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Harvest can affect the ecology and evolution of wild species. The removal of key individuals, such as matriarchs or dominant males, can disrupt social structure and exacerbate the impact of hunting on population growth. We do not know, however, how and when the spatiotemporal reorganization takes place after removal and if such changes can be the mechanism that explain a decrease in population growth. Detailed behavioral information from individually monitored brown bears, in a population where hunting increases sexually selected infanticide, revealed that adult males increased their use of home ranges of hunter-killed neighbors in the second year after their death. Use of a hunter-killed male’s home range was influenced by the survivor’s as well as the hunter-killed male’s age, population density, and hunting intensity. Our results emphasize that hunting can have long-term indirect effects which can affect population viability.
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Scientific RepoRts | 7:45222 | DOI: 10.1038/srep45222
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Hunting promotes spatial
reorganization and sexually
selected infanticide
M. Leclerc1, S. C. Frank2, A. Zedrosser2,3, J. E. Swenson4,5 & F. Pelletier1
Harvest can aect the ecology and evolution of wild species. The removal of key individuals, such as
matriarchs or dominant males, can disrupt social structure and exacerbate the impact of hunting on
population growth. We do not know, however, how and when the spatiotemporal reorganization takes
place after removal and if such changes can be the mechanism that explain a decrease in population
growth. Detailed behavioral information from individually monitored brown bears, in a population
where hunting increases sexually selected infanticide, revealed that adult males increased their use
of home ranges of hunter-killed neighbors in the second year after their death. Use of a hunter-killed
male’s home range was inuenced by the survivor’s as well as the hunter-killed male’s age, population
density, and hunting intensity. Our results emphasize that hunting can have long-term indirect eects
which can aect population viability.
Human activities are a major evolutionary force aecting wild populations1. ere is increasing evidence that
human exploitation leads to changes in morphological and life history traits worldwide1–4. For example, recent
studies have shown that size-selective harvest by commercial sheries and trophy hunting can induce evolution of
heritable traits5–9. Harvest-induced evolution might not be desirable as the selection induced by human exploita-
tion can be in the opposite direction of natural selection10–12.
Hunting can also have indirect eects on wildlife, although such eects are oen ignored by managers, even
though the removal of key individuals by hunting could change a populations social structure13. For example,
simulations suggest that the social networks of killer whales (Orcinus orca) may be vulnerable to targeted removal
of individuals14. In African elephants (Loxodonta africana) the enhanced discriminatory abilities of the oldest
individuals inuences the social knowledge and reproductive success of entire groups15, suggesting that the loss
of older individuals could decrease the tness of all females within the group. In social species, the removal of
any individual could aect social dynamics by changing the social structure. However, empirical evidence link-
ing hunting and spatiotemporal reorganization of the social structure is lacking and the data needed to investi-
gate this question are rarely available. Given the large number of species targeted by harvest, understanding the
potential eects of removal on subsequent space use, social structure, and the tness consequences for surviving
individuals is critical to achieve sustainable hunting practices.
Here, we used detailed individual behavioral information from a Scandinavian brown bear (Ursus arctos) pop-
ulation (monitored from 2008–2015) to evaluate whether surviving adult males (hereaer referred to as survivors)
shi their home range use aer a neighboring adult male has been killed by hunting (TableS1). We further investi-
gated the intrinsic and extrinsic factors driving the spatiotemporal reorganization of male spatial structure. In this
population, the removal of adult males through hunting increases the risk of sexually selected infanticide (SSI)16,17,
which is a major determinant of population growth18. Although important for sustainable wildlife manage-
ment19, the mechanism behind the harvest-induced increase of SSI remains unknown [but see Loveridge et al.20].
1Canada Research Chair in Evolutionary Demography and Conservation & Centre for Northern Studies, Département
de biologie, Université de Sherbrooke, Sherbrooke, J1K2R1, Canada. 2Faculty of Technology, Natural Sciences, and
Maritime Sciences, Department of Natural Sciences and Environmental Health, University College of Southeast
Norway, N-3800 Bø i Telemark, Norway. 3Department of Integrative Biology, Institute of Wildlife Biology and Game
Management, University of Natural Resources and Life Sciences, Vienna, Gregor Mendel Str. 33, A - 1180 Vienna,
Austria. 4Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life
Sciences, PO Box 5003, NO - 1432 Ås, Norway. 5Norwegian Institute for Nature Research, NO-7485 Trondheim,
Norway. Correspondence and requests for materials should be addressed to M.L. (email: Martin.Leclerc2@
USherbrooke.ca)
Received: 01 December 2016
Accepted: 21 February 2017
Published: 23 March 2017
OPEN
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Scientific RepoRts | 7:45222 | DOI: 10.1038/srep45222
Spatial reorganization due to hunting of males may be the responsible mechanism, by increasing the probability
that a female will encounter a new male that is unlikely to be the father of her cubs13,16.
