Effects of hurricanes Katrina and Rita on Louisiana black bear habitat

Jennifer L. Murrow1 and Joseph D. Clark1,2,3

1Department of Forestry, Wildlife and Fisheries, University of Tennessee, 274 Ellington Plant Sciences, Knoxville,TN 37996, USA

2US Geological Survey, Southern Appalachian Research Branch, University of Tennessee, 274 Ellington PlantSciences, Knoxville, TN 37996, USA

Abstract: The Louisiana black bear (Ursus americanus luteolus) is comprised of 3 subpopu-lations, each being small, geographically isolated, and vulnerable to extinction. Hurricanes

Katrina and Rita struck the Louisiana and Mississippi coasts in 2005, potentially altering

habitat occupied by this federally threatened subspecies. We used data collected on radio-

telemetered bears from 1993 to 1995 and pre-hurricane landscape data to develop a habitat

model based on the Mahalanobis distance (D2) statistic. We then applied that model to post-

hurricane landscape data where the telemetry data were collected (i.e., occupied study area) and

where bear range expansion might occur (i.e., unoccupied study area) to quantify habitat loss or

gain. The D2 model indicated that quality bear habitat was associated with areas of high mast-producing forest density, low water body density, and moderate forest patchiness. Cross-

validation and testing on an independent data set in central Louisiana indicated that prediction

and transferability of the model were good. Suitable bear habitat decreased from 348 to 345 km2

(0.9%) within the occupied study area and decreased from 34,383 to 33,891 km2 (1.4%) in the

unoccupied study area following the hurricanes. Our analysis indicated that bear habitat was

not significantly degraded by the hurricanes, although changes that could have occurred on a

microhabitat level would be more difficult to detect at the resolution we used. We suggest that

managers continue to monitor the possible long-term effects of these hurricanes (e.g., vegetationchanges from flooding, introduction of toxic chemicals, or water quality changes).

Key words: forest, habitat modeling, Hurricane Katrina, Hurricane Rita, Louisiana black bear, Mahalanobis

distance, Ursus americanus luteolus, wetland

Ursus 23(2):192–205 (2012)

Impacts of hurricanes have been evaluated for a

variety of animal taxa including fish (Dolloff et al.

1994), herpetofauna (Reagan 1991, Schriever et al.

2009), birds (Wiley and Wunderle 1993, Torres and

Leberg 1996, White et al. 2005, Brown et al. 2011),

and mammals, mostly ungulates (Craig et al. 1994,

Saıd and Servanty 1995, Swilling et al. 1998, Labisky

et al. 1999, Lopez et al. 2003, Widmer et al. 2004,

Storms et al. 2006). Effects have varied from bene-

ficial to detrimental depending on storm severity,

species habitat requirements, and species status.

Small, isolated populations that are highly vulnera-

ble to stochastic environmental events warrant

particular attention because they often lack the

capability to move to more suitable habitat and do

not have the demographic resilience to withstand a

dramatic population reduction (Burkey 1989, Lande

1993, Simberloff 1995).

The Louisiana coast is home to 1 of 3 small,

isolated subpopulations of Louisiana black bears

(Ursus americanus luteolus), the other 2 subpopulations

being located along the Tensas River in northeastern

Louisiana and along the upper Atchafalaya River

in central Louisiana. The coastal subpopulation was

estimated at 77 individual bears (Triant et al. 2004) and

is restricted to a narrow band of forest habitat between

coastal marsh to the south and urban and agricultural

development to the north (Nyland 1995, Wagner 1995).

Because of the small size and vulnerability of these 3

subpopulations, Louisiana black bears were listed as

threatened under the US Endangered Species Act (US

Fish and Wildlife Service 1992), requiring federal

agencies to conserve habitat and protect this species

from further decline (US Fish and Wildlife Service

1995, 1999).3jclark1@utk.edu

192

Hurricane Katrina struck the Louisiana and

Mississippi coasts on 29 August 2005, followed by

Hurricane Rita on 24 September. The 2 storms

caused the loss of approximately 562 km2 of wetland

(Barras 2006). Hurricanes can affect forest ecology

(Stoneburner 1978, Breininger et al. 1999, Widmer et

al. 2004), and Chambers et al. (2007) reported a

dramatic increase in non-photosynthetic land cover

classifications (e.g., felled trees) in southern Mis-

sissippi following the 2 hurricanes, suggesting that

considerable tree fall may have occurred in some

forest types. The coastal bear population is small,

coastal forest habitat is extremely limited in quantity

and highly fragmented, and no work has been

performed to evaluate effects of hurricanes on large

carnivores. Thus, our objectives were to estimate

how hurricanes Katrina and Rita affected black bear

habitat quantity and quality. To perform that

evaluation, our approach was to use pre-hurricane

landscape and bear radiotelemetry data to develop a

habitat model, estimate changes in land cover type

based on data collected after the hurricanes, and

apply the habitat model to the post-hurricane

landscape to quantify and characterize bear habitat

loss or gain to evaluate the potential impacts to this

threatened subpopulation of bears.

