CSIRO PUBLISHING

Wildlife Research, 2020, 47, 426–435
https://doi.org/10.1071/WR19161

Black bear translocations in response to nuisance behaviour
indicate increased effectiveness by translocation distance andlandscape context
A,D
Javan M. Bauder, Nathan M. Roberts B, David Ruid C, Bruce Kohn B andA
Maximilian L. AllenA

Illinois Natural History Survey, Prairie Research Institute, University of Illinois, 1816 South Oak Street,Champaign, IL 61820, USA.
B
Wisconsin Department of Natural Resources, 107 Sutliff Avenue, Rhinelander, WI 54501, USA.
C
United States Department of Agriculture Animal and Plant Health Inspection Service, Wildlife Services,
P.O. Box 1064, Rhinelander, WI 54501, USA.
D
Corresponding author. Email: javanvonherp@gmail.com

Abstract
Context. Translocation is a widely used non-lethal tool to mitigate human–wildlife conflicts, particularly forcarnivores. Multiple intrinsic and extrinsic factors may influence translocation success, yet the influence of release-site
landscape context on the success of translocations of wildlife involved in nuisance behaviour is poorly understood.
Moreover, few studies of translocated wildlife involved in nuisance behaviour have provided estimates of translocation
success under different scenarios.
Aims. We evaluated the role of intrinsic (age, sex) and extrinsic (translocation distance, landscape composition)
features on translocation success of American black bears (Ursus americanus) involved in nuisance behaviour and provide
spatially explicit predictions of success under different scenarios.
Methods. We analysed data from 1462 translocations of 1293 bears in Wisconsin, USA, from 1979 to 2016 andevaluated two measures of translocation success: repeated nuisance behaviour and probability of returning to a previous
capture location.
Key results. Translocation distances ranged from 2 to 235 km (mean ¼ 57 km). Repeated nuisance behaviour wasrecorded following 13.2% of translocation events (192 of 1457) and was not significantly affected by translocation
distance. Bears repeated nuisance behaviour and were recaptured at their previous captures site (i.e. returned) after 64% oftranslocation events (114 of 178). Return probability decreased with an increasing translocation distance, and yearlingbears were less likely to return than were adults. The proportions of agriculture and forest within 75 km and 100 km
respectively, of the release site had positive and negative effects on return probability.
Conclusions. Mangers can use bear characteristics and landscape context to improve translocation success. For
example, achieving a 10% predicted probability of return would require translocation distances of 49–60 km for yearlings
in low-agriculture and high-forest landscapes. In contrast, estimated return probability for adults was $38% across alltranslocation distances (0–124 km) and almost all landscape contexts.
Implications. Our results emphasise the importance of considering the effects of landscape conditions for developing
spatially explicit guidelines for maximising translocation success.
Additional keywords: agriculture, human–wildlife conflict, nuisance wildlife, Ursus americanus.
Received 8 September 2019, accepted 18 March 2020, published online 9 July 2020

Introduction
Managing human–wildlife conflicts is an on-going concern inmany areas because growing human populations result inincreasing encounter rates with wildlife (Messmer 2000; Can
et al. 2014). These conflicts often result in negative impacts toboth humans (e.g. property damage, zoonotic diseases, livestock loss, personal injury or death) and wildlife (e.g. fitness
costs, fear or mortality; Conover et al. 1995; Inskip andJournal compilation Ó CSIRO 2020

Zimmermann 2009) and require substantial investments intime and resources from wildlife management agencies(Beringer et al. 2002; Spencer et al. 2007). Lethal control isapplied in many human–wildlife conflicts (Baker and Harris
2006; Inskip and Zimmermann 2009), but sociological, ethicaland conservation (e.g. endangered species) concerns haveincreased the demand for non-lethal alternatives (Fall andJackson 2002).
www.publish.csiro.au/journals/wr

