspect 19403.58 7.77 28

Slope, elevation, aspect 19404.06 8.24 28

Slope 19418.21 22.40 22

Habitat 19419.53 23.71 32

Elevation*aspect 19421.61 25.79 22

Winter additive 19426.68 30.87 22

Aspect 19430.13 34.31 241All models include the following parameters: aspect, days on range, elevation, mean pellets/year, habitat,slope, slope2, elevation, elevation2, elevation3, slope*aspect, aspect*elevation.

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Pend d’Oreille white-tailed deer winter range habitat use 27

Bad Winter

0

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Elevation

400 650 900 1150 1400

0.35

Slope

0.30

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Good Winter

0

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80

Elevation

400 650 900 1150 1400

Slope

0.35

0.300.25

0.200.15

Figure 12. Predicted proportional use of habitats as a function of winter severity, slope(%), and elevation (m). Proportional use for each contour is listed in the graphs. Theelevation plots were standardized for forage habitat class, moderate population size andsouth /west aspects.

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Pend d’Oreille white-tailed deer winter range habitat use 28

The shapes of plot contours suggest that selection for steeper slopes and low to moderateelevation increased in bad winters (Fig. 12). In contrast, deer selected similarly for mid-steepness slopesacross mid to higher elevations in good winters. Analysis of GEE estimates for the winter*habitatinteraction term for the AIC selected model also suggested significant selection for cover classes in badwinters (Table 8). These results corresponded to the direction of selection in bad winters compared togood winters. Resource selection coefficient estimates showed strongest positive selection towardsoptimal cover and good cover, and negative selection towards forage. In terms of proportional use, thecover habitat classes were generally utilized more in bad winters as opposed to good winters (Fig. 13). Incontrast, the recruitment cover and forage classes were utilized less in bad winters.

Table 8. Interactions between habitat class (habitat) and winter severity (winter).

Interaction Habitat classResource Selection

Function (β) Z-score P-valueHabitat*winter Optimal cover 0.312 3.698 0.000

Habitat*winter Good cover 0.197 2.133 0.033

Habitat*winter Marginal cover 0.083 0.763 0.445

Habitat*winter Recruitment cover -0.151 -1.106 0.269

Habitat*winter Forage -0.139 -3.212 0.001

0.15

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Figure 13. Predicted proportional use of habitat classes as a function ofwinter severity. Results were standardized for a south and west aspect,moderate slope (30%), moderate elevation (790 m) and moderate populationlevels.

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Pend d’Oreille white-tailed deer winter range habitat use 29

Forest cover based analyses

The AIC model selected in the previous section was further developed by using forest coverattributes in place of the pre-defined habitat classes. The general development of this model wasproblematic due to uneven sample sizes for combinations of species, crown closure, and age class.Modeling age class and crown closure as a continuous variable partially mitigated this problem, however,the degree of inference for stands that did not have similar age class and crown closure distributions waslimited.

The distribution of crown closure was more even than age class for Douglas-fir and other treespecies, which made the use of this variable less problematic (Fig. 14). Crown closure and age class werecorrelated (r = 0.74, P < 0.0001, n = 1,534) suggesting that the age class potentially determines the crownclosure of a stand (and vice versa).

One potential modeling method to confront the problem of non-even crown closure (or age class)for each species was to pool crown closure for all species, therefore circumventing sample size issues forany particular species. This method had the restrictive assumption that the response of deer pelletdistribution was similar for each tree species (which is biologically unlikely). Models that explicitlyconsidered the relationship between crown closure and individual species (i.e. crown closure*speciesinteraction) were compared with models that pooled crown closure (and age class) to determine if poolingof age class or crown closure was a valid strategy. Note that models that consider species alone (and notcrown closure or age class) would not be valid for this study due to the species-specific distributions ofage class and crown closure.

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Figure 14. Distribution of crown closure class and age class for tree/habitat speciesassociation groups, Pend d’Oreille. Sample sizes reflect plots available in 1990.

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Pend d’Oreille white-tailed deer winter range habitat use 30

Table 9. AIC model selection results for forest cover-based models.

Additive terms1,2 Interactions AIC Delta AICNumber ofparameters

Species Species*crown closure 1,2 35374.00 0.00 26

Species Species*crown closure 1,2,3 35378.19 4.20 30

Crown closure 1,2 35428.52 54.52 20

Species, crown closure 1,2,3 35444.91 70.92 20

Species Species*ageclass 1,2,3 35478.40 104.40 30

Species, crown closure, age class Crown closure*age class 35481.02 107.03 21

Species, crown closure, age class 35483.31 109.32 20

Species, age class 35491.33 117.33 19

Species, age class 1,2,3 35492.20 118.21 21

Species Species*age class 35493.90 119.90 22

Species, crown closure 35578.46 204.46 19

Species Species*crown closure 35581.89 207.89 22

Age class 35709.25 335.25 15

Pre-defined habitat 35850.69 476.70 20

Crown closure 35866.20 492.21 151Topographic model includes the following parameters: aspect, days on range, elevation, meanpellets/year, and slope, slope2, elevation, elevation2, elevation3, slope*aspect, aspect*elevation.2Superscripts refer to the powers in which a parameter was modeled (i.e. crown closure1,2)=crownclosure1, crown closure2

AIC model selection results suggest that models with the interaction of leading species and crownclosure were most supported by the data (Table 9). Models that pooled crown closure were much lesssupported by the data, suggesting that there were species and crown closure interactions.

Proportional use of deer pellet groups as a function of leading species and crown closure variedamong tree species (Fig. 15). The interaction curves for deciduous-larch and grand fir-cedar were notsignificant (α = 0.05), which may have been due to low sample sizes. The Douglas-fir interaction termwas significant, and suggested selection for lower and higher crown closure classes. Note that in generalthe interactions of the Douglas-fir and grand fir-cedar species with crown closure appeared to be similarat lower crown closures, but diverged at higher crown closures (Fig. 15); however, this finding waslimited by the small range of crown closure values for grand fir-cedar stands. The deciduous-larchshowed a different response, but again the actual response curve should be interpreted cautiously due toinsignificant slope estimates and low sample sizes. However, in general a species-specific crown closure(interaction) model was more supported by the data than a pooled crown closure model, suggestingspecies-specific habitat selection (Table 9).

