Human-black bear interactions in Missoula Montana

The increasing frequency and distribution of human-wildlife interactions is a direct result of a growing human footprint worldwide. Specifically, the effects of urbanization can be significant for many species, including American black bears (Ursus americanus). Human-black bear interactions (HBI) resulting in property damage, injury or death to humans, or fear of injury or death to humans are increasing in number and extent throughout North America, and wildlife management agencies are interested in reversing this trend. Using a case study of HBI in Missoula, Montana, my objectives were to examine temporal patterns of human behaviors and attitudes regarding HBI, develop a model capable of predicting the spatial distribution of HBI, and determine forage-related variables that predict use of the urban landscape by bears. Based upon questionnaires sent to a sample of residents in 2004 and 2008, the prevalence of outdoor garbage storage decreased, and support for management actions used to deal with HBI increased. These results suggest that human behaviors and attitudes in urban areas exposed to HBI may be changing. Based on phone complaints regarding HBI recorded by Montana Fish, Wildlife & Parks from 2003 to 2008, the probability of HBI is highest when residents live close to large forest patches, close to rivers and streams, and in intermediate housing densities (approx. 7 houses/ha). These results provide a wildlife management tool and a repeatable statistical framework that can be used to predict future HBI in areas where the potential for development is high. Using GPS collared black bears and a time-to-event modeling framework, the probability of an individual black bear being located within the urban landscape was driven by anthropogenic forage availability (i.e., urban green-up, apple availability) as opposed to wildland forage scarcity. Black bears will forage within the urban areas even when wildland foods are available outside the urban area, suggesting that bears shift their behavior in response to the availability of multiple anthropogenic food items (e.g., fruit trees, garbage). Wildlife managers developing management plans for HBI should incorporate possible changes in human dimensions, models that can predict where HBI will occur in the future, and bear populations that are becoming increasingly reliant on anthropogenic food items

Predicting the Spatial Distribution of Human-Black Bear Interactions Across an Urban Area

Human (Homo sapiens)-black bear (Ursus americanus) interactions are increasing throughout North America. Information that assists managers in developing methods to reduce conflicts is lacking. We used human-bear incident data, i.e., phone complaints and conflicts, collected in Missoula, Montana, by Montana Fish, Wildlife and Parks from 2003-2008 to describe the attractants and human impacts of incidents, and develop a model that predicts the spatial probability of incidents. We combined the locations of black bear sightings (n = 307), other incidents, e.g., bear seen feeding on garbage (n = 549), and sites where proactive management actions were carried out (n = 108), and compared them to 5000 random locations using logistic regression. Based on literature, we used distance to forested patches, distance to water, and housing density as variables in our model. Garbage (38%), fruit trees (10%), and bird feeders (7%) were the most common attractants at incident sites, and some incidents resulted in threats to human safety (9%) and property damage (7%). All variables were significant in the predictive model, and the model performed well at discriminating the relative spatial probability of incidents (rs = 0.782; P < 0.01). The probability of incidents increased when residents lived close to forested patches, close to water, and in intermediate housing densities (~ 6.6 houses/ha). Our results suggest that spatial patterns in human-black bear interactions are predictable and these patterns can be used to understand the potential for conflict in developing areas and to identify areas where preventative management is necessary

Parsing the Effects of Demography, Climate and Management on Recurrent Brucellosis Outbreaks in Elk

Zoonotic pathogens can harm human health and well‐being directly or by impacting livestock. Pathogens that spillover from wildlife can also impair conservation efforts if humans perceive wildlife as pests. Brucellosis, caused by the bacterium Brucella abortus, circulates in elk and bison herds of the Greater Yellowstone Ecosystem and poses a risk to cattle and humans. Our goal was to understand the relative effects of climatic drivers, host demography and management control programmes on disease dynamics. Using \u3e20 years of serologic, demographic and environmental data on brucellosis in elk, we built stochastic compartmental models to assess the influences of climate forcing, herd immunity, population turnover and management interventions on pathogen transmission. Data were collected at feedgrounds visited in winter by free‐ranging elk in Wyoming, USA. Snowpack, hypothesized as a driver of elk aggregation and thus brucellosis transmission, was strongly correlated across feedgrounds. We expected this variable to drive synchronized disease dynamics across herds. Instead, we demonstrate asynchronous epizootics driven by variation in demographic rates. We evaluated the effectiveness of test‐and‐slaughter of seropositive female elk at two feedgrounds. Test‐and‐slaughter temporarily reduced herd‐level seroprevalence but likely reduced herd immunity while removing few infectious individuals, resulting in subsequent outbreaks once the intervention ceased. We simulated an alternative strategy of removing seronegative female elk and found it would increase herd immunity, yielding fewer infections. We evaluated a second experimental treatment wherein feeding density was reduced at one feedground, but we found no evidence for an effect despite a decade of implementation. Synthesis and applications. Positive serostatus is often weakly correlated with infectiousness but is nevertheless used to make management decisions including lethal removal in wildlife disease systems. We show how this can have adverse consequences whereas efforts that maintain herd immunity can have longer‐term protective effects. Climatic drivers may not result in synchronous disease dynamics across populations unless vital rates are also similar because demographic factors have a large influence on disease patterns