Factors influencing bear translocation

Translocation (i.e. capturing an individual and transporting it
to and releasing it at a different location within its geographic
range) is a widespread non-lethal approach for managing
nuisance wildlife behaviour (Fischer and Lindenmayer 2000;
Massei et al. 2010). However, translocations are potentiallystressful to the animals involved (Cromwell et al. 1999;
Beringer et al. 2002) and may lead to increased mortality(Reinert and Rupert 1999; Beringer et al. 2002). Moreover,translocations are often financially and logistically costly to
implement (Riley et al. 1994) and may vary in their effectiveness of mitigating nuisance behaviour (Rogers 1986; Linnell
et al. 1997). Understanding the conditions under which translocation mitigates nuisance behaviour is important for increasingtranslocation efficiency and efficacy. Previous studies acrossmultiple terrestrial vertebrate taxa have found relationships
between intrinsic factors, such as age or sex (Moehrenschlager
and Macdonald 2003; Pinter-Wollman et al. 2009; Bauder et al.
2014; Gedir et al. 2018), and extrinsic factors, such as releasemethod or habitat features (Tuberville et al. 2005; Moorhouse
et al. 2009; Devineau et al. 2011; McCoy et al. 2014), andvarious post-translocation behaviours. Incorporating such factors into analyses examining post-translocation nuisance behaviour could be used to predict the absence of such behaviours (i.e.
translocation success) as a function of those factors. However,
the effects of release-site environment on nuisance wildlife
translocation success have received less focus than haveconservation-based translocations (Linnell et al. 1997; cf. Jarvie
et al. 2014; Stone and Guy 2017) and few nuisance wildlife
studies have provided model-based predictions of translocation
success under different spatially explicit scenarios (Landriault
et al. 2009).
Conflict management between humans and American blackbears (Ursus americanus) involves substantial effort from wildlife management agencies (Spencer et al. 2007) and translocation is widely used to mitigate human–bear conflicts (Rogers
1986; Linnell et al. 1997). Previous studies of translocated black
bear involved in nuisance behaviour have defined translocation
success as an absence of repeated nuisance behaviour or as atranslocated bear not returning to its previous capture area
(Linnell et al. 1997; Landriault et al. 2009). Either measure of
success may vary across sites and studies (Landriault et al. 2009;
Shivik et al. 2011; Lewis et al. 2019); yet, most previous studies
of bear translocation have focussed on the probability of the bear
returning to the previous capture area. These studies have foundthat a bear’s age, sex and translocation distance may all influence its probability of returning (Rogers 1986; Fies et al. 1987;
Landriault et al. 2009; Alldredge et al. 2015). However, less isknown about factors that influence the probability of repeatednuisance behaviour. Moreover, relatively little is known about
how the post-release behaviour of translocated bears is affectedby the habitat characteristics or the landscape context of therelease site compared with the effects of age, sex, or translocation distance. McArthur (1981) found that translocation successfor black bears involved in nuisance behaviour increased with anincreasing topographic relief and elevation gain between capture and release sites. Both natural and anthropogenic land
covers can restrict or facilitate black bear movements (Karelus
et al. 2017; Ditmer et al. 2018), which, in turn, may affect theability or inclination of translocated individuals to return.

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427

Beginning in 1979, the Wisconsin Department of Natural
Resources (WDNR) and United States Department of Agriculture’s Animal Plant Health Inspection Service (APHIS) havemanaged a black bear nuisance abatement program. Complaints
have been resolved by providing technical assistance and advice(Voyles et al. 2015), with translocation generally reserved forbears deemed to have a high probability of repeating thenuisance behaviour (e.g. Shivik et al. 2011). The black bear
population in Wisconsin has increased to over 20 000 individuals (Allen et al. 2018), which has coincided with a range
expansion into central and southern Wisconsin, an area withhigher human populations and agricultural activity (Malcolm
and Van Deelen 2010). This may bring bears into an increasing
conflict with humans, making it important to both understandthe efficacy of the Wisconsin bear translocation program andprovide guidelines for maximising the program’s efficiency
and efficacy.
We used a long-term dataset (1979–2016) of black bear
captures and translocations in Wisconsin to address threeobjectives. First, we compared two definitions of translocation
success, namely (1) an absence of repeated nuisance behaviour,and (2) an absence of repeated nuisance behaviour within the
bear’s original home range (i.e. a bear not returning to its
capture area). Second, we evaluated how translocation successis influenced by multiple intrinsic (age, sex) and extrinsic(translocation distance, landscape composition) factors. Third,
we used the results of our analyses to provide model-based
predictions of translocation success under different spatially
explicit scenarios.
Methods
Study area
The WDNR manages black bear populations in four distinct
zones with unique quotas and hunting regulations (Allen et al.
2018). Most of the bear population resides in the northern half of
Wisconsin (Management zones A, B and D, Fig. 1). These zones
cover ,84 000 km2 north of where southern broadleaf forest
transitions to northern mixed broadleaf–conifer forest (Curtis1959). The most common habitat in this region is maple–
basswood forest, including sugar maple (Acer saccharum),
northern red oak (Quercus rubra), American beech (Fagus
grandifolia) and eastern hemlock (Tsuga canadensis; Curtis1959). Mean monthly temperature and precipitation during 1979–2016 ranged from 108C to 218C and from 3 to 11 cm respectively. Seasonal snowfall ranged from 68 to 204 cm (all weather
data are available at http://www.aos.wisc.edu/,sco/clim-history/
state/index.html#Variable, accessed 26 February 2020).
Black bear captures and translocation
Black bears in the present study were captured from 1979 to
2016, primarily by WDNR staff led by B. Kohn from 1979 to
1989, and under a cooperative agreement with APHIS from1990 to 2016. Reports of bear nuisance behaviour were classified as either nuisance reports (problematic bear behaviour,
damage to bird feeders, garbage cans, gardens, bears present inurban environments, or campgrounds), agricultural-damage
reports (primarily corn, but also other agriculture and bee
hives) or property-damage reports (Willging and Kohn 1992).

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J. M. Bauder et al.

N

Number of black bears

11–25

101–2000

26–50

201–358

1–10

51–100
0

100

200

400
km

Fig. 1. Number of translocated black bear captures by county and management zone (denoted by letters) in Wisconsin from 1975 to 2016.