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Pend d’Oreille white-tailed deer winter range habitat use 31

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Figure 15. The interaction of crown closure and tree species groups. The grand fir-cedarand deciduous-larch curves should be interpreted cautiously due to non-significance ofinteraction slope parameters. Crown closure values are offset to ease interpretation.Confidence interval bars are given for each prediction.

AIC results suggested that a forest cover species and crown closure-based model was moresupported by the data than the model using pre-defined habitat classes. The limitations of this result arediscussed below. Recommendations for future work to further develop forest cover-based models aregiven later in the report.

DISCUSSIONAnalysis of this 20-year pellet count data set suggests that the main driving force in terms of

winter habitat selection by white-tailed deer in the Pend d�Oreille valley was local topography (slope,aspect, and elevation), with habitat class or habitat attributes selected on an additive basis (Table 10).Over-riding all selection was changes in the size of the deer population on the study area as indexed bymean pellet group counts; this was the single strongest determinant of pellet group distribution.Essentially this means that within the confines of changing population size, habitat selection occurred onthe subset of areas that tend to accumulate low snow depths as determined by topographic constraints.Our results also suggest that selection of habitat classes was mainly towards the �best� 2 of the pre-defined cover classes, optimal and good, with greatest selection for the optimal cover and during winterswith the highest snow accumulation (Table 10). Optimal cover (defined as Douglas-fir or grand fir −leading stands with age class ≥6 and crown closure ≥5) had the highest age and crown closure within thestudy area, and would provide the greatest snow interception and thermal cover. Douglas-fir stands arealso important as a source of forage (Dawson et al. 1990). We found significant selection for both lowerand higher crown closure classes for Douglas-fir.

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Pend d’Oreille white-tailed deer winter range habitat use 32

Table 10. Summary of results, white-tailed deer winter habitat use in the Pend d’Oreillevalley.Predictor variable Summary of results

1) Aspect • South and west aspects selected at most elevations and slopes (Fig. 10)• North and east aspects only selected at lower elevations and less steep

slopes (where aspect is of little importance; Fig. 10)• Interacts with elevation and slope (Table 6)

2) Slope • Interacts with aspect: moderate to steeper slopes selected on south and westaspects (Table 6 and Fig. 10)

• Lower angles slopes selected on north and east aspects at lower elevations• Interacts strongly with winter severity: steeper slopes (40-60%) selected for

in bad winters (Fig. 12)3) Elevation • Mid-elevations selected for on south and west aspects. Lower elevations

selected for on north and west aspects (Fig. 10)• Interacts with winter severity: selection for lower to middle elevations (450-

1000 m.) and steeper slopes in bad winters (Fig. 12)4) Habitat class Primary selection determined by topographical variables (above). Habitat level

selection was additive. Basically, habitat selection occurs on the subset of areasthat have reduced snow as determined by topographical constraints (factorslisted above)

a) Optimal cover • The most selected habitat class in terms of resource selection coefficients(Table 6) and proportional use (Fig. 13)

• Interacts with winter severity: higher selection in bad wintersb) Good cover • The second most selected habitat class in terms of resource selection (Table

6) and proportional use (Fig. 13)c) Marginal cover • Not selected for in terms of resource selection (Table 6) and proportional

use (Fig. 13)d) Recruitmentcover

• Not selected for in terms of resource selection (Table 6) and proportionaluse (Fig. 13)

e) Forage • Marginally selected for in terms of resource selection (Table 6) andproportional use (Fig. 13). Forage was the most abundant habitat class anduse was substantial, and therefore greater selection occurs for the morelimiting optimal and good cover habitat classes

5) Crown closure • Only Douglas-fir has suitable range of crown closure values to fullyevaluate crown closure effects (Fig. 14)

• Interacts significantly with Douglas-fir forest species; selection for higherand lower crown closure classes of Douglas-fir (Fig. 15)

• Interacts with winter severity: selection for higher crown closure in badwinters

• Highly correlated with age class6) Forest coverspecies

Pooled to 3 categories due to sample size constraints

a) Douglas-fir • Significant selection for lower and higher crown class categories forDouglas-fir (Fig. 15)

b) Deciduous-larch • Mild selection for lower crown closure categories (Fig. 15)• Small sample size precludes full investigation of selection for this species

group

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Pend d’Oreille white-tailed deer winter range habitat use 33

Table 10. Continued.c) Grand fir-cedar • No selection for this species, however this may be due to low sample sizes

(Fig. 15)7) Mean pellet groupcount

• The strongest determinant of pellet group distribution as documented by avery large resource selection coefficient (Table 6)

8) Yearly days onrange

• A significant predictor of pellet group distribution suggesting that timespent on the winter range also determines pellet group distribution (Table 6)

Winter habitat selection patterns and winter severityWe modeled winters with overall low and high average snow accumulation (�good� versus �bad�

winters) in an attempt to elucidate selection of topographic and habitat attributes during periods of higheststress (deepest snows), and presumably greatest winter habitat selection pressure. We found selection forsteeper slopes and lower elevation increased in bad winters, which suggests that these variables are notsimple correlates of habitat quality. We also found strong positive selection towards optimal cover andgood cover (generally higher proportions of higher crown closure Douglas-fir) during bad winters, andnegative selection towards forage. Proportional use of optimal and good cover habitat classes was greaterand use of recruitment cover and forage classes less in bad winters as opposed to good winters, inagreement with how the animals should react when adopting an energy conservation mode of behaviour(Moen 1978) with increasing snow accumulation (Parker et al. 1984). We did not find significant use ofgrand fir-cedar stands with higher crown closure during severe winters, in agreement with Woods (1984),although low sample size compromised the power of this analysis.

Using radiotelemetry techniques, other studies in northwestern North American have founddifferent habitat selection by white-tailed deer during different periods of the winter. Habitat selectionduring snow depths of <30 cm (Pauley et al. 1993) to <40 cm (Woods 1984; early and late winter)appears to be non-selective (Woods 1984) or directed towards stands that furnish little canopy cover orsnow interception relative to mature forest stands, but provide the greatest abundance of preferred forage(Pauley et al. 1993). During these seasons of low snow accumulation, white-tailed deer should choosehabitats somewhat irrespective of terrain and canopy cover, except to the extent that these factors affectforage availability (Pauley et al. 1993). During mid-winter and the greatest snow accumulation, deerselect advanced forest age classes that provided the most optimum snow conditions, primarily throughhigher canopy closure, but may provide little available forage (Pauley et al. 1993, Secord 1994).

