Research Article Effects of Deer Settling Stimulus and...

Hindawi Publishing CorporationInternational Journal of Forestry ResearchVolume 2013 Article ID 690213 11 pageshttpdxdoiorg1011552013690213

Research ArticleEffects of Deer Settling Stimulus and Deer Density onRegeneration in a Harvested Southern New England Forest

Kevin J Barrett12 and Oswald J Schmitz1

1 School of Forestry amp Environmental Studies Yale University 370 Prospect Street New Haven CT 06511 USA2NEON Inc 1685 38th Street Suite 100 Boulder CO 80301 USA

Correspondence should be addressed to Kevin J Barrett kbarrettneonincorg

Received 22 May 2013 Accepted 3 August 2013

Academic Editor Friedrich Reimoser

Copyright copy 2013 K J Barrett and O J SchmitzThis is an open access article distributed under theCreative CommonsAttributionLicense which permits unrestricted use distribution and reproduction in anymedium provided the originalwork is properly cited

Elevated deer densities have led to reports of forest regeneration failure and ecological damage However there is growing evidencethat the biophysical conditions of a forest thatmake it attractive to deermay be a contributing factor in determining browsing levelsThus an understanding of settling stimulusmdashhow attractive an area is to deer in terms of food-independent habitat requirementsmdashis potentially important to manage deer browsing impacts We tested the settling stimulus hypothesis by evaluating the degreeto which thermal settling stimulus and deer density are related to spatial variation in browsing intensity across different forestharvesting strategies over the course of a year We determined if deer were impacting plant communities and if they resulted inchanges in plant coverWequantified the thermal environment around each harvest and tested to see if it influenced deer density andbrowsing impact We found that deer had an impact on the landscape but did not alter plant cover or diminish forest regenerationcapacity Deer density and browse impact had a relationship with thermal settling stimulus for summer and fall months and deerdensity had a relationship with browse impact in the winter on woody plants We conclude that thermal settling stimulus is animportant predictor for deer density and browsing impact

1 Introduction

Theslowing or failure of the regeneration of high value timberspecies in the northeastern USA is often attributed to highwhite-tailed deer (Odocoileus virginianus) abundances [1 2]In particular above threshold densities of 10 to 15 deer kmminus2deer are reported to alter forest composition via selectivebrowsing of woody plant species such as eastern hemlock(Tsuga canadensis) red maple (Acer rubrum) sprouts andRubus spp [1 3ndash5] Heavy browsing may in turn cause forestregeneration failure and decreased vertical structural com-plexity of forest stands that may impact wildlife habitat andecosystem functions such as nutrientmineralization rates [6ndash10] Selective browsing by highly abundant deer populationsmay also interact with forest management causing areas toentrain into altered stable states [11] and creating savannah-like areas dominated by ferns grasses and sedges that inhibitforest regeneration [1]

Nevertheless deer density and selective browsing alonemay not always explain spatial and temporal variation in veg-etation impacts [12 13] Instead available forage in relation

to settling stimulusmdashhow attractive an area is in terms ofthe food-independent habitat requirements of deer such asthermal and hiding cover edge effect and level of disturbance[14ndash19]mdashmay be a contributing factor determining levels ofbrowsing impact [13 17 20 21] Because deer must managetradeoffs between eating andmaintaining thermal homeosta-sis [22 23] high deer impacts may occur wherever areas thatsupply high forage production (such as sites that have recentlyundergone timber harvest) are juxtaposed with favorablethermal cover [14ndash19] Accordingly deer impacts may bealleviated via forest management strategies that mediatesettling stimulus and thereby make economically valuableforest stands less attractive to deer [1 19 24] To this endforest harvesting strategies that consider settling stimulusmay reduce a standrsquos predisposition to heavy browsing bydeer [15 19] Although this is a well-tested hypothesis forEuropean ungulates such as roe deer (Capreolus capreolus)this remains an untested hypothesis for white-tailed deerTheobjective of this study was to experimentally test the extent towhich thermal settling stimulus determined by the thermal

2 International Journal of Forestry Research

environment and deer density determine spatial variationin browsing intensity We quantified the effects of thermalsettling stimulus and deer activity density under differentforest harvest strategies over the course of a year

2 Materials and Methods

21 Study Area We conducted our study in the 3213 ha Yale-Myers Forest (YMF) in northeastern Connecticut (41∘57

1015840

N72∘28

1015840

W) YMF is a mixed hardwood forest dominated byred oak (Quercus rubra) black oak (Q velutina) red maplesugar maple (Acer saccharum) black birch (Betula lenta)yellow birch (B alleghaniensis) white ash (Fraxinus ameri-cana) pignut hickory (Carya glabra) and eastern hemlockwith interspersed white pine (Pinus strobus) stands Theunderstory is comprised of a diverse community of shrubsand forbs including mountain laurel (Kalmia latifolia) hay-scented fern (Dennstaedtia punctilobula) Canada mayflower(Maianthemum canadense) wild sarsaparilla (Aralia nudi-caulis) star flower (Trientalis borealis) and Rubus speciesState parks forests and large private forest holdings surroundthe forest making it a large habitat island within a semiurbanlandscape that fostered deer densities between 8 and 10 perkm2 in 1995 [25]

YMF implemented a variety of forest harvesting treat-ments to compare their efficacy to regenerate oak We super-imposed our study on this harvested landscape and evaluatedseveral shelterwood (119899 = 6 replicates) crown thinning(119899 = 4 replicates) and reserve (119899 = 5 replicates) harvestingstrategies to determine if and how these strategies influencedeer herbivory patterns Shelterwood harvests removed 60ndash90 of the original basal area (BA) leaving 45ndash138m2 haminus1of BA Slash was not treated andwas scattered along the forestfloor These harvests promote high forage production haveabundant edge habitat and are designed to regenerate a newcohort of oak Crown thinning harvests removed 25ndash35 ofthe original BA leaving 184ndash207m2 haminus1 of BA These har-vests had lower forage production and less slash on the forestfloor and typically result in a pulse of understory regenerationthat is later shaded out by the remaining canopyThe reservesare managed for purposes other than timber harvestingincluding education and research and thus serve as experi-mental controls because no harvesting has taken place withinthemThese areas typically have little to no forage production

22 Herbivory Sampling We selected areas that were har-vested between 2008 and 2010 Within each harvesting strat-egy replicate (hereafter replicate) we paired deer exclosuresand open plots to evaluate herbivory patterns Plot locationswithin the harvested sites were selected to control for siteconditions such as slope distance from the nearest edge andunderstory plant communities The fenced exclosure plotsmade of welded wire fencing were 25m2 times 15m tall We didnot detect evidence of deer presencewithin the exclosure afterthe fences were constructed The open plots were also 25m2and allowed deer to forage freely within them The pairedplots were constructed in March and April of 2011

