How Climate Impacts the Composition of Wolf-Killed Elk in Northern Yellowstone National Park

While the functional response of predators is commonly measured, recent work has revealed that the age and sex composition of prey killed is often a better predictor of prey population dynamics because the reproductive value of adult females is usually higher than that of males or juveniles. Climate is often an important mediating factor in determining the composition of predator kills, but we currently lack a mechanistic understanding of how the multiple facets of climate interact with prey abundance and demography to influence the composition of predator kills. Over 20 winters, we monitored 17 wolf packs in Yellowstone National Park and recorded the sex, age and nutritional condition of kills of their dominant prey—elk—in both early and late winter periods when elk are in relatively good and relatively poor condition, respectively. Nutritional condition (as indicated by per cent marrow fat) of wolf‐killed elk varied markedly with summer plant productivity, snow water equivalent (SWE) and winter period. Moreover, marrow was poorer for wolf‐killed bulls and especially for calves than it was for cows. Wolf prey composition was influenced by a complex set of climatic and endogenous variables. In early winter, poor plant growth in either year t or t − 1, or relatively low elk abundance, increased the odds of wolves killing bulls relative to cows. Calves were most likely to get killed when elk abundance was high and when the forage productivity they experienced in utero was poor. In late winter, low SWE and a relatively large elk population increased the odds of wolves killing calves relative to cows, whereas low SWE and poor vegetation productivity 1 year prior together increased the likelihood of wolves killing a bull instead of a cow. Since climate has a strong influence on whether wolves prey on cows (who, depending on their age, are the key reproductive components of the population) or lower reproductive value of calves and bulls, our results suggest that climate can drive wolf predation to be more or less additive from year to year

Predicting Bison Migration out of Yellowstone National Park Using Bayesian Models

Long distance migrations by ungulate species often surpass the boundaries of preservation areas where conflicts with various publics lead to management actions that can threaten populations. We chose the partially migratory bison (Bison bison) population in Yellowstone National Park as an example of integrating science into management policies to better conserve migratory ungulates. Approximately 60% of these bison have been exposed to bovine brucellosis and thousands of migrants exiting the park boundary have been culled during the past two decades to reduce the risk of disease transmission to cattle. Data were assimilated using models representing competing hypotheses of bison migration during 1990–2009 in a hierarchal Bayesian framework. Migration differed at the scale of herds, but a single unifying logistic model was useful for predicting migrations by both herds. Migration beyond the northern park boundary was affected by herd size, accumulated snow water equivalent, and aboveground dried biomass. Migration beyond the western park boundary was less influenced by these predictors and process model performance suggested an important control on recent migrations was excluded. Simulations of migrations over the next decade suggest that allowing increased numbers of bison beyond park boundaries during severe climate conditions may be the only means of avoiding episodic, large-scale reductions to the Yellowstone bison population in the foreseeable future. This research is an example of how long distance migration dynamics can be incorporated into improved management policies