The type of nuisance activity associated with each translocation
was not recorded but most translocations of marked bears annually were in response to nuisance- and property-damage reports(e.g. Engstrom et al. 2015). Agency personnel first attempted toresolve all complaints over the phone, but attempted to capture
and translocate bears when deemed necessary.
Our data consisted of 1916 translocation events from 1659
bears marked with unique ear tags, lip tattoos, or both. We used
1462 events from 1293 marked and translocated individuals forwhich capture and release location data were available. Sample
sizes for subsequent analyses varied depending on the completeness of the data for any given analysis (see below). Bears
designated for translocation were primarily captured using
culvert traps (nculvert trap ¼ 918, ncage trap ¼ 2, nunrecorded ¼ 542),
and immobilised using chemical immobilisation (Shivik et al.
2011; Kreeger and Arnemo 2012). Each bear was classified as ayearling or adult ($2 years) and sexed before release. All bears
were hard released and all capture, handling and release procedures were consistent with the American Society of Mammologist’s Guidelines for Use of Wild Mammals in Research and
Education (Sikes et al. 2016). Personnel also recorded whether abear was harvested through hunting or died of other causes (e.g.
road mortality) and we considered all mortalities as deadrecoveries. All live recaptures were made in association withnuisance behaviour and we, therefore, consider recaptures as
indicative of repeated nuisance behaviour.
Translocation and return distances
Capture and release locations were recorded to the nearest section using the public-land survey system (PLSS), which weconverted to geographic coordinates using a shapefile of
Wisconsin townships–ranges–sections (hpps://navigator.blm.

gov/api/share/5ff91e1972c9ac84, accessed 9 November 2018).
We used only records including the township, range, range
direction, and section (median polygon size ¼ 0.91 km) or, in aminority of cases (,1%), the township, range and range
direction (median polygon size ¼ 5.44 km). We measured
the distance between capture and release locations as a translocation distance.
We followed the approach of previous studies of black beartranslocations and considered a bear to have returned if it wasrecaptured within a distance approximating the diameter of ahome range (Fies et al. 1987; Landriault et al. 2009; Alldredge
et al. 2015). Because male black bears typically have larger
home ranges than do females (Alt et al. 1980; Garshelis and
Pelton 1981), we used separate diameters for each sex. We
performed a systematic review of all published home-range
estimates for black bears in habitats similar to that of bears innorthern Wisconsin (the northern mixed-forest ecotone), following the methods of Allen et al. (2018). We tabulated the
maximum home-range sizes (Table S1, available as Supplementary material to this paper) after excluding outliers noted byauthors. We converted the reviewed home-range sizes to diameters by assuming circular home ranges and calculated themean diameter across studies by sex (8 km for females and
13 km for males). We used maximum, rather than mean, homerange sizes, because our method for estimating home-range
diameter may underestimate the diameter of elongate homeranges. However, our mean diameters corresponded to breaks inthe distribution distances between release locations and subsequent capture locations for bears in our study (Fig. S1, available
as Supplementary material to this paper).
Land cover
We used the National Land Cover Data (NLCD) from 2001,
2006 and 2011 (www.mrlc.gov, accessed 13 December 2018,
and re-projected to WGS84 in ArcMap ver. 10.6, ESRI, Inc.
2017) to quantify release-site landscape composition. We
measured the proportion of urban (NLCD Classes 21, 22, 23 and24), agriculture (81 and 82), forest (41, 42,and 43) and wetland(90 and 95) land covers within circular buffers around eachbear’s release location with the extract function in the package
RASTER (Hijmans 2018) in Program R (ver. 5.5.1, R Core Team2018). We excluded open water before calculating proportions.
Because animals may respond differently to landscape featuresat varied spatial scales (Thompson and McGarigal 2002;
Bowyer and Kie 2006), we used a range of buffer sizes (10-, 20-,30-, 50-, 75- and 100-km radii) to measure landscape composition at scales ranging from immediately around the release site
up to the entire potential return route.
Statistical analyses
We conducted all analyses in Program R. We used chi-square
goodness-of-fit tests to test for equal proportions of captures
between sexes and age classes.
To test for effects on the probability of recapture following atranslocation (i.e. probability of repeated nuisance behaviour)while accounting for both the high proportion of bears withsingle records and the non-independence of multiple records
from the same bear, we used a bootstrapping approach combined