Within the confines of our methodology our results followed this general pattern, with less use ofthe more closed-canopy stands during winters of lower snow accumulation, and more use of high crownclosure during winters of high snow accumulation. A potential drawback to the pellet group method ofdetermining habitat use is that pellet plots provide an index of average use of a given habitat over anentire year, or at least an entire winter. We have assumed that there is little use of our study area duringsummer and fall (Woods 1984). Still, the pellet plots provide an indication of use over the entire winter,which generally runs from arrival on the winter range during low and accumulating snow depths, throughthe deepest snows (and presumably the highest habitat selection pressure) in mid-winter, to springdeparture from the winter range after much of the snow has melted from lower and mid-elevation sites.As noted, habitat selection pressures and habitat use differ among periods of the winter, primarily relatedto snow depth (i.e., Woods 1984, Pauley et al 1993, Mackie et al. 1998). Therefore, pellet group countscannot readily highlight habitats used during the mid-winter �critical� period. Identification of habitatsselected during a particular period of the winter may be easier using techniques that provide a moreinstantaneous index to habitat selection, such as radio-telemetry (Woods 1984, Pauley et al. 1993, Mackieet al. 1998, Poole et al. 2000) or track counts (Waterhouse et al. 1990, D�Eon 2000). We used

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Pend d’Oreille white-tailed deer winter range habitat use 34

comparisons of habitat selection between winters of low and high overall snow accumulation as asurrogate for having data to identify periods of greatest stress and selection pressure during each winter.

Forest cover versus pre-defined habitat class modelsThe pre-defined habitat classes and forest cover-based vegetation structure variables displayed

similar predictions in terms of habitat use. Results from the pre-defined habitat class model (Table 6, andFigs. 11 and 13) suggest weak selection for forage classes (with no crown closure), no selection for midcrown closure habitat classes (recruitment and marginal cover) and increased selection for cover classeswith greater crown closure (good and optimal cover). A similar trend is seen with the AIC model usingseparate forest cover structural attributes (Fig. 15). Unlike the pre-defined habitat class model, therelationship between crown closure and proportional use was statistically estimated with the forest cover-based model. In addition, species such as larch and grand fir were modeled as a separate class instead ofbeing pooled (within the pre-defined cover classes). All of these differences result in a comparativelybetter fit of the forest cover-based model to the data (Table 9). However, the general applicability of theforest cover-based crown closure and species interaction model is limited due to the limited range ofcrown closure classes for the leading combinations of species. The interaction terms of crown closureand species will only be applicable to the narrow range of crown closure classes for each species used inestimating model parameters. For example, this model could not be used in an area that had grand fir-cedar stands with crown closure class >6 since there were no such stands in the Pend d'Oreille whichcould be used to estimate parameters for greater crown closures. However, it does highlight some of thestronger selection components (i.e., selection towards greater crown closure in Douglas-fir) that wouldmost likely drive habitat selection by deer in areas of similar biogeoclimatic subzones.

Topographic variables and sample size issuesOne of the main findings of this analysis was the strong interaction of deer habitat selection

among slope, aspect, and elevation (Figs. 10 and 12). This interaction was likely because only certaincombinations of these topographic strata shed snow and accumulate low levels of snow, and thereforeonly these areas were available to deer in all but the lowest snow accumulation winters. Given this, oneof the main challenges in interpreting the results was the adequate modeling of habitat classes and treespecies that do not occur evenly across these topographic strata. The direct modeling interactionsbetween slope, aspect, and elevation partially accounted for this by modeling the topographic location ofeach plot. However, yearly sample sizes between some combinations of strata were low (i.e., northaspects at lower elevations).

Influence of population on deer distributionThe large influence of deer population size on distribution of deer was documented by the large

value of model slope parameters for mean pellets per year. This result suggests that deer will expand orcontract areas of use based on population size. Therefore, managers and researchers should consider therelative population size of deer in study areas when generalizing the results of studies for any given year,an aspect of ungulate habitat use that is rarely considered in habitat use studies.

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Pend d’Oreille white-tailed deer winter range habitat use 35

Shapes of response curvesMany of the predictor variables were modeled as higher order polynomial terms to relax the

restrictive assumption of a logistic relationship between response and predictor variables. This allowednon-uniform response surfaces (e.g., Fig. 12). It should be obvious that non-linear responses should beconsidered in all habitat studies. Many of the published studies of habitat selection blindly use logisticregression with little attention to non-linear relationships and other restrictive assumptions of thistechnique.

Limitations to findingsThis study was not designed as a controlled study of winter habitat selection by white-tailed deer.

The main objective of the pellet group transects was to estimate population size on the winter range(using the methods of Smith et al. 1969), and therefore the distribution of predictor variables was not arepresentative sample of all white-tailed deer habitat in the area. Therefore, the results of this study mayonly apply to winter range selection within the Pend d�Oreille study area. Wide scale extrapolations toother areas should be done cautiously, and include rigorous comparisons of the ranges of predictorvariables in the Pend d�Oreille study with those found in other areas. For example, it would not be validto use the model derived in this exercise for an area that has a large percentage of north-facing aspects,nor would it be applicable to use this model for areas that have vastly different classes and distributions oftree species.

Maps of predicted proportional useWe generated maps that display predicted proportional use of the Pend d'Oreille study area under

a variety of scenarios (Figs. 16 and 17; see Appendix 2 for detailed map). The scenarios, the models usedto generate the proportional use, and the input parameters are described in Table 11. Other inputparameters (i.e. topographic and habitat) were taken from TRIM and forest cover data.

Table 11. Summary of input parameters for GIS maps (Figs. 16 and 17) displayingpredicted proportional use of the Pend d’Oreille winter range.Map title Model used to generate predictions Population parameters1 Winter severityMinvalue AIC model from pooled 20 year data set

(Table 6)Minimum observed Average

Meanvalue AIC model from pooled 20 year data set(Table 6)

Average observed Average

Maxvalue AIC model from pooled 20 year data set(Table 6)

Maximum observed Average

Goodwinter AIC Good and bad winter only model(Table 7)

Average observed Good

Badwinter AIC Good and bad winter only model(Table 7)

Average observed Bad

1Population parameters are mean pellets/year and days on range.