We sampled herbivory patterns once a month betweenMay 2011 and March 2012 We did not sample in December2011 for logistical reasons Within each plot we sampledfour 1m2 subplots for percent cover and browse impactSubplots were located at the corners of the open and exclosureplots following Rutherford and Schmitz [13] The subplotswere delineated with PVC sampling squares We estimatedpercentage of the ground covered by hardwoods conifersshrubs and forbs that were less than 12m tall which iswithin the optimumheight range for deer browse [26 27]Wecalculated browse index a relative measurement of browsingimpact [28 29] as the ratio of browsed terminal twigsrelative to total terminal twigs on a plant for every speciesencountered below a height of 12m We considered a plantto be browsed by deer when the apical bud was missingand there were remnants of bark strips We noted an otherevidence of browsing impact such as bark fraying flowerremoval and stem damage for each subplot We calculatedbrowse indices for each plant group (conifer hardwoodshrub and forb) We averaged the individual subplot valuesfor browse index and percent cover to obtain a single estimatefor each replicate exclosed and open plot pair

23 Deer Activity Density We estimated an index of localdeer activity and thus potential herbivory pressure in March2011 and once per month thereafter from May 2011 throughFebruary 2012 within each replicate using deer pellet groupcounts [25 30 31] Although deer pellet transects have beencriticized as not being a direct indicator of deer density theyare a useful and logistically feasible method for indicating therelative density and use preferences of animals among dif-ferent habitats [32 33] Therefore these estimates explainedthe relative use of different harvest strategies by deer givingus an index of their activity density We implemented four20m times 1m linear transects within each replicate We clearedall deer pellets from the transects as theywere tallied countedthe number of newly deposited deer pellet groups per monthand estimated local deer activity density as [30]

Deer kmminus2 = 119909 times 500 times 100119910

times 13 (1)

where 119909 is the average number of pellet groups per transect500 is the number of transects per hectare 100 is the numberof hectares in a square kilometer 119910 is the number of dayssince leaf fall (for March 2011 calculations) or the last timethe transect was run (for May 2011 through February 2012calculations) and 13 is the average number of pellet groupsper deer per day [25] There was no long-term snowpackduring the deer activity density sampling period thereforeno pellets were missed due to being buried in snow Weassumed leaf fall to be 15 October 2010 based onOctober 2011observations

24 Thermal Settling Stimulus Estimation We evaluated theeffects of the thermal environment and hence quantifiedthermal settling stimulus on deer browsing by estimatingrealized thermal energy exchange (net heat gained or lost bythe animal 119879119864) within each replicate and surrounding cover

International Journal of Forestry Research 3

types (classified as open hardwood mixed hardwood andconifer) High positive 119879119864 values indicate high heat gain bydeer more negative 119879119864 values indicate that high heat loss bydeer and 119879119864 values near zero indicate the environment isthermally neutral for deer We estimated 119879119864 for each harvestin March 2011 and monthly fromMay 2011 through February2012 using the equation for heat flux [22 34]

119879119864 = 119872 + 119876abs minus 1205741205761198794

119903

minus ℎ119888

(119879119903

minus 119879119886

) (2)

where119879119864 is the net heat gained or lost by the animal (Wmminus2)119872 is the metabolic heat produced by the animal (Wmminus2)119876abs is the solar radiation absorbed by the animal (Wmminus2)120574 is the Stefan-Boltzmann constant (Wmminus2∘Kminus1) 120576 is theemissivity of the animalrsquos surface (097) 119879

119903

is the animalrsquos furtemperature (∘K) 119879

119886

is the ambient air temperature (∘K) andℎ119888

is the convection coefficient (Wmminus2∘Kminus1)We calculated119872as abW075211 such that 119886 is a constant depending on season(winter = 19 spring = 20 summer = 366 and fall = 30 [35])119887 is a constant (18) that refers to deer activity [22 23]119882 isthe averagemass of the deer (assumed to be 135 kg) and 211 isthe animalrsquos surface area We calculated 119879

119903

as 6559 + 0944119879119886

[36] where 119879119886

is the ambient air temperature We calculatedℎ119888

as 55119881119911

05 where 119881119911

is the wind speed at height 119911 abovethe groundWe calculated the solar radiation absorbed as (see[22])

119876abs = 119886119911119878 + 05 (119886119904 + 119886119903 (119878 + 119904)) + 120576120574119888 (051198791198864

+ 05119879Vminus1198924

)

(3)

where 119886 is the absorptivity of a deerrsquos surface for shortwaveradiation (assumed to be 074) 119911 is the fraction of the deerrsquossurface area exposed to shortwave radiation (assumed to be0185 average of the values when the animal is parallel andperpendicular to the suns path) 119878 is direct shortwave radi-ation (Wmminus2) 119904 is diffuse shortwave radiation (calculated as01 119878 Wmminus2) 119903 is the environmentrsquos reflectivity of shortwaveradiation (assumed to be 02) 119888 is a constant describing thecover type (1 for open 119 for hardwood 124 for mixedhardwood and 128 for conifer) and 119879Vminus119892 is the groundtemperature We measured air and ground temperatureswind speed and direct shortwave radiation in each replicateand surrounding cover type once a month at each replicateWe measured wind speed and air temperature using a digitalanemometer (La Crosse Technology La Crosse WI) groundtemperature with a pocket digital thermometer (Taylor OakBrook IL) and direct shortwave radiation with a light meter(General New York NY) We made biophysical measure-ments for each hour between 0800 and 1600 hours in eachmonth and averaged the values to obtain one representativemeasurement for the month

25 Data Analysis We tested for deer impacts on vegetation(based on browse index and percent cover) using mixedmodels repeated-measures analysis of variance (ANOVA)with harvesting strategies exclosure presence and themonthsampled as fixed effects and plot identity as a randomeffect allowing us to account for correlation due to therepeated monthly measures of each plot [37] Browse index

and percent cover data were arcsine square root trans-formed to meet normality assumptions We constructedour models in the R statistical package (R Version 2121httpwwwR-projectorg accessed 10 January 2011)We usedTukeyrsquos procedure on all significant effects (119875 le 005) todetermine significant separation of means (119875 le 005)

We calculated average air and ground temperatureswind speed and direct shortwave radiation for each harveststrategy by season (spring summer fall andwinter)We usedthese values in (2) to estimate 119879119864 by harvesting strategy andcover type for each season of the year We defined spring asFebruary 1st through April 30th summer as May 1st throughAugust 31st fall as September 1st through October 31st andwinter as November 1st through January 31st

