Global Positioning System Collars Research Articles

An individual-based movement model for contacts between mule deer (Odocoileus hemionus)

Contacts between individuals are key for the spread of infectious disease. Although essential to understanding disease spread, contact rates are difficult to predict, based simply on population demographics in wildlife populations, because contact rates depend upon environmental features as well as the nature of social interactions within and between groups of individuals. We developed a detailed, behaviorally structured, individual-based model (IBM) in Netlogo to simulate contacts between- and within-groups of individual mule deer (Odocoileus hemionus), a species particularly susceptible to chronic wasting disease. The model tracks contacts (defined as two individuals coming within five meters of one another), recorded as between- or within-group depending on the social group membership of the two individuals (dyad). We parameterized the model with data from mule deer with global positioning systems (GPS) collars in east-central Alberta, Canada. Individuals move according to habitat preferences, home range attraction, and grouping behaviours. Animals were tracked at two-hour time steps and were modelled as selecting locations relative to preferred resources based on sex-specific integrated step-selection functions (iSSFs) with steps biased toward a home range centroid. Total within-group contacts increased with group size and were sensitive to changes in movement cohesion of the group and movement persistence, particularly movement cohesion. Total between-group contacts were sensitive only to the number of groups. We compared model predictions for where the locations of deer contacts occurred against an existing statistical model for the relative contact probabilities (RCP) on the same landscape (Dobbin et al. 2023). Predicted locations of deer contacts generally were consistent with higher predicted RCP values. When disease transmission is a function of contact rate, the model can be used to assess the interaction between model components (e.g., movement rates, grouping rules, home ranges, animal densities) and the spatial distribution of key natural and artificial resources that may attract deer and potentially increase disease spread.

Resource selection by Sarda cattle in a Mediterranean silvopastoral system.

Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning systems (GPS) to track animals, together with geographic information systems (GIS), can relate animal distribution to landscape features. With the aim to develop a general spatial model that provides accurate prediction of cattle resource selection patterns within a Mediterranean mountainous silvopastoral area, free-roaming Sarda cows were fitted with GPS collars to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to five environmental predictor variables, were fitted to data. To identify a final model providing a robust prediction of cattle resource selection pattern across the different seasons, the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that watering point, elevation, and distance to fences were important factors affecting cattle resource-selection patterns. The prediction performances (as Spearman rank correlation scores) of the final model, when fitted to each season, ranged between 0.7 and 0.94. The cows were more likely to select areas lower in elevation and farther from the watering point in winter than in summer (693 ± 1 m and 847 ± 13 m vs. 707 ± 1 m and 635 ± 21 m, respectively), and in spring opted for the areas furthest from the water (963 ± 12). Although caution should be exercised in generalizing to other silvopastoral areas, the satisfactory Spearman correlations scores from the final RSF model applied to different seasons indicate resource selection function is a powerful predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model's ability to interpret changes in animal behavior at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviors of cows grazing in Mediterranean silvopastoral areas and could therefore be helpful in managing and preserving ecosystem services of these areas.

The utility of Global Positioning System collars in inventorying the diet of lions and its shifts following an anthrax outbreak

AbstractLions (Panthera leo) are a keystone species of many wildlife areas and, as such, understanding their dietary ecology is important for holistic wildlife management. Knowledge of lion feeding ecology enables researchers and wildlife managers to better understand predator–prey dynamics and ecological limits governing their local contexts. Where kill sites can be physically located in the field, they can provide important baseline information on lion diet. In this study, Global Positioning System (GPS) collar data were used together with field observations to investigate the dietary breadth of lions at Malilangwe Wildlife Reserve, Zimbabwe. Results showed that lions in our study had a moderately specialised diet with zebra (Equus quagga) constituting the largest proportion of prey species consumed. A massive anthrax outbreak occurred during the study, drastically reducing numbers of several prey species, and this provided an opportunity to test possible shifts in lion diet in response to changes in prey abundance. Prey species selection before the anthrax outbreak was significantly different to the period after the outbreak, indicating a shift in diet post‐outbreak. Notable increases were observed in kill rates of species that were not severely affected by anthrax, namely warthog (Phacochoerus africanus), zebra, wildebeest (Connochaetes taurinus) and eland (Taurotragus oryx). Quantifying lion dietary requirements is important for assessing the ecological and economic impacts they have on prey populations. In addition to space‐use ecology, this study shows that GPS collar data can facilitate diet investigation in large carnivores thereby providing useful information for ecological management.