Study areasWe evaluated 2 landscape extents in our analysis.

First, we modeled habitat quality within occupied

bear range (hereafter, occupied) in an area consist-

ing of the 36.5-km2 Bayou Teche National Wildlife

Refuge (NWR) and surrounding private land near

Franklin, Garden City, and Centerville, Louisiana,

USA (29u279–29u589N, 91u149–91u559W). This area

was within the Outer Coastal Plain Mixed Forest

Province within the Mississippi Alluvial Valley and

was gently sloping with elevations ,90 m (Bailey

1988). Average annual temperatures ranged from 16

to 21uC. Rainfall was abundant and well distributed

throughout the year; precipitation ranged from 102

to 153 cm annually. Bayou Teche NWR was

surrounded by private lands that were a mix of

forested, agricultural, and industrial lands used for

hunting, sugarcane farming, and industry such as oil,

gas, salt, and carbon black production. Marshes,

swamps, and lakes were numerous. Live oak

(Quercus virginiana), water oak (Quercus nigra),

sugarberry (Celtis laevigata), and American elm

(Ulmus americana) were common overstory species.

The area was similar to much of coastal Louisiana in

that it was undergoing active subsidence.

We also characterized a broader landscape that

included unoccupied bear habitat (hereafter, unoc-

cupied) from eastern Texas to western Florida, USA

(28u539–32u189N, 87u279–95u389W). This area was

primarily made up of the Outer Coastal Plain Mixed

Forest Province but also encompassed portions of

the Lower Mississippi Riverine Forest Ecosystem

Province (Bailey 1988). The Lower Mississippi

Riverine Forest Province is characterized by broad

floodplains and low terraces made up of alluvium

and loess (Bailey 1988). Most of the area was flat,

with an average southward slope of ,127 mm/km.

The only significant changes in elevation were sharp

terrace scarps and natural levees that rose a few

meters above adjacent bottomlands or stream

channels. Historically, this province was covered by

bottomland deciduous forest with an abundance of

ash (Fraxinus spp.), elm (Ulmus spp.), cottonwood

(Populus deltoides), sugarberry, sweetgum (Liquid-

ambar styraciflua), water tupelo (Nyssa aquatica),

oak (Quercus spp.), and baldcypress (Taxodium

distichum). Much of the province has since been

converted to agriculture (Stavins 1986, Neal 1990).

MethodsTelemetry data

Coastal Louisiana black bears were radiomoni-

tored weekly from aircraft during 1991–1995

(Wagner 1995, 2003; Pace et al. 2000). Locations of

those bears were estimated during daylight hours

and plotted directly on 1:24,000 USGS quadrangle

maps. Median location error was estimated to be

281 m (147 m inner quartile, 417 m outer quartile,

n 5 106; Wagner 2003).

We retained individual bears in the data set only if

.30 radiolocations had been collected within a 2-

year period comprised of .10 of the 12 calendar

months. We estimated straight-line distances be-

tween consecutive weekly locations (5–13 days apart)

to calculate average weekly movement. We used the

Animal Movement Extension (Hooge and Eichen-

laub 1997) in ArcViewH GIS (Environmental Sys-

tems Research Institute, Redlands, California, USA

[use of trade, product, or firm names does not imply

endorsement by the United States government]) to

calculate 75% and 95% kernel home ranges (Worton

1989) for the animals meeting our inclusion criteria.

EFFECTS OF HURRICANES ON BEARS N Murrow and Clark 193

Ursus 23(2):192–205 (2012)

Habitat variables

We created 13 habitat variables for potential

inclusion in the model from land cover data, water

body data, and roads data with ArcInfoH GIS

(Environmental Systems Research Institute, Red-

lands, California, USA) and Fragstats software

(McGarigal et al. 2002). All 13 variables were 30-m

resolution continuous-valued grid metrics. We ob-

tained pre- and post-hurricane land cover data based

on 30-m resolution Landsat Thematic Mapper (TM)

and Landsat Enhanced Thematic Mapper satellite

imagery (TM 5 or 7, National Oceanic and

Atmospheric Administration 2009). The pre-hurri-

cane Landsat data were collected from 5 November

2004 to 15 August 2005, and the post-hurricane data

were collected from 22 November 2005 to 2 March

2006. At-satellite reflectance was performed on each

scene, and the tasseled cap transformation was

applied (Crist and Cicone 1984). Each Landsat TM

scene was geo-referenced by the US Geological

Survey Earth Resources Observation and Science

Data Center and tested for spatial accuracy to within

2 pixels (60 m). Water body data was obtained from

the most current National Hydrography Dataset

(NHD; US Geological Survey 2009) and road

information from TIGERH line data from the

redistricting 2000 census (US Bureau of the Census

2009).