We addressed how the thermal environment within thevicinity of a harvest site affects settling stimulus We usedArcGIS v10 (ESRI Redlands California USA) to createa 500m buffer around each site (see [12]) and calculatethe percentage of each surrounding cover type within thebuffer We also calculated the percentage of the area that washarvested within the bufferWemultiplied each percentage ofsurrounding cover type and harvested area by its respective119879119864 estimate to obtain an indexed119879119864 valueWe then summedall of the indexed values to obtain an overall index of thermalsettling stimulus for each replicate Using regression analysisin R we regressed local deer activity density on thermalvalues to determine if the thermal environment could explainvariation in local deer abundance We regressed browseindex on thermal values to assess if thermal settling stimuluscould explain variation in deer browsing patterns Finallywe regressed browse index on local deer activity density todetermine if estimated deer activity densities could explainvariation in deer browsing patterns We used AIC values todetermine if linear polynomial or exponential regressionmodels provided the best fit Models were deemed highlysignificant when 119875 le 005 and moderately significant when005 lt 119875 le 010

3 Results

Average measured air temperatures (∘C) across YMF for thesummer spring fall and winter were 2378 plusmn 133 (1 SE)530 plusmn 064 (1 SE) 1223 plusmn 124 (1 SE) and 467 plusmn 036 (1 SE)respectively Estimated local activity density indices (deerkmminus2) indicated that deer used shelterwoods themost (509plusmn97 [1 SE]) followed by thinnings (289 plusmn 69 [1 SE]) andreserves (187 plusmn 43 [1 SE]) Given the variation in estimateddeer activity densities we expected some of our sites tobe subject to high potential deer herbivory pressure Ourexclosure experiment revealed that harvest strategy and deerbrowsing did indeed have impacts on some vegetation classesand that the effects varied by season

31 Impacts on Forest Plant Groups Dominant plants basedon percent cover included white pine for the conifer plantgroup birches oaks and maples for the hardwoods Rubusspp blueberry (Vaccinium spp) and mountain laurel forthe shrubs and Canada mayflower wild sarsaparilla and


Table 1 Three-factor repeated measures ANOVA used to examine the mean browse index and percent cover for conifer forb shrub andhardwood plant groups at YMF Independent factors were harvest strategy and exclosure presence and the repeated factor was month Onlythe main effects are presented

ConifersBrowse index Percent cover

Source DF 119865 119875 Source DF 119865 119875

Harvest strategy 2 147 0251 Harvest strategy 2 105 0366Exclosure 1 107 0312 Exclosure 1 001 0908Residuals 168 Residuals 120

ForbsBrowse index Percent cover


ShrubsBrowse index Percent cover

Harvest strategy 2 1390 lt0001lowast Harvest strategy 2 2726 lt0001lowast

Exclosure 1 3603 lt0001lowast Exclosure 1 001 0956Residuals 168 Residuals 120

HardwoodsBrowse index Percent cover

Harvest strategy 2 076 0476 Harvest strategy 2 822 0002lowast

Exclosure 1 1897 lt0001lowast Exclosure 1 005 0823Residuals 168 Residuals 120lowastDenotes significant results (119875 le 005)

starflower for the forbs ANOVA revealed that deer herbivory(presence or absence of an exclosure) and harvest strategydid not have a significant effect on conifers and forbs (asmeasured by browse index or percent cover Table 1) ANOVArevealed that deer herbivory did not have a significanteffect on shrub percent cover regardless of harvest strategy(Table 1) Harvesting strategy however had a significanteffect on shrub percent cover Tukey pairwise comparisonsindicated that shelterwood harvests had higher shrub percentcovers than thinnings or reserves (Figure 1(a)) ANOVArevealed that harvest strategy had an effect on shrub browseindex Tukey pairwise comparisons indicated that shelter-wood and thinning harvest strategies had higher browseindices than reserves (Figure 1(a)) ANOVA revealed thatdeer herbivory had an effect on shrub browse index (Table 1)Tukey analysis indicated that shelterwood and thinningharvest strategies caused higher shrub browse indices thanreserves (Figure 1(a)) ANOVA revealed that deer herbivoryhad a significant impact on the hardwood browse indexbut harvest strategy did not (Table 1) Tukey pairwise com-parisons suggested that deer impacts on hardwoods weresignificant in all three forest harvest types (Figure 1(b))ANOVA revealed that only harvest strategy had an effect onthe percent cover of hardwood species while deer herbivorydid not (Table 1) Tukey pairwise comparisons suggestedthat shelterwood harvests had higher percent covers thanthinning or reserves (Figure 1(b))

32 Mechanism of Impact Thermal Settling Stimulusor Deer Activity Density

321 Thermal Environment versus Deer Activity DensityRegression analysis revealed a significant relationshipbetween heat flux (119879119864) and deer activity density for thesummer season (119865

112

= 1404 1198772 = 054 119875 = 0003)Deer activity density varied with 119879119864 values according to therelationship deer kmminus2 = 161 times 10minus32 times 1198791198642254 meaningthat warmer sites had higher deer activity density estimatesthan cooler sites (Figure 2(a)) This significant effect wasderived after removing one outlier that had an unusuallylow estimated 119879119864 value (2477Wmminus2) given a high deeractivity density estimate of 67 deer kmminus2 The estimated 119879119864value was 155 lower than the mean 119879119864 value of all sites(2933Wmminus2) This site is a reserve located at the border ofYMF and in close proximity to several houses thereby havinggreater habitat fragmentation than the surrounding forestmatrix possibly explaining the relatively high deer activitydensity estimate for a relatively cool site Regression analysisrevealed a moderately significant relationship between119879119864 and estimated deer activity density for the fall season(119865212

= 317 1198772 = 035 119875 = 0078) in which deer activitydensity varied with 119879119864values according to the relationshipdeer kmminus2 = 013(119879119864)2 minus 1312(119879119864) + 32664 meaning thatwarmer and cooler sites had higher deer density activity


ShelterwoodThinningReserve

X

X

Yx

y y

X

YY

X

Y Y

0

5

10

15

20

Open BI Closed BI Open () Closed ()

()

(a)

X

X

X

xx

x

X

Y Y

X

Y Y

10

20

30

40

0Open BI Closed BI Open () Closed ()

()


(b)

Figure 1 Browse index (left BI) and percent cover (right ) for the shrub plant group (a) and hardwood plant group (b) at YMF amongopen and closed plots by harvest strategy Upper case versus lower case letters above standard error bars indicate values that are statisticallydifferent between open and closed plots by Tukey pairwise comparisons Unlike letters of the same case indicate values that are statisticallydifferent between harvest strategies using Tukey pairwise comparisons

estimates than intermediate sites (Figure 2(b)) There wereno significant relationships between 119879119864 and deer activitydensity for the winter and spring seasons (Figures 2(c) and2(d))