Factors influencing space-use and kill distribution of sympatric lion prides in a semi-arid savanna landscape.

Understanding lions' (Panthera leo) space-use is important for the management of multi-species wildlife systems because lions can have profound impacts on ecosystem-wide ecological processes. Semi-arid savanna landscapes are typically heterogeneous with species space-use driven by the availability and distribution of resources. Previous studies have demonstrated that lions select areas close to water as encounter rates with prey are higher and hunting success is greater in these regions. Where multiple lion prides exist, landscape partitioning is expected to follow a despotic distribution in which competitively superior prides occupy high-quality areas while subordinates select poorer habitats. In this study, Global Positioning System collar data and logistic regression were used to investigate space-use and hunting success among 50% of lion prides at Malilangwe Wildlife Reserve, Zimbabwe. Our findings show that lion space-use was driven by surface water availability and that home range selection was socially hierarchical with the dominant pride occupying habitat in which water was most abundant. In addition, we found that the effect of shrub cover, clay content and soil depth on kill probability was area specific and not influenced by hierarchical dominance. Where multiple lion prides are studied, we recommend treating prides as individual units because pooling data may obscure site and pride specific response patterns.

Survival, cause‐specific mortality, and population growth of white‐tailed deer in western Virginia

AbstractUnderstanding the role of recruitment in population dynamics of white‐tailed deer (Odocoileus virginianus) is important for management. In the central Appalachian Mountains, deer are part of a largely forested ecosystem that supports 3 carnivore species thought to be capable of influencing white‐tailed deer recruitment: black bears (Urus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Yet little is known about predation, how other environmental factors influence recruitment, or the importance of neonate survival to white‐tailed deer population performance in the region. Our objectives were to identify causes of mortality for neonates, analyze effects of landscape attributes on survival of neonates, estimate survival rates for neonates and adult female white‐tailed deer, and to model population growth trends based on current vital rates and hypothetical harvest and neonate survival scenarios. During 2019–2020, we captured 57 neonate deer in Bath County, Virginia, USA, by monitoring 38 pregnant females equipped with global positioning system collars and vaginal implant transmitters and by conducting transect searches for recently born neonates. We observed 37 neonate mortalities and identified cause of death using field and genetic evidence. Mortalities included 28 predation events and 9 deaths from other causes (e.g., abandonment, malnutrition, disease). Black bears accounted for 48.6% of neonate mortalities, and 64.2% of predation events (n = 18), followed by bobcats (n = 5) and coyotes (n = 3). Annual survival for adult female deer was 0.871 and neonate survival to 12 weeks old was 0.310. Elevation was a significant predictor of neonate survival; mortality risk increased 20% for every 100‐m increase in elevation. Models of annual population growth using observed vital rates predicted an increasing population (λ = 1.10). A 10% increase in female harvest would still result in a potential population increase of 2% (λ = 1.02), but a 20% increase in harvest rate would result in a potential 7% decline (λ = 0.93). Neonate survival was higher near fertile valley bottoms and lower along forested ridges characterized by shallow, infertile soils and limited edge or early successional forests. While predation, largely influenced by black bears, was the leading cause of neonate mortality and contributed to low neonate survival, we observed little evidence of population decline, and suggest there is opportunity for a modest increase in harvest of female deer.

Estimating individual exposure to predation risk in group-living baboons, Papio anubis.

In environments with multiple predators, vulnerabilities associated with the spatial positions of group-living prey are non-uniform and depend on the hunting styles of the predators. Theoretically, coursing predators follow their prey over long distances and attack open areas, exposing individuals at the edge of the group to predation risk more than those at the center (marginal predation). In contrast, ambush predators lurk unnoticed by their prey and appear randomly anywhere in the group; therefore, isolated individuals in the group would be more vulnerable to predators. These positions of vulnerability to predation are expected to be taken by larger-bodied males. Moreover, dominant males presumably occupy the center of the safe group. However, identifying individuals at higher predation risk requires both simultaneous recording of predator location and direct observation of predation events; empirical observations leave ambiguity as to who is at risk. Instead, several theoretical methods (predation risk proxies) have been proposed to assess predation risk: (1) the size of the individual 'unlimited domain of danger' based on Voronoi tessellation, (2) the size of the 'limited domain of danger' based on predator detection distance, (3) peripheral/center position in the group (minimum convex polygon), (4) the number and direction of others in the vicinity (surroundedness), and (5) dyadic distances. We explored the age-sex distribution of individuals in at-risk positions within a wild baboon group facing predation risk from leopards, lions, and hyenas, using Global Positioning System collars. Our analysis of the location data from 26 baboons revealed that adult males were consistently isolated at the edge of the group in all predation risk proxies. Empirical evidence from previous studies indicates that adult male baboons are the most frequently preyed upon, and our results highlights the importance of spatial positioning in this.