We combined land cover types to form a binary

classification consisting of forest (1) and non-forest

(0) categories, excluding areas that were permanent-

ly inundated. We defined the forest category as

deciduous forest, mixed forest, or palustrine forest

Landsat cover types, which have been shown to be

important to Louisiana black bears (Nyland 1995,

Benson and Chamberlain 2007). The non-forest

category represented all other land cover types

including urban and barren areas. Using that data

layer, we calculated forest density (FOREST) based

on a neighborhood analysis within a circular moving

window (any cell center encompassed by the circle

was included in the processing of the neighborhood),

the diameter of which was equivalent to the average

weekly movements of radiocollared bears (1,500 m;

see Results). We used the neighborhood analysis to

characterize the habitat conditions at a particular

pixel within the context of the surrounding habitats.

We then assigned the average values of all cells in the

window (i.e., forest density) to the original center 30-

m2 grid cell (i.e., number of cells classified as forest/

total number of cells). We continued this analysis

until every cell was assigned an average forest

density. Water body density (WATER) was deter-

mined with a combination of an NHD line layer,

which represented rivers and streams, and an NHD

water bodies layer (water 5 1, non-water 5 0). We

calculated road density (ROADS) based on the

TigerH primary roads data whereby pixels containing

roads were assigned values of 1 and the remaining

pixels had values of 0. We used the same neighbor-

hood analysis technique to calculate WATER and

ROADS at the 30-m2 resolution.

We used Fragstats software (McGarigal and

Marks 1995, Haines-Young and Chopping 1996,

Turner et al. 2001) to calculate 9 additional variables

that quantified broad-scale patterns of forest–

wetland edge and patch configuration that were

potentially important for bear movement and

metapopulation dynamics. All 9 variables were

calculated using the forest–non-forest land cover

data. Contagion (CONTAG) was a measure of both

dispersion and interspersion of patches and was

inversely related to edge density (McGarigal and

Marks 1995). Connectivity (CONNECT) was a

measure of the connectedness of the landscape and

had a threshold based on 25% of the estimated mean

distance of weekly female bear movements. That

threshold was arbitrary but we considered it

reasonable to depict what was easily accessible by

female bears within normal movement patterns. If 2

forest patches were within that threshold, those 2

patches were considered connected. Splitting index

(SPLIT) was a measure of the patchiness of the

landscape; SPLIT increased as the landscape was

increasingly subdivided into smaller patches. Cohe-

sion (COHESION) and percent of like adjacent

(PLADJ) were connectivity measures and required

no threshold. Fractal dimensions (FRACTAL) and

landscape shape index (LSI) were measures of

landscape shape, including land cover aggregation

or clumpiness. Patch density (PD) and patch richness

(PR) were measures of landscape-level forest patch

characteristics, and Simpson’s diversity index (SIDI)

was an ecological measure of patch richness. We

used the same sized neighborhood analysis to

perform these calculations for these 9 variables at

the 30-m2 resolution.

Statistical model

Mahalanobis distance (D2) has been used to assess

habitat for a wide range of plant and animal species

(e.g., Clark et al. 1993, Knick and Dyer 1997, Corsi

194 EFFECTS OF HURRICANES ON BEARS N Murrow and Clark

Ursus 23(2):192–205 (2012)

et al. 1999, Farber and Kadmon 2003, Buehler et al.

2006, Thompson et al. 2006, Griffin et al. 2010). We

chose D2 for habitat modeling because it requires

only presence data and the method performed well

among several presence-only modeling techniques

evaluated (Alldredge et al. 1998, Tsoar et al. 2007).

In addition, D2 does not require that assumptions of

multivariate normality be met, variables can be in

different units of measurement, and co-varying

habitat variables do not bias the statistic value

(Rao 1952, Clark et al. 1993, Knick and Rotenberry

1998).

Mahalanobis distance is a multivariate measure of

dissimilarity, with small values of D2 (distances)

representing landscape conditions similar to those

associated with the location data, whereas larger

values represent increasingly different conditions

(Rao 1952). This technique predicts habitat suitabil-

ity based on species presence data (e.g., radio-

locations) and geographic variables (e.g., land cover

data in a GIS grid) with the following equation:

D2~ x�{u_

�

� �’X{1 x�{u

_

�

� �,

where x is a vector of landscape characteristics, u is

the mean vector of landscape characteristics esti-

mated from the animal location data, and g21 is the

inverse of the variance–covariance matrix of the

landscape variables (Rao 1952). For example,

consider a model consisting of the 2 variables of

forest density and road density. If the mean forest

density and road densities within all home ranges

were 0.75 km2/km2 and 440 m/km2, respectively (u),

and the values for a particular pixel were 0.70 and

420, respectively (x), then the difference (x 2 u)

would be 0.05 and 20, respectively. Those numbers

would constitute a row vector for that pixel, which

would then be multiplied by the inverse of the

variance–covariance matrix of those 2 variables.

