Animal Conservation. Print ISSN 1367-9430

Effects of translocation on survival of nuisance bears
J. M. Bauder1, D. Ruid2, N. M. Roberts3, B. Kohn3 & M. L. Allen1

1 Illinois Natural History Survey, Prairie Research Institute, University of Illinois, Champaign, IL, USA
2 United States Department of Agriculture Animal and Plant Health Inspection Service, Wildlife Services, Rhinelander, WI, USA
3 Wisconsin Department of Natural Resources, Rhinelander, WI, USA

Keywords
American black bear; harvest mortality;
human–wildlife conflict; mark-recapture;
multi-state models; Ursus americanus.
CorrespondenceJavan M. Bauder, Illinois Natural History
Survey, Prairie Research Institute, University
of Illinois, Champaign, IL 61820, USA.
Email: javanvonherp@gmail.com
Editor: Vincenzo Penteriani
Associate Editor: Carolyn Hogg
Received 17 July 2020; accepted 25
February 2021
doi:10.1111/acv.12684

Abstract
Effective mitigation of human–wildlife conﬂict should aim to reduce conﬂicts while
also minimizing wildlife mortality. Translocation is often used to mitigate human–
wildlife conﬂict but translocated individuals may have reduced survival, which
could negatively affect population growth and social acceptance of translocation asa management tool. Yet, non-translocated nuisance individuals may also have low
survival due to inherent risks associated with nuisance behavior. We used a 38-year
dataset of 1233 marked and translocated nuisance American black bears (Ursus
americanus) as a model system with which to evaluate the impacts of translocation
on nuisance bear survival. We used multi-state mark-recapture models to estimate
annual harvest and non-harvest mortality rates and tested for effects of translocation distance and harvest rate on recapture and both mortality rates. Recapture
probability increased with translocation distance but 75% of translocated bears
were translocated ≤75 km and recapture probabilities were <0.05 across these distances. Survival was 0.43 for adult males, 0.56 for adult females, and 0.38–0.40
for yearlings. However, increasing translocation distance reduced both harvest andnon-harvest mortality (β = −0.0044, 95% CI = −0.0081 to −0.0006 andβ = −0.0020, 95% CI = −0.0051 to 0.0011, respectively) showing that increasingtranslocation distance does not negatively impact survival. Our survival estimates
were generally lower than those reported for non-nuisance American black bear
populations (0.67–0.83), which likely reﬂects risks associated with nuisance behavior, such as proximity to human dwellings, agriculture, or roads which in turn may
increase harvest and/or road mortality. Our results show that translocation is a useful approach for mitigating human–bear conﬂict that does not always negatively
affect survival. Lower survival of nuisance bears suggests that biologists should
focus efforts on reducing the incidences of human–wildlife conﬂicts (e.g., removing
anthropogenic food sources).

Introduction
Many large carnivores are declining globally due to habitat
loss and human persecution (Laliberte and Ripple, 2004;
Woodroffe, 2000). Yet, some areas have seen increases inlarge carnivore populations due to protection and/or reintroduction (Linnell, Swenson and Anderson, 2001). Increases inlarge carnivore populations may lead to increased human–carnivore conﬂicts (e.g., Can et al., 2014; Holland, Larson
and Powell, 2018; Raithel et al., 2017). Human–carnivore
conﬂicts are important to address because they can result ininjury or death to people or domestic animals, and negativeinteractions often fuel demand for lethal carnivore control(Baker et al., 2008; Can et al., 2014; Inskip and Zimmermann, 2009). Illegal lethal control can jeopardize carnivore
populations, inhibit efforts to promote human–carnivore

820

coexistence, and ultimately fail to reduce conﬂicts (Lennox
et al., 2018). It is therefore important to evaluate different
methods for mitigating human–carnivore conﬂicts (e.g., Holland et al., 2018) while understanding their effects on carnivore population demographics.
One non-lethal approach for mitigating human–carnivore
conﬂict is translocation of offending individuals, yet the factors affecting the outcomes of mitigation through translocation are varied and often poorly understood (Linnell, Aanes
and Swenson, 1997; Massei et al., 2010). Previous studies
evaluating translocation outcomes for carnivore conﬂicts
have generally framed success in terms of the resumption of
nuisance behavior (Athreya et al., 2011; Bauder et al., 2020;
Bradley et al., 2005) or the return of individuals to their capture area (Landriault et al., 2009; Rogers, 1986), and studies
often report variable success rates. Translocated carnivores

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J. M. Bauder et al.