Multi-mode movement decisions across widely ranging behavioral processes

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed populationlevel, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.DATA AVAILABILITY STATEMENT : The data are available at: https://osf.io/v5pnc/SUPPLEMENTARY MATERIAL : S1 Appendix. Calculation of average travelled distance using coefficient estimates associated to step length. https://doi.org/10.1371/journal.pone.0272538.s001S1 Table. Values and definition [from c] of model parameters used to simulate multi-state correlated random walks in three scenarios of landscape patchiness. https://doi.org/10.1371/journal.pone.0272538.s002S2 Table. Coefficient estimates along with their 95% confidence interval (95% CI) of the mixed-effects generalized linear model with binomial distribution (HMM-SSF + GLMM) and the multi-state correlated random walk model (HMM-CRW) to predict probability of switching from encamped to travelling mode, in 500 simulated foragers moving among resource patches and avoiding a predator. In resource patch is a dummy variable indicating whether the forager is within a resource patch (i.e., patch quality >0), equals the actual distance of the predator from the forager (dPredator) when dPredator ≤ 0.8 km and 0.8 km, otherwise. log(dPredator) is the natural logarithm of dPredator. https://doi.org/10.1371/journal.pone.0272538.s003S3 Table. Coefficient estimates along with their 95% confidence interval (95% CI) of mixed-effects generalized linear models with binomial distribution to predict probability of switching from encamped to travelling mode of movement, in plains bison during summer in Prince Albert National Park (SK, Canada). Each table represents estimates for a specific threshold probability (Pthreshold) used to categorized transition and non-transition from the conditional probabilities of being in encamped or travelling state, obtained from the fit of the HMM-SSF to plains bison trajectories. was set to the actual distance between bison and wolf (dwolf) when dwolf≤dthreshold and dthreshold, otherwise. https://doi.org/10.1371/journal.pone.0272538.s004S1 Fig. Simulated heterogeneous landscape used in the multi-state biased correlated random walk simulations, from gaussian random field with an exponential covariance function with variance = 1, nugget = 0 and a set of patch concentration (μQ) and patch size (γQ) resulting in three level of patchiness: 1) low (μQ = -1.5, γQ = 2), 2) intermediate (μQ = -0.5, γQ = 2) and 3) high (μQ = 1, γQ = 10). https://doi.org/10.1371/journal.pone.0272538.s005S2 Fig. Distribution of distance to the closest waterhole according to the mode of movement estimated from the HMM-SSF for 18 zebras in Hwange National Park during the dry hot season. The conditional probabilities of being in each state, obtained from the fit of the HMM-SFF, were dichotomized to 0–1 based on a 0.5 threshold to determine the state of the individual at each step on its trajectory. https://doi.org/10.1371/journal.pone.0272538.s006S3 Fig. Log-likelihood profile from mixed-effects generalized linear model with binomial distribution to predict probability of switching from encamped to travelling mode of movement, according to a gradient of threshold distance, dthreshold. https://doi.org/10.1371/journal.pone.0272538.s007S4 Fig. Total number of switches from encamped to travelling mode of movement according to day time, estimated using conditional probabilities of being in each state, obtained from the fit of the HMM-SFF to plains bison trajectories followed during the summers 2005–2016. We then separated the day in four periods: Night: 22:00–02:00, Dawn: 03:00–06:00, Day: 07:00–15:00 and Dusk: 16:00–21:00. https://doi.org/10.1371/journal.pone.0272538.s008http://www.plosone.orgdm2022Mammal Research InstituteZoology and Entomolog