Factors influencing bear translocation

with generalised linear models (GLM) with binomial error
distributions and logit links. We first randomly subsampled
75% of all translocation events, while ensuring that only a single
event was randomly selected from bears with multiple events. We
then fit the binomial GLM and recorded the b estimate for the
independent variable and the predicted probabilities of recapture.
We repeated this process 10 000 times, and report the 2.5th, 50thand 97.5th quantiles (i.e. median and 95% CI) of the betas andpredicted probabilities. We drew inference from effects whose
95% CI did not include zero. We tested for effects of sex, age,translocation distance, whether or not the translocation was abear’s first translocation or a subsequent translocation (e.g. secondor third translocation), and whether or not a bear was eventually
recovered dead at any point following its first translocation.
We evaluated the effects of age, sex and translocation distanceon the probability of returning using generalised linear mixed
models (GLMM) with binomial error distributions and the logit
link with random intercepts for individuals. All bears used inthese analyses repeated nuisance behaviour after their firsttranslocation; so, the probability of returning is synonymous withthe probability of repeating nuisance behaviour within a bear’s
original home range. To minimise the effects of location error andwithin-home range familiarity, we removed translocations lessthan our home-range diameters (e.g. Landriault et al. 2009).
Because bears translocated further generally have lower returnprobabilities (Rogers 1986; Landriault et al. 2009), we retainedtranslocation distance in each model and tested for additive andinteractive effects of distance with sex and age. We fit all GLMM
using LME4 (Bates et al. 2015, 2019) and z-score standardisedtranslocation distance to facilitate model convergence. We
ranked candidate models using Akaike’s information criterion
adjusted for small sample sizes (AICc; Burnham and Anderson
2002) and calculated AICc weights (w) and evidence ratios foreach model. We calculated model-averaged b estimates and 95%
CI across models containing a given covariate (Grueber et al.
2011) and drew inferences from effects whose model-averaged
95% CI did not include zero.
To evaluate the effect of release-site landscape composition on
return probability, we first identified the characteristic scale (i.e.
the scale with the lowest AICc; Holland et al. 2004) for each landcover covariate individually. We added land cover to the best
supported model from the previous analysis, to test our predictionthat land cover would explain additional variation in returnprobability. For our land-cover analyses, we discarded all entries
before 1995 (corresponding to a resumption of marking bears
after cessation from 1991 to 1994, Fig. S2) to minimise the timelag between the date of translocation and the date of our earliest
land-cover data (2001). We assigned land-cover data to theremaining entries by using the NLCD data whose year was
closest to the capture year of each entry. We evaluated a single
model for each land-cover covariate because of limited sample
sizes and high collinearity between forest and agriculture(r ¼ 0.78). We z-score-standardised continuous covariates
and visualised effect sizes for age, sex and translocation distanceby using model-averaged predicted values across all land-covermodels. Because we observed support for interactive effects ofage and translocation distance, we conducted a post hoc analysis
where we fit four additional land-cover models, each including aninteraction between age and land cover. We drew inference from

Wildlife Research429

these post hoc land-cover models only if they received greater
empirical support than their corresponding additive model.
To estimate translocation success under different spatially
explicit scenarios, we calculated the translocation distances
required to achieve a predicted probability of 0.10, 0.25, 0.38
(for comparison with Landriault et al. 2009) and 0.50 of
returning for different age–sex combinations. We note that theseguidelines apply to bears that resumed nuisance behaviour (i.e.
were recaptured following translocation). Because only twoland-cover covariates (forest and agriculture) had greater empirical support than did our null model (containing only age, sex,
and translocation distance), we calculated model-averaged predicted probabilities across all models (additive and interactive)
containing these covariates and the null model. We visually
examined the bivariate distribution of the percentages of agriculture and forest land-cover values at their characteristic scales
across all release locations and selected the following fivecombinations spanning the domain of our data at which toestimate predicted probabilities of return (land-cover percentages reported in parentheses): high agriculture (12%) and low
forest (52%), medium agriculture (7%) and low forest (52%),
medium agriculture (7%) and medium forest (59%), low agriculture (4%) and medium forest (59%), and low agriculture(4%)
and high forest (64%). Finally, we predicted the return probabilities at all observed release locations used in the land-coveranalyses for each age–sex combination.
Results
Bear demography and spatial distribution
Significantly (P , 0.0001) more males (n ¼ 872, 70%) thanfemales (n ¼ 382, 30%) were caught and translocated. Significantly (P , 0.0001) more adults (n ¼ 830) than yearlings(n ¼ 225) were caught and translocated. There was no significant (P ¼ 0.29) difference in the proportion of males and females
captured in each age class. Most (92%) incidents that led totranslocation were in Zones A and D (nA ¼ 762, nD ¼ 576) withsmaller numbers in Zones B and C (nB ¼ 77, nC ¼ 34). Fivecounties had .100 captures (Fig. 1), including Sawyer(n ¼ 358), Ashland (n ¼ 239), Bayfield (n ¼ 214), Price(n ¼ 176), and Douglas (n ¼ 157).
Bear translocations and repeated nuisance behaviour
Mean translocation distance across 1449 translocations (1282
bears) was 57 km (s.d. ¼ 22 km, range 2–235; Fig. S3). Translocated bears were not recaptured following the majority(86.8%) of translocation events (n ¼ 1457) and most translocated bears (n ¼ 1289) were not recaptured after their firsttranslocation event (87.7%). Of 197 recaptures of translocatedbears, most of these recaptures were a bear’s first recapture(n ¼ 161, 82%), although three recaptures were the fourth
recapture for those bears (n ¼ 3, 0.2%). Most translocated bears
that were recovered dead (n ¼ 490, 38.0%) were harvested(n ¼ 463, 94.5%), followed by vehicle mortalities (n ¼ 24,
4.9%) and other mortality sources (n ¼ 3, 0.6%). The percentageof translocated bears that were not recovered dead (n ¼ 799) andthat were never recaptured was 86%.
After evaluating factors influencing the probability of being
recaptured following a translocation (i.e. repeated nuisance430

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J. M. Bauder et al.