Finally, the resulting row vector would be multiplied

by the corresponding column vector (x 2 u)9, thus

producing a single squared value (D2) for that GIS

pixel. The same calculation would be made for every

pixel on the landscape map. The resulting D2 statistic

for each pixel provides a unitless index of similarity

to the multivariate landscape conditions associated

with the location data (Knick and Rotenberry 1998).

We used 75% kernel home ranges as our sampling

units rather than individual radiolocations and

evaluated their placement on the landscape as our

habitat selection criterion (Type II; Johnson 1980).

The use of individual radiolocations can bias habi-

tat analyses due to disparate numbers of telemetry

locations among animals, telemetry error resulting in

misclassification of individual locations, and tempo-

ral biases in the telemetry data, so our use of home

ranges helped to reduce those effects (Kauhala and

Tiilikainen 2002, Moser and Garton 2007). We only

used home ranges of female bears for model

development and testing because male home ranges

were large and were likely to include extensive

amounts of area not actually used by the bears,

which could result in an overly optimistic character-

ization of model performance.

Before developing the model, we reduced the

number of variables considered. We did this for

simplicity, ease of interpretation, and because some

variables may have poor or redundant explanatory

power. We selected variables for elimination by

identifying means, variances, or ranges that differed

little among bear home ranges and the overall study

area (i.e., poor explanatory power). We retained

variables if we believed they represented unique

landscape characteristics (e.g., ROADS, and

SPLIT).

Model evaluation

After constructing the final D2 model with SAS/

STATH software (SAS Institute, Inc. 2004), we used

a principal components analysis to assess the

relationship between and among the final variables.

We did so by calculating the eigenvalues for each

eigenvector based on the correlation matrix and

identifying the significant components of the model

(Jackson 1991, Morrison et al. 1992). To determine

the number of components to interpret from the

principal components analysis, we used the broken-

stick method as a stopping rule (Jackson 1993). The

broken-stick distribution is essentially the expected

distribution of eigenvalues based on random vari-

ables. We evaluated any principal components that

explained .10% of the total variance and retained

those components if observed eigenvalues were

higher than the corresponding random broken-stick

components.

Although defining study area boundaries is not

necessary to calculate D2, we did so for purposes of

model testing. We defined our occupied study area

as the area that was physically available (land cover

types excluding open water) for home range estab-

lishment by radiocollared bears. To quantify this

area, we merged radiolocations that were included in

EFFECTS OF HURRICANES ON BEARS N Murrow and Clark 195

Ursus 23(2):192–205 (2012)

the kernel home range estimates and calculated a

minimum convex polygon (MCP) around those

locations using ArcViewH. We then applied a buffer,

which corresponded to the radius of the average

female home range, to the outside periphery of the

MCP to delineate the study area boundaries. We

created random home ranges with the same size

distribution as the bear home ranges until .50% of

the area within our study area boundaries, as defined

above, was sampled (Katnik and Wielgus 2005). We

then plotted cumulative frequency distributions of

D2 values for the original bear home ranges and the

random home ranges, using the Kolmogorov-Smir-

nov test to detect differences. If differences were

detected, we established a cutoff value for suitable

habitat by determining the D2 value where the

difference between the 2 cumulative frequency

distributions was greatest (Browning et al. 2005).

We considered pixels with Mahalanobis distance

values below the cutoff to represent suitable bear

habitat and used that cutoff to classify each random

and bear home range. Home ranges with mean D2

values below the cutoff were classified as being in

suitable habitat and those above the cutoff were not.

To assess model consistency, identify outliers in

the data used to construct the model, and evaluate

predictive performance, we used a cross-validation

technique (Larson 1931, van Manen et al. 2002)

whereby we excluded 1 bear home range from the

data set, recalculated D2, and assessed whether the

mean D2 value within the excluded home range was

above or below the cutoff for that particular model.

We repeated that procedure until each home range

had been excluded and tested, calculating the overall

proportion of correctly classified home ranges for

each (Verbyla and Litvaitis 1989). Although our

sample size for developing the habitat model was

small, Katnik and Wielgus (2005) found that the

Type I error rates for comparing random to

observed home ranges with the methods we em-

ployed was low (,0.04 for tests using 10 home

ranges 10 km2 in size).

We also performed a model validation based on

the Boyce index (Boyce et al. 2002, Hirzel et al.

2006). To do so, we first scaled the D2 scores into 10

value categories based on equal percentiles. For

example, D2 values for the lowest one-tenth percen-

tile of the study area were categorized as 10.0, the

next tenth percentile as 9.0, and so on. We used those

scaled D2 scores to calculate the Boyce index based

on a 10-fold cross-validation with 3 home ranges

removed per iteration (i.e., we randomly removed 3

home ranges and recalculated the model; we did this

10 times). Because of our relatively small sample

sizes and the arbitrary nature of selecting a num-

ber of classes or bins for ratio comparisons, we

calculated the Boyce ratio for 3, 4, and 5 equally

distributed bins of the scaled D2 values. The bin

values were independently established by equally

dividing mean D2 values from the 250 randomly

located home ranges into 3, 4, and 5 equal parts, and

identifying the corresponding D2 value. An increas-

ing relationship across binned categories and Spear-

man-rank correlations (rs . 0.5) between bin ranks

and area-adjusted frequencies for model sets indi-

cates a strong relationship and supports the validity

of the model.

