GPS Telemetry Data Research Articles

Vegetation cover, topography, and low-traffic roads influence Sonoran desert tortoise (Gopherus morafkai) movement and habitat selection

BackgroundAnthropogenic activities occurring throughout the Sonoran Desert are replacing and fragmenting habitat and reducing landscape connectivity for the Sonoran desert tortoise (Gopherus morafkai). Understanding how the structure of the landscape influences tortoise habitat use and movement can help develop strategies for mitigating the impacts of these landscape alterations, which are conservation actions needed to support the species’ long-term persistence. However, how natural and anthropogenic features influence fine-scale habitat use and movement of Sonoran desert tortoises remains unclear.MethodsThe goals of this study were to (1) understand how characteristics of the landscape shape tortoise habitat use and movement in order to (2) identify factors that may reduce habitat use or threaten landscape connectivity for the species by discouraging or restricting movement. We collected GPS telemetry data from 17 adult tortoises tracked for two summer monsoon seasons, when tortoises are most active, in a U.S. National Monument along the international border between Arizona, USA and Sonora, Mexico. We used Hidden Markov Models (HMMs) to assign GPS locations to an encamped or a moving state. We used the moving state data in integrated Step Selection Analyses (iSSA) to examine how range-resident Sonoran desert tortoises select habitat and respond to landscape features while moving.ResultsTortoises selected to move through areas of intermediate vegetation cover and terrain ruggedness and avoided areas far from desert washes and close to low-traffic roads. Tortoises increased their speed when approaching or crossing low-traffic roads but showed no detectable response to a highway.ConclusionBare earth or high vegetation cover, flat or extremely rugged terrain, areas far from desert washes, and low-traffic roads may discourage or restrict tortoise movement. Therefore, preventing the development of roads, activities that degrade washes, and activities that thin, remove, or greatly increase vegetation cover may encourage tortoise habitat use and movement within those habitats.

Identification of landscape features structuring movement connectivity for Namibian elephants

ContextHuman modification of landscapes poses a significant threat to wildlife, particularly in Africa where population growth and land conversion are expected to increase. Habitat loss and fragmentation have led to declines in wildlife populations, highlighting the need to identify and preserve critical habitats, including core use areas and connectivity between them. Most recently, the identification of habitat corridors has become a key objective.ObjectivesOur study objectives are to (1) empirically quantify connectivity across the Kunene—Etosha landscape in Northwestern Namibia using GPS tracking data on wild African elephants, and (2) assess the landscape features (i.e., geologic, biotic, and human-made) influencing connectivity and corridor types (e.g., fast movement corridors versus slow multi-use movement corridors).MethodsWe used GPS telemetry data from 66 elephants collared in Northwestern Namibia to empirically quantify connectivity using a graph theoretic approach and assess landscape features influencing connectivity. Based on the ‘movescape’ approach, we identify different types of corridors and examined how landscape features differed across these corridors using multiple regression models on locations classified into different types of use categories by machine learning algorithms.ResultsOur results revealed strong variation in connectivity across the landscape, with paths of high connectivity near water sources between the study areas. We found that factors related to water sources and human presence primarily influenced connectivity. Water holes serve as hubs across the ecosystem for both male and female elephants with lower use areas peripheral to areas with water. Connectivity between Kunene and Etosha National Park was relatively rare among the collared elephants, but we highlight the key areas used to move between the two regions.ConclusionWater was the key feature structuring space use, and human presence influenced connectivity between water points, highlighting the importance of landscape planning in relation to limited water sources and human activities. Our results suggest that focusing management efforts on areas where water is limited for both elephants and humans will be important to reduce conflict and maintain ecosystem connectivity.