Table 1. Candidate model rankings based on Akaike’s information criterion adjusted for small sample sizes (AICc) for the effects of translocation
distance (DST), age (AGE; yearling, adult) and sex (SEX) on the probability of returning by translocated black bears, with random intercepts forindividuals, excluding one observation of an adult male not returning after being translocated 165 km
w, AICc weightParameterK

Log-likelihood

AICc

DAICc

w

Cumulative w

Evidence ratio

SEX þ AGE  DST
SEX þ AGE þ DST
AGE
AGE þ SEX  DST
SEX
Null
DST

6
5
4
6
4
2
3

74.85
76.53
78.12
76.23
85.72
89.3089.04

162.33163.52164.53165.09179.74
182.69184.26

0.00
1.18
2.20
2.76
17.40
20.35
21.93

0.47
0.26
0.16
0.12
0.00
0.00
0.00

0.47
0.73
0.88
1.00
1.00
1.00
1.00

1.00
1.81
3.00
3.97
6010.1726 289.26
57 839.39

behaviour), we found that only two covariates had coefficientestimates whose 95% CI did not include zero. Translocatedbears were less likely to be recaptured following their firsttranslocation (0.07, 95% CI ¼ 0.05–0.08) than any subsequenttranslocation (i.e. their second, third, or fourth translocation;
0.14, 95% CI ¼ 0.07–0.21) (b ¼ 0.81, 95% CI ¼ 0.05–1.36).
Translocated bears that were eventually recovered dead (0.05,
95% CI ¼ 0.04–0.07) were also less likely to be recaptured
before being recovered dead than were translocated bears thatwere never recovered dead (0.08, 95% CI ¼ 0.06–0.10;
b ¼ 0.46, 95% CI ¼ 0.93 to 0.06). While males had alower recapture probability than did females (males: 0.07, 95%
CI ¼ 0.05 to 0.08; females: 0.09, 95% CI ¼ 0.06 to 0.11), the
95% CI overlapped zero (b ¼ 0.29, 95% CI ¼ 0.69 to 0.14).
Adults had a higher recapture probability than did yearlings(adults: 0.08, 95% CI ¼ 0.06 to 0.09; yearlings: 0.06, 95%
CI ¼ 0.03 to 0.08), but the 95% CI overlapped zero (b ¼ 0.34,
95% CI ¼ 0.97 to 0.13). The effect of translocation distance
was negative, but the 95% CI also overlapped zero (b ¼ 0.09,
95% CI ¼ 0.31 to 0.10) and the predicted recapture probabilities at the minimum, mean and maximum observed translocation
distances (3, 57 and 235 km respectively) were 0.09 (95%CI ¼ 0.05 to 0.14), 0.07 (95% CI ¼ 0.06 to 0.08) and 0.04
(95% CI ¼ 0.01 to 0.14) respectively.
Probability of repeated nuisance behaviour around thecapture site
We examined the 178 translocation events from 147 bears(nmales ¼ 103, nfemales ¼ 72; nadults ¼ 119, nyearlings ¼ 23) thatwere followed by repeated nuisance behaviour (i.e. recaptured)and had sufficient data with which to determine post-release
movements. Most of these translocation events were a bear’s
first translocation (n ¼ 124, 84%), but 17 events were a bear’s
second translocation (12%), four were a bear’s third translocation (3%) and two were a bear’s fourth translocation (1%).
Median translocation distance for these 178 translocations was
53 km (IQR ¼ 41–66 km, range ¼ 19 to 167 km). Bears returnedto their capture area and repeated nuisance behaviour (i.e.
returned) following 64% (n ¼ 114) of these translocation events.
We had 138 translocation events from 115 bears with sufficient data to evaluate the effects of sex, age, and translocation
distance on return probability. One 165-km translocation of an
adult male that was not recaptured within its original home range
was an outlier relative to other translocated adults (see below);

so, we report results without this translocation event (for results
including this translocation event, see Tables S2 and S3). Fourmodels had 100% of the cumulative AICc weight and allcontained age (Table 1). The top-ranked model included aninteractive effect of age and translocation distance and had 1.81
times the empirical support of the model with an additive effectof age and translocation distance. The model-averaged 95% CI
for the main effect for age excluded zero and indicated thatjuveniles had lower return probabilities than did adults(b ¼ 2.74, 95% CI ¼ 4.78 to 0.70), whereas the modelaveraged 95% CI for the main effect of sex included zero(b ¼ 0.79, 95% CI ¼ 1.65 to 0.07). The coefficient estimate
for the interactive effect between age and translocation distance
was almost identical regardless of whether the outlying observation was excluded (excluded: b ¼ 1.40, 95% CI ¼ 3.27 to
0.46; included: b ¼ 1.41, 95% CI ¼ 3.41 to 0.60). Returnprobability decreased with an increasing translocation distancefor juveniles (Fig. 2). However, removing the outlying observation virtually removed any relationship between return probability and translocation distance for adults (Fig. 2).
Effect of landscape composition on probability of returning
We had 106 translocation events (excluding the potential outlier
described above) from 87 bears with sufficient data to evaluate
the effects of land cover on return probability. Urban landscape
had the strongest effect at the 10-km scale, agriculture had thestrongest effect on return probability at the 75-km scale, andforest and wetland had the strongest effect at the 100-km scale(Fig. S4). The means and ranges of the percentages of each land
cover within the buffer at their characteristic scales were 7.6%
(3.0 to 26.8%) for agriculture, 58.8% (47.3 to 64.6%) for forest,
21.9% (14.1 to 29.6%) for wetland, and 0.003% (0.0 to 0.01%)
for urban landscape.
Adding agriculture or forest to the top-ranked model with
only age, sex and translocation distance resulted in 4.9 and 2.3
times more support respectively, relative to the model with only
age, sex and translocation (Table 2). Return probability
increased and decreased with increasing amounts of agriculture
and forest respectively (Fig. 3). These results were similar when
including the aforementioned outlier (Table S3). The post hoc
model including an interaction between agriculture and age had
1.1 times the empirical support of the model including an
additive effect of agriculture and age (w ¼ 0.43 and 0.38
respectively) and 14.9 and 13.1 times the empirical support