322 Thermal Environment versus Browse Index Regressionrevealed a moderately significant relationship between 119879119864and browse index for the summer season (119865

113

= 3491198772

= 021 and 119875 = 0085) Browse index varied with119879119864 values according to the relationship browse index =165 times 10

minus5

times 119879119864249 meaning that warmer sites had higher

browse indices than cooler sites (Figure 3(a)) Regressionrevealed a moderately significant relationship between 119879119864and browse index for the fall season (119865

112

= 284 1198772 =032 and 119875 = 0098) Browse index was described by therelationship browse index = 00005(119879119864)2minus0042(119879119864)+1054meaning that warmer and cooler sites had higher browseindices than intermediate sites (Figure 3(b)) None of themodels describing the relationship between 119879119864 and browseindex were significant for winter and spring (Figures 3(c) and3(d))

323 Deer Activity Density versus Browse Index Regressionrevealed a moderately significant relationship between deeractivity density estimates and browse index for the winterseason (119865

212

= 312 1198772 = 034 and 119875 = 0081) Browseindex varied with deer activity density according to therelationship browse index = minus3127 times 10minus5(deer density)2 +438times10

minus3

(deer density)+0075 meaning that locations withlowest and highest estimates of deer activity density had lower

browse indices than locations with intermediate deer den-sities (Figure 4(c)) There were no significant relationshipsbetween deer density and browse index for the summer fallor spring seasons (Figures 4(a) 4(b) and 4(d))

4 Discussion

The goal of this study was to evaluate using an exclosureexperiment superimposed on a landscape-scale comparisonof forest harvest strategies for regenerating oak whetherthermal settling stimulus or deer activity density affects deerbrowsing impact on a northeastern forest The rationale forthe study (sensu [13]) was that if deer activity density orthermal settling stimulus was an important determinant ofdeer impacts on forests then these variables should explaina high degree of variation in browse impact and hencepotential forest regeneration failureWe examined the impacton four major forest plant groups (forbs shrubs conifersand hardwoods) The plant species comprising forbs shrubsand hardwoods were well suited for examinations of deerimpacts because deer prefer to consume these species in thenortheast whereas the conifer group primarily white pine isnot preferred by deer [2 13] All plant groups sampled wereless than 12 meters tall and hence at risk of browsing impactbecause they fell within the optimal reach of deer [26 27]Comparisons of exclosure and open plots revealed that deerdid indeed browse in the harvest areas but impact variedseasonally

We found thermal settling stimulus to be related toestimated deer activity density in the summer and fall seasons


Summer TE22 24 26 28 30 32 34

0

20

40

60

80

100

Dee

r act

ivity

den

sity

(sqmiddot

km)

(a)

Fall TE35 45 55 65

10

20

30

40

50

0

Dee

r act

ivity

den

sity

(sqmiddot

km)

(b)

Winter TE

31 32 33 34 35 36 37 38 39 40 41 42

20

40

60

80

0

100

120

Dee

r act

ivity

den

sity

(sqmiddot

km)


(c)

Spring TE50 55 60 65 70 75 80

20

40

60

80

0

100

Dee

r act

ivity

den

sity

(sqmiddot

km)


(d)

Figure 2 Relationship between the thermal environment (TE) and deer activity density at YMF for the (a) summer (MayndashAugust) (b)fall (SeptemberndashOctober) (c) winter (NovemberndashJanuary) and (d) spring seasons (February-March) Regression lines are provided whensignificant models were produced The black point in panel (a) indicates an outlier that was removed from analysis

but not during the winter and spring Even though thermalsettling stimulus and deer activity density were correlatedin some seasons the resulting deer impacts could not beexplained by both variables equally Thermal settling stim-ulus appeared to be a statistically better predictor of deerbrowse impact than deer activity density during summerand fall Because trees are leafed out during the summer

and fall months creating shade throughout the landscape119879119864 values are generally more thermally neutral across allhabitat types during the summer and fall seasons withthe exception of a few sites (representing all three forestharvest strategies) during the fall Daily feeding time formany ungulates is determined by the thermal environment[22 23] therefore thermal settling stimulus is likely a better


Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)

Figure 3 Relationship between the thermal environment (119879119864) and browse index (BI) at YMF for (a) the summer (MayndashAugust) (b)fall (SeptemberndashOctober) (c) winter (NovemberndashJanuary) and (d) spring seasons (February-March) Regression lines are provided whensignificant models were produced

predictor of browsing impact during the summer and fallbecause the more sheltered landscape gives deer a greaterscope to reduce thermal stress allowing them to devote moretime to browsing in thermally attractive areas even whenat lower densities That is while perhaps at lower densitiesincreased per capita browsing effort by deer in thermallyfavorable habitat can result in as high of an impact or

higher impact than that realized when deer are under higherdensities in less thermally favorable habitat Although theseresults may be density dependent and territorial behaviorby white-tailed deer could cause some deer to disperseinto suboptimal habitats deer should still concentrate theirforaging activity in places that are relatively more favorableIndeed thermal settling stimulus did not predict browsing


Sum

mer

BI

0 20 40 60 80 100 120

02

01

0

Deer activity density (sqmiddotkm)

(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0

0 20 40 60 80 100Deer activity density (sqmiddotkm)


(d)

Figure 4 Relationship between activity deer density and browse index (BI) at YMF for (a) the summer (MayndashAugust) (b) fall (SeptemberndashOctober) (c) winter (NovemberndashJanuary) and (d) spring seasons (February-March) Regression lines are provided when significant modelswere produced

impact as well as deer activity density in the cooler wintermonths due to a more thermally homogeneous landscape inwhich deer are more exposed to weather conditions Moreexposure to harsh weather conditions likely resulted in alower scope for deer to mediate browsing behavior basedon the thermal environment This combined with the factthat deer metabolism slows during the winter leading to

concentrated foraging on select plant groups [35] resultsin areas with higher deer activity densities having higherbrowsing impacts because there are fewer thermally attractiveoptions for deer to choose to spend time foraging in andfewer species that deer browse upon Despite the correlationbetween deer activity density and thermal settling stimulusthe two variables differentially determined impacts because


plots within a particular harvest strategy had similar 119879119864values but more variable deer activity densities For exampleshelterwood 119879119864 values ranged from 275 to 304Wmminus2 inthe summer while deer activity densities spanned almostthe entire range of observed densities for all harvest types(refer to Figure 2) This suggests that thermal environmentmay be a consistently better predictor of variation in deerimpact across the forested landscape at YMF than deeractivity density These results are consistent with earlier casestudies about settling stimulus and deer impacts in forestenvironments (see [15 16 18])