Learning and animal movement

Authors acknowledge the following grants for supporting this research: NSERC Discovery (ML and MA-M), NSF DMS-1853465 (WF and EG), and Canada Research Chairs Program (ML and MA-M).Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.Publisher PDFPeer reviewe

Drivers of site fidelity in ungulates

1. While the tendency to return to previously visited locations—termed ‘site fidelity’—is common in animals, the cause of this behaviour is not well understood. One hypothesis is that site fidelity is shaped by an animal's environment, such that animals living in landscapes with predictable resources have stronger site fidelity. Site fidelity may also be conditional on the success of animals’ recent visits to that location, and it may become stronger with age as the animal accumulates experience in their landscape. Finally, differences between species, such as the way memory shapes site attractiveness, may interact with environmental drivers to modulate the strength of site fidelity. 2. We compared inter‐year site fidelity in 669 individuals across eight ungulate species fitted with GPS collars and occupying a range of environmental conditions in North America and Africa. We used a distance‐based index of site fidelity and tested hypothesized drivers of site fidelity using linear mixed effects models, while accounting for variation in annual range size. 3. Mule deer Odocoileus hemionus and moose Alces alces exhibited relatively strong site fidelity, while wildebeest Connochaetes taurinus and barren‐ground caribou Rangifer tarandus granti had relatively weak fidelity. Site fidelity was strongest in predictable landscapes where vegetative greening occurred at regular intervals over time (i.e. high temporal contingency). Species differed in their response to spatial heterogeneity in greenness (i.e. spatial constancy). Site fidelity varied seasonally in some species, but remained constant over time in others. Elk employed a ‘win‐stay, lose‐switch’ strategy, in which successful resource tracking in the springtime resulted in strong site fidelity the following spring. Site fidelity did not vary with age in any species tested. 4. Our results provide support for the environmental hypothesis, particularly that regularity in vegetative phenology shapes the strength of site fidelity at the inter‐annual scale. Large unexplained differences in site fidelity suggest that other factors, possibly species‐specific differences in attraction to known sites, contribute to variation in the expression of this behaviour. 5. Understanding drivers of variation in site fidelity across groups of organisms living in different environments provides important behavioural context for predicting how animals will respond to environmental change

Body size and digestive system shape resource selection by ungulates : a cross-taxa test of the forage maturation hypothesis

The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small-bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.DATA AVAILABILITY STATEMENT : The dataset used in our analyses is available via Dryad repository (https://doi.org/10.5061/dryad.jsxksn09f) following a year-long embargo from publication of the manuscript. The coordinates associated with mountain zebra data are not provided in an effort to protect critically endangered black rhino (Diceros bicornis) locations. Interested researchers can contact the data owner (Minnesota Zoo) directly for inquiries.https://wileyonlinelibrary.com/journal/elehj2022Mammal Research InstituteZoology and Entomolog

Mule deer movements spring 2016 Wyoming

The data file represents GPS relocations from 28 adult (> 1:5 years of age) female mule between March and August 2016. Deer were captured in March 2016 using a netgun fired from a helicopter near Cody, Wyoming (USA). Collars (Advanced Telemetry Systems, Iridium, Isanti, Minnesota, USA) were programmed to take a location every 2 hours. All deer were captured following protocols consistent with the University of Wyoming standards. Note, this database is a small subset of data from a larger collaring project. Program R was used to create the datafile in April 2017. Each row in the database represents a single GPS relocation. Description of the column headings are as follows: AID - unique identifier for each animal; hour - hour as decimal number, 0 to 23; min - minute as decimal number, 0 to 59; year - year with century; month - month as decimal number, 1 to 12; day - day as decimal number 1 to 31; jul - julian date as decimal number, 0 to 365; date - date in POSIX format (local time; Mountain Standard Time) for program R; Long - longitude; Lat - latitude. There are 54,528 rows in the database, with an average of 1,947 relocations per individual. Please contact Jerod Merkle ([email protected]) or Matt Kauffman ([email protected]) at University of Wyoming prior to using these data. If neither of these people can be reached, please contact the Wyoming Cooperative Fish and Wildlife Research Unit at University of Wyoming