Factors influencing bear translocation

Wildlife Research

Age

431

Sex

Yearling

Adult

FemaleMale

All data

Without outlier

Probability of returning

0.75

0.50

0.25

0
40

80

120

160

40

80

120

160

Translocation distance (km)Fig. 2. Probability of returning for translocated black bears by sex and age classes at the time of capture as a function oftranslocation distance with (n ¼ 107) and without (n ¼ 106) one observation of an adult male not returning after beingtranslocated 165 km. Predicted values were model-averaged across all additive and interactive land-cover models, with landcover covariates being held constant at their mean values.

Table 2. Candidate model rankings for the effects of translocation distance, age (yearling, adult), sex and land-cover covariates on the probability of
returning by translocated black bears excluding one observation of an adult male not returning after being translocated 165 km
Each land-cover covariate was included at its characteristic scale (10 km for urban, 75 km for agriculture, 100 km for forest and wetland) and the null model
includes only non-land-cover covariates. The evidence ratio indicates the support of Model i relative to the null model. b, the fixed-effect parameter estimate
with its 95% CI; w, AICc weightParameter

K

Loglikelihood

AICc

DAICc

w

Cumulativew

Evidence
ratio

b

Lower
95% CI

Upper
95% CIAGRICULTURE þ
SEX þ AGE  DST
FOREST þ SEX þ AGE  DST
SEX þ AGE  DST
WETLAND þ SEX þ AGE  DST
URBAN þ SEX þ AGE  DST7

51.72118.59

0.00

0.76

0.76

14.93

1.00

0.15

1.86

7
6
7
7

53.61
55.57
54.53
54.85122.36124.00124.20124.84

3.77
5.41
5.61
6.25

0.11
0.05
0.05
0.03

0.87
0.92
0.97
1.00

2.27
1.00
0.90
0.66

0.53

0.37
0.26

1.10

0.89
0.69

0.03

0.14
0.16

respectively, of the model with only age, sex and translocation(w ¼ 0.03). The post hoc model including an interaction between
forest and age had less support than its corresponding additivemodel (w ¼ 0.02 and 0.07 respectively).
Guidelines for translocation distances to minimise repeat
nuisance behaviour
The model-averaged predicted return probabilities indicatedthat the translocation distance needed to achieve a specified
probability of returning varied widely by age class (Table 3).
Yearlings needed to be translocated the shortest distancesand had a 0.10 probability of returning when translocated$49–60 km, depending on sex and landscape context. In contrast, predicted returning probabilities for adults did not fall

below 0.38 for any of the landscape contexts we evaluated acrossnearly the entire range of translocation distances used in our
analyses (up to 124 km). Predicting return probabilities acrossactual release sites used in our landscape-composition analyses
indicated that return probability was lowest in the northern
regions of Zones A and D (Fig. S5).
Discussion
We found that our definition of translocation success strongly
influenced the apparent success of translocation in mitigating
human–black bear conflicts in Wisconsin. Translocated blackbears in our study were recaptured resuming nuisance behaviour
following 13.2% of translocation events, suggesting that translocation acts as a strong deterrent to repeated nuisance behaviour

432

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J. M. Bauder et al.

Age

Sex

Yearling

Adult

FemaleAgriculture

Male

ForestProbability of returning

1.00

0.75

0.50

0.25

0
0.10

0.20

0.50

0.55

0.60

0.65

Proportion of buffer
Fig. 3. Model-averaged predicted probabilities of returning by translocated black bears as a function of the proportion ofagriculture and forest land cover within 75 km and 100 km respectively, of a bear’s release point, excluding one observation of an
adult male not returning after being translocated 165 km. Results were virtually identical when this individual was included.
Predictions were averaged across the two models (additive and interactive) containing each land cover and the null model
containing only age, sex and translocation distance (held constant at its mean value).