We did not investigate the possible impact deer haveon height growth and species composition in hardwoodforests However despite ample evidence of browse impacton hardwoods in all three harvest types throughout thestudy area we could not detect adverse effects on forestregeneration measured as percent cover (refer to Figure 1)Similarly deer impacted shrubs in shelterwood and thinningharvests but the impact did not affect ground cover

The conventional strategy to mitigate the effects of deerherbivory on the landscape is culling deer populations tolower deer abundances thus decreasing the browsing impactsdeer have on plant communities [1 4 5] This strategywould only be warranted when deer density is stronglypositively related to browse impact Although this may bethe case under certain landscape contexts we found thatdeer density was not generally the cause of browse impact atYMF Indeed intermediate deer densities in some cases ledto more browsing impact but herbivory did not necessarilyinhibit forest regeneration capacity or alter understory plantcommunities This was especially the case when a givensitersquos thermal conditions led to less favorable deer thermalsettling stimulus This suggests that the effects deer haveon understory plant communities can be mediated by thethermal environment whereby deer concentrate foragingactivity in sites with favorable thermal energy exchanges withthe environment in order tomanage tradeoffs between eatingand maintaining thermal homeostasis [23]

There is increasing awareness that high deer abundancesdo not necessarily translate into diminished forest regen-eration capacity or detrimental impacts on forest under-story communities [12 13 38] Indeed Russell et al [38]acknowledge that although deer density is a contributingfactor in determining browsing impact on vegetative com-munities little is known about contributing factors thatmay modify these effects across landscapes Landscape-scaleevaluations of deer impacts in western Connecticut revealedthat relationships between deer abundance and plant speciesabundance or diversity or the ability of forests to regeneratevaried considerably with other landscape-scale features suchas land use and management that promote deer impactsat local scales [13] Similarly Hurley et al [12] found thatalthough deer abundance explained 19 of the variation innative herb cover across Indiana models that accounted fordeer abundance and the interspersion and juxtaposition ofperennial forb habitat explained 84of the variation in nativeherb cover This indicates that landscape context indepen-dently of deer abundance can have substantial effects on

deer browsing impact Our study quantified thermal aspectsof that landscape context to reveal that spatial variation inbiophysical conditions among different forest cover typesjuxtaposed with food availability across the landscape is astronger predictor of browsing impact on the forest thanmerely deer activity densities Moreover because thermalconditions of different harvests changed seasonally therewas much context dependency in browse impact throughoutthe year Consequently lowering deer abundances alone isunlikely to be the single best strategy for mitigating browseimpacts

Most deer impacts to woody browse regeneration occurduring winter Our study indicates this as browse impactwas generally lower during the warmer summer months(Figures 3 and 4) Deer generally prefer different environ-ments depending on weather conditions For example deermay seek warm sunny environments on colder days andforested areas during wind events Our study revealed thatshelterwood harvests are likely the most susceptible to deerimpacts because of ample understory browse production incombination with warm thermal conditions that make theseharvests attractive to deer Many shelterwoods are also inclose proximity to more heavily forested sites giving themquick reprieve from adverse conditions thereby creating anenvironment conducive to deer spending time browsing inOur research shows that forest management may mediatedeer impacts by balancing production of regeneration withdeer impact using forest thinning harvest strategies Even soour research shows that the presence of deer on the landscapemay not necessarily always lead to impaired regeneration

5 Conclusions

Understanding the underlyingmechanisms determining spa-tial variation in deer browsing behavior is key to makingdeermanagement decisions aimed at forest regenerationTheconventional strategy is to directly alter deer densities viahunting or culling to mitigate deer herbivory effects [1 4 5]This presumes that deer density consistently explains themajority of variation in deer impacts [13] We foundminimalevidence that deer activity density consistently affects forestplant communities within YMF When density was a factorit was intermediate rather than high densities that resulted inthe greatest impact A greater understanding of deer habitatselection and foraging behavior with respect to biophysicalhabitat components may give us a more nuanced approach tomitigating the effects of deer herbivory in the future

Instead of using conventional deer management strate-gies to meet forest regeneration objectives it may be moreeffective to implement forest management strategies toaddress the effects deer herbivory has on forest regenerationWe found that thermal settling stimulus determined bythe thermal energy exchange between deer and the thermalenvironment was a contributing factor in determining deerbrowsing impact The implication is that forest managementcan mitigate browse impact by implementing harvest strate-gies that alter forest microhabitats and thereby modify diur-nal and seasonal temperature fluctuations in ways that make


the forest sites less conducive to deer presence and henceimpact For example intensive harvesting strategies generallycreate warmer environments during the summermonths andmore variable environments during the fall months withthe attendant seasonal trend in browsing impact [39] Lessintense harvesting strategies generally create cooler andmorevariable thermal environments and lower browse indices(see Figure 3) It is noteworthy that heavily harvested areastend to have the highest plant productivity a contributingfactor to higher browse index values Nevertheless whenforest regeneration is a management objective implementingharvests that minimize the creation of warmer thermalenvironments in the surrounding land matrix can reduce ormitigate browsing damage even in areas with high browseabundance

Conflict of Interests

None of the authors are affiliated with any commercialidentities mentioned in this paper thus there is no conflictof interests in this paper

Acknowledgments

The authors thank the Carpenter-Sperry Research Fund andthe Schiff Fund for their financial support The LandscapeManagement System Laboratory generously provided labfacilitiesThey thank the Yale Myers Forest for allowing themto conduct their study on their property They thank RCampbell for providing valuable information regarding thelogistics of the forest They thank M Ashton E Hooper KMack H Bement T Usrell J Miller C Donihue H GlickK McLean and A Trainor for comments and discussion

References

[1] W S Alverson D M Waller and S L Solheim ldquoForests toodeer edge effects in Northern wisconsinrdquoConservation Biologyvol 2 no 4 pp 348ndash358 1988

[2] W M Healy ldquoInfluence of deer on the structure and compo-sition of oak forests in central Massachusettsrdquo in The Scienceof Overabundance Deer Ecology and Population ManagementW J McShea H B Underwood and J H Rappole EdsSmithsonian Institution Press Washington DC USA 1997

[3] E W Beals G Cottam and R J Vogl ldquoInfluence of deer onvegetation of the Apostle Islands Wisconsinrdquo The Journal ofWildlife Management vol 24 no 1 pp 68ndash80 1960