may also have reduced survival, generally attributed toincreased post-release movements which may increase theirsusceptibility to anthropogenic mortality (e.g., harvest, roadmortality, and management killings) (Bradley et al., 2005;
Fonturbel and Simonetti, 2011; Linnell et al., 1997), yet population-level consequences of reduced post-release survival
are often unknown. Additionally, reduced post-release survival may negatively affect societal acceptance of translocation as a management option. However, nuisance behavior isinherently risky for carnivores because of increased proximity to anthropogenic development or agriculture and susceptibility to legal or illegal killings which may result in lower
survival of non-translocated nuisance individuals (Baker
et al., 2008; Raithel et al., 2017). It is therefore important tounderstand how translocation affects carnivore survival andidentify strategies to minimize speciﬁc mortality risks. However, many studies evaluating translocated carnivore survival
are short term (Fonturbel and Simonetti, 2011; but see Bradley et al. 2005) and few report model-based estimates ofcause-speciﬁc mortality from long-term translocation programs for nuisance carnivores (c.f., Raithel et al., 2017).
Long-term studies can evaluate the effects on survival
beyond those associated with moving an individual and its
immediate responses to the release area and minimize theeffects of short-term temporal idiosyncrasies (e.g., weather
and variation in food availability). Cause-speciﬁc mortality
estimates are particularly important for identifying and mitigating the greatest sources of mortality.
We used 38 years of data from translocated nuisance
American black bears (Ursus americanus, hereafter bears) as
a model system with which to evaluate the effects of translocation on the survival of nuisance carnivores. Bears provide
an excellent model system for such a purpose for several
reasons. First, translocation is widely employed to mitigate
human–bear conﬂicts (Spencer, Beausoleil and Martorello,2007). Second, bears involved in nuisance behavior are often
in close proximity to human dwellings or agriculture (Baruch-Mordo et al., 2008; Beckmann and Berger, 2003), which
may increase their susceptibility to road mortality, recreational harvest, or lethal control (Raithel et al., 2017).
Translocated bears may also make extensive post-release
movements (Landriault et al., 2006; Rogers, 1986). Therefore, translocated nuisance bears may have lower survival
than non-nuisance bears (Alldredge et al., 2015; Hebblewhite, Percy and Serrouya, 2003). Finally, bear population
growth can be sensitive to changes in survival, particularly
adult female survival (Beston, 2011; Hebblewhite et al.,2003). We had two objectives in our study. First, we estimated legal recreational harvest (hereafter harvest) and nonharvest mortality rates and survival of translocated nuisancebears by sex and age class. We predicted that survival oftranslocated nuisance bears would be lower than survival inother bear populations and tested this prediction by conducting a Bayesian meta-analysis of bear survival based on areview of black bear demographics across North America by
Beston (2011). Second, we tested for effects of translocation
distance and county-level harvest pressure on bear mortality
rates. We predicted that mortality rates would increase withAnimal Conservation 24 (2021) 820–831 ª 2021 The Zoological Society of London

Nuisance bear survival after translocation

increased translocation distance through exposure to unfamiliar landscapes and greater post-release movements.

Materials and methods
Study area and management regulations
We used mark-recapture data and harvest records fromtranslocated nuisance bears (primarily bears associated withproblematic behavior, damage to bird feeders or garbage
cans, in close proximity to urban areas or campgrounds)
across Wisconsin, USA. The Wisconsin Department of Natural Resources (WDNR) manages bears in the state in four
distinct management zones (A, B, C, and D, Fig. 1), eachwith unique quotas and variable hunting regulations (Allenet al., 2018). Recreational bear harvest was not initially subject to annual quota limits before 1985. The harvest season
was closed for the 1985 season, after which recreational harvest was reinitiated in 1986 using a quota system (Kohn,
1992). Recreational harvest has since occurred annually for
28–35 days beginning in early September (Dhuey et al.,2017; Kohn, 1992), and was open to Ojibwe tribal hunters
during our study (Kohn, 1992). Data on Ojibwe harvested
bears were provided by the Great Lakes Indian Fish andWildlife Commission (Dhuey et al., 2017). Use of bait for
hunting was allowed in all zones and hound hunting was
also allowed in zones A, B, and D. All non-tribal hunterswere legally required to report their harvest and submit anupper ﬁrst premolar for age determination.
Bears occur primarily in northern Wisconsin (zones A, B,
and D, Fig. 1), an area of approximately 84 000 km2 ofnorthern mixed forest (Curtis 1959). Dominant tree species
in this region include sugar maple (Acer saccharum), northern red oak (Quercus rubra), American beech (Fagus grandifolia), and eastern hemlock (Tsuga canadensis) (Curtis1959). Mean temperature and precipitation are −8°C and9 cm during winter and 19°C and 31 cm during summer,
respectively (http://www.aos.wisc.edu/~sco/clim-history/state/4700-climo.html, accessed 7 January 2020).

Field methods
Bears were captured in response to nuisance complaints from1979 to 2016. Captures during 1979–1989 were performed
by WDNR staff (led by BK) and during 1990–2016 by theUnited States Department of Agriculture-Wildlife Services.
Agency personnel ﬁrst attempted to resolve all complaints
over the phone with technical assistance and only attempted
to capture, mark, and translocate bears for repeated highlevel nuisance behavior (e.g., bears present during the day,could not be hazed away, damaged property, etc.). We did
not record the speciﬁc nuisance activity associated with each
capture and we refer to all study animals as nuisance bears.
However, most translocated bears in our study were in
response general nuisance complaints with a relatively small
percentage due to property damage (e.g., Engstrom, Willging
and Ruid, 2015). We trapped nuisance bears (nculvert trap =930, nfootsnare = 7, and nunrecorded = 296), and immobilized
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Nuisance bear survival after translocation

D
B

A

C

Number of Black Bear

4-8

44 - 1570

9 - 20158 - 284

1-3

21 - 43
0

100

200

400
Kilometers

Figure 1 American black bear (Ursus americanus) harvest management zones in Wisconsin, USA, with color shades representing thenumber of translocated nuisance black bear captures by county
during 1979–2016 (n = 1047) that were used in our survival analyses.

them using different drug combinations (most recently a
combination of ketamine and xylazine; Kreeger and Arnemo(2012)). Bears were aged (yearling or adult [≥2 years]),
sexed, weighed, and marked with unique ear tags and/or lip
tattoos, before being translocated and hard-released. We followed the standard ethical guidelines and procedures for capture, handling, and release of wild mammals (Sikes et al.,2016). Post-release live recaptures of marked bears were primarily associated with subsequent nuisance complaints andtranslocations. Dead recoveries were predominately fromrecreational harvest or road mortalities.