94 Characterizing Yearling Beef Steers Grazing on Smooth Bromegrass Pasture Using Global Positioning Technology

Abstract Extensive grazing systems often vary in pasture size and terrain, making it challenging for ranchers to accurately locate livestock through visual appraisal. Using satellite-based global positioning systems (GPS), accurate position data can be provided to track animal movements that could inform grazing management decisions. This study aimed to: 1) assess two modern GPS devices for functionality by calculating fix success rate, and 2) evaluate the relationship between average daily distance traveled and average daily weight gain (ADG) performance on yearling steers grazing smooth bromegrass pasture using the GPS devices. Yearling steers (n = 25) grazed a 10.8-ha smooth bromegrass pasture for 156 days from May to September 2021. The temperature ranged from 4.1 to 38.8ºC with an average 22.7ºC and received 490 mm of rainfall. Cattle ADG was 0.65 ± 0.13 kg/d. Each steer was assigned two GPS collars for 28 days to compare two types of sensors. The sensors evaluated were i-gotU GT-600 travel loggers (GU) and Yabby LoRaWAN GPS sensors (YB) utilizing the IoT (Internet of Things) technology. The YB sensor possessed real-time monitoring and remote data access, while data from the GU sensor needed to be removed from the animals and manually downloaded. The YB sensors had a manufacturer declared finest positioning accuracy of 4.6 m, while that of the GU sensor being 9.1 m. With a 10-min sampling rate, battery life of the YB sensors lasted 4 weeks, while the GU sensors demonstrated unreliable battery performance that ranged between 1 to 4 weeks. Fixed success rate (FSR) was calculated for each sensor. Using PROC MIXED of SAS (v 9.4), on average the GU sensors had a 67.1% FSR and the YB sensors observed an 89.9% FSR (P < 0.0001). The YB sensor outweighed the GU sensor on battery life and FSR, thus was utilized throughout the duration of the grazing season. Individual animal traveled distance was calculated after converting the GPS coordinates in decimal degrees to Universal Transverse Mercator coordinates (WGS 1984 UTM zone 14N) in meters. The Euclidean distance between consecutive coordinates was calculated and summed daily to represent the daily traveled distance of an animal. Using all coordinates recorded from YB, steers traveled 2,718 ± 809 m/d. Statistical analysis was conducted using PROC CORR of SAS to determine the correlation between the daily averages of distance traveled and ADG. All values were considered significant α ≤ 0.05. There was no correlation observed between distance traveled and ADG (P ≥ 0.76). Results demonstrate that modern GPS sensors utilizing IoT technology have benefits over traditional GPS sensors and can be used as a tool for extensive grazing livestock tracking.

Change-point models for identifying behavioral transitions in wild animals

Animal behavior can be difficult, time-consuming, and costly to observe in the field directly. Innovative modeling methods, such as hidden Markov models (HMMs), allow researchers to infer unobserved animal behaviors from movement data, and implementations often assume that transitions between states occur multiple times. However, some behavioral shifts of interest, such as parturition, migration initiation, and juvenile dispersal, may only occur once during an observation period, and HMMs may not be the best approach to identify these changes. We present two change-point models for identifying single transitions in movement behavior: a location-based change-point model and a movement metric-based change-point model. We first conducted a simulation study to determine the ability of these models to detect a behavioral transition given different amounts of data and the degree of behavioral shifts. We then applied our models to two ungulate species in central Pennsylvania that were fitted with global positioning system collars and vaginal implant transmitters to test hypotheses related to parturition behavior. We fit these models in a Bayesian framework and directly compared the ability of each model to describe the parturition behavior across species. Our simulation study demonstrated that successful change point estimation using either model was possible given at least 12 h of post-change observations and 15 min fix interval. However, our models received mixed support among deer and elk in Pennsylvania due to behavioral variation between species and among individuals. Our results demonstrate that when the behavior follows the dynamics proposed by the two models, researchers can identify the timing of a behavioral change. Although we refer to detecting parturition events, our results can be applied to any behavior that results in a single change in time.