Results
We found that survivors increased their use of the home ranges of hunter-killed males in the second year aer
their death (Fig.1, TableS2). is time lag in the response likely is related to the bear’s ecology. Bears den from
October to April21,22, shortly aer the hunting season in late August—September. e size of the annual home
range in our study population is mainly dened by space use during the mating season (May to mid-July), when
males exhibit a roam-to-mate behavior23. erefore, we hypothesize that survivors do not readjust their home
range until aer the rst mating season without the hunter-killed neighbor. is could explain the two-year time
lag in spatial reorganization. Our results support the contention that the spatiotemporal reorganization of male
home ranges is an important mechanism linking hunter harvest to an increase in SSI, described above. It is also
consistent with earlier studies in the same population showing lower cub survival following a two-year time lag
aer a male had been killed16,17.
We further investigated which intrinsic (ages of hunter-killed and surviving males) and extrinsic factors (pop-
ulation density and hunting intensity) modulated the speed and strength of the survivors’ response to hunting
removals (Fig.2, TablesS3 and S4). e use of a hunter-killed male’s home range by its surviving neighbors
was inuenced by (in order of decreasing relative importance) survivor’s age ( BIC = 115), hunting intensity
( BIC = 76), population density ( BIC = 74), and hunter-killed male’s age ( BIC = 6). Older survivors used
a hunter-killed male’s home range less strongly following the hunter-killed male’s death than younger survivors
(Fig.2A). is suggests that older males may already have held home ranges with better resources, including food
and females. Age-dependent home range quality could also explain why survivors increased their use of an old
hunter-killed male’s home range more than that of a younger hunter-killed male (Fig.2D).
Survivors more strongly increased their use of a hunter-killed male’s home range in the second year aer its
death when hunting intensity was greater (Fig.2B). As increasing hunting intensity will increase the number
of openings for surviving males, this should lead to a higher degree of spatial reorganization. We previously
reported that the killing of an adult male within 25 km of a female strongly reduced the survival of her cubs, with
a two-year time lag, although an increase in the number of killed males within 25 km had no signicant additive
eect17. Even though the degree of spatial reorganization increased with increased hunting intensity, this might
not always translate into a correspondingly lower cub survival, because even though more surviving males may
respond to increased hunting removal, only one infanticidal male is sucient to kill most of females’ cubs. e
other extrinsic factor aecting shis in a survivor’s home range use was population density (Fig.2C). Survivors
at higher densities had higher initial overlap with the hunter-killed male and showed a weaker reorganization
response than survivors at lower densities (Fig.2C). Stronger competition for space between neighbors might
explain why we observed higher initial overlap, with a weaker response at higher densities.
Discussion
We identied a key behavioral mechanism linking hunting to an increase in SSI and show how post-hunt spa-
tiotemporal reorganization of males was modulated by both intrinsic and extrinsic factors. By removing males
from the population, hunters destabilized the spatial organization of the population for at least two years aer a
male had been killed. is period of two years might be specic to brown bears, due to their denning period and
could be dierent in other harvested species with SSI, such as lions (Panthera leo)20 or cougars (Puma concolor)24.
Nevertheless, hunting increases shis in home range use by surviving males and increases the probability of
SSI16,17. Male bears seem to assess their paternity through their mating history25, and increasing the magnitude
of shis in home range use would increase the probability that a male could encounter a female with whom he
had not previously mated. Such a pattern is expected regardless of the cause of death (e.g., vehicle collision, man-
agement kill, natural mortality). However, hunting is oen additive to natural mortality, as in our study system26,
which increases the occurrence of SSI compared to unharvested systems.
e spatial distribution of the hunting mortality of bears was not homogenous in our study area27. Spatial
and social relationships of bears are likely to change more rapidly in areas with higher hunting mortality, thereby
potentially decreasing the cohesion of their social network28,29 but see ref. 30. Such eects could also inuence
Figure 1. Changes in surviving male brown bears use of hunter-killed neighboring males’ home ranges
over time. Shown are the coecients and 95% condence intervals for three consecutive years, i.e. the year the
hunter-killed male was shot (baseline) and the following two years.