Estimating cause-specific mortality andsurvival
We estimated cause-speciﬁc mortality using multi-state models (Koons et al., 2014; Raithel et al., 2017) with threestates: alive (A), dead from harvest (H), and dead from allother causes (O). We estimated cause-speciﬁc mortality astransition probabilities from state A to either H (harvest mortality, μH) or O (non-harvest mortality, μO) between year t
and year t + 1 with survival being calculated as:
1

μH

μO

We speciﬁed our capture histories following Gauthier andLebreton (2008) wherein an individual recovered dead in822

year t was coded as being recovered dead in year t + 1. We
ﬁxed transition probabilities out of each dead state to 0
(Schaub and Pradel, 2004). Recapture parameters included
the probability of being recaptured alive (pA), the probabilityof being recovered after being harvested (rH), and the probability of being recovered after dying from other causes (rO).
We ﬁxed rH to 1 during each year following Raithel et al.
(2017) because recreational hunters were legally required toreport harvested bears and Ojibwe harvests are routinely
reported. This makes our estimates of μH mortality from legally reported harvest.
We estimated model parameters as functions of covariatesthat we hypothesized a priori would affect recapture/recovery and mortality probabilities of translocated nuisance bears.
We considered categorical effects of sex and age class (yearling or adult) on pA, rO, μH, and μO and modeled selectinteractive effects. Because we deﬁned adults as >1-year-old,estimates of pA for yearlings represented the probability ofrecapturing an individual marked as a yearling during theﬁrst year post-release. Recaptures and harvest numbers variedmarkedly across our study with no recaptures or harvests insome years (Figure S1; Tables S1 and S2). We therefore
compared three temporal terms for pA, μH, and μO (interactive with sex for adults only) in lieu of a fully time-dependent term (Raithel et al., 2017): quadratic and cubic effectsof year and a three-level categorical factor denoting the periods 1979–1995, 1996–2004, and 2005–2016. We hypothesized that pA, μH, and μO might be higher in the year
immediately following release (hereafter Post) as translocatedbears may be more susceptible to recapture, harvest, or roadmortality while trying to return to their initial capture location. We ﬁt our models using MARK v. 9.0 (White andBurnham, 1999) through the package RMARK (v. 2.2.6,
Laake, 2013) in Program R (v. 3.6.1, R Core Team, 2019,see Appendix S4 for annotated code). We used simulated
annealing and modeled μ using the logit link to allow μHand μO to vary independently. We calculated survival using
the TransitionMatrix function in RMARK and report modelaveraged predicted survival estimates.

Model selection
We evaluated the effects of age class, sex, Post, and year
using our complete dataset (n = 1233 bears) and a modiﬁed secondary candidate set strategy from Morin et al.
(2020) described as follows. Each multi-state model had
two sub-models (recapture/recovery or transition probability). We evaluated multiple a priori primary (sensu Morinet al. 2020) terms (i.e., combinations of covariates) for thetarget sub-model while holding the non-target sub-model
constant at a “global” (i.e., heavily parameterized) term
(Lebreton et al., 1992). We retained all primary terms with
ΔAICc < 5 from each sub-model and created a secondarymodel set (sensu Morin et al. 2020) by combining all combinations of primary terms from each sub-model (Morinet al., 2020). Our modiﬁcation was to create two different
primary model sets for each sub-model where the ﬁrst primary model set was used to select the best-supported
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J. M. Bauder et al.

temporal terms for pA, μH, and μO while holding the nontarget sub-model constant with the global term. We considered additive and interactive temporal effects with age andsex for pA and additive temporal effects for μH and μO.
Within the ﬁrst primary model set for pA, we considered
all combinations of the temporal terms for pA with our foura priori candidate terms for rO (Table S3). We modeled
one transition parameter (μH or μO) as a function of eachtemporal term in combination with eight a priori terms forthe other transition parameter for a total of 24 models eachfor μH and μO (Tables S4 and S5). The best-supported temporal terms were carried forward to the second primarymodel sets for each sub-model. We drew inferences on ourcovariates using the effect sizes of model-averaged predictions across the secondary model set.