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Pend d'Oreille white-tailed deer winter range habitat use 36

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Figure 16. Predicted proportional use of habitat in the Pend d'Oreille winter range underlow (minvalue), average (meanvalue), and high (maxvalue) population levels

Proportional Use

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Pend d'Oreille white-tailed deer winter range habitat use 37

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Figure 17. Predicted proportional use of habitat in the Pend d'Oreille winter range undergood (low snow; goodwinter) and bad (deep snow; badwinter) snow levels

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Pend d’Oreille white-tailed deer winter range habitat use 38

The minvalue, meanvalue, and maxvalue maps (Fig. 16) display how the expected range ofproportional use predicted in a winter range area is highly dependent on the population of deer present onthe winter range and the amount of time spent on range. These maps are based upon the same logisticmodel, and therefore similar areas are highlighted on the maps. However, the range of proportional usevaries as a function of population size on the winter range area. For example, in the case of the minvaluemap (low population size), so few deer were present that the actual range of proportional use seen wassmall because of the small number of pellets deposited during the winter. In this case, not enough pelletswere deposited to highlight the ranges of proportional use.

The good and bad winter maps (Fig. 17) display how the predicted winter range proportional usedepended on winter severity. In the case of the bad winter (deep snow), key areas (in green) are usedmore than other areas. In a good winter (low snow), selection does not occur for these areas, andtherefore only general selection for larger areas occurs.

These maps highlight some important findings and recommendations from this study. First, theactual proportional use of winter range by deer as indexed by pellet plots is a function of the number ofdeer present, the amount of time they spend on the winter range, and the severity of the winter.Therefore, observations of deer use for any given year may not be indicative of long-term winter rangeuse. As a result, researchers should attempt to get an approximate idea of relative population size andwinter severity when attempting to extrapolate yearly results of selection studies to long-term winterrange use. For example, a study conducted in a mild winter with low population size will probably notdetect key habitat attributes that would be detected in a bad winter with large population size. Moredirect methods of assessing habitat selection, such as radio telemetry, may allow a finer picture of habitatuse in critical winter areas when compared to pellet plot indices for short-term studies. Second, managersshould determine the key attributes in which winter range should be managed for. For example, therelative importance of winter severity and population size should be considered when interpreting studiesand drawing up management guidelines.

Recommendation for future analysesOur analysis presents a baseline exploration of the dominant factors influencing winter habitat

selection by deer in the Pend d�Oreille. The magnitude of the data set alone allowed a large array ofpotential analysis strategies to be employed. The model used to analyze the fit of species and crownclosure interactions was somewhat crude due to sample size constraints (i.e. 3 tree species-based classes).It may be possible to further develop this model into more species-based categories. However, to do thiswill require a more intensive study of the distribution of tree species topographical distribution. Forexample, it may be possible to split the Douglas-fir �species� into components based on secondaryspecies.

There was some disparity between models that used corrected and uncorrected forest cover datain terms of selection of habitat types (Appendix 1). It is difficult to determine the exact reason for thedifferences because both the corrected and uncorrected data are models or approximations of the truedistribution of habitat types. Therefore, we can only ascertain that the models give different results ratherthan make the conclusion that one model is "right" or "wrong". One potential explanation for thedisparity in habitat selection is that corrected forest cover data is centered around a plot based scale,whereas the forest cover data extends to a landscape scale in terms of the measurement of habitatavailability. Even though the forest cover data often is erroneous at a plot scale, it may be a betterdescriptor of landscape scale availability in certain cases. Therefore, models that use corrected anduncorrected data may be really assessing selection at different scales. The issue of scale can be exploredby defining habitat types at different distances from plots using a moving window approach (Apps andKinley 1998). In this case we would define habitat availability at a plot by multiple habitat types (i.e.

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Pend d’Oreille white-tailed deer winter range habitat use 39

20% forage, 80% optimal cover), so that there would be multiple habitat-based predictor variables in theanalysis. This type of analysis would also provide more inference in terms of optimal interspersion ofhabitat types.

The best way to test this model would be to compare the selection of habitat based on analysis ofradio-collared points with predicted use from the pellet group-based model. It is important to rememberthat the model has averaged selection across the 20-year data set. The best comparison would be to runthe model for the approximate winter severity in which the radio-collared deer were monitored (ifsufficient data are available), as well as the population levels (as reflected by mean pellets per year) andthe days on range estimated for the year of study.

WINTER RANGE MANAGEMENT GUIDELINESCurrent KBLUP ungulate winter range guidelines for white-tailed deer in the ICHxw

biogeoclimatic subzone call for a minimum of 30% forest cover retention of >100 year old (≥ class 6)trees with an average of 50% crown closure (class 5) in units >20 ha in size every 250 ha (KootenayInter-Agency Management Committee 1997). On slopes >50% the minimum amount of mature forestcover is reduced to 15%. In the ICHdw subzone these requirements are increased to a minimum of 40%forest cover with an average crown closure of 60%, with no allowance for slope. These habitatmanagement objectives are to: maintain suitable security cover, snow interception cover and connectivityhabitat value; maintain mature forest cover at an optimum distance to forage sites; to maintain high forageto cover differentiation; and to particularly maintain mature Douglas-fir stands (Kootenay Inter-AgencyManagement Committee 1997).

Our pellet plot analysis of the Pend d�Oreille winter range did not allow examination of spatiallyexplicit habitat prescriptions. We observed the strongest selection for Douglas-fir � leading stands,supporting KBLUP guidelines and highlighting the importance of this species. Age class 6 (101-120years) stands were most prevalent forested stands within the study area, most of which were Douglas-fir-leading stands (Fig. 14). We were unable to successfully model age class within our data set because ofuneven sample sizes by species, but the KBLUP age class recommendation appears reasonable sinceapproximately two-thirds of crown closure class 6 and greater stands within the study area werecomposed of age class 6-8 Douglas-fir � leading stands. Age class was highly correlated with crownclosure, and our model demonstrated significant selection for lower and higher crown closure classes forDouglas-fir (Fig. 15). AIC model selection results support the model with the interaction of leadingspecies and crown closure as best supporting the data (Table 9). Our models suggest that Douglas-firstands with crown closure equal or greater than 6 are most selected for by white-tailed deer. We did notdifferentiate our analysis by biogeoclimatic subzone; however, our analyses suggest that the value of acover stand increases with increasing crown closure. Whether crown closure class 5 stands are�adequate� for deer during periods of high snow accumulation is difficult to ascertain; there wasincreased proportional use of crown closure class 5 stands compared to lower crown closure stands.Because crown closure class 6-8 stands were most selected for during bad winters, the crown closurerequirements for ungulate winter range guidelines pertaining to this area could be increased to reflectthese higher crown closures.