Behavioral responses of terrestrial mammals to COVID-19 lockdowns

DATA AND MATERIALS AVAILABILITY : The full dataset used in the final analyses (33) and associated code (34) are available at Dryad. A subset of the spatial coordinate datasets is available at Zenodo (35). Certain datasets of spatial coordinates will be available only through requests made to the authors due to conservation and Indigenous sovereignty concerns (see table S1 for more information on data use restrictions and contact information for data requests). These sensitive data will be made available upon request to qualified researchers for research purposes, provided that the data use will not threaten the study populations, such as by distribution or publication of the coordinates or detailed maps. Some datasets, such as those overseen by government agencies, have additional legal restrictions on data sharing, and researchers may need to formally apply for data access. Collaborations with data holders are generally encouraged, and in cases where data are held by Indigenous groups or institutions from regions that are under-represented in the global science community, collaboration may be required to ensure inclusion.COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals’ 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.The Radboud Excellence Initiative, the German Federal Ministry of Education and Research, the National Science Foundation, Serbian Ministry of Education, Science and Technological Development, Dutch Research Council NWO program “Advanced Instrumentation for Wildlife Protection”, Fondation Segré, RZSS, IPE, Greensboro Science Center, Houston Zoo, Jacksonville Zoo and Gardens, Nashville Zoo, Naples Zoo, Reid Park Zoo, Miller Park, WWF, ZCOG, Zoo Miami, Zoo Miami Foundation, Beauval Nature, Greenville Zoo, Riverbanks zoo and garden, SAC Zoo, La Passarelle Conservation, Parc Animalier d’Auvergne, Disney Conservation Fund, Fresno Chaffee zoo, Play for nature, North Florida Wildlife Center, Abilene Zoo, a Liber Ero Fellowship, the Fish and Wildlife Compensation Program, Habitat Conservation Trust Foundation, Teck Coal, and the Grand Teton Association. The collection of Norwegian moose data was funded by the Norwegian Environment Agency, the German Ministry of Education and Research via the SPACES II project ORYCS, the Wyoming Game and Fish Department, Wyoming Game and Fish Commission, Bureau of Land Management, Muley Fanatic Foundation (including Southwest, Kemmerer, Upper Green, and Blue Ridge Chapters), Boone and Crockett Club, Wyoming Wildlife and Natural Resources Trust, Knobloch Family Foundation, Wyoming Animal Damage Management Board, Wyoming Governor’s Big Game License Coalition, Bowhunters of Wyoming, Wyoming Outfitters and Guides Association, Pope and Young Club, US Forest Service, US Fish and Wildlife Service, the Rocky Mountain Elk Foundation, Wyoming Wild Sheep Foundation, Wild Sheep Foundation, Wyoming Wildlife/Livestock Disease Research Partnership, the US National Science Foundation [IOS-1656642 and IOS-1656527, the Spanish Ministry of Economy, Industry and Competitiveness, and by a GRUPIN research grant from the Regional Government of Asturias, Sigrid Rausing Trust, Batubay Özkan, Barbara Watkins, NSERC Discovery Grant, the Federal Aid in Wildlife Restoration act under Pittman-Robertson project, the State University of New York, College of Environmental Science and Forestry, the Ministry of Education, Youth and Sport of the Czech Republic, the Ministry of Agriculture of the Czech Republic, Rufford Foundation, an American Society of Mammalogists African Graduate Student Research Fund, the German Science Foundation, the Israeli Science Foundation, the BSF-NSF, the Ministry of Agriculture, Forestry and Food and Slovenian Research Agency (CRP V1-1626), the Aage V. Jensen Naturfond (project: Kronvildt - viden, værdier og værktøjer), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy, National Centre for Research and Development in Poland, the Slovenian Research Agency, the David Shepherd Wildlife Foundation, Disney Conservation Fund, Whitley Fund for Nature, Acton Family Giving, Zoo Basel, Columbus, Bioparc de Doué-la-Fontaine, Zoo Dresden, Zoo Idaho, Kolmården Zoo, Korkeasaari Zoo, La Passarelle, Zoo New England, Tierpark Berlin, Tulsa Zoo, the Ministry of Environment and Tourism, Government of Mongolia, the Mongolian Academy of Sciences, the Federal Aid in Wildlife Restoration act and the Illinois Department of Natural Resources, the National Science Foundation, Parks Canada, Natural Sciences and Engineering Research Council, Alberta Environment and Parks, Rocky Mountain Elk Foundation, Safari Club International and Alberta Conservation Association, the Consejo Nacional de Ciencias y Tecnología (CONACYT) of Paraguay, the Norwegian Environment Agency and the Swedish Environmental Protection Agency, EU funded Interreg SI-HR 410 Carnivora Dinarica project, Paklenica and Plitvice Lakes National Parks, UK Wolf Conservation Trust, EURONATUR and Bernd Thies Foundation, the Messerli Foundation in Switzerland and WWF Germany, the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions, NASA Ecological Forecasting Program, the Ecotone Telemetry company, the French National Research Agency, LANDTHIRST, grant REPOS awarded by the i-Site MUSE thanks to the “Investissements d’avenir” program, the ANR Mov-It project, the USDA Hatch Act Formula Funding, the Fondation Segre and North American and European Zoos listed at http://www.giantanteater.org/, the Utah Division of Wildlife Resources, the Yellowstone Forever and the National Park Service, Missouri Department of Conservation, Federal Aid in Wildlife Restoration Grant, and State University of New York, various donors to the Botswana Predator Conservation Program, data from collared caribou in the Northwest Territories were made available through funds from the Department of Environment and Natural Resources, Government of the Northwest Territories. The European Research Council Horizon2020, the British Ecological Society, the Paul Jones Family Trust, and the Lord Kelvin Adam Smith fund, the Tanzania Wildlife Research Institute and Tanzania National Parks. The Eastern Shoshone and Northern Arapahoe Fish and Game Department and the Wyoming State Veterinary Laboratory, the Alaska Department of Fish and Game, Kodiak Brown Bear Trust, Rocky Mountain Elk Foundation, Koniag Native Corporation, Old Harbor Native Corporation, Afognak Native Corporation, Ouzinkie Native Corporation, Natives of Kodiak Native Corporation and the State University of New York, College of Environmental Science and Forestry, and the Slovenia Hunters Association and Slovenia Forest Service. F.C. was partly supported by the Resident Visiting Researcher Fellowship, IMéRA/Aix-Marseille Université, Marseille. This work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society.https://www.science.org/journal/sciencehj2023Mammal Research InstituteZoology and Entomolog