[4] S B Horsley S L Stout andD S DeCalesta ldquoWhite-tailed deerimpact on the vegetation dynamics of a northern hardwoodforestrdquo Ecological Applications vol 13 no 1 pp 98ndash118 2003

[5] T P Rooney and D M Waller ldquoDirect and indirect effectsof white-tailed deer in forest ecosystemsrdquo Forest Ecology andManagement vol 181 no 1-2 pp 165ndash176 2003

[6] J F Franklin T A Spies R V Pelt et al ldquoDisturbances andstructural development of natural forest ecosystems with silvi-cultural implications using Douglas-fir forests as an examplerdquoForest Ecology and Management vol 155 no 1ndash3 pp 399ndash4232002

[7] A F Hough ldquoA twenty-year record of understory vegetationalchange in a Virgin Pennsylvania Forestrdquo Ecology vol 46 no 3pp 370ndash373 1965

[8] N G Tilghman ldquoImpacts of white-tailed deer on forestregeneration in northwestern Pennsylvaniardquo Journal of WildlifeManagement vol 53 no 3 pp 524ndash532 1989

[9] D S DeCalesta ldquoDeer and ecosystem managementrdquo in TheScience of Overabundance Deer Ecology and Population Man-agement W J McShea H B Underwood and J H RappoleEds pp 267ndash297 Smithsonian Institution Press WashingtonDC USA 1997

[10] W J McShea H B Underwood and J H Rappole ldquoDeermanagement and the concept of overabundancerdquo inThe Scienceof Overabundance Deer Ecology and PopulationManagmentWJ McShea H B Underwood and J H Rappole Eds pp 1ndash7Smithsonian Institution Press Washington DC USA 1997

[11] O J Schmitz and A R E Sinclair ldquoRethinking the role of deerin forest ecosystemdynamicsrdquo inTheScience of OverabundanceDeer Ecology and Population Management W J McShea H BUnderwood and J H Rappole Eds pp 201ndash223 SmithsonianInstitution Press Washington DC USA 1997

[12] P M Hurley C R Webster D J Flaspohler and G R ParkerldquoUntangling the landscape of deer overabundance reserve sizeversus landscape context in the agriculturalMidwestrdquoBiologicalConservation vol 146 no 1 pp 62ndash71 2012

[13] A C Rutherford and O J Schmitz ldquoRegional-scale assessmentof deer impacts on vegetation within western ConnecticutUSArdquo Journal of Wildlife Management vol 74 no 6 pp 1257ndash1263 2010

[14] A Leopold Game Management Charles Scribnerrsquos Sons NewYork NY USA 1933

[15] F Reimoser and H Gossow ldquoImpact of ungulates on forestvegetation and its dependence on the silvicultural systemrdquoForest Ecology and Management vol 88 no 1-2 pp 107ndash1191996

[16] F Reimoser ldquoSteering the impacts of ungulates on temperateforestsrdquo Journal for Nature Conservation vol 10 no 4 pp 243ndash252 2003

[17] S Reimoser E Partl F Reimoser and S Vospernik ldquoRoe-deer habitat suitability and predisposition of forest to browsingdamage in its dependence on forest growth-Model sensitivity inan alpine forest regionrdquo EcologicalModelling vol 220 no 18 pp2231ndash2243 2009

[18] E Partl V Szinovatz F Reimoser and J Schweiger-AdlerldquoForest restoration and browsing impact by roe deerrdquo ForestEcology and Management vol 159 no 1-2 pp 87ndash100 2002

[19] S Vospernik and S Reimoser ldquoModelling changes in roe deerhabitat in response to forest managementrdquo Forest Ecology andManagement vol 255 no 3-4 pp 530ndash545 2008

[20] S D Cote T P Rooney J-P Tremblay C Dussault and DMWaller ldquoEcological impacts of deer overabundancerdquo AnnualReview of Ecology Evolution and Systematics vol 35 pp 113ndash147 2004

[21] J-P Tremblay J Huot and F Potvin ldquoDensity-related effects ofdeer browsing on the regeneration dynamics of boreal forestsrdquoJournal of Applied Ecology vol 44 no 3 pp 552ndash562 2007

[22] G E Belovsky ldquoOptimal activity times and habitat choice ofmooserdquo Oecologia vol 48 no 1 pp 22ndash30 1981

[23] O J Schmitz ldquoThermal constraints and optimization of winterfeeding and habitat choice in white-tailed deerrdquo HolarcticEcology vol 14 no 2 pp 104ndash111 1991


[24] D M Waller and W S Alverson ldquoThe white-tailed deer akeystone herbivorerdquo Wildlife Society Bulletin vol 25 no 2 pp217ndash226 1997

[25] D B Kittredge and P M S Ashton ldquoImpact of deer browsingon regeneration in mixed stands in southern New EnglandrdquoNorthern Journal of Applied Forestry vol 12 no 3 pp 115ndash1201995

[26] M A Barrett and P Stiling ldquoEffects of Key deer herbivoryon forest communities in the lower Florida Keysrdquo BiologicalConservation vol 129 no 1 pp 100ndash108 2006

[27] R M A Gill ldquoA review of damage by mammals in northtemperate forests 1 Deerrdquo Forestry vol 65 no 2 pp 145ndash1691992

[28] T P Rooney R J McCormick S L Solheim and D M WallerldquoRegional variation in recruitment of hemlock seedlings andsaplings in the upperGreat Lakes USArdquoEcological Applicationsvol 10 no 4 pp 1119ndash1132 2000

[29] T P Rooney S L Solheim and D M Waller ldquoFactors affectingthe regeneration of northern white cedar in lowland forestsof the Upper Great Lakes region USArdquo Forest Ecology andManagement vol 163 no 1ndash3 pp 119ndash130 2002

[30] L Eberhardt and R C V Etten ldquoEvaluation of the pelletgroup count as a deer census methodrdquo The Journal of WildlifeManagement vol 20 no 1 pp 70ndash74 1956

[31] D J Neff ldquoThe pellet-group count technique for big gametrend census and distribution a reviewrdquoThe Journal ofWildlifeManagement vol 32 no 3 pp 597ndash614 1968

[32] D M Forsyth R J Barker G Morriss and M P ScroggieldquoModeling the relationship between fecal pellet indices and deerdensityrdquo Journal of Wildlife Management vol 71 no 3 pp 964ndash970 2007

[33] F F C Marques S T Buckland D Goffin et al ldquoEstimatingdeer abundance from line transect surveys of dung sika deer inSouthern Scotlandrdquo Journal of Applied Ecology vol 38 no 2 pp349ndash363 2001

[34] W P Porter and D M Gates ldquoThermodynamic equilibria ofanimals with environmentrdquo Ecological Monographs vol 39 no3 pp 227ndash244 1969