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the female reproductive rate because female brown bears exhibit kin-related spatial structures31, where neighbors
negatively aect each other’s probability of having cubs32,33. e direct eect of removals due to hunting, in addi-
tion to the indirect eects of increasing cub mortality due to SSI and the potential impacts of decreasing social
network cohesion, all increases heterogeneity in survival and reproductive rates. ese eects combined could
increase demographic variability and ultimately aect eective population size34,35. erefore, we expect spatially
structured demographic variability that could potentially result in source-sink dynamics35,36.
Our study sheds light on the importance of animal behavior to explain time lags in the responses to hunting
in the wild. Understanding the indirect consequence of hunting over long time scales is critical for developing
sustainable management practices and for the viability of harvested populations.
Methods
e study area was in south-central Sweden (61°N, 15°E) and was composed of bogs, lakes, and intensively man-
aged coniferous forest stands. e dominant tree species were Norway spruce (Picea abies), Scots pine (Pinus
sylvestris), lodgepole pine (Pinus contorta), and birch (Betula spp.). Elevations ranged between 150 and 725 m asl.
Gravel roads (0.7 km/km2) were more abundant than paved roads (0.14 km/km2). See Martin et al.37 for further
information about the study area.
We captured brown bears from a helicopter using a remote drug delivery system (Dan-Inject® ,
Børkop, Denmark). We determined sex at capture and extracted a tooth from unknown individuals for age
Figure 2. Inuence of intrinsic and extrinsic factors on the speed and strength at which a surviving male
will use hunter-killed neighboring males’ home ranges. Shown are the coecients and 95% condence
intervals for three consecutive years, i.e. the year the hunter-killed male was shot (baseline) and the following
two years, depending on the surviving male’s age (A), hunting intensity (B), population density (C), and hunter-
killed male’s age (D), e low and high values in each panel represent the 25th and 75th percentiles, respectively,
observed in the database.
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determination38. We equipped bears with GPS collars (GPS Plus; Vectronic Aerospace GmbH, Berlin, Germany)
programed to relocate a bear with varying schedules ( 1 hour intervals). See Fahlman et al.39 for details on
capture and handling. All captured bears were part of the Scandinavian Brown Bear Research Project and all
experiments, captures and handling were performed in accordance with relevant guidelines and regulations and
were approved by the appropriate authority and ethical committee (Naturvårdsverket and Djuretiska nämden i
Uppsala, Sweden).
Spatial analysis. We used adult male bears 4 years in the analysis to exclude natal dispersers40. We did
not include natal dispersers because all male dispersers moved outside the study area where too few or no other
males were GPS-collared. In addition, females actively defend their cubs during infanticide attempts. erefore,
younger dispersing males that have not yet attain full body size are less likely to successfully commit SSI than
older, larger and better established males41. We screened the relocation data of adult males and removed GPS xes
with dilution of precision values > 10 to increase spatial accuracy. To reduce autocorrelation, we used a 6-hour
minimum interval between successive positions for a given bear. We excluded bears in years for which an individ-
ual had < 75% of days with GPS locations from 1 May to 30 September.
We used an approach adapted from resource selection functions [RSFs;42] developed by Bischof et al.43. For
each GPS-collared hunter-killed male we (1) determined its annual 95% kernel home range for the active period
(1 May to 30 September or the day before he was killed) of the year in which he was killed and (2) calculated a
40-km radius circular buer centered on its home range centroid. is radius was used because it represents the
distance within which 95% of home range centroids of successful mates occurred44 and the distance at which
the eect of male removal on cub survival seems to disappear17. In a given year, we used GPS relocations of the
hunter-killed male and all the GPS locations of surviving adult males within the buer (hereaer called survivors)
to (3) calculate a 95% kernel isocline (hereaer called sampling space). For each survivor, we (4) generated as
many random than GPS relocations within the sampling space and (5) determined if GPS and random relocations
were inside or outside the hunter-killed bear’s home range. We repeated steps 3–5 for 3 consecutive years, i.e. the
year a hunter-killed male had been killed and the two following years. We updated the sampling space annually
by keeping the hunter-killed males’ relocations the year he was killed constant for the three years, and used the
appropriate relocations of survivors for each year. We only used survivors that were alive and monitored during
the three-year period. We repeated these steps for each hunter-killed male. is enabled us to test whether survi-
vors increased their use of a hunter-killed male’s home range the years following its death.