Effects of translocation distance andharvest on mortality
We tested for effects of translocation and recreational harvest
on pA, μH, and μO using data from bears with complete capture and release location data. We measured the distance
between capture and release locations (hereafter translocation
distance). We obtained the annual recreational harvest ofbears from WDNR by county (Allen et al., 2018) and calculated harvest pressure as each county’s proportion of theannual statewide recreational harvest to control for increasing
bear harvests over time (Kirby, Macfarland and Pauli, 2017).
We predicted that greater harvest would reﬂect greater hunter
numbers, effort, or pressure from more skilled hunters, all ofwhich could increase mortality risk. We re-ran our initial
analyses using only bears with complete capture and releaselocation data to create a secondary model set (n = 33 models, hereafter “base models”) with which to evaluate covariate effects.
We tested for linear and quadratic effects of a bear’s
annual cumulative translocation distance during the year(s) it
was translocated using time-varying individual covariates(TVIC; e.g., Dinsmore, White and Knopf, 2002). We also
evaluated linear and quadratic effects of annual cumulativetranslocation distance as individual covariates (IC) to test foreffects over a bear’s entire post-release period. We did notmodel pA using TVIC because of model convergence issues.
We tested for effects of annual county-level bear harvest andharvest pressure as both TVIC and IC. We tested for covariate effects on pA and mortality using two separate analyses
by including an additive effect of a single TVIC or IC on
the parameter(s) of interest in each of our 33 base models.
This resulted in a total of 99 models for pA and 297 models
for mortality. Additive covariate effects were estimated separately for μH and μO. We evaluated the empirical support foreach covariate using the cumulative AICc weights (hereafter
wCum.) across all models containing that particular covariate
and comparing wCum. to that of the base models. We calculated model-averaged coefﬁcient estimates and 95% CI
across the 33 models containing the covariate of interest andmodel-averaged predicted values across all models containing

Animal Conservation 24 (2021) 820–831 ª 2021 The Zoological Society of London

Nuisance bear survival after translocation

either a linear or quadratic effect of that covariate and thebase models.

Meta-analysis of American black bearsurvival
We re-examined the 73 studies reviewed by Beston (2011)
on bear demographics across North America to obtain survival estimates and standard errors for male and female
adults and yearlings in populations with and without recreational hunting. We used the Bayesian meta-analysis fromBeston (2011) to estimate mean survival and 95% highest
posterior density intervals. We also conducted a literature
review of studies reporting survival estimates for nuisancebears and present a detailed description of our methods andresults in Appendices S2 and S3.

Results
We used data from 1233 nuisance bears with known age
class and sex (hereafter “full dataset”), including 81 recaptured and 427 harvested marked bears (Table S1). Most captures, recaptures, and harvests occurred during 1995–2004(70.7%, 58.0%, and 71.4%, respectively) coinciding withresearch conducted by WDNR (Figure S1). Most bears(n = 1158, 93.9%) were not recaptured in subsequent years,but 6.1% were captured in at least one subsequent year
(n1 = 70, n2 = 4, and n3 = 1).
Most captures and harvests in our study population were
adult males (nCaptures = 687, nHarvests = 290) or adult females(nCaptures = 298, nHarvests = 89) with fewer captures of yearling males (nCaptures = 162, nHarvests = 29) or yearling
females (nCaptures = 86, nHarvests = 19). Thirty-two bears(2.6%) were recorded dead from non-harvest causes(nvehicles = 29, nunknown = 2, neuthanization = 1). The proportions of captures and harvests among sex and age classes
varied markedly (χ2 = 23.83, p < 0.0001) with a greater percentage of adult males harvested (68%) than captured (56%),
and a lower proportion of yearling males harvested (7%)
than captured (13%). Annual recreational harvest and harvestpressure per year per county ranged from 0 to 513 bears and
0 to 0.104, respectively.

Survival analyses for all bears
The term for rO with the greatest empirical support was thequadratic effect of year (wCum. = 0.38) followed by the constant term (wCum. = 0.30, Table S3). Subsequent results werevirtually identical using either the quadratic effect of year or
the constant term for rO. We therefore report results using
the quadratic effect of year for rO in which the model-averaged probability of dead recovery ranged from 0.002 (95%CI = 0.000–0.097) to 0.053 (95% CI = 0.034–0.083, Figure S2).
The best-supported temporal term for pA included separate
quadratic effects of year for adult males and adult females(wCum. = 0.67, Table S3). The interactive effect of sex and the823

J. M. Bauder et al.

Nuisance bear survival after translocationgroups (e.g., Table 2). At the midpoint of our study, μH was
higher for males than for females (Table 2), μO was higher
for yearlings than adults (Table 2), and survival was higher
for adults (Smale = 0.433, Sfemale = 0.563) than yearlings(Table 2).

quadratic effect of year (w = 0.78) and the interactive effectof sex, age, and the quadratic effect of year (w = 0.18) on pA
together had overwhelming empirical support (Table S4).
Median probability of live recapture for adult males was 0.061(range = 0.039–0.197) and highest at the beginning and endsof the study (Fig. 2). In contrast, median probability of live
recapture for adult females was 0.102 (range = 0.001–0.204)
and peaked during approximately 1986–1991 (Fig. 2). Medianprobabilities of live recapture for yearling males and females
were 0.062 (range = 0.043–0.191) and 0.098 (range =0.015–0.171), respectively.
The best-supported temporal term for μH was year modeled as a cubic polynomial (wCum. = 0.62, Table S5), while
the best-supported temporal term for μO was a quadratic
effect of year (wCum. = 0.62, Table S6). Two terms forrecapture/recovery and 13 terms for mortality were carriedforward to create our secondary model set of 26 models(Tables S4 and S7).
All but four secondary candidate set models for all bears
included an interactive effect of age class and sex on μH(wCum. = 0.95, Table 1). However, the magnitude of temporal change in μH during our study was low as model-averaged estimates of harvest mortality within each age–sex
group differed by ≤0.05 (Fig. 3). Six models included aneffect of age on μO (wCum. = 0.49) and 12 models included
an interactive effect of age class and sex on μO (wCum. =
0.38, Table 1). An effect of post-capture period on either μH
or μO was included in four models (wCum. ≤ 0.10) but themagnitude of this effect was small (≤0.02) for all age–sex