[35] A N Moen ldquoSeasonal changes in heart rates activitymetabolism and forage intake of white-tailed deerrdquoThe Journalof Wildlife Management vol 42 no 4 pp 715ndash738 1978

[36] A N Moen ldquoSurface temperatures and radiant heat loss fromwhite-tailed deerrdquoThe Journal of Wildlife Management vol 32no 2 pp 338ndash344 1968

[37] W M Tzilkowski and G L Storm ldquoDetecting change usingrepeated measures analysismdashwhite-tailed deer abundance atGettysburg National Military Parkrdquo Wildlife Society Bulletinvol 21 no 4 pp 411ndash414 1993

[38] F L Russell D B Zippin and N L Fowler ldquoEffects of white-tailed deer (Odocoileus virginianus) on plants plant populationsand communities a reviewrdquo American Midland Naturalist vol146 no 1 pp 1ndash26 2001

[39] R T Brooks and T D Kyker-Snowman ldquoForest floor tem-perature and relative humidity following timber harvesting insouthern New England USArdquo Forest Ecology and Managementvol 254 no 1 pp 65ndash73 2008

Submit your manuscripts athttpwwwhindawicom

Forestry ResearchInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Environmental and Public Health

Journal of



EcosystemsJournal of


MeteorologyAdvances in

EcologyInternational Journal of


Marine BiologyJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Advances in


Environmental Chemistry

Atmospheric SciencesInternational Journal of



Waste ManagementJournal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics


Geological ResearchJournal of

EarthquakesJournal of


BiodiversityInternational Journal of


ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


ClimatologyJournal of


environment and deer density determine spatial variationin browsing intensity We quantified the effects of thermalsettling stimulus and deer activity density under differentforest harvest strategies over the course of a year

2 Materials and Methods

21 Study Area We conducted our study in the 3213 ha Yale-Myers Forest (YMF) in northeastern Connecticut (41∘57

1015840

N72∘28

1015840

W) YMF is a mixed hardwood forest dominated byred oak (Quercus rubra) black oak (Q velutina) red maplesugar maple (Acer saccharum) black birch (Betula lenta)yellow birch (B alleghaniensis) white ash (Fraxinus ameri-cana) pignut hickory (Carya glabra) and eastern hemlockwith interspersed white pine (Pinus strobus) stands Theunderstory is comprised of a diverse community of shrubsand forbs including mountain laurel (Kalmia latifolia) hay-scented fern (Dennstaedtia punctilobula) Canada mayflower(Maianthemum canadense) wild sarsaparilla (Aralia nudi-caulis) star flower (Trientalis borealis) and Rubus speciesState parks forests and large private forest holdings surroundthe forest making it a large habitat island within a semiurbanlandscape that fostered deer densities between 8 and 10 perkm2 in 1995 [25]

YMF implemented a variety of forest harvesting treat-ments to compare their efficacy to regenerate oak We super-imposed our study on this harvested landscape and evaluatedseveral shelterwood (119899 = 6 replicates) crown thinning(119899 = 4 replicates) and reserve (119899 = 5 replicates) harvestingstrategies to determine if and how these strategies influencedeer herbivory patterns Shelterwood harvests removed 60ndash90 of the original basal area (BA) leaving 45ndash138m2 haminus1of BA Slash was not treated andwas scattered along the forestfloor These harvests promote high forage production haveabundant edge habitat and are designed to regenerate a newcohort of oak Crown thinning harvests removed 25ndash35 ofthe original BA leaving 184ndash207m2 haminus1 of BA These har-vests had lower forage production and less slash on the forestfloor and typically result in a pulse of understory regenerationthat is later shaded out by the remaining canopyThe reservesare managed for purposes other than timber harvestingincluding education and research and thus serve as experi-mental controls because no harvesting has taken place withinthemThese areas typically have little to no forage production

22 Herbivory Sampling We selected areas that were har-vested between 2008 and 2010 Within each harvesting strat-egy replicate (hereafter replicate) we paired deer exclosuresand open plots to evaluate herbivory patterns Plot locationswithin the harvested sites were selected to control for siteconditions such as slope distance from the nearest edge andunderstory plant communities The fenced exclosure plotsmade of welded wire fencing were 25m2 times 15m tall We didnot detect evidence of deer presencewithin the exclosure afterthe fences were constructed The open plots were also 25m2and allowed deer to forage freely within them The pairedplots were constructed in March and April of 2011

We sampled herbivory patterns once a month betweenMay 2011 and March 2012 We did not sample in December2011 for logistical reasons Within each plot we sampledfour 1m2 subplots for percent cover and browse impactSubplots were located at the corners of the open and exclosureplots following Rutherford and Schmitz [13] The subplotswere delineated with PVC sampling squares We estimatedpercentage of the ground covered by hardwoods conifersshrubs and forbs that were less than 12m tall which iswithin the optimumheight range for deer browse [26 27]Wecalculated browse index a relative measurement of browsingimpact [28 29] as the ratio of browsed terminal twigsrelative to total terminal twigs on a plant for every speciesencountered below a height of 12m We considered a plantto be browsed by deer when the apical bud was missingand there were remnants of bark strips We noted an otherevidence of browsing impact such as bark fraying flowerremoval and stem damage for each subplot We calculatedbrowse indices for each plant group (conifer hardwoodshrub and forb) We averaged the individual subplot valuesfor browse index and percent cover to obtain a single estimatefor each replicate exclosed and open plot pair

23 Deer Activity Density We estimated an index of localdeer activity and thus potential herbivory pressure in March2011 and once per month thereafter from May 2011 throughFebruary 2012 within each replicate using deer pellet groupcounts [25 30 31] Although deer pellet transects have beencriticized as not being a direct indicator of deer density theyare a useful and logistically feasible method for indicating therelative density and use preferences of animals among dif-ferent habitats [32 33] Therefore these estimates explainedthe relative use of different harvest strategies by deer givingus an index of their activity density We implemented four20m times 1m linear transects within each replicate We clearedall deer pellets from the transects as theywere tallied countedthe number of newly deposited deer pellet groups per monthand estimated local deer activity density as [30]

Deer kmminus2 = 119909 times 500 times 100119910

times 13 (1)

where 119909 is the average number of pellet groups per transect500 is the number of transects per hectare 100 is the numberof hectares in a square kilometer 119910 is the number of dayssince leaf fall (for March 2011 calculations) or the last timethe transect was run (for May 2011 through February 2012calculations) and 13 is the average number of pellet groupsper deer per day [25] There was no long-term snowpackduring the deer activity density sampling period thereforeno pellets were missed due to being buried in snow Weassumed leaf fall to be 15 October 2010 based onOctober 2011observations