Beyond protected areas: The importance of mixed-use landscapes for the conservation of Sumatran elephants (Elephas maximus sumatranus).

Elephants were once widely distributed across the Indonesian island of Sumatra but now exist in small, isolated populations. Using the best data available on elephant occurrence, we aimed to (a) predict potential habitat suitability for elephants (Elephas maximus sumatranus) across the island of Sumatra and (b) model landscape connectivity among the extant elephant populations. We used direct sightings and indirect observations of elephant signs, as well as six remotely sensed proxies of surface ruggedness, vegetation productivity and structure, and human land use and disturbance, to model habitat suitability in a Google Earth Engine (GEE) environment. We validated the habitat suitability prediction using 10-fold spatial block cross validation and by calculating the area under the precision-recall curve (AUC-PR), sensitivity, and specificity for each model iteration. We also used a geolocation dataset collected from global positioning system (GPS) collars fitted on elephants as an independent validation dataset. Models showed good predictive performance with a mean AUC-PR of 0.73, sensitivity of 0.76, and specificity of 0.68. Greater than 83% of the independent GPS collar geolocations were located in predicted suitable habitat. We found human modification, surface ruggedness, and normalized difference vegetation index to be the most important variables for predicting suitable elephant habitat. Thirty-two percent, or 135,646 km2, of Sumatra's land area was predicted to be suitable habitat, with 43 patches of suitable habitat located across Sumatra. Areas with high connectivity were concentrated in the Riau and North Sumatra provinces. Though our analysis highlights the need to improve the quality of data collected on Sumatran elephants, more suitable habitat remains on Sumatra than is used by known populations. Targeted habitat conservation, especially of the suitable habitat in and around the Lamno, Balai Raja, Tesso Tenggara, Tesso Utara, Bukit Tigapuluh, Seblat, Padang Sugihan, and Bukit Barisan Selatan ranges, may improve the long-term viability of this critically endangered species.

Berries and bullets: influence of food and mortality risk on grizzly bears in British Columbia