Revealing functional responses in habitat selection of rocky features and rugged terrain by Eurasian lynx (Lynx lynx) using LiDAR data

ContextMany carnivores are attracted to rugged terrain, rocky areas, and conspicuous relief features. However, most of the previous research is limited to general topographical habitat characteristics and rarely consider the effects of microhabitat characteristics.ObjectivesWe used the Eurasian lynx (Lynx lynx) as a model species to investigate the effects of microhabitat characteristics and human infrastructure on habitat selection. We also tested whether there is evidence for a functional response in habitat selection across a large gradient of habitat availability.MethodsWe developed a new approach for detecting rocky outcrops from airborne LiDAR data. In combination with other remote sensing techniques and GPS-telemetry data, we assessed lynx habitat selection and functional responses across two geologically contrasting areas in Europe.ResultsWe detected > 1 million rocky outcrops and confirmed their strong selection by lynx. Lynx also selected steep, rugged, and rocky areas, especially for day-resting sites. Furthermore, lynx avoided paths during the day but selected them and other linear anthropogenic infrastructure during the night, indicating the behaviour-specific impact of human infrastructure. We also observed a functional response in the selection of rocky and rugged areas, as lynx’ selection of such habitats increased with their lower availability. This highlights the importance of preserving such terrains, especially when they are rare in a landscape.ConclusionsOur results highlight the importance of incorporating remote sensing techniques and data on microhabitat features in animal habitat selection research. We also recommend caution when developing new infrastructure for human recreation or promoting its use near geomorphological features and in rugged terrain.

Cozy den or winter walk: the effects of climate and supplementary feeding on brown bear winter behavior

AbstractHibernation is a key adaptation for coping with unfavorable climatic conditions and low food availability in areas with severe winter conditions. While understanding the physiology and phenology of this adaptation has received considerable attention, comparatively little information is available on how hibernation will be affected by changing climate conditions. We used GPS telemetry data from 20 free‐ranging brown bears monitored over 31 winters between 2007 and 2022, to identify behavioral strategies of bears during winter. We applied behavioral change point analysis to quantify brown bears’ hibernation phenology in a population close to the bear's southern latitudinal range limit in Europe where supplementary food is available to bears year‐round. We observed winter behavior patterns that varied across age and reproductive classes but also within individuals between winters. Among 31 winter events, we registered six cases in which bears exhibited a single hibernation/stationary period and 19 events where hibernation was split into up to five stationary periods. Moreover, six winter events did not show behaviors consistent with hibernation and individuals remained partly or completely active throughout winter. The movement of these active bears decreased with increasing snow depth. In addition, these winter‐active bears showed higher fidelity to supplementary feeding sites during the winter period compared to the rest of the year. Our data suggest that an abundance of human‐provided food resources during winter may facilitate the emergence of different wintering strategies in brown bears. Furthermore, supplemental feeding sites in combination with predicted mild winters and prolonged natural food availability suggest that the use of hibernation as an energy‐saving strategy to overcome severe environmental conditions may decrease in the future.

Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk

Interactions between wildlife and livestock can lead to cross‐species disease transmission, which incurs economic costs and threatens wildlife conservation. Wild waterfowl are natural hosts of avian influenza viruses (AIVs), are often abundant near poultry farms, and have been linked to outbreaks of AIVs in poultry. Interspecific and seasonal variation in waterfowl movement and habitat use means that the risk of disease transmission between wild birds and poultry inevitably varies across species, space, and time. Here, we used GPS telemetry data from 10 waterfowl species in and near California's Central Valley, a region where both wild waterfowl and domestic poultry are abundant, to study selection of poultry farms by waterfowl across diel, seasonal, and annual cycles. We found that waterfowl selected for wetlands, open water, protected areas, and croplands, which meant that they generally avoided habitats that were likely to be used for poultry farming. These selection patterns were linked to species' ecology and diel behavioral patterns, such that avoidance of poultry habitats was stronger for local or partial migrants than for long‐distance migrants, and stronger during daytime than at night. We then combined these habitat selection results with data on poultry farm locations to map risk of waterfowl–poultry contact across the Central Valley. Average selection strength at poultry farms was low, suggesting that current placement of poultry farms is generally effective for limiting risk of contact with wild birds. When we combined these habitat selection results with data on species' abundances and AIV infection prevalence, we found dramatic variation in potential AIV transmission risk among species. These results could be used to prioritize surveillance and biosecurity efforts for regions and times of relatively high risk. More generally, these results highlight that fine‐scale movement data can help identify interspecific, seasonal, and diel patterns in animal behaviors that affect wildlife and poultry health.