Additionally, we tested the original D2 model with

independent bear presence data collected at barbed-

wire hair-sampling sites for a mark–recapture study

in Pointe Coupee Parish in the Upper Atchafalaya

River basin (Lowe 2011). Pixels containing hair-

sampling sites where bears were detected were paired

with the D2 values for those pixels to determine the

proportion above and below our cutoff. We used a

Wilcoxon rank-sum test to compare the D2 values of

a random sample of 104 locations, generated within

an area the size of the average female home range

and centered on the hair-sampling sites, to the D2

values of the hair stations.

Model application

We used the finalized D2 model to estimate the

overall loss of suitable bear habitat. To do that, we

recalculated all model variables with the post-

hurricane landscape data and recalculated the D2

statistic using the pre-hurricane model coefficients.

To identify shifts in habitat quality, we examined the

change in mean D2 scores across each bear home

range and each randomly placed home range. We

also estimated the area above and below our

suitability cutoffs, both before and after the hurri-

canes and for both the occupied and unoccupied

study areas.

ResultsThe telemetry data set consisted of 601 locations

representing 13 (2M:11F) individuals after we

applied our criteria for data inclusion in the analysis.

The average 95% kernel home-range was 11.8 km2

(n 5 11, SE 5 2.6) for females and 51.1 km2 (n 5 2,

196 EFFECTS OF HURRICANES ON BEARS N Murrow and Clark

Ursus 23(2):192–205 (2012)

SE 5 36.2) for males. The 75% home ranges for the

11 females averaged 5.07 km2 (SE 5 1.66), ranging

from 1.16 to 19.76 km2. Average weekly movements

of bears with consecutive weekly locations from April

to November was 1,454 m (n 5 12, SE 5 146.8) for all

bears and 1,353 m (n 5 10, SE 5 153.8) for females.

The 75% kernel home ranges of the 11 females and the

250 randomly generated home ranges covered 47 km2

(5%) and 670.5 km2 (68%) of the occupied area,

respectively. Based on these data, we used 1,900 m

(95% kernel female home range radius) as our buffer

for study area delineation for model testing (983 km2),

used 375 m (25% of mean weekly female movement)

as the cutoff for the CONNECT habitat variable, and

used 1,500 m (average weekly movement of bears) as

the diameter of the circular moving window for the

neighborhood analyses.

We identified 1 outlier home range during the

initial cross-validation calculations that dispropor-

tionately impacted the model parameters when

included and resulted in .50% of the home ranges

to be classified as unsuitable bear habitat. This animal

had a home range that included (11%) an industrial

complex consisting of a salt mine and an electronics

materials plant. Therefore, we considered that indi-

vidual to be an outlier, removed her home range from

the data set, and reparameterized the model. The

second cross-validation resulted in 7 of the 10 test

home ranges being correctly classified. However,

mean D2 values for the 3 outlier home ranges

exceeded the model cutoff by an average of only

2.3% (SD 5 0.02). Therefore, we did not exclude these

home ranges from the model parameterization.

Of the 13 variables considered, 6 were used in the

final model: CONTAG, LSI, SPLIT, FOREST,

WATER, and ROADS (Table 1). Areas selected by

bears were generally associated with areas of

variable landscape complexity, limited permanent

standing water, and extensive amounts of palustrine

or deciduous forest. The principal components

analysis indicated the first 2 components explained

91.3% of the model variation (74.2 and 17.1%,

respectively), but the observed distribution of eigen-

values was less than the distribution based on random

variables for the second component (Table 2).

The model variables with the strongest correlation

and greatest loadings (.0.4) in the first principal

component were evenly distributed between FOR-

EST, CONTAG, SPLIT, and LSI (Table 3), all of

which were related to landscape shape or degree of

fragmentation of forest cover.

Calculation of the Boyce index based on 10-fold

cross-validation indicated a positive correlation for

the 3-, 4-, and 5-binned values (mean rs 5 1.0, 0.8,

and 0.9, respectively), although the 4-binned values

were not statistically significant (P , 0.001, P 5

0.200, and P 5 0.037, respectively). Graphically, all

binned values showed the expected relationship if

habitat selection was occurring (Fig. 1).

The mean D2 value for the occupied study area

was 1,460.3 (SD 5 3,518.2), whereas mean values for

individual home ranges ranged from 20.4 to 143.0 (��x5 80.8, SE 5 45.2). The random home ranges had

mean D2 values ranging from 13.7 to 27,362.6 (��x 5

1,417.1, SE 5 2,750.1) and differed from the original

bear home ranges (D 5 0.80, P , 0.01), suggesting

that bear habitat selection differed from random.