For each hunter-killed male we also extracted a population density index derived from county-level scat col-
lections in Sweden. We used the method of Jerina et al.45 and summed the weighted values of an individual bear’s
multiple scats across a grid of 10 × 10 km. is was carried out for each county separately, aer which the distri-
bution was corrected temporally, using county-level trends of the Large Carnivore Observation Index46,47, pro-
vided by the Swedish Association for Hunting and Wildlife Management. Lastly, we calculated a proxy of hunting
intensity based on the number of dead adult males located within the 40-km radius circular buer centered on
a given hunter-killed male’s home range centroid over a 3-year period prior to its death [see Gosselin et al.17 for
further details].
Statistical analysis. As a first step, we determined if surviving males shifted their home range use in
response to the removal of a hunter-killed male. To do so, we used a generalized linear mixed model (GLMM)
with binomial distributed errors. We coded the dependent variable either as GPS (coded 1) or random (coded
0) relocation. As independent variables we used a dummy variable representing whether the relocations were
inside (coded 1) or outside (coded 0) the hunter-killed males home range, as well as a variable representing the
period of the relocations (3-level factor; the year of the hunter-killed male’s death, as well as 1 and 2 years aer
the hunter-killed male’s death). We evaluated 4 candidate models (TableS1) and selected the most parsimonious
based on the Bayesian information criterion (BIC)48. To control for the eect of year and unequal sample sizes
across individuals, we included Year and the survivor ID nested within the hunter-killed males’ ID as random
intercepts in all candidate models.
In a second step, we examined how intrinsic (i.e., age of survivor and hunter-killed males) and extrinsic (i.e.,
population density and hunting intensity) factors inuenced the speed and strength at which a survivor would
adjust its home range use in response to the removal of a hunter-killed male. We used a GLMM with binomial
distributed errors and coded the dependent variable either as GPS (coded 1) or random (coded 0) relocation. We
evaluated the eect of six independent variables; inside vs outside the hunter-killed male home range, period,
age of the survivor, age of the hunter-killed male, population density, and hunting intensity to build 17 candidate
models (TableS3). We selected the most parsimonious model based on BIC. To control for the eect of year and
unequal sample sizes across individuals, we included Year and the survivor ID nested within the hunter-killed
males’ ID as random intercepts in all candidate models. To facilitate model convergence, we scaled (mean = 0,
variance = 1) all numerical covariates. We assessed the relative importance of variables within the most parsimo-
nious model by dropping each variable and monitoring the BIC. e larger the relative dierence in BIC com-
pared to the most parsimonious model, the more important we considered a variable. For all candidate models
tested, the variance ination factor (VIF) value was < 249. We used R version 3.2.3 for all statistical analyses50.
We captured and GPS-monitored a total of 15 adult males between 2008 and 2015. e database contained
19,133 GPS and 19,133 random relocations of 11 hunter-killed males and 7 survivors, for a total of 23 survivor –
hunter-killed male pairs.
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Acknowledgements
We would like to thank M. Festa-Bianchet, D. Garant, J. Van de Walle, one anonymous reviewer and the editor
for useful comments on earlier version of the manuscript. ML was supported nancially by NSERC and FRQNT.
FP was funded by NSERC discovery grant and by the Canada research Chair in Evolutionary Demography
and Conservation. is is scientic publication No. 232 from the SBBRP, which was funded by the Swedish
Environmental Protection Agency, the Norwegian Directorate for Nature Management, the Research Council
of Norway, the Austrian Science Fund, and the Swedish Association for Hunting and Wildlife Management.