High-resolution species distribution modelling reveals spatio-temporal variability of habitat suitability in a declining grassland bird

Abstract Context Species distribution models (SDMs) may provide accurate predictions of species occurrence across space and time, being critical for effective conservation planning. Objectives Focusing on the little bustard (Tetrax tetrax), an endangered grassland bird, we aimed to: (i) characterise the drivers of the species distribution along its key phenological phases (winter, breeding, and post-breeding); and (ii) quantify spatio-temporal variation in habitat suitability across phenological phases and over the years 2005–2021. Methods Combining remotely sensed metrics at high temporal resolution (MODIS) with long-term (> 12 years) GPS telemetry data collected for 91 individuals at one of the species’ main strongholds within the Iberian Peninsula, we built SDMs (250 m resolution) for the species key phenological phases. Results The use of both dynamic and static predictors unveiled previously unknown ecological responses by little bustards, revealing a marked change in the spatial distribution of suitable habitat among phenological phases. Long-term habitat suitability trends showed considerable fluctuations, mainly in the breeding and post-breeding phases. Overall, SDM projections into the past revealed that while the species’ winter and post-breeding habitats apparently increased since 2005, suitable habitat during the species’ most critical phenological phase, breeding, apparently reduced in area over time. Conclusions Our findings show that matching remotely sensed data with GPS tracking data results in accurate habitat suitability predictions throughout the yearly cycle. Additionally, our findings stress the importance of quantifying habitat loss and its potential impact on little bustard decline over nearly 20 years. Spatio-temporal variations in habitat suitability are also identified in this work, which can help prioritize conservation areas, particularly the breeding areas that have remained stable over time, as this is a key requirement for little bustard lek breeding system.

Comparison of daily activity patterns across seasons using GPS telemetry and camera trap data for a widespread mammal

AbstractDaily activity patterns of animals can be dynamic across seasons in response to changing environmental conditions. Daily activity, though, has rarely been evaluated in relation to multiple factors (e.g., seasons, demographics, and data collection methods), which could be important for understanding what drives activity patterns. Here, we evaluated the daily activity patterns of a widespread invasive species, wild pigs (Sus scrofa), across two ecologically different study areas at Buck Island Ranch, FL, and Tejon Ranch, CA, from 2015 to 2018. Using GPS telemetry data (62 individuals in FL, 21 individuals in CA) and detections from grids of systematically placed motion‐activated cameras (44 cameras in FL, 48 cameras in CA), we compared activity patterns among seasons, sexes, and data collection methods (GPS and camera traps). Overall, wild pigs were mostly active during crepuscular and nocturnal periods, with their lowest activity during diurnal periods; however, activity patterns varied due to several factors. Daily activity patterns were similar between methods, among seasons, and between sexes in the subtropical climate of Florida. In contrast, daily activity patterns exhibited greater differences between methods and among seasons in California, where seasonal differences in temperature and precipitation were more pronounced. Overall, daily activity patterns estimated using GPS telemetry and camera‐based methods not only exhibited a high degree of overlap in several comparisons but also exhibited marked differences that should be recognized. Given the increasing evaluation of daily activity patterns of animals using multiple types of datasets, our study provides ecologists with valuable information to consider when designing ecological studies, interpreting their results, and comparing research across systems and studies.

Snowmelt progression drives habitat selection and vegetation disturbance by an Arctic avian herbivore