AbstractThe influence of bottom‐up food resources and top‐down mortality risk underlies the demographic trajectory of wildlife populations. For species of conservation concern, understanding the factors driving population dynamics is crucial to effective management and, ultimately, conservation. In southeastern British Columbia, Canada, populations of the mostly omnivorous grizzly bear (Ursus arctos) are fragmented into a mosaic of small isolated or larger partially connected sub‐populations. They obtain most of their energy from vegetative resources that are also influenced by human activities. Roads and associated motorized human access shape availability of food resources but also displace bears and facilitate human‐caused mortality. Effective grizzly bear management requires an understanding of the relationship between habitat quality and mortality risk. We integrated analyses of bottom‐up and top‐down demographic parameters to understand and inform a comprehensive and efficient management paradigm across the region. Black huckleberry (Vaccinium membranaceum) is the key high‐energy food for grizzly bears in much of southeastern British Columbia. Little is known about where and why huckleberries grow into patches that are useful for grizzly bears (i.e., densely clustered fruiting shrubs that provide efficient access to high energy food) and how forage supply and mortality risk influence population vital rates. By following 43 grizzly bears tracked with global positioning system (GPS) collars (57 bear years) in a 14,236‐km2 focal area spanning the Selkirk and Purcell mountain ranges, we developed a model to identify huckleberry patches from grizzly bear use data. Over 2 years we visited 512 sites used by bears, identifying more than 300 huckleberry patches. We used boosted regression tree modeling associating geophysical, ecological, soil, climate, and topographical variables with huckleberry patches. We integrated this modeled food layer depicting an important pre‐hibernation resource, into broader bottom‐up and top‐down analyses. In addition to berries, we examined bottom‐up variables indexing vegetative productivity that were previously found to be predictive of bear use (e.g., alpine, canopy cover, greenness, riparian). We also examined top‐down variables including road presence, road density, distance‐to‐road, secure habitat (defined as 500 m away from a road open to vehicular access), highways, human development, and terrain ruggedness. We evaluated the relationship of these variables to female habitat selection, fitness, and population density, testing the predictability and interrelatedness of covariates relative to bottom‐up and top‐down influences. We estimated resource selection functions with 20,293 GPS telemetry locations collected over 10 years from 20 female grizzly bears. We modeled fitness using logistic regression of spatially explicit reproductive data derived from genetically identified family pedigrees consisting of a mother, father, and offspring. Data included 33 mothers and 72 offspring (1–8 offspring per female). We estimated density through spatial capture‐recapture analysis of 126 grizzly bears detected with hair‐sampled DNA 287 times between 1998 and 2005. In all 3 analyses (habitat selection, fitness, and density), huckleberry patches were the most influential bottom‐up factor and secure habitat was the most consistent top‐down variable (road density was similarly predictive). All of the best supported models contained bottom‐up and top‐down variables except the male density model, which only contained a top‐down variable (secure habitat). These results suggest that both bottom‐up and top‐down forces drive several population processes of grizzly bears in the region, especially for females. We found that 38% of huckleberry patches (235 km2) predicted by the top model were in non‐secure habitat and that these patches were associated with lower fitness and density relative to those in secure habitat. Grizzly bear density was 2.6 times higher in habitat with road densities <0.6 km/km2, supporting the use of this road density target for management. The models predict that applying motorized access controls to backcountry areas with huckleberry patches would increase grizzly bear abundance by 23% on average across the region and 125% in the lowest density portion of the study area (Yahk). Managing both bottom‐up and top‐down influences is necessary to best mitigate the expanding human footprint, which is affecting many carnivore species worldwide. We provide evidence that bottom‐up forces were more influential for female habitat selection, fitness, and density than top‐down effects. We also uncovered a critical pattern in the magnitude of top‐down and bottom‐up influences on behavioral (habitat selection) and demographic (population density and fitness) responses. We show that the relative influence of top‐down influences on habitat selection and fitness are relatively weak compared to bottom‐up influences, whereas top‐down pressures exert much stronger limiting forces on population density. Forming conservation decisions around behavioral responses alone may misdirect actions and have limited benefits to populations. This insight can facilitate more effective decision‐making for grizzly bear conservation. Our findings highlight the importance of considering both bottom‐up and top‐down influences, suggesting cautious interpretation of habitat selection models for any species. A comprehensive examination with population‐level metrics such as density, vital rates, and fitness may be needed for effective management.

Disentangling wildlife–cattle interactions in multi‐host tuberculosis scenarios: systematic review and meta‐analysis

Abstract Ecological interactions involving wildlife (wild mammals) and cattle Bos taurus are considered fundamental drivers of animal tuberculosis (TB) caused by Mycobacterium bovis at the wildlife–livestock interface. Despite recent insights about the role of direct and indirect interactions on TB dynamics, a mechanistic evaluation of studies addressing patterns of wildlife–cattle interaction at the global level is lacking, and the most likely factors explaining interaction rates under different epidemiological scenarios remain poorly understood. We began by reviewing the main criteria used to define a wildlife–cattle interaction relevant to Mycobacterium bovis transmission under different methodological approaches (camera‐trapping, proximity loggers and Global Positioning System collars). Secondly, we applied a generic framework to estimate and characterise interaction patterns between susceptible wildlife and cattle hosts worldwide, testing the effect of potential ecological and methodological factors on interaction rates. We synthesise two main criteria to define direct interactions and five criteria to define indirect interactions between wildlife and cattle. Using data from 31 studies, our meta‐analysis showed that wildlife–cattle direct interaction rates were low (mean = 0.03 interactions/month per species pair, range: 0.00–0.12). In contrast, indirect interaction rates were 154 times higher than the mean of direct interaction rates (mean = 4.63 interactions/month per species pair, range: 0.16–30.00). To prevent TB transmission to cattle, attention should be given to indirect interactions between wildlife and cattle in shared environments. Indirect interactions significantly increase with increasing wildlife density, which, hypothetically, could result in a higher TB transmission risk for cattle. We outline recommendations to achieve harmonised integration and comparison of results in future studies. Consolidation of knowledge in this field will contribute towards guiding control and biosecurity measures, also applicable to other infectious diseases at the wildlife, domestic species and human interfaces.