We acknowledge the support of the Center for Advanced Study in Oslo, Norway, that funded and hosted our
research project “Climate eects on harvested large mammal populations” during the academic year of 2015–
2016 and funding from the Polish-Norwegian Research Program operated by the National Center for Research
and Development under the Norwegian Financial Mechanism 2009–2014 in the frame of Project Contract No
POL-NOR/198352/85/2013.
Author Contributions
All authors participated in the study design. M.L. and S.C.F. carried out statistical analyses, F.P., J.E.S. and
A.Z. secured funding, J.E.S. and A.Z. coordinated the Scandinavian Brown Bear Research Project. All authors
participated in writing the manuscript.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing Interests: e authors declare no competing nancial interests.
How to cite this article: Leclerc, M. et al. Hunting promotes spatial reorganization and sexually selected
infanticide. Sci. Rep. 7, 45222; doi: 10.1038/srep45222 (2017).
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© e Author(s) 2017
... Dans le troisième chapitre de cette thèse, j'ai documenté la restructuration spatiotemporelle des domaines vitaux suite à la récolte d'un mâle (Leclerc et al., 2017a). Cette restructuration est considérée comme l'un des mécanismes pouvant expliquer le lien entre la chasse et l'augmentation de l'infanticide sexuellement sélectionné. ...
... Cette restructuration est considérée comme l'un des mécanismes pouvant expliquer le lien entre la chasse et l'augmentation de l'infanticide sexuellement sélectionné. Mes résultats montrent que les mâles adultes résidents augmentaient leur utilisation du domaine vital d'un ours tué à la chasse, mais seulement deux ans après la mort de ce dernier (Leclerc et al., 2017a;. Ce délai de deux ans coïncide également avec les résultats obtenus sur la survie juvénile dans le deuxième chapitre. ...
... En effet, la survie des oursons était influencée par la distance au mâle tué le plus près deux ans auparavant . La restructuration spatiotemporelle des domaines vitaux de mâles variait également en fonction de l'âge des ours, de la densité de la population et de l'intensité de la chasse (Leclerc et al., 2017a). Les résultats obtenus dans les chapitres deux et trois de cette thèse soulignent l'importance d'étudier les effets écologiques comportementaux de la récolte sur plusieurs années. ...
Thesis
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La planète Terre fait face à sa sixième extinction massive des espèces. Cette fois, l’Homme (Homo sapiens) est considéré comme la cause principale de ce phénomène. Une des menaces les plus importantes qui pèsent sur la survie des populations animales est la surexploitation, telle que la pêche et la chasse. L’exploitation diminue la survie des classes d’âges et de sexes qui sont récoltées, mais elle peut également induire des effets écologiques et évolutifs sur les populations sauvages. Bien que les effets écologiques et évolutifs de la récolte puissent influencer le taux de croissance de la population, ils sont peu documentés et quantifiés dans la littérature scientifique et sont rarement pris en compte lors de la gestion des populations exploitées. Cette thèse a donc comme objectif principal de quantifier les effets écologiques et évolutifs de la chasse chez une population de grand carnivore ; l’ours brun (Ursus arctos) scandinave. Cette population est suivie de manière longitudinale depuis 1985 par le Scandinavian Brown Bear Project. Ce projet de recherche cumule des informations, entre autres, sur l’âge, le sexe, la reproduction, la survie et le comportement des ours bruns, ce qui en fait une des bases de données des plus complètes au monde chez un grand carnivore. Dans un premier temps, j’ai évalué les effets écologiques de la chasse. La chasse peut déstabiliser la structure spatiale et sociale d’une population récoltée et ainsi augmenter la probabilité d’observer de l’infanticide sexuellement sélectionné. Par conséquent, la chasse a le potentiel de diminuer la survie juvénile même si cette classe d’âge n’est pas directement visée par la récolte. En combinant des informations sur la survie juvénile et la localisation des mâles récoltés à la chasse, j’ai mis en évidence une diminution de la survie d’une portée d’une femelle lorsqu’un mâle était tué à proximité (Chapitre deux). De plus, j’ai montré que la mortalité d’un mâle à la chasse résulte en une restructuration spatiale qui perdure pendant deux années (Chapitre trois). Les résultats obtenus suggèrent que la chasse a des effets écologiques à long terme qui peuvent influencer la viabilité des populations. Dans un deuxième temps, j’ai évalué les conséquences évolutives potentielles de la chasse. J’ai débuté en montrant qu’il existe de la variabilité comportementale en sélection d’habitat chez l’ours brun (Chapitre quatre), soit une des conditions de l’évolution par sélection naturelle. Ensuite, j’ai colligé de l’information issue de la littérature scientifique afin de montrer que les