Effects of translocation and harvestpressure on mortality
We used data from 1047 nuisance bears (with 52 recapturesand 372 harvests) to evaluate the effects of translocation andharvest pressure on mortality (Table S2). Of these 1047 bears,
991 (95%) were translocated annual cumulative distance of 4392 km (mean = 65 km, SD = 35 km). The remaining 56
bears (5%) were not translocated. Our secondary model candidate set included 144 models (Table S8–S14) and indicatedsimilar patterns in recapture and mortality probability fortranslocated bears as for all bears. To limit the number ofmodels in our base model set, we only used models with
ΔAICc < 5 (n = 33) for all subsequent analyses.
Models with a quadratic effect of cumulative translocation
distance on pA had more support (wCum. = 0.96) than models with or without a linear effect of cumulative translocation
distance (wCum. = 0.04 and 0.00, respectively). Live recapture probability was low across years and sexes at cumulative translocation distances below 65 km but generally
increased markedly above approximately 100 km (Fig. 2).
However, wide model-averaged 95% CI indicated substantial
uncertainty for estimates at large distances (Fig. 2).
Female (Translocated)
1.00

0.75

0.75

0.50

0.50

Probability of Live Recapture

Probability of Live Recapture

Female
1.00

0.25
0.00

Male
1.00
0.75

0.25
0.00

Male (Translocated)
1.00
0.75

0.50

0.50

0.25

0.25

0.00

0.00
1980 1985 1990 1995 2000 2005 2010 2015

0

Year

50

100 150 200 250 300 350 400

Cumulative Translocation Distance (km)

Year
1980

1997

2015
A

Figure 2 Model-averaged estimates and 95% CI of probability live recapture (p ) for marked adult translocated nuisance American blackbears (Ursus americanus) in Wisconsin, USA, during 1979–2016. The left panels are estimates across all bears (n = 1233). The right panels
show probability of live recapture as a function of cumulative translocation distance for individuals with complete capture/release data(n = 1047) model-averaged across the final 99 models with individual covariates. The vertical line is the mean cumulative translocation distance (65 km).

824

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J. M. Bauder et al.

Nuisance bear survival after translocation

Table 1 Model rankings from all secondary model terms for translocated nuisance American black bears (Ursus americanus) in Wisconsin,
USA, during 1979–2016 (n = 1233)
Probability of live recapture (pA)2

Sex*Time
Sex*Time2
Sex*Time2
Sex*Time2
AM*Time2 + AF*Time2 + YM+YF
Sex*Time2
Sex*Time2
Sex*Time2
Sex*Time2
AM*Time2 + AF*Time2 + YM+YF
Sex*Time2
AM*Time2 + AF*Time2 + YM+YF
Sex*Time2
Sex*Time2
Sex*Time2
AM*Time2 + AF*Time2 + YM+YF
Sex*Time2
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF
AM*Time2 + AF*Time2 + YM+YF

Probability of harvest mortality(μH)

Probability of non-harvest mortality (μO)

Ka

Devb

ΔAICc

wc

AM + AF+YM + YF+Time
AM + AF+YM + YF
AM + AF+YM + YF+Time3
AM + AF+YM + YF+Time3
AM + AF+YM + YF+Time3
AM*Post + AF*Post + YM+YF
AM + AF+YM + YF
AM*Post + AF*Post + YM+YF
AM + AF+YM + YF
AM + AF+YM + YF
AM + AF+YM + YF
AM + AF+YM + YF+Time3
AM + AF+YM + YF+Time3
AM + AF+YM + YF
Sex + Time3
AM + AF+YM + YF+Time3Sex
AM + AF+YM + YF+Time3
AM*Post + AF*Post + YM+YF
AM + AF+YM + YF
AM + AF+YM + YF
AM + AF+YM + YF
AM*Post + AF*Post + YM+YF
Sex + Time3Sex
AM + AF+YM + YF

AgeAge
AM + AF+YM + YF+Time2
AM + AF+YM + YF
Age
AM*Post + AF*Post + YM+YF
AM + AF+YM + YF
Age
AM + AF+YM + YF+Time2Age
Sex
AM + AF+YM + YF+Time2Sex
AM*Post + AF*Post + YM+YF
Sex + Time2
AM + AF+YM + YF
Sex + Time2Sex
AM*Post + AF*Post + YM+YF
AM + AF+YM + YF
Sex
AM + AF+YM + YF+Time2Age
Sex + Time2
Sex + Time2
AM*Post + AF*Post + YM+YF

18
15
22
20
20
21
17
17
19
17
15
24
18
19
18
22
15
20
23
19
17
21
19
20
17
21

793.91800.87787.74791.86792.21790.64799.24799.60795.57799.68803.89785.72798.17796.26798.48790.72805.07795.09789.38798.07802.31794.27798.37796.42803.13795.03

0.00
0.84
2.02
2.04
2.39
2.86
3.29
3.64
3.70
3.72
3.86
4.11
4.26
4.39
4.57
5.00
5.04
5.27
5.71
6.20
6.35
6.49
6.51
6.60
7.18
7.26

0.21
0.14
0.08
0.08
0.06
0.05
0.04
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01

3

We fixed probability of recovering a harvest mortality (rH) equal to one. In all models, probability of recovering a non-harvest mortality (rO)
was held constant using a quadratic effect of year. Covariate abbreviations are: AM = adult males, AF = adult females, YM = yearling
males, YF = yearling females, Post = first occasion/interval and all other occasions/intervals following release, Time2 = quadratic effect ofyear, Time3 = cubic effect of year (.) = constant.
a
Number of parameters.
b
Deviance.
c
AICc model weight for models with cumulative w = 0.95.