Resources and Habitat Requirements for Giraffes' (Giraffa camelopardalis) Diet Selection in the Northwestern Kalahari, South Africa.

Diet selection concerning browse availability of giraffes (Giraffa camelopardalis) was studied over 15 months in an arid environment in South Africa. A global positioning system collar was fitted to a giraffe individual to assess the specific areas, consisting of different vegetation types, that the population utilised during different seasons. Results are provided on diet selection in relation to browse availability between seasons and vegetation types, including tree densities and the amount of the total evapotranspiration tree equivalents. Diet selections of the giraffe population changed in response to the availability of browse material from July to October. The availability of important resource areas had a significant (p < 0.05) effect on the spatial ecology, and an increase in home range size was noted. Information that is important for the well-being of giraffes was identified. This included nutritional stress and the limited variety of the most utilised tree species available for browsing, especially during critical dry periods. The results demonstrate the importance of assessment of giraffes' diet selection in relation to browse availability, especially before introduction to a new area, to limit the lack of population growth and underperformance. This study provides valuable information towards understanding the resources and habitats required for successful giraffe management.

Elk Shift Resource Selection Temporally in Response to Natural Gas Development

Resource selection is a process in which animals maximize resource acquisition through unequal use of heterogeneous resources on the landscape. Thus, shifts in resource and disturbance gradients should cause animals to shift resource use and distribution. Although there is an increasing body of evidence about the impacts of disturbance risk on wildlife populations, relatively few studies have assessed fine-scale impacts of human disturbance on wildlife. Our objective was to identify differences in day and night resource selection. Elk (Cervus canadensis) may be able to reduce consequences of coal bed natural gas development through expansion of resource use at night, thus ameliorating population impacts. To collect fine-scale temporal data, we used locations from 55 female elk equipped with Global Positioning System collars, as well as habitat and human disturbance variables for a study system in northeastern Wyoming. We used a mixed-effects model framework integrating frequency of individual elk to quantify shifts between day and night elk resource selection within a coal bed natural gas field. Elk exhibited differences in day and night resource selection, whereby they used areas with decreased forested cover and closer to roads at night. However, there was greater variation in resource selection at night compared with daytime. Our results suggest across each day, elk shifted fine-scale resource selection to take advantage of local resource gradients and response to disturbance impacts.

Detection of goat herding impact on vegetation cover change using multi-season, multi-herd tracking and satellite imagery

The frequency and severity of Mediterranean forest fires are expected to worsen as climate change progresses, heightening the need to evaluate understory fuel management strategies as rigorously as possible. Prescribed small-ruminant foraging is considered a sustainable, cost-effective strategy, but demonstrating a link between animal presence and vegetation change is challenging. This study tested whether the effect of small-ruminant herd presence in Mediterranean woodlands can be detected by integrating remote sensing and herd tracking at the landscape scale. The daily foraging routes of seven shepherded goat herds that exploited a 100-km2 forested area of the Judean Hills, Israel, were tracked over six years using GPS (Global Positioning System) collars. Herd locations were converted to stocking rates, with units of animal-presence-days per unit area per defined time period, and mapped at a spatial resolution of 10 m. We estimated pixel-level vegetation cover change based on a time series of 63 monthly Landsat-8 images expressed as the normalized soil-adjusted vegetation index (SAVI). Spatiotemporal trend analysis assessed the magnitude and direction of change, and a random forest machine-learning algorithm estimated the relative impact on vegetation cover change of environmental factors as well as the herd-related factors of stocking rate that accrued over six years and distance to the closest corral. The last two factors were among the most influential factors determining vegetation cover change in the regional and individual-herd analyses. In some respects, the permanent herds differed in their spatial pattern of stocking rate from the mobile herds that periodically relocated their night corral throughout the year, but stocking rate scaled logarithmically for all herds individually and combined. The combination of multi-season GPS tracking, remote sensing, and machine-learning techniques, applied at a regional scale, detected herd impacts on vegetation cover trends, consistent with livestock foraging being an effective tool for fuel reduction in Mediterranean woodlands.