Hierarchical models provide insight into wildlife and disease management

2014 Summer.Wildlife diseases can alter host populations with cascading effects throughout ecosystems and human economies that rely on those wildlife. Pathological effects can be the ultimate cause of wildlife population decline through depressing host reproduction and survival. Otherwise, less virulent pathogens can harm host populations indirectly, through management actions imposed on wildlife populations harboring diseases that harm people or their livelihoods. Hierarchical Bayesian methods provide a framework for factoring highly dimensional problems into lower dimensional ones. These techniques decompose a problem into data, the underlying process, and parameters, and identify uncertainty associated with each component. Appropriately quantifying uncertainty fosters clearer understanding of wildlife and disease management problems. Bison (Bos bison) migrating from Yellowstone National Park into the state of Montana during winter and spring concern ranchers on lands surrounding the park because bison can transmit brucellosis (Brucella abortus) to cattle. Migrations have been constrained with bison being lethally removed or moved back into the park. I, and several coauthors (we) developed a state-space model to support decisions on bison management aimed at mitigating conflict with landowners outside the park. The model integrated recent GPS observations with 22 years (1990-2012) of aerial counts to forecast monthly distributions and identify factors driving migration. Wintering areas were located along decreasing elevation gradients and bison accumulated in wintering areas prior to moving to progressively lower elevation areas. Bison movements were affected by time since the onset of snow pack, snow pack magnitude, standing crop, and herd size. Migration pathways were increasingly used over time, suggesting experience or learning influenced movements. To support adaptive management of Yellowstone bison, we forecast future movements to evaluate alternatives. Our approach of developing models capable of making explicit probabilistic forecasts of large herbivore movements and seasonal distributions is applicable to managing the migratory movements of large herbivores worldwide. These forecasts allow managers to develop and refine strategies in advance, and promote sound decision-making that reduces conflict as migratory animals come into contact with people. Chronic wasting disease (CWD) is a fatal, neurodegenerative prion disease that affects members of the deer family (Cervidae). There is worldwide concern that the disease may harm ecosystems and human economies by causing demise of deer populations. Little is known about effects of the disease on population dynamics. We studied a mule deer population where CWD has been present for at least four decades. We developed a disease model to estimate the effect of CWD on population growth rate and extent that the epidemic is increasing. Our model integrated capture-mark-recapture histories of adult female mule deer during a four year study with long-term population monitoring data on abundance, composition, and CWD prevalence. Our model was capable of deciphering probabilities of infection and correct identification of infected individuals from disease tests. We provide compelling evidence that prion epidemics can affect mule deer populations both locally and at coarse spatial scales. Chances of population decline were greatest at the wintering subpopulation scale, but differences in infection rate among subpopulations caused CWD to have virtually no effect on growth in some wintering subpopulations. At larger scales, deer populations showed some natural resistance against CWD by localizing areas of higher infection. Overall, disease effects were subtle and the protracted time-scale of the epidemic is likely much longer than the thirty year time span of our research. As a result, we could not identify the inevitable fate of deer populations with CWD. Our findings do suggest, in the nearer-term (e.g., decades), mule deer populations persisting at lower levels after disease establishment