Models with linear or quadratic effects of cumulativetranslocation distance (hereafter distance) as IC had 100% of
the empirical support among TVIC/IC models for mortality(wCum. = 0.86 and 0.14, respectively). The only other covariate whose cumulative w exceeded that of the base models wasannual county harvest (hereafter harvest) as an IC (wCum. =0.74 and 0.26, respectively). We then added the quadratic
effect of distance to pA in our 33 base models and in the models with distance or harvest to create 99 models with which to
calculate model-averaged coefﬁcient and predicted mortality
estimates. The linear effect of distance on mortality had thegreatest support among this ﬁnal model set (wCum. = 0.49)
followed by harvest (wCum. = 0.27) and models with IC for
only pA (wCum. = 0.24). Mortality decreased with increased
distance (Fig. 4), but the effect was stronger for harvest mortality (β = −0.0044, 95% CI = −0.0081 to −0.0006) than for
non-harvest mortality (β = −0.0020, 95% CI = −0.0051 to
0.0011, respectively). Harvest mortality increased at a greaterrate than non-harvest mortality with increased harvest(β = 0.0013, 95% CI = 0.0001–0.0025, and 0.0003, 95%
CI = −0.0010 to 0.0015, respectively, Fig. 4).
Animal Conservation 24 (2021) 820–831 ª 2021 The Zoological Society of London

Survival of non-nuisance, non-translocatedAmerican black bears
Annual bear survival from non-nuisance studies was higher
than our estimates (Fig. 5). Mean posterior estimates and
95% highest posterior density intervals were 0.72
(0.66–0.77) for adult males, 0.83 (0.78–0.87) for adultfemales, 0.67 (0.48–0.81) for yearling males, and 0.80
(0.74–0.86) for yearling females (Appendix S2). These estimates were generally higher in study areas not permitting
legal recreational hunting, although there was substantial
overlap among studies with and without hunting.

Discussion
Our results indicate that nuisance behavior per se, rather thantranslocation, led to lower survival of translocated nuisancebears in our study. Our survival estimates were generally
lower than those of non-nuisance populations in areas also
permitting legal recreational harvest. Only two of 32 studies
reporting adult survival reported lower survival than our
825

J. M. Bauder et al.

Nuisance bear survival after translocation

Adult Males

Adult Females

Yearling Males

Yearling Females

1.00
0.75

Probability of Mortality

0.50
0.25
0.00

1.00
0.75
0.50
0.25
0.00
1980 1985 1990 1995 2000 2005 2010 2015 1980 1985 1990 1995 2000 2005 2010 2015

Year
Mortality
Non−Harvest Mortality

Harvest Mortality

H

Figure 3 Model-averaged probabilities and 95% CI of harvest mortality (μ ) and non-harvest mortality (μO) for marked translocated nuisance
American black bears (Ursus americanus) in Wisconsin, USA, during 1979–2016.

Table 2 Model-averaged estimates of annual harvest (μH) and non-harvest (μO) mortality and survival (S) with 95% CI for translocatednuisance American black bear (Ursus americanus) in Wisconsin, USA, at the midpoint of our study (1997) (n = 1233)
Harvest mortality

Non-harvest mortality

Survival

Sex & age at capture

Post-release year*

μH

95% CI

μO

95% CIS

95% CI

Adult Male
Adult Male
Adult Female
Adult Female
Yearling Male
Yearling Female

SubsequentFirst
Subsequent
First
NA
NA

0.22
0.22
0.12
0.11
0.19
0.19

0.18–0.26
0.19–0.26
0.09–0.15
0.09–0.15
0.13–0.25
0.12–0.30

0.34
0.33
0.32
0.32
0.45
0.43

0.30–0.40
0.25–0.41
0.28–0.37
0.25–0.40
0.32–0.59
0.28–0.60

0.43
0.45
0.56
0.57
0.38
0.40

0.39–0.48
0.36–0.53
0.50–0.62
0.48–0.66
0.24–0.51
0.19–0.62*

Post-release year is either the first year post-release or all subsequent years.

study (Table A2), although our meta-analysis did not accountfor other factors (e.g., road density, harvest quotas, and season length) that could inﬂuence variation in non-translocatedbear survival. However, the negative relationship between
both harvest and non-harvest mortality and translocation distance indicates that lower survival was not due to translocation. While we did not directly compare survival between
translocated and non-translocated nuisance bears, our lower
survival estimates are consistent with results from studies
where nuisance bears were (SAdult = 0.50; SSubadult = 0.28,(Alldredge et al., 2015); SAdult Male = 0.31; SAdult Female =0.58, (Comly-Gericke and Vaughan, 1997); SAdult = 0.66
(Hebblewhite et al., 2003)) and were not (SYearling Problem =0.36; SAdult Problem = 0.29, (Raithel et al., 2017); SAdult =0.83 (Clark, van Manen and Pelton, 2003)) translocated(Appendix S2). Nuisance wildlife often occur in close proximity to humans (Baruch-Mordo et al., 2008), which may
increase mortality risk through acclimation to anthropogenic