MAINTENANCE AND ENHANCEMENT OF PEND D’OREILLE WINTER RANGEOur analysis suggests that although higher crown closure stands on steeper slopes are utilized to a

greater degree during winters with higher snow accumulation, stands that produce forage are alsoextensively used. This use did not translate into selection, but given the great proportion of forage standsavailable within the study area, access to forage is of obvious importance. This suggests support for an

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Pend d’Oreille white-tailed deer winter range habitat use 40

interspersion of multi-aged stands, which provide juxtaposition of snow interception cover and forage(Woods 1984).

We found that stand age was not as useful a variable as crown closure in describing deer winterhabitat selection, and that stands of high crown closure (primarily Douglas-fir) were always usedregardless of age. Our results suggest strong selection for high crown closure stands; even though standsof Douglas-fir with crown closure classes 7 and 8 were poorly represented within the study area (Fig. 14),proportional use of these stands was high relative to lower crown closure classes (Fig. 15). Mackie et al.(1998) reported that white-tailed deer consistently preferred overstory canopy coverages ≥50% duringsevere weather conditions. Thus, we suggest that timber management should emphasize retention ofDouglas-fir stands of the highest possible crown closure; reduction of crown closure is not desirable inany age class stand.

Our analysis showed preference for certain combinations of topography, with increased selectionfor moderate to steep slopes on south and west-facing aspects at lower to mid-elevations. Logging ofmature Douglas-fir � leading stands on these slopes should be avoided or minimized. Logging directed athigher elevation sites and on north and east aspects would minimize the reduction in preferred deer winterrange.

KBLUP ungulate winter range guidelines for white-tailed deer call for 30-40% mature forestcover within winter range, depending upon biogeoclimatic subzone, with an allowance down to 15%forest cover on >50% slopes (Kootenay Inter-Agency Management Committee 1997). Using correctedforest cover data for the Pend d�Oreille winter range, it appears that currently about 16% of the range iscomprised of stands with ≥40% Douglas-fir of crown closure class ≥5. This suggests that these mature,Douglas-fir � leading stands with the recommended crown closure are well below the minimum amountrecommended by KBLUP for slopes <50%, and that further harvest of these stands should be curtaileduntil more crown closure class ≥5 stands become available through forest maturation. Approximately12% of the study area is currently comprised of Douglas-fir � leading stands of crown closure class 4.

This simple analysis makes the assumption that forest cover mapping accurately depicts crownclosure of mapped polygons. Forest cover provides the average crown closure for a stand, but in realitythe same crown closure may look very different on the ground. Deer may view a stand that is composedof an even distribution of 40% crown closure less favorably than one that averages 40% crown closurebut is composed of clumps of mature trees with 70% crown closure interspersed with more open areaswith 10% crown closure. Finer-scale mapping than forest cover may be required to explore thesedifferences.

Examination of the spatial distribution of habitats was not possible with the pellet plot data set,but it seems prudent to develop a juxtaposition of forage with high crown closure stands within the winterrange. Approximately two-thirds of crown closure class 6 and greater stands within the study area werecomposed of age class 6-8 Douglas-fir � leading stands, thus retention of these age class stands should beencouraged. Selective logging may enhance forage production within stands, but will reduce the value ofthe stand for mid-winter cover. Thus, use of small clear-cuts while retaining un-touched stands of highcrown closure may provide the greatest benefit for deer. Enhancement of juxtaposition of cover andforage on mid-winter deer habitat could involve cut-blocks of small size (0.5-1.0 ha [Woods 1984], or0.1-0.6 ha [Mackie et al. 1998]). Silviculture prescriptions that involve commercial thinning of conifercanopies or reduction in understory conifer density are not desirable on mid-winter ranges (Mackie et al.1998).

Given the limitations of forest cover data in identifying forage characteristics of areas selected,we are reluctant to propose management considerations for forage enhancement. Prescribed burning andslashing of decadent shrubs can increase forage production that can benefit ungulates during winter andearly spring green-up (Woods 1984, Mackie et al. 1998).

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Pend d’Oreille white-tailed deer winter range habitat use 41

ACKNOWLEDGEMENTSPend d�Oreille/Seven Mile Fish and Wildlife Compensation Program, B.C. Ministry of

Environment, Lands and Parks, B.C. Hydro and Power Authority, and the Columbia Basin Fish andWildlife Compensation Program provided funding for this study. This study would have not beenpossible without the assistance of numerous field workers. In particular, we would like to thank D.Houghton, D. Ross, R. Clarke, and E. Parton for their diligent assistance in the field and office. We thankG. Mowat for reviewing drafts of this manuscript.

LITERATURE CITEDAGRESTI, A. 1990. Categorical data analysis. John Wiley and Sons, New York, New York, USA.

APPS, C. D., AND T. A. KINLEY. 1998. Development of a preliminary habitat assessment andplanning tool for mountain caribou in southeast British Columbia. Rangifer Special Issue No. 10:61-72.

ARMLEDER, H. M., R. J. DAWSON, AND R. N. THOMSON. 1986. Handbook for timber and Muledeer management co-ordination on winter ranges in the Cariboo forest region. B.C. Ministry ofForests. Land Management Handbook No. 13.

ARMLEDER, H. M., M. J. WATERHOUSE, D. G. KEISHER, AND R. J. DAWSON. 1994. Winterhabitat use by mule deer in the central interior of British Columbia. Canadian Journal of Zoology72:1721-1725.

BIO, A. M. F., R. ALKEMADE, AND A. BARENDREGT. 1998. Determining alternative models forvegetation response analysis: a non-parametric approach. Journal of Vegetation Science 9:5-16.

BENNETT, L. J., P. F. ENGLISH, AND R. MCCAIN. 1940. A study of deer populations by use ofpellet group counts. Journal of Wildlife Management 4:398-403.