comportements des animaux peuvent influencer leur vulnérabilité à la récolte, que ce soit la pêche ou la chasse (Chapitre cinq). Ensuite, j’ai montré que les chasseurs peuvent induire des pressions sélectives sur le comportement des ours, soit la deuxième condition de l’évolution par sélection naturelle (Chapitre six). Bien que je n’aie pas quantifié l’héritabilité comportementale dans cette thèse, les résultats des chapitres quatre, cinq et six suggèrent néanmoins qu’il pourrait y avoir de l’évolution induite par la récolte pour les traits comportementaux héritables. Finalement, j’ai comparé, pour la même population, les données du suivi longitudinal du Scandinavian Brown Bear Project aux données du registre d’abattages de chasse. J’ai mis en lumière que les données de registre d’abattages de chasse peuvent être biaisées par rapport à des données issues d’un suivi longitudinal, et ce, même dans un système d’étude où la chasse est considérée comme peu sélective (Chapitre sept). Cette conclusion est importante pour la gestion, la conservation et l’étude des effets écologiques et évolutifs de la chasse qui utilisent souvent les données dans les registres d’abattages de chasse. En effet, ces données constituent souvent une des seules sources d’informations disponibles pour plusieurs espèces exploitées. Bien que les données de registres d’abattages de chasse soient souvent abondantes, elles devraient être utilisées de manière prudente dans les décisions de gestion et de conservation considérant qu’elles sont souvent biaisées. Les résultats de cette thèse ont permis de quantifier certains effets écologiques et évolutifs de la récolte et de souligner l’importance de ces effets pour la viabilité à long terme des populations exploitées. Mieux documenter les effets des activités anthropiques est primordial afin de pouvoir décider des actions à poser pour réduire ces effets dans une ère où l’Homme est la principale menace à la biodiversité de la planète.
... Our study includes yearlings consuming solid food, however its relative contribution to the diet is unknown. Access to food resources (mainly berries in autumn [43]), and thus foraging opportunities, should be similar among litter mates, although scramble competition or compensatory feeding is possible [44]. ...
... In addition, it is possible that intraspecific killing of yearlings, an important cause of mortality for female yearlings 42 , may be higher today. Indeed, because surviving male bears reorganize their homes ranges after the death of a nearby male 43 , hunting can promote spatial reorganization, which, combined with higher numbers of bears in the population in general, leads to higher probabilities of deadly encounters between young bears and adult males 42,44 . By staying with their mother an additional year, yearlings not only gain protection against hunting, but also against their other main cause of mortality; intraspecific killing 28,42 . ...
... These densities were then corrected for temporal variations using the Large Carnivore Observation Index 66 . Finally, annual density cells were summed over the study area and scaled (from 0 to 1) to obtain an index of annual density that reflects temporal changes in bear population density 43,44 . As for hunting pressure, effect sizes (log-odds) of density (Appendix 5.1: Table A5.3) on survival components were then used to generate posterior predictions of λ dependent on population density index. ...
... High territory turn-over and opportunistic male natal philopatry can further alter dispersal and metapopulation structure in leopards (Fattebert et al., 2015;Milner et al., 2007). This has also been illustrated in many other harvested large carnivores, such as pumas (Puma concolor; Newby et al., 2013), brown bears (Ursus arctos; Leclerc et al., 2017) and wolves (Canis lupus; Webb et al., 2011). ...
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... Hunting can have direct and indirect effects on animals. A typical example is either a direct reduction in the animals population or an indirect effect on the social structure of the population by eliminating key individuals (Leclerc et al., 2017). Along the borderland, where hunting is prohibited, which could change the trade-offs of animals (Wilson et al., 2020). ...
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... Given that ovulation in female bears can be potentially induced by the loss of cubs (Boone et al. 2004, Curry et al. 2014Stewart 2016, it is conceivably beneficial for a male to kill cubs assuming they are not his from a previous mating, and assuming that the female does indeed ovulate and then become receptive to his advances (e.g., Bunnell & Tait 1981, Larivière & Ferguson 2003, Himelright et al. 2014). There is increasing evidence for the existence of this somewhat speculate sequence, consistent with well-documented increases in infanticide under conditions were there is an influx of non-resident male bears (Bellemain et al. 2006;Gosselin et al. 2015Gosselin et al. , 2017Leclerc et al. 2017). ...