826

food sources (Kirby et al., 2017), susceptibility to harvest(Raithel et al., 2017), road mortality, and lethal control. Whileprevious studies of translocated nuisance carnivores haveoften found reduced survival of translocated individuals(Boast, Good and Klein, 2016; Bradley et al., 2005; Fonturbel
and Simonetti, 2011), our results suggest that nuisance behavior itself, rather than translocation, is responsible for our
lower survival estimates.
The positive relationship between survival and translocation distance was counter to our hypothesis. Translocation
may expose individuals to unfamiliar landscapes, potential
conﬂict with resident conspeciﬁcs (Athreya et al., 2011), andresult in extensive post-release movements (Landriault et al.,2006; Rogers, 1986). Nuisance behavior in bears is often a
response to food stress (Baruch-Mordo et al., 2008; Lewis
et al., 2019; Zack, Milne and Dunn, 2003) and translocation
may reduce an individual’s ability to ﬁnd food which could
contribute to recidivism. However, translocating individuals

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J. M. Bauder et al.

Nuisance bear survival after translocation

1.00

Probability of Mortality

Probability of Mortality

1.00

0.75

0.50

0.25

0.00

0.75

0.50

0.25

0.00
0

100

200

300

400

0

Cumulative Translocation Distance (km)Harvest Mortality

100

200

300

400

500

Annual County−Level Harvest

Non−Harvest Mortality

Figure 4 Model-averaged probabilities and 95% CI of harvest mortality and non-harvest mortality for marked translocated nuisance American
black bears (Ursus americanus, results presented for adult females at the midpoint of our study [1997]) in Wisconsin, USA, as a function of
cumulative annual translocation distance and annual county-level harvest modeled as individual covariates. A quadratic effect of cumulative
annual translocation distance was included for probability of live recapture in all models. Vertical lines represent mean covariate values.

Adult Male Hunting

Adult Female Hunting

Adult Male No Hunting

Adult Female No Hunting

Yearling Male Hunting

Yearling Female Hunting

Yearling Male No Hunting

Yearling Female No Hunting

6
4
2
0

Number of Studies

6
4
2
0

6
4
2
0

6
4
2
0
0.00

0.25

0.50

0.75

1.00 0.00

0.25

0.50

0.75

1.00

Annual Survival
Figure 5 Annual survival estimates and 95% CI of translocated nuisance American black bears (Ursus americanus) from this study (black circles and error bars) and mean posterior estimates and 95% highest posterior density intervals from a Bayesian meta-analysis of previous
studies (gray diamonds and error bars) of non-translocated and non-nuisance black bear populations across North America from Beston(2011) (see Supporting Information for additional details). Studies are presented with (Hunting) and without recreational hunting (No Hunting), with histogram bin widths of 0.02.

to “less risky” landscapes (e.g., large tracts of undeveloped
landscapes) may increase survival. While such landscapes in
Wisconsin likely receive more bear hunting effort, they may
also provide fewer opportunities for repeated nuisance
Animal Conservation 24 (2021) 820–831 ª 2021 The Zoological Society of London

behavior. Conversely, short translocations may allow bears toreturn to sites where nuisance behaviors were previously
committed or remain in landscapes with greater risk of roador harvest mortality. Older individuals in our study were827

Nuisance bear survival after translocation

often translocated further, and higher survival of older individuals could also contribute to the positive effect of translocation distance on survival. Wildlife managers should
therefore consider the landscape context of translocation
release sites and seek to better understand how it can affectpost-release movement and behavior.
Our results indicated that recapture probability increased
with translocation distance, suggesting that, while longer
translocations may reduce mortality, they may not necessarily
reduce recidivism. A previous study found that translocatednuisance bears in our study area resumed nuisance behavior
following only 13% of translocations (Bauder et al., 2020).
However, 75% of translocated bears in our study had cumulative translocation distances <75 km and model-averaged
recapture estimates were <0.05 across 0–75 km. Our results
are therefore consistent with our previous ﬁndings suggestingthat translocation acts as an effective deterrent to repeatednuisance behavior for bears in our study area. Our lower
recapture estimates could be due to the fact that our multistate models estimated recapture conditional upon being
alive.
Translocation is a viable management option for mitigating human–carnivore conﬂict in some situations. Previous
research in our study area indicated low recidivism oftranslocated nuisance bears (Bauder et al., 2020) and our
results indicate that translocation does not negatively impact
survival of nuisance bears. Our ﬁnding is important for large
carnivore conservation, both because human–wildlife conﬂicts are limiting factors for large carnivore populations inmany areas (Can et al., 2014; Inskip and Zimmermann,2009) and the efﬁcacy of many carnivore translocation
efforts have been equivocal (Fonturbel and Simonetti, 2011;
Linnell et al., 1997). Translocation may also inadvertently
erode social tolerance if translocated individuals resume nuisance behavior around the release site. Successfully mitigating those conﬂicts without negatively affecting population
persistence is important for maintaining carnivore populations. Bear populations in Wisconsin have consistently
increased since the 1980s (Allen et al., 2018; Kirby et al.,2017) concurrent with the combined use of education andtranslocation to mitigate conﬂicts. Our results also suggestthat strategies for reducing the incidence of human–wildlife
conﬂict could have greater demographic beneﬁts for populations of wildlife involved in nuisance behavior than directintervention (e.g., hunting, retaliatory killing, or translocation; Holland et al., 2018). These strategies can focus on
maintaining or increasing social carrying capacity througheducation or compensation programs (Holland et al., 2018;
Hristienko and McDonald, 2007) and preventing or reducing
wildlife nuisance behavior by removing anthropogenic foodsources, installing wildlife-proof infrastructure, protecting
livestock and agriculture, and utilizing wildlife deterrents(Baker et al., 2008; Baruch-Mordo et al., 2013). Such efforts
provide an initial response to human–carnivore conﬂicts towhich translocation can be added as necessary.
We found that survival was lower for males and yearlings
compared to females, likely due to their higher harvest mortality. Male bears have higher harvest rates in our study area
828