Table 3. Minimum translocation distances (km) needed to reach a certain probability of returning for translocated black bears, excluding one
observation of an adult male not returning after being translocated 165 km
Probabilities represent the model-averaged predicted probabilities of returning across all additive and interactive models with agriculture (Ag.) or forest and anull model with only age, sex and translocation distance. Percentages of agriculture and forest are the percentages of each land cover within 75-km- and100-km-radius buffers respectively, at the 5th (L), 50th (M) and 95th (H) quantiles. Values of ‘.124’ mean that model-averaged predicted probabilities did not
fall below the specified threshold across the range of translocation distances used in the analyses (max. ¼ 124 km)
Sex

Age

Agriculture

Forest

0.10

0.25

0.38

0.50

Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
FemaleFemaleFemaleFemaleFemaleFemaleFemaleFemaleFemaleFemaleYearling
Yearling
Yearling
Yearling
Yearling
Adult
Adult
Adult
Adult
Adult
Yearling
Yearling
Yearling
Yearling
Yearling
Adult
Adult
Adult
Adult
Adult

H Ag. (12%)
L Ag. (4%)
L Ag. (4%)
M Ag. (7%)
M Ag. (7%)
H Ag. (12%)
L Ag. (4%)
L Ag. (4%)
M Ag. (7%)
M Ag. (7%)
H Ag. (12%)
L Ag. (4%)
L Ag. (4%)
M Ag. (7%)
M Ag. (7%)
H Ag. (12%)
L Ag. (4%)
L Ag. (4%)
M Ag. (7%)
M Ag. (7%)

L Forest (52%)
H Forest (64%)
M Forest (59%)
L Forest (52%)
M Forest (59%)
L Forest (52%)
H Forest (64%)
M Forest (59%)
L Forest (52%)
M Forest (59%)
L Forest (52%)
H Forest (64%)
M Forest (59%)
L Forest (52%)
M Forest (59%)
L Forest (52%)
H Forest (64%)
M Forest (59%)
L Forest (52%)
M Forest (59%)

60
49
49
54
52
.124
.124
.124
.124
.124
71
60
61
65
63
.124
.124
.124
.124
.124

45
34
35
39
37
.124
.124
.124
.124
.124
56
45
46
50
48
.124
.124
.124
.124
.124

37
26
26
31
29
.124
.124
.124
.124
.124
48
37
37
42
40
.124
.124
.124
.124
.124

30
19
20
24
23
.124
19
27
.124
.124
41
30
31
35
34
.124
.124
.124
.124
.124

Factors influencing bear translocation

among black bears involved in nuisance behaviour in Wisconsin. This may be due to capture, immobilisation and transportation having deterrent effects (Clark et al. 2002). Lewis et al.
(2019), Landriault et al. (2009) and Fies et al. (1987) reported
that 25%, 30% and 3% of translocated black bears respectively,
repeated nuisance behaviour. Shivik et al. (2011) recaptured 4%of black bear involved in nuisance behaviour in Wisconsin thatwere trapped at agricultural areas and translocated 40 to 64 km.
Our observations may underestimate the degree of nuisancebehaviour by excluding complaints resolved with technicalassistance over the phone (Voyles et al. 2015). However, thesecomplaints are generally deemed less serious (e.g. not involving
agriculture or property damage) than are those resulting intranslocation. In Wisconsin, most of complaints not involving
agriculture or property damage were resolved over the phone (D.
Ruid, unpubl. data), highlighting the value of public educationand outreach in these situations. The small proportion ofrecaptured translocated bears, therefore, suggests a low repeatedincidence of more serious nuisance behaviours, although we
lacked the data to explicitly test this hypothesis. Our low
recapture rates could also be due to hunter harvest, because
38% of translocated bears were harvested by hunters, and othernon-harvest moralities. However, our overall recapture rates
were similar with and without excluding bears that wererecovered dead.
In contrast to the low recurrence of nuisance behaviour, 36%of translocated black bears involved in nuisance behaviour thatwere recaptured occurred outside of what we defined as their
original home range. Although this percentage refers only tobears that were recaptured resuming nuisance behaviour, returnprobability may still be an important measure of translocation
success because the objective of many translocations may,indeed, be to remove nuisance individuals from particular areas
or properties. However, although translocation may reduce theincidence of nuisance behaviour for particular individuals, it
may have less efficacy for reducing nuisance complaints onparticular properties by other bears (Voyles et al. 2015).
Our results also show how the definition of translocation
success can influence subsequent management recommendations. We found a weak association between translocation
distance over a range of 2 to 235 km and the probability of
repeated nuisance behaviour. In contrast, we observed a strong
negative association between return probability and translocation distance over a range of 19 to 124 km for yearling bears.
Despite the low probability of repeated nuisance behaviour in
our study, we still recommend that managers consider increasing translocation distance when deciding where to translocate
black bears in Wisconsin, as this will likely reduce the probability of a bear returning to its original home range. Our higher rates
of repeated nuisance behaviour for bears with multiple translocation events may also suggest that some individuals are more
prone to repeating nuisance behaviour; however, additional
work is needed to test this hypothesis.
Our estimated relationships between translocation distanceand age and return probability are consistent with results fromprevious studies. Multiple studies have reported negative relationships between translocation distance and return probability(Rogers 1986; Linnell et al. 1997; Landriault et al. 2009). Shivik
et al. (2011) translocated black bears involved in nuisance