The largest separation between the 2 cumulative

frequency distributions occurred at a D2 value of

Table 1. Mean (xx�), standard error (SE), and range ofMahalanobis distance values (D2) associated withbear home ranges and study area for landscape-scale variables included in the model of occupiedbear habitat in coastal Louisiana, 1993–95. Variablesare forest density (FOREST), road density (ROADS),water body density (WATER), contagion (CONTAG),splitting index (SPLIT), and landscape shapeindex (LSI).

Variable

Bear home ranges (n = 10)Study area x

(SE)x (SE) Range

FOREST 0.73 (0.14) 0.55–0.95 0.39 (0.34)

ROADS 0.03 (0.04) 0.00–0.11 0.03 (0.06)

WATER 0.06 (0.01) 0.04–0.08 0.12 (0.13)

CONTAG 41.67 (25.76) 9.04–86.61 57.08 (27.67)

SPLIT 2.01 (0.55) 1.13–2.64 1.66 (0.73)

LSI 4.15 (2.20) 1.43–7.21 2.87 (1.38)

Table 2. Eigenvalues and observed proportion ofvariance explained by 6 principal componentsassociated with the variables used to model bearhome ranges in coastal Louisiana, 1993–95. Theexpected proportion of variance is based on thebroken-stick distribution.

Principalcomponentvector Eigenvalue

Observedproportion of

variance

Expectedproportion of

variance

1 4.46 0.7424 0.4083

2 1.02 0.1705 0.2417

3 0.40 0.0658 0.1583

4 0.11 0.0180 0.1028

5 0.01 0.0018 0.0611

6 0.01 0.0012 0.0278

EFFECTS OF HURRICANES ON BEARS N Murrow and Clark 197

Ursus 23(2):192–205 (2012)

143, with the mean D2 value of all 10 bear home

ranges falling below that cutoff (Fig. 2) compared

with 49 of the 250 random home ranges (19.6%).

From the 104 hair-sampling stations in Pointe

Coupee Parish where bear presence was documented,

81 (78%) were located in pixels with D2 values less

than the 143 cutoff, 45 of which occurred where D2

values were ,40. The 104 random locations created

for comparison had an average D2 value of 336.3, 61

(58.7%) of which fell below the cutoff. The Wilcoxon

rank-sum test confirmed that the random locations

came from a different distribution than the active hair

sampling locations (U 5 8,507.5, P , 0.001).

Following the hurricanes, ,0.5% of the land cover

classifications changed on the occupied study area

(Table 4). Of the cover types making up .1% of the

study area, open water increased by the greatest

proportion (1.7%). Forest classifications (deciduous,

mixed, and palustrine forest) decreased by 2.8%.

Mean D2 values increased in 8 of the 10 bear home

ranges following the hurricanes, but no home ranges

would have been reclassified as being non-habitat.

Average D2 values within the home ranges increased

from 80.8 to 86.2. Mean D2 values in the occupied

study area before the hurricanes changed from

1,460.3 (SD 5 3,518.2) to 1,495.4 (SD 5 3,715.1;

Fig. 3) afterwards. Suitable habitat (i.e., D2 , 143)

decreased from 348 km2 before the hurricanes to

345 km2 afterward (0.9%). In the unoccupied study

area, suitable habitat decreased from 34,383 km2 to

33,891 km2 (1.4%; Fig. 4).

DiscussionHabitat quality can be affected in divergent ways

following hurricanes depending on the species of

interest. For example, Florida scrub jay (Aphelocoma

coerulescens) coastal populations experienced a 10–

30% increase in extinction rates (Breininger et al.

1999), whereas Florida key deer (Odocoileus virgi-

nianus clavium) experienced an increase in produc-

tivity because of the increase in available browse

Table 3. Principal component loading values of thefirst 2 principal components calculated for aMahalanobis distance (D2) model of occupied bearhabitat in coastal Louisiana, 1993–95. Variables areforest density (FOREST), road density (ROADS),water body density (WATER), contagion (CONTAG),splitting index (SPLIT), and landscape shapeindex (LSI).

VariablesPrincipal

component 1Principal

component 2

FOREST 20.45a 0.02

ROADS 0.39 20.16

WATER 0.08 0.97a

CONTAG 20.47a 20.04

SPLIT 0.47a 0.09

LSI 0.45a 20.14

aVariables with principal components loadings .0.4.

Fig. 1. Ratio of observed and expected frequency of home ranges within 3 (black), 4 (dashed), and 5 (gray)classes (bins) of bear habitat index values based on 10-fold cross-validation of American black bear habitatindex values, coastal Louisiana, 1993–95.