BROWN, D., AND P. ROTHERY. 1993. Models in biology: Mathematics, statistics, and computing.John Wiley and Sons, New York, New York, USA.

BURNHAM, K. P., AND D. R. ANDERSON. 1998. Model selection and inference: A practicalinformation theoretic approach. Springer, New York, New York, USA.

COLLINS, W. B., AND P. J. URNESS. 1981. Habitat preferences of mule deer as rated by pellet-groupdistributions. Journal of Wildlife Management 45:969-972

CROWDER, M. 1995. On the use of a working corellation matrix in using generalized linear models forrepeated measures. Biometrika 82:407-410.

DAWSON, R. J., H. M. ARMLEDER, AND M. J. WATERHOUSE. 1990. Preferences of mule deer forDouglas-fir foliage from different sized trees. Journal of Wildlife Management 54:378-382.

D�EON, R. 2000. Deer winter range use and habitat associations within TFL #3: final report. Reportsubmitted to Slocan Forest Products Ltd., Slocan, B.C., Canada.

DUSEK, G. L., R. J. MACKIE, J. D. HERRIGES, JR., AND B. B. COMPTON. 1989. Populationecology of white-tailed deer along the lower Yellowstone River. Wildlife Monographs 104:1-68.

EDGE, W. D., AND C. L. MARCUM. 1989. Determining elk distribution with pellet-group andtelemetry techniques. Journal of Wildlife Management 53:621-624.

FLACK, V. F., AND P. C. CHANG. 1987. Frequency of selecting noise variables in subset regressionanalysis: a simulation study. American Statistician 41:84-86.

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Pend d’Oreille white-tailed deer winter range habitat use 42

GILBERT, P. F., O. C. WALLMO, AND R. B. GILL. 1970. Effect of snow depth on mule deer inMiddle Park, Colorado. Journal of Wildlife Management 34:15-23.

HARESTAD, A. S. 1985. Habitat use by black-tailed deer on northern Vancouver Island. Journal ofWildlife Management 49:946-950.

HURLBERT, S. H. 1984. Pseudoreplication and the design of ecological field experiments. EcologicalMonographs 54:187-211.

KEAY, J. A., AND J. M. PEAK. 1980. Relationship between fires and winter habitat of deer in Idaho.Journal of Wildlife Management 44:372-380.

KOOTENAY INTER-AGENCY MANAGEMENT COMMITTEE. 1997. Kootenay/Boundary land useplan implementation strategy. Ministry of Environment, Lands and Parks. Nelson, B.C., Canada.

LEOPOLD, B. D., P. R. KRAUSMAN, AND J. J. HERVERT. 1984. Comments: the pellet-groupcensus technique as an indicator of relative habitat use. Wildlife Society Bulletin 12:325-326.

LIANG, K.-Y., AND S. ZEGER. 1986. Longitudional data analysis using generalized linear models.Biometrika 73:13-22.

LIPSITZ, S. R., G. M. FITZMAURICE, E. J. ORAV, AND N. M. LAIRD. 1994. Performance ofgeneralized estimate equations in practical situations. Biometrics 50:270-278.

LOFT, E. R., AND J. G. KIE. 1988. Comparison of pellet-group and radio triangulation methods forassessing deer habitat use. Journal of Wildlife Management 52:524-527.

MACKIE, R. J., D. F. PAC, K. L. HAMLIN, AND G. L. DUSEK. 1998. Ecology and management ofmule deer and white-tailed deer in Montana. Montana Fish, Wildlife and Parks. Federal Aid ProjectW-120-R.

MANLY, B. F. J., L. L. MCDONALD, AND D. L. THOMAS. 1993. Resource selection by animals:statistical design and analysis for field studies. Chapman and Hall, New York, New York, USA.

McCULLOUGH, P., AND J. A. NELDER. 1989. Generalized linear models. Chapman and Hall, NewYork, New York, USA.

MEIDINGER, D., AND J. POJAR. 1991. Ecosystems of British Columbia. Special Report Series 6,B.C. Ministry of Forests, Research Branch, Victoria, B.C., USA.

MINISTRY OF FORESTS. 1999. Vegetation resource inventory; the B.C. land cover classificationscheme. Version 1.1. B.C. Ministry of Forests, Resources Inventory Branch, Victoria, B.C., Canada.

MOEN, A. N. 1978. Seasonal changes in heart rates, activity, metabolism, and forage intake of white-tailed deer. Journal of Wildlife Management 42:715-738.

MOWAT, G. 1999. Literature review for deer and elk winter range modeling. Final report prepared forJ. H. Huscroft Ltd., Creston, B.C., and B.C. Ministry of Environment, Lands and Parks, Nelson, B.C.,USA.

NEFF, D. J. 1968. The pellet-group count technique for big game trend, census, and distribution: areview. Journal of Wildlife Management 32:597-614.

NYBERG, J. B., AND D. W. JANZ. 1990. Deer and elk habitats in coastal forests of southern BritishColumbia. B.C. Ministry of Forests, Special Report Series 5, Victoria, B.C. USA.

OWENS, T. E. 1981. Movement patterns and determinants of habitat use of white-tailed deer in northernIdaho. M.S. Thesis, University of Idaho, Moscow, Idaho, USA.

PARKER, K. L., C. T. ROBBINS, AND T. A. Hanley. 1984. Energy expenditures for locomotion bymule deer and elk. Journal of Wildlife Management 48:474-488.

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PAULEY, G. R., J. M. PEEK, AND P. ZAGER. 1993. Predicting white-tailed deer habitat use innorthern Idaho. Journal of Wildlife Management 57:904-913.

POOLE, K. G., R. SERROUYA, AND R. D�EON. 2000. Habitat selection and seasonal movements bymule deer in the Lemon Creek drainage, southeastern British Columbia. Unpublished report preparedfor B.C. Environment, Lands and Parks, Nelson, B.C., Canada.

PROGULSKE, D. R., AND D. C. DUERRE. 1964. Factors influencing spotlighting counts of deer.Journal of Wildlife Management 28:27-34.

SAS INSTITUTE. 1997. SAS/STAT software: changes and enhancements through release 6.12. SASInstitute, Cary, North Carolina, USA.

