Using Visual Observations to Compare the Behavior of Previously Immobilized and Non-Immobilized Wild Polar Bears

During 17 field seasons between 1973 and 1999, we conducted a long-term study of the behavior of undisturbed wild polar bears in Radstock Bay, southwest Devon Island, Nunavut. In a subset of 11 seasons (6 spring and 5 summer) between 1975 and 1997, we used three different drug combinations to chemically immobilize a small number of adult and subadult polar bears on an opportunistic basis and applied a temporary dye mark so that individual bears could be visually reidentified. We then used multinomial logistic regression to compare the behavior of 35 previously immobilized bears of five different demographic classes (sex, age, and reproductive status) to the behavior of non-immobilized bears of the same demographic classes in the same years and seasons. During the first two days after immobilization, bears slept significantly more and spent less time hunting than did bears that had not been immobilized. However, previously immobilized bears returned to the same behavioral patterns and proportion of total time spent hunting as non-immobilized bears within two days and no further negative behavioral effects were detected in the following 21 d. We visually confirmed successful hunting by three adult bears within 0.4 to 2.1 d of being immobilized, all of which went on to make additional kills within the following 24 h. The return to normal behavior patterns, including the ability to hunt successfully, within 48 h of immobilization appears consistent with the hypothesis that polar bears do not experience longer-term behavioral effects following brief chemical immobilization for conservation and management purposes. Durant 17 saisons de recherche, entre 1973 et 1999, nous avons effectué l’étude à long terme du comportement d’ours polaires sauvages non perturbés à la baie Radstock, dans le sud-ouest de l’île Devon, au Nunavut. Dans un sous-ensemble de 11 saisons (six printemps et cinq étés) échelonnées de 1975 à 1997, nous avons utilisé trois combinaisons de drogues différentes pour immobiliser chimiquement un petit nombre d’ours polaires adultes et d’ours polaires immatures de manière opportuniste, puis nous avons appliqué une marque de colorant temporaire sur les ours afin de pouvoir les réidentifier individuellement. Ensuite, nous avons recouru à la régression logistique multinomiale pour comparer le comportement de 35 ours précédemment immobilisés faisant partie de cinq catégories démographiques différentes (sexe, âge et état reproducteur) au comportement d’ours non immobilisés faisant partie des mêmes catégories démographiques pour les mêmes années et les mêmes saisons. Au cours des deux premières journées suivant l’immobilisation, les ours dormaient beaucoup plus et consacraient moins de temps à la chasse que les ours qui n’avaient pas été immobilisés. Cependant, les ours qui avaient été immobilisés ont repris les mêmes habitudes de comportement et consacré le même temps à la chasse que les ours non immobilisés en dedans de deux jours, et aucun autre effet négatif sur leur comportement n’a été décelé au cours des 21 jours qui ont suivi. Nous avons eu la confirmation visuelle d’une chasse réussie par trois ours adultes dans la période de 0,4 à 2,1 jours suivant l’immobilisation, tous trois ayant réussi à faire d’autres prises dans les 24 heures qui ont suivi. Le retour aux habitudes de comportement normales, y compris l’aptitude à faire une chasse réussie, dans les 48 heures suivant l’immobilisation semble cadrer avec l’hypothèse selon laquelle les ours polaires ne subissent pas d’effets comportementaux de longue haleine après une brève immobilisation chimique à des fins de conservation et de gestion.

The Acute Physiological Response of Polar Bears to Helicopter Capture

Many wildlife species are live captured, sampled, and released; for polar bears (Ursus maritimus) capture often requires chemical immobilization via helicopter darting. Polar bears reduce their activity for approximately 4 days after capture, likely reflecting stress recovery. To better understand this stress, we quantified polar bear activity (via collar‐mounted accelerometers) and body temperature (via loggers in the body core [Tabd] and periphery [Tper]) during 2–6 months of natural behavior, and during helicopter recapture and immobilization. Recapture induced bouts of peak activity higher than those that occurred during natural behavior for 2 of 5 bears, greater peak Tper for 3 of 6 bears, and greater peak Tabd for 1 of 6 bears. High body temperature (\u3e39.0°C) occurred in Tper for 3 of 6 individuals during recapture and 6 of 6 individuals during natural behavior, and in Tabd for 2 of 6 individuals during recapture and 3 of 6 individuals during natural behavior. Measurements of Tabd and Tper correlated with rectal temperatures measured after immobilization, supporting the use of rectal temperatures for monitoring bear response to capture. Using a larger dataset (n = 66 captures), modeling of blood biochemistry revealed that maximum ambient temperature during recapture was associated with a stress leukogram (7–26% decline in percent lymphocytes, 12–21% increase in percent neutrophils) and maximum duration of helicopter operations had a similar but smaller effect. We conclude that polar bear activity and body temperature during helicopter capture are similar to that which occurs during the most intense events of natural behavior; high body temperature, especially in warm capture conditions, is a key concern; additional study of stress leukograms in polar bears is needed; and additional data collection regarding capture operations would be useful