State-space modeling to support management of brucellosis in the Yellowstone bison population

The bison (Bison bison) of the Yellowstone ecosystem, USA, exemplify the difficulty of conserving large mammals that migrate across the boundaries of conservation areas. Bison are infected with brucellosis (Brucella abortus) and their seasonal movements can expose livestock to infection. Yellowstone National Park has embarked on a program of adaptive management of bison, which requires a model that assimilates data to support management decisions. We constructed a Bayesian state-space model to reveal the influence of brucellosis on the Yellowstone bison population. A frequency-dependent model of brucellosis transmission was superior to a density-dependent model in predicting out-of-sample observations of horizontal transmission probability. A mixture model including both transmission mechanisms converged on frequency dependence. Conditional on the frequency- dependent model, brucellosis median transmission rate was 1.87 yr-1. The median of the posterior distribution of the basic reproductive ratio (R0) was 1.75. Seroprevalence of adult females varied around 60% over two decades, but only 9.6 of 100 adult females were infectious. Brucellosis depressed recruitment; estimated population growth rate k averaged 1.07 for an infected population and 1.11 for a healthy population. We used five-year forecasting to evaluate the ability of different actions to meet management goals relative to no action. Annually removing 200 seropositive female bison increased by 30-fold the probability of reducing seroprevalence below 40% and increased by a factor of 120 the probability of achieving a 50% reduction in transmission probability relative to no action. Annually vaccinating 200 seronegative animals increased the likelihood of a 50% reduction in transmission probability by fivefold over no action. However, including uncertainty in the ability to implement management by representing stochastic variation in the number of accessible bison dramatically reduced the probability of achieving goals using interventions relative to no action. Because the width of the posterior predictive distributions of future population states expands rapidly with increases in the forecast horizon, managers must accept high levels of uncertainty. These findings emphasize the necessity of iterative, adaptive management with relatively short-term commitment to action and frequent reevaluation in response to new data and model forecasts. We believe our approach has broad applications

Data from: State-space modeling to support management of brucellosis in the Yellowstone bison population

The Yellowstone bison (Bison bison) exemplifies the challenge of conserving large mammals that migrate across the boundaries of conservation areas. Bison in Yellowstone are infected with brucellosis (Brucella abortus). Their seasonal movements can expose livestock to infection. We developed a Bayesian state-space model to reveal the influence of brucellosis on the dynamics of the Yellowstone bison population and to inform decisions on bison management. A model of frequency dependent transmission was superior to a density dependent model in its ability to predict out-of-sample observations of the probability of horizontal transmission (mean square prediction error frequency model = 0.78, density dependent model = 0.91). Conditional on the frequency dependent model, the median transmission rate of brucellosis was 1.86 year-1 (95% equal-tailed credible interval, BCI = 1.5, 2.2). The median of the posterior distribution of the basic reproductive ratio (R0) was 1.76 (BCI = 1.47, 2.36). Seroprevalence of adult females varied around 60% during the last two decades; however only 13 of 100 adult females were infectious (BCI = 0.1, 0.15). Estimation of population growth rate (_) in the presence of brucellosis reflected the depressing effect of the disease on recruitment; _ for an infected population averaged 1.07 (BCI = 1.03, 1.11) and for a healthy population _ = 1.12 (BCI = 1.07, 1.16). We used forecasting with a five year horizon to evaluate the ability of different actions to meet goals for management relative to a no action alternative. Annually removing 200 seropositive female bison increased the probability of reducing seroprevalence below 40% by 30-fold relative to no action and increased the probability of achieving a 50% reduction in transmission probability by a factor of 110 relative to no action. Annually vaccinating 200 seronegative animals increased the probability of achieving a 50% reduction transmission probability by five fold over no action. Forecasts of the future state of the population became increasingly uncertain with increases in the forecast horizon. Our findings emphasize the necessity of iterative, adaptive management with a relatively short term commitment to action, a commitment that must be reevaluated frequently in response to new data and model forecasts