SECORD, M. L. 1994. Winter habitat use, migration, and spring and summer use of clearcuts by white-tailed deer in the Priest Lake watershed of northern Idaho. M.Sc. Thesis. University of Montana,Missoula, Montana, USA.

SMITH, R. H., D. J. NEFF, AND C. Y. McCULLOCH. 1969. A model for the installation and use of adeer pellet group survey. Special Report No. 1, Research Division, Arizona Game and FishDepartment, Phoenix, Arizona, USA.

STOKES, M. E., C. S. DAVIS, AND G. G. KOCH. 1997. Categorical data analysis using the SASsystem. SAS Institute, Cary, North Carolina, USA.

THOMAS, J. W., editor. 1979. Wildlife habitats in managed forests: the Blue Mountains of Oregon andWashington. U.S. Forest Service Agricultural Handbook 553. Washington, D.C., USA.

TREXLER, J. C., AND J. TRAVIS. 1993. Nontraditional regression analyses. Ecology 74: 1629-1637.

VOLD, T., R. F. FERSTER, T. K. OVANIN, R. D. MARSH, AND G. P. WOODS. 1980. Soil andvegetation resources of the Pend d�Oreille valley, B.C. Assessment and Planning Division Bulletin 2,B.C. Ministry of Environment, Victoria, B.C., Canada.

WALLMO, O. C., L. H. CARPENTER, W. L. REGLIN, R. B. GILL, AND D. L. BAKER. 1977.Evaluation of deer habitat on a nutritional basis. Journal of Range Management 30:122-127.

WATERHOUSE, M. J., H. M. DAWSON, AND H. M. ARMLEDER. 1990. The effects of juvenilespacing on wildlife habitat use during winter in the interior Douglas-fir zone of British Columbia.B.C. Ministry of Forests Research Report 89003-CA, Victoria, B.C., Canada.

WELLES, R. W., AND F. B. WELLES. 1961. The bighorn of Death Valley. U.S. National Park ServiceFauna Series 6.

WHITE, G. C., K. P. BURNHAM, AND D. R. ANDERSON. 1999. Advanced features of programMARK. Department of Fishery and Wildlife Biology. Colorado State Univ. Ft. Collins, Colorado,USA.

WOODS, G. P. 1983. Pend d�Oreille wildlife management plan. Fish and Wildlife Branch, B.C.Ministry of Environment, Nelson, B.C., USA.

WOODS, G. P. 1984. Habitat selection of white-tailed deer in the Pend d�Oreille Valley, BritishColumbia. M.S. Thesis, University of Idaho, Moscow, Idaho, USA.

ZIEGLER, A., AND G. ULRIKE. 1998. The generalised estimating equations: A comparison ofprocedures available in commercial statistical software packages. Biometrical Journal 40:245-260.

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Pend d’Oreille white-tailed deer winter range habitat use 44

APPENDIX 1 – STATISTICAL DETAILS

Shapes of response curvesWhen logistic regression is used for habitat selection analysis it is assumed that the relationship

between pellet group distribution (the response variable) and the continuous predictor variables (elevationand slope) is logistic. The logistic curve can accommodate near-linear, exponential, and sigmoidalrelationships between predictor and response variables. However, it cannot accommodate non-linearrelationships between variables. We therefore introduced higher order polynomial terms (i.e. elevation2,elevation3) to test for and account for non-linear relationships (Trexler and Travis 1993). Both slope andelevation were standardized by mean and standard deviation to aid in model convergence with the higherorder polynomial terms. AIC methods and significance tests using GEE methods were used to evaluatethe fit of higher order polynomial terms. However, the detail in terms of response shapes was still limitedgiven that each polynomial term could only accommodate a restricted range of shapes. An alternative tothe use of logistic regression was generalized additive modeling, which allows the fitting of splinefunctions and other less restricted response curve shapes (Bio et al. 1998). Unfortunately, this procedurewas still in the theoretical realm for SAS (SAS Institute 1997), and other mainstream statistical softwaredo not support this type of analysis. In the future, it may be productive to reassess the findings of thisstudy using this newer technique.

The Information Theoretic (AIC) approach to model selectionTraditional hypothesis testing methods, such as ANOVA techniques and stepwise model selection

procedures, become limited as model complexity increases (Flack and Chang 1987). In addition, thetheoretical basis for a hypothesis testing approach becomes muddled with ecological data in which avariety of hypotheses can explain the outcome of a data set (Johnson 1999). Therefore, we usedinformation theoretic approaches to model selection as an alternative to traditional approaches. Theinformation theoretic approach and associated Akaike Information Criterion (AIC) model selectionmethod has been shown to be the best method for selection of models from complex data sets (Burnhamand Anderson 1998). Much of the application of the AIC approach has been for mark-recapture analysis;however, the general approach was applicable to any analysis that utilizes generalized linear models(Burnham and Anderson 1998). The information theoretic approach was based upon the logic that theoptimal models for a set of data are the ones that explain the most variation using the least number ofparameters (the most parsimonious model as indexed by AIC scores). This philosophy acknowledges thefact that a "true model" would contain infinite parameters and was not obtainable or desirable forstatistical inference. Given this constraint, a set of candidate models based on the biology of the questionof interest should be proposed, and from these appropriate model(s) should be selected based on samplesize and other factors that affect model fit.

The information theoretic method was superior to traditional stepwise model selection proceduresfor many reasons. First, it was not affected by multiple tests, so no adjustment of scores was needed(such as the Bonferroni adjustment to P-values when multiple tests are done on the same data set).Second, it was less affected by correlation among variables, given that full models are compared, asopposed to individual predictor variables within a model (Burnham and Anderson 1998). Third, it wasmore robust to "noise" in data sets, which causes erroneous variable selection in stepwise regressionprocedures (Flack and Chang 1987).

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Pend d’Oreille white-tailed deer winter range habitat use 45

The use of Generalized Estimating Equations for longitudinal dataThe GEE method utilizes a "working correlation matrix", which has many potential structures

dependant on the class of data being analyzed. There has been some controversy over the best forms ofworking correlation matrices to use with longitudinal data or repeated measure data (Lipsitz et al. 1994,Crowder 1995). An unstructured working correlation structure as implemented in SAS PROC GENMOD(SAS Institute 1997) was used for the GEE model. The unstructured correlation matrix made the leastnumber of assumptions about the correlation structure in the data and therefore should produce the mostrobust estimates(Ziegler and Ulrike 1998).