Wildlife Research433

behaviour in Wisconsin agricultural areas 40 to 64 km andfound that 15 of 21 recaptures associated with repeated nuisancebehaviour occurred #10 km of the bear’s capture area. Similar
to our study, Landriault et al. (2009) found that translocatedadult black bears in southern Ontario were far more likely toreturn than were juveniles (73% vs 29% respectively) and alsothat the negative effect of translocation distance was stronger forjuveniles than for adults. Yearling male black bears are theprimary dispersing age class and may disperse .200 km,
whereas yearling females generally remain close (,15 km) totheir natal home range (Rogers 1987; Costello 2010). However,translocated adult and juvenile bears may still return to their
original home range when translocated long distances (100 to
398 km; Rogers 1986; Fies et al. 1987; Landriault et al. 2006).
Adults show a greater degree of home-range fidelity but are also
capable of extensive movements outside of their normal homerange (Rogers 1987; Costello 2010) and are likely to havegreater familiarity with their surrounding landscape because ofprevious dispersal and extra-home range movements. Higher
return rates for adults may, therefore, reflect a greater fitness
need for returning to their home range (via familiarity with thelocation of food resources and potential competitors or mates),increased navigational abilities (Landriault et al. 2009), or both.
Our results indicated that the landscape composition aroundthe release site also affects return probability and we observed
the strongest effects from agriculture (positive) and forest(negative) land covers. This was reflected in the spatial distribution of return probabilities, wherein probability of return waslowest in the northern, most-forested parts of Wisconsin(Fig. S4). Agricultural landscapes may offer less food, depending on the season (Ditmer et al. 2018), shelter, and greatermortality risks, thereby increasing the relative costs of remaining in the release area. Bears may also move more rapidly andextensively in landscapes containing higher proportions ofanthropogenically disturbed habitat (Karelus et al. 2017; Ditmer
et al. 2018). Agriculture and forest land covers had the strongest
effects at large ($75 km) scales, indicating that the landscapecontext along the entire return route may affect a bear’s ability,
willingness to return to its capture area, or both. Alternatively,
these broad-scale associations with land cover may reflect
regional differences in landscape-scale habitat suitability rather
than direct influences of particular land covers. Nevertheless, werecommend that managers not only consider habitat features atpotential release sites, but also the broader landscape contexts ofthe sites.
Most translocated bears in our study were adults, and males
were slightly more likely to be recaptured than were females. A
similar male-bias has been reported for translocated black bears
in Montana (59%, McArthur 1981), Pennsylvania (57%, Alt
et al. 1977) and the southern Appalachian Mountains (87%,
Beeman and Pelton 1976). Landriault et al. (2009) found that thesex ratio of captured black bears involved in nuisance behaviour
in southern Ontario varied among study sites from equal to 3 : 1
male-biased, indicating that male-bias in black bear nuisancereports is not universal. Male-biased captures of bears involvedin nuisance behaviour may be due to more extensive malemovement patterns, which may increase their encounters ofhuman food resources or human-dominated landscapes. However, sex had a relatively weak effect on return probability

434

Wildlife Researchcompared with other covariates, which is consistent with thefindings of Landriault et al. (2009); however, both our study andtheirs found that males were slightly less likely to return thanfemales. This pattern is consistent with the hypothesis that moredispersal-prone individuals (i.e. males) are less likely to return totheir release site; however, our limited sample sizes prevented amore rigorous test of this hypothesis.
Our study has shown that translocation can be an effectivemeans of reducing nuisance behaviour and human–wildlife
conflict, and has provided guidelines for wildlife managers
regarding translocating black bears involved in nuisance behaviour. First, a small minority of translocated bears in our study
were recaptured, indicating that translocation across a range of
distances (2 to 235 km) strongly reduces nuisance behaviour.
Second, translocating yearlings will have the greatest success inremoving the individual from the area where nuisance activity
occurred. Third, our results also suggest that it may be difficult
to prevent adults from returning to their capture location, given
their high probability of return across the range of translocation
distances employed in our study (i.e. #124 km). Fourth, thelandscape context of the release site should be considered acrossmultiple spatial scales, with priority given to release sites inhighly forested landscapes. Yet, we encourage additional
research to better understand the mechanisms by which landscape context affects post-release movement behaviour of blackbears translocated in response to nuisance behaviour. Finally,
our results illustrated the management value of providing
spatially explicit empirical estimates of translocation successunder different scenarios and we recommend the adoption of thisapproach across studies.
Conflicts of interest
The authors declare no conflicts of interest.
Acknowledgements
This project was funded by the Wisconsin Department of Natural Resources,
the Illinois Natural History Survey, and the University of Illinois. We thankthe many staff at Wisconsin Department of Natural Resources and the UnitedStates Department of Agriculture Animal and Plant Health Inspection Service that contributed to the black bear captures over the years, and S. Hull,
R. Rolley, D. MacFarland for their support. The comments of M. Cheong,
N. Parsons, and H. Pavlatos greatly improved this manuscript.

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