24 Thermal Settling Stimulus Estimation We evaluated theeffects of the thermal environment and hence quantifiedthermal settling stimulus on deer browsing by estimatingrealized thermal energy exchange (net heat gained or lost bythe animal 119879119864) within each replicate and surrounding cover



119879119864 = 119872 + 119876abs minus 1205741205761198794

119903

minus ℎ119888

(119879119903

minus 119879119886

) (2)


119903


119886



119903

as 6559 + 0944119879119886

[36] where 119879119886


as 55119881119911

05 where 119881119911


119876abs = 119886119911119878 + 05 (119886119904 + 119886119903 (119878 + 119904)) + 120576120574119888 (051198791198864

+ 05119879Vminus1198924

)

(3)






3 Results



















112





X

X

Yx

y y

X

YY

X

Y Y

0

5

10

15

20


()

(a)

X

X

X

xx

x

X

Y Y

X

Y Y

10

20

30

40


()


(b)




113

= 3491198772


minus5



112



212


minus3



4 Discussion




Summer TE22 24 26 28 30 32 34

0

20

40

60

80

100

Dee

r act

ivity

den

sity

(sqmiddot

km)

(a)

Fall TE35 45 55 65

10

20

30

40

50

0

Dee

r act

ivity

den

sity

(sqmiddot

km)

(b)

Winter TE

31 32 33 34 35 36 37 38 39 40 41 42

20

40

60

80

0

100

120

Dee

r act

ivity

den

sity

(sqmiddot

km)


(c)

Spring TE50 55 60 65 70 75 80

20

40

60

80

0

100

Dee

r act

ivity

den

sity

(sqmiddot

km)


(d)





Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)





Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics
















119879119864 = 119872 + 119876abs minus 1205741205761198794

119903

minus ℎ119888

(119879119903

minus 119879119886

) (2)


119903


119886



119903

as 6559 + 0944119879119886

[36] where 119879119886


as 55119881119911

05 where 119881119911


119876abs = 119886119911119878 + 05 (119886119904 + 119886119903 (119878 + 119904)) + 120576120574119888 (051198791198864

+ 05119879Vminus1198924

)

(3)






3 Results



















112





X

X

Yx

y y

X

YY

X

Y Y

0

5

10

15

20


()

(a)

X

X

X

xx

x

X

Y Y

X

Y Y

10

20

30

40


()


(b)




113

= 3491198772


minus5



112



212


minus3



4 Discussion




Summer TE22 24 26 28 30 32 34

0

20

40

60

80

100

Dee

r act

ivity

den

sity

(sqmiddot

km)

(a)

Fall TE35 45 55 65

10

20

30

40

50

0

Dee

r act

ivity

den

sity

(sqmiddot

km)

(b)

Winter TE

31 32 33 34 35 36 37 38 39 40 41 42

20

40

60

80

0

100

120

Dee

r act

ivity

den

sity

(sqmiddot

km)


(c)

Spring TE50 55 60 65 70 75 80

20

40

60

80

0

100

Dee

r act

ivity

den

sity

(sqmiddot

km)


(d)





Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)





Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics






























112





X

X

Yx

y y

X

YY

X

Y Y

0

5

10

15

20


()

(a)

X

X

X

xx

x

X

Y Y

X

Y Y

10

20

30

40


()


(b)




113

= 3491198772


minus5



112



212


minus3



4 Discussion




Summer TE22 24 26 28 30 32 34

0

20

40

60

80

100

Dee

r act

ivity

den

sity

(sqmiddot

km)

(a)

Fall TE35 45 55 65

10

20

30

40

50

0

Dee

r act

ivity

den

sity

(sqmiddot

km)

(b)

Winter TE

31 32 33 34 35 36 37 38 39 40 41 42

20

40

60

80

0

100

120

Dee

r act

ivity

den

sity

(sqmiddot

km)


(c)

Spring TE50 55 60 65 70 75 80

20

40

60

80

0

100

Dee

r act

ivity

den

sity

(sqmiddot

km)


(d)





Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)





Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics
















X

X

Yx

y y

X

YY

X

Y Y

0

5

10

15

20


()

(a)

X

X

X

xx

x

X

Y Y

X

Y Y

10

20

30

40


()


(b)




113

= 3491198772


minus5



112



212


minus3



4 Discussion




Summer TE22 24 26 28 30 32 34

0

20

40

60

80

100

Dee

r act

ivity

den

sity

(sqmiddot

km)

(a)

Fall TE35 45 55 65

10

20

30

40

50

0

Dee

r act

ivity

den

sity

(sqmiddot

km)

(b)

Winter TE

31 32 33 34 35 36 37 38 39 40 41 42

20

40

60

80

0

100

120

Dee

r act

ivity

den

sity

(sqmiddot

km)


(c)

Spring TE50 55 60 65 70 75 80

20

40

60

80

0

100

Dee

r act

ivity

den

sity

(sqmiddot

km)


(d)





Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)





Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics















Summer TE22 24 26 28 30 32 34

0

20

40

60

80

100

Dee

r act

ivity

den

sity

(sqmiddot

km)

(a)

Fall TE35 45 55 65

10

20

30

40

50

0

Dee

r act

ivity

den

sity

(sqmiddot

km)

(b)

Winter TE

31 32 33 34 35 36 37 38 39 40 41 42

20

40

60

80

0

100

120

Dee

r act

ivity

den

sity

(sqmiddot

km)


(c)

Spring TE50 55 60 65 70 75 80

20

40

60

80

0

100

Dee

r act

ivity

den

sity

(sqmiddot

km)


(d)





Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)





Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics















Summer TE

Sum

mer

BI

24 26 28 30 32

02

015

01

005

0

(a)

Fall TE

Fall

BI

30 40 50 60

04

03

02

01

0

(b)

Winter TE

Win

ter B

I

32 34 36 38 40 42

04

03

02

01

0


(c)

Spring TE

Sprin

g BI

50 55 60 65 70 75 80

04

03

02

01

0


(d)





Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics















Sum

mer

BI

0 20 40 60 80 100 120

02

01

0


(a)Fa

ll BI

0 10 20 30 40 50 60

035

025

015

005

0


(b)

Win

ter B

I

0 20 40 60 80 100 120

035

025

015

005

0



(c)

Sprin

g BI

04

03

02

01

0



(d)











5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics





















5 Conclusions







Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics


















Acknowledgments


References













































Journal of












Volume 2014

Advances in









Geophysics



































Journal of












Volume 2014

Advances in









Geophysics


















Journal of












Volume 2014

Advances in









Geophysics














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Research Article Effects of Deer Settling Stimulus and...

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