J. M. Bauder et al.

(Allen et al., 2018) and other study areas (Diefenbach, Laakeand Alt, 2004; Lee and Vaughan, 2005). We also found thatharvest mortality rates were higher in counties with higher
numbers of harvested bears, consistent with previous ﬁndingsthat bear harvest rates were higher with increased hunter
density (Diefenbach and Alt, 1998). Managers could therefore potentially use translocation as a management tool toincrease the harvest of nuisance bears particularly if seeking
to limit bear population growth (Raithel et al., 2017),
although additional data are needed to test this hypothesis.
However, despite increasing recreational bear harvest in Wisconsin over the past three decades (Kirby et al., 2017), we
found that harvest mortality for translocated nuisance bears
was relatively constant during our study. This may reﬂect
concurrent increases in Wisconsin’s bear population during
our study (Kirby et al., 2017). Unreported road mortality
may have contributed toward our relatively high non-harvest
mortality rate for males and females, particularly since there
were no systematic efforts to monitor road-killed bears during our study, as well as illegal or un-reported harvest (Beringer et al., 1998; Comly-Gericke and Vaughan, 1997).
Nuisance wildlife translocation programs provide opportunities to address research questions related to post-release
movement, cause-speciﬁc mortality, and behavior of translocated bears. Managers should therefore consider marking
translocated individuals, particularly for harvested species as
this provides an additional means of tag recovery. For example, all bears in our study were initially captured in response
to nuisance reports allowing us to obtain sample sizes and astudy duration that would be impossible by monitoring bears
with radio telemetry (Alldredge et al., 2015; Comly-Gerickeand Vaughan, 1997). However, maintaining constant marking
effort may be logistically difﬁcult. Our recapture rates andnumber of harvests varied strongly over the course of ourstudy reﬂecting changes in research and management objectives which limited our ability to precisely estimate model
parameters. Finally, individuals involved in nuisance behavior may not represent unbiased samples with respect to age
or sex, which warrants caution when inferring broader demographic patterns from such data.
Our research indicates that translocation is a viable management option for mitigating human–wildlife conﬂict, leading us to offer four recommendations for future research onlarge carnivores involved in nuisance behavior. First, we
encourage future studies comparing cause-speciﬁc mortality
among translocated nuisance, non-translocated nuisance, andnon-nuisance carnivores within and among studies to guide
conﬂict reduction strategies. Second, biologists should continue to evaluate and report multiple translocation outcomes
including incidence of recidivism, post-release movements,and survival. Third, biologists should continue to determine
the extent to which conﬂict management strategies, includingtranslocation, reduce the incidence of nuisance behavior
across species and landscapes with particular emphasis given
to understanding how the landscape context (e.g., land cover,resident conspeciﬁc density, and spatial distribution of bear
baiting sites) of the release site affects translocation outcomes in nuisance wildlife. Finally, we echo calls for
Animal Conservation 24 (2021) 820–831 ª 2021 The Zoological Society of London

J. M. Bauder et al.

interdisciplinary approaches that consider human–wildlife
conﬂicts within a broader context of human–wildlife relationships that address ecological, cultural, political, and economic concerns of relevant stakeholders (Pooley et al.,2017).

Acknowledgments
We thank the Wisconsin Department of Natural Resources,
the University of Illinois, and the Illinois Natural History
Survey for their support. This project was funded by Federal
Aid in Wildlife Restoration Grant WI-W-160-R. We thank J.
Rees and P. Engstrom for additional support. J. Price-Tack,
T. Benson, K. Stodola, J. Suraci, and two anonymous
reviewers provided valuable comments on earlier drafts ofthis manuscript.

Data Availability Statement
The data used in the survival analyses contained in thismanuscript are available in the Illinois Data Bank (https://doi.org/10.13012/B2IDB-5471143_V1)

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Supporting information
Additional supporting information may be found online in
the Supporting Information section at the end of the article.

Appendix S1. Additional information on the number ofAmerican black bear (Ursus americanus) captures across ourstudy area, the number of annual bear captures, recaptures,

Animal Conservation 24 (2021) 820–831 ª 2021 The Zoological Society of London

Nuisance bear survival after translocationand harvests, and model rankings from the cause-speciﬁcsurvival analyses.
Appendix S2. Methodology and results of our literature
review and meta-analysis of American black bear (Ursus
americanus) survival.
Appendix S3. R script for conducting our Bayesian metaanalysis.
Appendix S4. R script to conduct the cause-speciﬁc survival analyses.

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