198 EFFECTS OF HURRICANES ON BEARS N Murrow and Clark

Ursus 23(2):192–205 (2012)

following hurricanes (Lopez et al. 2003). The

ecological literature suggests that habitat generalists

may fare better when environmental perturbations

occur (Devictor et al. 2008). Black bears are con-

sidered habitat generalists (Pelton 2003), so it is

perhaps not surprising that our analysis indicated that

bear habitat was only minimally affected by the 2005

hurricanes. Additionally, the effects of the hurricanes

on forested habitats as detected by the TM data were

minimal. Consequently, we speculate that probabili-

ties of population persistence were also not dramat-

ically altered for the Louisiana black bear.

Our analysis was performed at a coarse resolution,

a necessity based on the scaling of the landscape TM

Fig. 2. Cumulative frequency distributions of black bear home ranges’ (solid line) and random home ranges’(dashed line) average Mahalanobis distance statistic values. The arrow line marks the largest separation of the2 distributions (143).

Table 4. Land-cover type percentages and area of the 2005 pre- and post-hurricane data used for the model ofoccupied bear habitat calculated in coastal Louisiana, 1993–95.

Landsat cover type (value)Percentage ofcover type

Pre-hurricanedata (km2)

Post-hurricanedata (km2)

Difference(% change)

High developed (2) ,1% 1.24 1.25 0.01 (0.8)

Medium developed (3) ,1% 2.27 2.20 20.07 (23.1)

Low developed (4) 3% 26.53 26.59 0.06 (0.2)

Developed open space (5) ,1% 0.91 0.91 0 (0.0)

Cultivated/row crops (6) 15% 143.03 143.03 0 (0.0)

Pasture/hay (7) 1% 7.54 7.54 0 (0.0)

Grasslands (8) ,1% 3.42 3.52 0.10 (2.9)

Deciduous forest (9) 1% 9.32 9.07 20.25 (22.7)

Evergreen forest (10) ,1% 0.30 0.27 20.03 (210.0)

Mixed forest (11) ,1% 0.15 0.15 0 (0.0)

Scrub–shrub (12) ,1% 3.28 3.42 0.14 (4.3)

Palustrine forested (13) 38% 374.93 374.64 20.29 (20.1)

Palustrine scrub–shrub (14) 2% 21.60 20.84 20.76 (23.5)

Palustrine emergent (15) 25% 244.16 245.21 1.05 (0.4)

Estuarine scrub–shrub (17) ,1% 0.82 0.77 20.05 (26.1)

Estuarine emergent (18) 7% 71.63 71.46 20.17 (20.2)

Unconsolidated shore (19) 1% 11.00 10.25 20.75 (26.8)

Barren (20) ,1% 0.52 0.52 0 (0.0)

Water (21) 6% 58.62 59.63 1.01 (1.7)

Palustrine aquatic bed (22) 0% 1.37 1.36 20.01 (20.7)

EFFECTS OF HURRICANES ON BEARS N Murrow and Clark 199

Ursus 23(2):192–205 (2012)

data available to us. Ramsey et al. (2009) found that

Landsat TM was similar to radar satellite data in its

ability to detect changes in forest structure following

Hurricane Katrina in the Pearl River Basin. Landsat

TM data have also been successfully used to identify

forests heavily damaged by Hurricane Hugo (Cablk

et al. 1994), and TM images have provided good

class separation following hurricanes when classes

dominated areas .30 m (Ramsey and Laine 1997).

However, Wang and Xu (2010) found that some

components of the TM Tasseled Cap transformation

may not as readily detect changes in forest areas with

lesser degrees of damage and with minimal leaf

wetness impacts. Thus, it was possible for small (,30

x 30 m) or slightly to moderately damaged forest

cover types to retain their pre-hurricane classifica-

tions. The loss of forest cover from these 2

hurricanes was perhaps greatest in the Pearl River

Basin of eastern Louisiana where Chapman et al.

(2008) reported post-Katrina annual tree mortality

rates of 20.5%. Our pre- and post-land cover data

also reflected greater loss in this specific area

compared with the broader region, but to a lesser

extent. Brown et al. (2011) reported reduced canopy

closure on their Pearl River study area following the

hurricanes, with increased understory density, most-

ly consisting of blackberries (Rubus spp.). This

suggests that forest cover with moderate wind

damage likely retains habitat value for bears because

production of berries and other soft mast foods

would probably increase due to the creation of more

light gaps in the forest canopy. Thus, the loss of

small patches of trees within a larger forest matrix

would not necessarily degrade bear habitat. Regard-

less, further evaluation to resolve the discrepancies

between the remotely sensed data and field observa-

tions of forest condition in areas affected by the

hurricanes is needed.

Some variables in the model were simple, espe-

cially the binary land-cover classification that we

used. We combined forest cover types to improve

performance of the model across a broader land-

Fig. 3. Mahalanobis distance values (D2) after hurricanes Katrina and Rita on unoccupied bear habitat alongthe Gulf Coast in Texas, Louisiana, Mississippi, Alabama, and Florida, 2005. The occupied study area adjacentto Bayou Teche National Wildlife Refuge is inset.