Fit of model to a binomial distributionLogistic regression analysis assumes that the pellet group data fit a binomial distribution. We

used analysis of deviance to evaluate the fit of models to the data using the Pearson chi-square test todetermine if the general assumption of model fit could be met (McCullough and Nelder 1989). Onecommon reason for reduced model fit was non-independence of observations. The degree of non-independence in observations was modeled by a dispersion parameter ( c ) estimated by the Pearson chi-square statistic divided by the degrees of freedom in the analysis. If c was >1.5 then QAICc values wereused to evaluate model fit rather than AICc values.

Sensitivity of predictions to unequal plot spacingSpacing between plots varied between 18 to 77 m. Theoretically, plots with close spacing may

exhibit increased spatial autocorrelation when compared to plots with greater spacing. Increasedautocorrelation with closely spaced plots will result in non-independence of observations, which couldlead to negatively biased variance estimates and a higher probability of class 1 errors in the statisticalanalysis. To investigate this we calculated the degree of autocorrelation between plots using both theSpearman and Pearson correlation coefficients and the formulas of Brown and Rothery (1993). Deerabundance was modeled in terms of a binomial or presence/absence variate. This presented a range ofdata with varying levels of mean deer abundance.

We also conduced a generalized linear model analysis to determine if the degree of correlationbetween plots was associated with plot spacing and the mean abundance of deer in any given year. Theresponse variables for this analysis were the Pearson correlation coefficient calculated between each plotand then summarized for each transect. The predictor variables were the mean plot spacing and meanabundance for each year. The correlation coefficients were modeled as a normal variate. Thesignificance tests were corrected for pseudoreplication using the GEE method and an unstructuredcorrelation matrix similar to the method discussed above.

Results of the generalized linear model (GLM) analysis suggested that the distance between plotsdid not affect the degree of autocorrelation between plots (GEE Z = -0.505, P = 0.96). The meanabundance of deer did affect the degree of correlation between all plots, regardless of spacing (Z = 2.205,P = 0.027). However, this effect appears to be independent of plot spacing, as indicated by a non-significant abundance*plot spacing interaction term (Z = -1.553, P = 0.12). This result makes intuitivesense in that when deer abundance was low most plots did not show presence of deer, and therefore thecorrelation was minimal. As deer abundance increased the probability of use of adjacent plots alsoincreased. However, this affect occured across all plots with minimal difference in terms of plot spacing.

In general, all tests suggested that there was little correlation between repeated (annual)observations on plots (as reflected by the values of the GEE scale parameter being close to 1 for all

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Pend d’Oreille white-tailed deer winter range habitat use 46

analysis). In addition, there was also minimal correlation between adjacent plots. There are severalpossible reasons for this result. First, the majority of plots in the study area (69.3%) recorded no deerpellet groups. Given this, no correlation would be found between these plots in terms of yearlycorrelation between repeated measures on individual plots, or spatial autocorrelation between adjacentplots. If the abundance of deer was higher and more plots were used, then correlation may have beendetected. The results of the adjacent plot analysis suggested that correlation does increase between plotsas density of deer increases; however, in no cases was the degree of correlation high. A second reasonwas that the model used does not have enough structure to adequately model the correlation between plotsover space and time. However, this was doubtful since the GEE analysis explicitly models yearlyobservations on individual plots. If this was the case then the GEE method would have a tendency toexhibit type 1 errors at a greater than 5% rate. In addition, the AIC method would have a tendency tooverfit models.

Sensitivity of predictions to uncorrected forest dataThe AIC selected model (Table 6) was run with uncorrected habitat classes to test the sensitivity

of model predictions to uncorrected forest cover data (Table 12). In terms of non-habitat parameters thefit of the model was similar to the model run with the corrected habitat class data. However, 1 differenceis that all habitat classes show significant selection, whereas only good and optimal cover showedsignificant selection with the model run with corrected forest data.

It is difficult to determine which model (corrected and uncorrected forest cover) is the bestdescriptor of the distribution of habitat classes over the entire duration of the study. This is because thetrue distribution of habitat types is unknown and both forest cover and corrected plot data will only beapproximations to this distribution. One likely reason for the disparity is the fact that the corrected forestcover data is mainly based upon the plot scale whereas forest cover data is based upon a landscape scale.Even though the forest cover data is often erroneous at a plot scale it may be a better descriptor oflandscape scale availability in certain cases. More discussion of this, including strategies to investigatethe scale issue, are given in the discussion section.

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Pend d’Oreille white-tailed deer winter range habitat use 47

Table 12: Results of GEE analysis of base model, Pend d’Oreille with uncorrected forestcover based habitat classes. A parameter was considered significant if at least 1 of itscategorical slope (ββββ) estimates was significant.

Parameter CategorySelection

coefficient (β) SE (β) Z P (β=0)Aspect South and west 0.17 0.07 2.32 0.0204

North and east 0.00 0.00 0.00 0.0000

Slope -0.16 0.06 -2.53 0.0114

Slope2 -0.08 0.03 -2.86 0.0042

Elevation -0.15 0.08 -1.84 0.0665

Elevation2 -0.14 0.03 -4.04 0.0001

Elevation3 0.02 0.01 1.64 0.1003

Habitat Optimal cover 0.59 0.19 3.13 0.0018

Good cover 0.66 0.19 3.52 0.0004

Marginal cover 0.66 0.20 3.29 0.0010

Recruitmentcover

0.40 0.22 1.83 0.0672

Forage 0.49 0.17 2.91 0.0036

Other 0.00 0.00 0.00 0.0000

Slope*aspect South and west 0.35 0.08 4.67 0.0000

North and east 0.00 0.00 0.00 0.0000

Elevation*aspect South and west 0.22 0.09 2.56 0.0103

North and east 0.00 0.00 0.00 0.0000

Mean pellets/year 2.07 0.06 32.10 0.0000

Days on range 0.00 0.00 -4.04 0.0001

Intercept -2.26 0.21 -10.80 0.0000

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Pend d'Oreille white-tailed deer winter range habitat use 48

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APPENDIX 2. Detailed GIS map of the Pend d'Oreille winter range underaverage population levels

Proportional Use

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Scale 1:50,000 - Projection UTM Zone 11 - Datum NAD 83 - Contour Interval 100 metres


Recommended