Climate change threatens polar bear populations : a stochastic demographic analysis

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 91 (2010): 2883–2897, doi:10.1890/09-1641.1.The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture–recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001–2003) and population decline in years with less ice coverage (2004–2005). LTRE (life table response experiment) analysis showed that the reduction in λ in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log λs, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log λs ≈ − 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with “business as usual” (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act.We acknowledge primary funding for model development and analysis from the U.S. Geological Survey and additional funding from the National Science Foundation (DEB-0343820 and DEB-0816514), NOAA, the Ocean Life Institute and the Arctic Research Initiative at WHOI, and the Institute of Arctic Biology at the University of Alaska–Fairbanks. Funding for the capture–recapture effort in 2001–2006 was provided by the U.S. Geological Survey, the Canadian Wildlife Service, the Department of Environment and Natural Resources of the Government of the Northwest Territories, and the Polar Continental Shelf Project, Ottawa, Canada

Appendix A. Model results examining relationships between the availability of sea ice habitat and measures of skull width, body length, mass, and condition of polar bears in the southern Beaufort Sea.

Model results examining relationships between the availability of sea ice habitat and measures of skull width, body length, mass, and condition of polar bears in the southern Beaufort Sea

Appendix B. Model results examining trends in measures of skull width, body length, mass, and condition of polar bears in the southern Beaufort Sea between 1983 and 2006.

Model results examining trends in measures of skull width, body length, mass, and condition of polar bears in the southern Beaufort Sea between 1983 and 2006

Regehr2018_PolarBear_recruitment

R code for separate recruitment analyses for Chukchi Sea polar bear

Regehr2018_PolarBear_N

R code for density extrapolation and abundance estimation for Chukchi Sea polar bear

Regehr2018_C1perAF_data

Data for separate recruitment analyse

Data from: Harvesting wildlife affected by climate change: a modelling and management approach for polar bears

The conservation of many wildlife species requires understanding the demographic effects of climate change, including interactions between climate change and harvest, which can provide cultural, nutritional or economic value to humans. We present a demographic model that is based on the polar bear Ursus maritimus life cycle and includes density-dependent relationships linking vital rates to environmental carrying capacity (K). Using this model, we develop a state-dependent management framework to calculate a harvest level that (i) maintains a population above its maximum net productivity level (MNPL; the population size that produces the greatest net increment in abundance) relative to a changing K, and (ii) has a limited negative effect on population persistence. Our density-dependent relationships suggest that MNPL for polar bears occurs at approximately 0·69 (95% CI = 0·63–0·74) of K. Population growth rate at MNPL was approximately 0·82 (95% CI = 0·79–0·84) of the maximum intrinsic growth rate, suggesting relatively strong compensation for human-caused mortality. Our findings indicate that it is possible to minimize the demographic risks of harvest under climate change, including the risk that harvest will accelerate population declines driven by loss of the polar bear's sea-ice habitat. This requires that (i) the harvest rate – which could be 0 in some situations – accounts for a population's intrinsic growth rate, (ii) the harvest rate accounts for the quality of population data (e.g. lower harvest when uncertainty is large), and (iii) the harvest level is obtained by multiplying the harvest rate by an updated estimate of population size. Environmental variability, the sex and age of removed animals and risk tolerance can also affect the harvest rate. Synthesis and applications. We present a coupled modelling and management approach for wildlife that accounts for climate change and can be used to balance trade-offs among multiple conservation goals. In our example application to polar bears experiencing sea-ice loss, the goals are to maintain population viability while providing continued opportunities for subsistence harvest. Our approach may be relevant to other species for which near-term management is focused on human factors that directly influence population dynamics within the broader context of climate-induced habitat degradation

Regehr2018_dextrp_data

Data for density extrapolation and abundance estimatio