Habitat selection and water dependency of feral burros in the Mojave Desert, California, USA

AbstractExpansion of feral burro (Equus asinus) populations across the southwestern United States is causing human–wildlife conflicts including rangeland degradation, competition with livestock and native species, and burro–vehicle collisions. On the Fort Irwin National Training Center (NTC) in California, feral burros interfere with military training and are involved in vehicle collisions and other conflicts (e.g., burros blocking access to buildings). Limited data on burro movements and resource use poses a challenge for the development of management plans and mitigation strategies. We estimated home range size, second‐ and third‐order seasonal resource selection, and water dependency of 10 adult female feral burros fitted with global positioning system (GPS) collars on the NTC from November 2015 to April 2017. Mean 95% autocorrelated kernel home range size of female burros (253.9 ± 30.7 km2 [SE]) did not differ among seasons or between burros that resided close to or far from urban areas. Burros selected areas closer to water in all seasons and at both spatial scales, but selection was stronger in the dry season and at the landscape scale. When available, burros strongly selected for areas closer to urban areas. Burros consistently selected for areas with green forage and at lower elevations, but selection for other topographical features was variable. Water use patterns were consistent with the resource selection results. Burros visited water sources twice as often (every 22.2 ± 6.3 hr) during the hot‐dry season (Apr–Oct) compared to the cool‐wet seasons (Nov–Mar; 2015: 45.9 ± 21.0; 2016: 39.7 ± 9.3 hr). Our results suggest that urban areas, and resources therein, and water sources have the biggest influence on burro resource selection, and management plans could focus mitigation programs on these areas.

Grazing-induced cattle behaviour modulates the secondary production in a Eurasian steppe ecosystem

Livestock-grassland interactions are among the most important relationships in grazed grassland ecosystems, where herbivores play a crucial role in plant community and ecosystem functions. However, previous studies primarily have focused on the responses of grasslands to grazing, with few focussing on the effects of livestock behaviour that in turn would influence livestock intake and primary and secondary productivity. Through a 2-year grazing intensity experiment with cattle in Eurasian steppe ecosystem, global positioning system (GPS) collars were used to monitor animal movements, where animal locations were recorded at 10-min intervals during the growing season. We used a random forest model and the K-means method to classify animal behaviour and quantified the spatiotemporal movements of the animals. Grazing intensity appeared to be the predominant driver for cattle behaviour. Foraging time, distance travelled, and utilization area ratio (UAR) all increased with grazing intensity. The distance travelled was positively correlated with foraging time, yielding a decreased daily liveweight gain (LWG) except at light grazing. Cattle UAR showed a seasonal pattern and reached the maximum value in August. In addition, the canopy height, above-ground biomass, carbon content, crude protein, and energy content of plants all affected cattle behaviour. Grazing intensity and the resulting change in above-ground biomass and forage quality jointly determined the spatiotemporal characteristics of livestock behaviour. Increased grazing intensity limited forage resources and promoted intraspecific competition of livestock, which induced longer travelling distance and foraging time, and more even spatial distribution when seeking habitat, which ultimately led to a reduction in LWG. In contrast, under light grazing where there were sufficient forage resources, livestock exhibited higher LWG with less foraging time, shorter travelling distance, and more specialized habitat occupation. These findings support the Optimal Foraging Theory and the Ideal Free Distribution model, which may have important implications for grassland ecosystem management and sustainability.

A multi-level modeling approach to guide management of female feral hogs in Great Smoky Mountains National Park

AbstractWe trapped, anesthetized, and fit 16 female feral swine (Sus scrofa) with Global Positioning System (GPS) collars in Great Smoky Mountains National Park (GRSM) to develop predictive summer and winter models for more effective population control efforts. Given the highly diverse habitat and topography in GRSM and the spatial extent of our dataset, we employed Step Selection Function (SSF) to evaluate resource selection at the 3rd-order level and Resource Selection Function (RSF) models at the 2nd-order level for both summer and winter seasons. The summer SSF and RSF models suggested relatively similar levels of selection, whereas the winter models differed by method. We created a straightforward consensus model to better visualize the agreement and constraints of each set of models. In summer, feral swine used lower slopes regardless of elevation, especially those closer to human-dominated spaces such as along paved and gravel roadways. In winter, feral swine maintained preference for lower slopes but preferred oak-dominated forest areas and selection for human development was less than in summer. Wildlife managers can use these models to better focus feral swine surveillance and management in GRSM. Managers can identify areas of high use by season and plan control activities that are both accessible and highly efficient. The combination and consensus framework presented here can be applied to other systems where species’ habitat selection may result in incongruous results across different levels of selection or seasons of interest.