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... Disruptions to social structures can affect mating patterns and enhance sexually selected infanticide (SSI) through increased contact between unfamiliar male and female mates during the breeding season Leclerc et al., 2017). In response, females adapt countermeasures against SSI and promiscuously mate with several males to reduce SSI risk . ...
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Harvest can disrupt wildlife populations by removing adults with naturally high survival. This can reshape sociospatial structure, genetic composition, fitness, and potentially affect evolution. Genetic tools can detect changes in local, fine-scale genetic structure (FGS) and assess the interplay between harvest-caused social and FGS in populations. We used data on 1614 brown bears, Ursus arctos, genotyped with 16 microsatellites, to investigate whether harvest intensity (mean low: 0.13 from 1990 to 2005, mean high: 0.28 from 2006 to 2011) caused changes in FGS among matrilines (8 matrilines; 109 females ≥4 years of age), sex-specific survival and putative dispersal distances, female spatial genetic autocorrelation, matriline persistence, and male mating patterns. Increased harvest decreased FGS of matrilines. Female dispersal distances decreased, and male reproductive success was redistributed more evenly. Adult males had lower survival during high harvest, suggesting that higher male turnover caused this redistribution and helped explain decreased structure among matrilines, despite shorter female dispersal distances. Adult female survival and survival probability of both mother and daughter were lower during high harvest, indicating that matriline persistence was also lower. Our findings indicate a crucial role of regulated harvest in shaping populations, decreasing differences among “groups,” even for solitary-living species, and potentially altering the evolutionary trajectory of wild populations. © 2020 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd
... Human-induced selection-intentional or unintentionalhas the potential to cause rapid phenotypic and evolutionary change in exploited populations that may reduce viability [11,12]. Overharvest may also cause changes in spatial organisation [13,14], social dynamics/interactions [15] and recruitment [16,17], which can significantly affect population growth [9]. Understanding the relationship between overharvesting and the indirect effects on population growth and viability is therefore crucial for the recovery of exploited species. ...
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... The populations of European large carnivores have been culled with varying intensity from sustainable management to eradication under different national and sub-national policies (Stoskpf, 2012;Penteriani et al., 2018), with the most intense persecution occurring in western, central and northern Europe (Boitani & Linnell, 2015). Culling causes abrupt changes in the behaviour of surviving animals and can strongly alter habitat use in large carnivores (Darimont et al., 2009;Frank et al., 2017;Leclerc et al., 2017;Van de Walle et al., 2018e Walle et al., 2018. Therefore, asymmetric and context-dependent intensity of persecution, human presence and human-mediated modifications in the environment may result in different patterns of habitat selection by large carnivores towards remote areas with less human activity (Llaneza, López-Bao, & Sazatornil, 2012;Holbrook et al., 2019). ...
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Network resilience to perturbation is fundamental to functionality in systems ranging from synthetic communication networks to evolved social organization [1]. While theoretical work offers insight into causes of network robustness, examination of natural networks can identify evolved mechanisms of resilience and how they are related to the selective pressures driving structure. Female African elephants (Loxodonta africana) exhibit complex social networks with node heterogeneity in which older individuals serve as connectivity hubs [2, 3]. Recent ivory poaching targeting older elephants in a well-studied population has mirrored the targeted removal of highly connected nodes in the theoretical literature that leads to structural collapse [4, 5]. Here we tested the response of this natural network to selective knockouts. We find that the hierarchical network topology characteristic of elephant societies was highly conserved across the 16-year study despite ∼70% turnover in individual composition of the population. At a population level, the oldest available individuals persisted to fill socially central positions in the network. For analyses using known mother-daughter pairs, social positions of daughters during the disrupted period were predicted by those of their mothers in years prior, were unrelated to individual histories of family mortality, and were actively built. As such, daughters replicated the social network roles of their mothers, driving the observed network resilience. Our study provides a rare bridge between network theory and an evolved system, demonstrating social redundancy to be the mechanism by which resilience to perturbation occurred in this socially advanced species.