Bayesian Modeling of Prion Disease Dynamics in Mule Deer Using Population Monitoring and Capture-Recapture Data

<div><p>Epidemics of chronic wasting disease (CWD) of North American <i>Cervidae</i> have potential to harm ecosystems and economies. We studied a migratory population of mule deer (<i>Odocoileus hemionus</i>) affected by CWD for at least three decades using a Bayesian framework to integrate matrix population and disease models with long-term monitoring data and detailed process-level studies. We hypothesized CWD prevalence would be stable or increase between two observation periods during the late 1990s and after 2010, with higher CWD prevalence making deer population decline more likely. The weight of evidence suggested a reduction in the CWD outbreak over time, perhaps in response to intervening harvest-mediated population reductions. Disease effects on deer population growth under current conditions were subtle with a 72% chance that CWD depressed population growth. With CWD, we forecasted a growth rate near one and largely stable deer population. Disease effects appear to be moderated by timing of infection, prolonged disease course, and locally variable infection. Long-term outcomes will depend heavily on whether current conditions hold and high prevalence remains a localized phenomenon.</p></div

Data from: State-space modeling to support management of brucellosis in the Yellowstone bison population

The Yellowstone bison (Bison bison) exemplifies the challenge of conserving large mammals that migrate across the boundaries of conservation areas. Bison in Yellowstone are infected with brucellosis (Brucella abortus). Their seasonal movements can expose livestock to infection. We developed a Bayesian state-space model to reveal the influence of brucellosis on the dynamics of the Yellowstone bison population and to inform decisions on bison management. A model of frequency dependent transmission was superior to a density dependent model in its ability to predict out-of-sample observations of the probability of horizontal transmission (mean square prediction error frequency model = 0.78, density dependent model = 0.91). Conditional on the frequency dependent model, the median transmission rate of brucellosis was 1.86 year-1 (95% equal-tailed credible interval, BCI = 1.5, 2.2). The median of the posterior distribution of the basic reproductive ratio (R0) was 1.76 (BCI = 1.47, 2.36). Seroprevalence of adult females varied around 60% during the last two decades; however only 13 of 100 adult females were infectious (BCI = 0.1, 0.15). Estimation of population growth rate (_) in the presence of brucellosis reflected the depressing effect of the disease on recruitment; _ for an infected population averaged 1.07 (BCI = 1.03, 1.11) and for a healthy population _ = 1.12 (BCI = 1.07, 1.16). We used forecasting with a five year horizon to evaluate the ability of different actions to meet goals for management relative to a no action alternative. Annually removing 200 seropositive female bison increased the probability of reducing seroprevalence below 40% by 30-fold relative to no action and increased the probability of achieving a 50% reduction in transmission probability by a factor of 110 relative to no action. Annually vaccinating 200 seronegative animals increased the probability of achieving a 50% reduction transmission probability by five fold over no action. Forecasts of the future state of the population became increasingly uncertain with increases in the forecast horizon. Our findings emphasize the necessity of iterative, adaptive management with a relatively short term commitment to action, a commitment that must be reevaluated frequently in response to new data and model forecasts