The ethology of wolves foraging on freshwater fish in a boreal ecosystem.

Through global positioning system (GPS) collar locations, remote cameras, field observations and the first wild wolf to be GPS-collared with a camera collar, we describe when, where and how wolves fish in a freshwater ecosystem. From 2017 to 2021, we recorded more than 10 wolves (Canis lupus) hunting fish during the spring spawning season in northern Minnesota, USA. Wolves ambushed fish in creeks at night when spawning fish were abundant, available and vulnerable in shallow waters. We observed wolves specifically targeting sections of rivers below beaver (Castor canadensis) dams, suggesting that beavers may indirectly facilitate wolf fishing behaviour. Wolves also cached fish on shorelines. We documented these findings across five different social groups at four distinct waterways, suggesting that wolf fishing behaviour may be widespread in similar ecosystems but has probably remained difficult to study given its annual brevity. Spawning fish may serve as a valuable pulsed resource for packs because the spring spawning season coincides with low primary prey (deer Odocoileus virginianus) availability and abundance, and when packs have higher energetic demands owing to newly born pups. We demonstrate the flexibility and adaptability of wolf hunting and foraging behaviour, and provide insight into how wolves can survive in a myriad of ecosystems.

Exploring 'Wether' Grazing Patterns Differed in Native or Introduced Pastures in the Monaro Region of Australia.

Monitoring livestock allows insights to graziers on valuable information such as spatial distribution, foraging patterns, and animal behavior, which can significantly improve the management of livestock for optimal production. This study aimed to understand what potential variables are significant for predicting where sheep spent the most time in native (NP) and improved (IP) paddocks. Wethers (castrated male sheep) were tracked using Global Positioning System (GPS) collars on 15 sheep in the IP and 15 in the NP, respectively, on a property located in the Monaro region of Southern New South Wales, Australia. Trials were performed over four six-day periods in April, July, and November of 2014 and March in 2015. Data were analyzed to understand various trends that may have occurred during different seasons, using random forest models (RFMs). Of the factors investigated, Normalized Difference Vegetation Index (NDVI) was significant (p < 0.01) and highly important for wethers in the IP, but not the NP, suggesting that quality of pasture was key for wethers in the IP. Elevation, temperature, and near distance to trees were important and significant for predicting residency of wethers in the IP, as well as the NP. The result of this study highlights the ability of predictive models to provide insights on behavior-based modelling of GPS data and further enhance current knowledge about location-based choices of sheep on paddocks.

Can GPS monitoring help farmers select the best nutritional management strategy for finishing sheep on pasture?

Global Positioning System (GPS) allows monitoring of grazing herds remotely through real-time data collection. We hypothesized that GPS monitoring will establish preferential routes and grazing sites to find the best feeding strategy (supplementation versus no-supplementation) for sheep finished in a Caatinga rangeland (seasonally dry tropical forest) throughout different seasons of the year (rainy period, transition period, and drought period). The experiment was conducted on a 37.9 ha-Caatinga rangeland and comprised 84 days, subdivided into three 28-day periods (rainy, transition, and drought period). Four rumen cannulated sheep were used for estimation of pasture composition during each collection period based on extrusa samples. Twenty-four intact ram lambs (Santa Ines × Dorper F1 crossbred) grazing on Caatinga rangeland were used to test the effects of supplementation on performance, nutrient intake, and nutrient digestibility during the different seasons. The animals were fitted with GPS collars before being released into the Caatinga to monitor their behavioral activity. Greater walking distance, longer grazing time, higher kernel density, and higher walking speed were detected (P < 0.05) for non-supplemented animals, irrespective of season. The supplemented sheep showed greater (P < 0.05) nutrient intake and nutrient digestibility across all experimental periods compared to the non-supplemented animals. Supplemented animals showed greater (P < 0.05) final body weight and average daily gain compared to non-supplemented animals. During the transition and drought periods, supplementation affected all performance variables. In conclusion, GPS is a useful tool to highlight grazing sites according to different seasons and monitor the behavior of F1 Santa Ines × Dorper sheep finished on Caatinga rangeland. Thus, GPS monitoring aids in establishing the best supplementation strategy and consequently reduces productive and environmental losses caused by the spatial distribution of animals and irregular forage use. In addition, supplementation can offset the negative impacts of low forage supply on sheep grazing Caatinga areas.