200 EFFECTS OF HURRICANES ON BEARS N Murrow and Clark

Ursus 23(2):192–205 (2012)

scape where we lacked home range data or where

bears did not occur. For example, had we defined

estuarine shrub–scrub as a separate cover type in the

model, the statistical procedure may well have

identified proximity to that cover type as a habitat

requirement because the target data set was located

near the coast. Consequently, the model would have

predicted that habitat was poor further inland,

which we knew to be untrue. Therefore, we chose

more generalized variables to avoid this problem of

model overspecificity. Regardless, cross-validation

and independent model testing in the Upper

Atchafalaya River Basin indicated that model

performance was good. Bears select habitat based

on landscape characteristics (Schoen 1990), so it is

not surprising that our model performed well even

though the variables were relatively coarse. Al-

though it was necessary to use a cutoff value to

discriminate between habitat and non-habitat, we

caution that the D2 values in our model represent a

continuum and an absolute threshold value is an

oversimplification.

The principal component analysis indicated that

forest density and fragmentation were underlying

drivers of black bear habitat selection. A post-hoc

stepwise logistic regression of the 6 variables resulted

in only FOREST and LSI variables being retained

and suggested that bears preferred to locate their

home ranges in areas where forests were more

prevalent (FOREST b 5 7.355, 95% CI 5 21.591–

13.119) and aggregated (LSI b 5 1.005, 95% CI 5

0.411–1.599). When we inspected home range

placement on the landscape, we noticed that all

bears selected areas of generally high forest density,

but individuals varied in the amount of fragmenta-

tion and edge they tolerated. Furthermore, bears

whose home ranges centered on deciduous forests

tended to tolerate not only higher fragmentation but

also the lowest forest densities. These observations

are again consistent with bears being habitat

generalists and suggest that the effect of forest

fragmentation on bear ecology in coastal Louisiana

may depend in part on the quality of the habitat

being fragmented.

Fig. 4. Black bear habitat lost (D2 values falling below 143 in gray) after hurricanes Katrina and Rita along theGulf Coast of Louisiana, 2005. The occupied study area is depicted with a black border.

EFFECTS OF HURRICANES ON BEARS N Murrow and Clark 201

Ursus 23(2):192–205 (2012)

We only evaluated immediate and direct effects of

the hurricanes. However, significant longer-term

impacts to wildlife as a result of hurricanes have

been documented following minimal immediate

effects (Wauer and Wunderle 1992, Wiley and

Wunderle 1993, Swilling et al. 1998). Future indirect

effects could include vegetation changes due to

saltwater flooding following storm surges (Conner

et al. 2005), the introduction of toxic chemicals

(Suedel et al. 2006), water quality changes due to

floodwater pumping (Ray 2006), and land subsi-

dence (Burkett et al. 2003). Also, fallen trees are

susceptible to insect infestations, and increased fuel

loads increase the chances of wildfire (Sheikh 2005).

These types of long-term impacts would be especially

important for small, vulnerable populations. Thus,

although existing damage from hurricanes Katrina

and Rita has not resulted in a significant reduction in

suitable bear habitat, there may be future impacts.

We suggest that managers continue to monitor

possible longer-term changes in bear habitat due to

hurricanes.

Despite our findings of little overall immediate

impact, the hurricanes served to illustrate the

precariousness of small, isolated populations. The

outcome could have been far different for the coastal

bear population had the trajectories or intensities of

the hurricanes been otherwise. Specifically, if Hur-

ricane Katrina had made a more westerly landfall or

Hurricane Rita had made a more easterly landfall,

the damages could have been much more significant

to bears. The Louisiana black bear recovery plan

(US Fish and Wildlife Service 1995) specifies that at

least 2 geographically distinct subpopulations should

be viable for delisting to occur. That multi-subpop-

ulation approach to recovery is warranted to help

spread the risk should weather or other environ-

mental perturbation negatively affect any single

population segment.

AcknowledgmentsWe would like to acknowledge and thank the US

Fish and Wildlife Service, especially D. Fuller, who

funded and supported this project. The Louisiana

Department of Wildlife and Fisheries helped coor-

dinate the contracting, for which we are grateful. We

are deeply indebted to D. Walther, M. Davidson, C.

Lowe, and the Black Bear Conservation Committee

for helping us better understand bear habitat issues

in southern Louisiana. Finally, we express our

appreciation to R. Pace, M. Chamberlain, J. Ertel,and the other researchers and managers that came

before us who collected the bear data, performed

earlier analyses, and permitted their use in our study.

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Received: 22 December 2011Accepted: 18 June 2012Associate Editor: J. McDonald

EFFECTS OF HURRICANES ON BEARS N Murrow and Clark 205

Ursus 23(2):192–205 (2012)