AbstractArctic tundra vegetation is affected by rapid climatic change and fluctuating herbivore population sizes. Broad‐billed geese, after their arrival in spring, feed intensively on belowground rhizomes, thereby disturbing soil, mosses, and vascular plant vegetation. Understanding of how springtime snowmelt patterns drive goose behavior is thus key to better predict the state of Arctic tundra ecosystems. Here, we analyzed how snowmelt progression affected springtime habitat selection and vegetation disturbance by pink‐footed geese (Anser brachyrhynchus) in Svalbard during 2019. Our analysis, based on GPS telemetry data and field observations of geese, plot‐based assessments of signs of vegetation disturbance, and drone and satellite images, covered two spatial scales (fine scale: extent 0.3 km2, resolution 5 cm; valley scale: extent 30 km2, resolution 10 m). We show that pink‐footed goose habitat selection and signs of vegetation disturbance were correlated during the spring pre‐breeding period; disturbances were most prevalent in the moss tundra vegetation class and areas free from snow early in the season. The results were consistent across the spatial scales and methods (GPS telemetry and field observations). We estimated that 23.4% of moss tundra and 11.2% of dwarf‐shrub heath vegetation in the valley showed signs of disturbance by pink‐footed geese during the study period. This study demonstrates that aerial imagery and telemetry can provide data to detect disturbance hotspots caused by pink‐footed geese. Our study provides empirical evidence to general notions about implications of climate change and snow season changes that include increased variability in precipitation.

Ocelots in the moonlight: Influence of lunar phase on habitat selection and movement of two sympatric felids.

Various landscape and environmental factors influence animal movement and habitat selection. Lunar illumination affects nocturnal visual perception of many species and, consequently, may influence animal activity and habitat selection. However, the effects of varying moon stage may differ across taxa. Prey species often reduce activity during highly visible periods of night while predators may increase activity or alter their habitat use. Ocelots (Leopardus pardalis) and bobcats (Lynx rufus), two nocturnal predatory felids that coexist in southern Texas, may also alter their behavior in response to the phase of the moon. To evaluate the effects of lunar phase on habitat selection of ocelots and bobcats, we executed a step selection analysis using high-frequency GPS-telemetry data collected on each species (ocelot, N = 8; bobcat, N = 13) in southern Texas during 2017-2021 and compared step length during new versus full moons. We predicted that ocelots would increase use of dense thornshrub to reduce their visibility during a full moon. However, as bobcats are habitat generalists and are more active during crepuscular periods, we predicted less influence of moon phase on activity. Ocelots did not alter habitat selection in response to lunar phase but moved shorter distances during full moon phases. Conversely, bobcats selected for greater vegetation cover during full moons, possibly to facilitate hunting during brighter periods, but exhibited no difference in movement across lunar phase. We provide, to our knowledge, the first example of habitat selection by predators in relation to lunar phase and show differences across new versus full moons by ocelots and bobcats such that ocelots alter step length but not habitat selection while bobcats altered habitat selection but not step length in response to shifting lunar phase. Further, we suggest the high potential for ocelot-vehicle collisions on darker nights due to increased movement by ocelots and poor visibility for drivers.

Habitat selection of resident and non-resident gray wolves: implications for habitat connectivity

Habitat selection studies facilitate assessing and predicting species distributions and habitat connectivity, but habitat selection can vary temporally and among individuals, which is often ignored. We used GPS telemetry data from 96 Gray wolves (Canis lupus) in the western Great Lakes region of the USA to assess differences in habitat selection while wolves exhibited resident (territorial) or non-resident (dispersing or floating) movements and discuss implications for habitat connectivity. We used a step-selection function (SSF) to assess habitat selection by wolves exhibiting resident or non-resident movements, and modeled circuit connectivity throughout the western Great Lakes region. Wolves selected for natural land cover and against areas with high road densities, with no differences in selection among wolves when resident, dispersing, or floating. Similar habitat selection between resident and non-resident wolves may be due to similarity in environmental conditions, when non-resident movements occur largely within established wolf range rather than near the periphery or beyond the species range. Alternatively, non-resident wolves may travel through occupied territories because higher food availability or lower human disturbance outweighs risks posed by conspecifics. Finally, an absence of differences in habitat selection between resident and non-resident wolf movements may be due to other unknown reasons. We recommend considering context-dependency when evaluating differences in movements and habitat use between resident and non-resident individuals. Our results also provide independent validation of a previous species distribution model and connectivity analysis suggesting most potential wolf habitat in the western Great Lakes region is occupied, with limited connectivity to unoccupied habitat.

High risk, high reward? Influence of experience level in the selection or avoidance of artificial feeding sites by Eurasian lynx

Artificial feeding of wildlife is a widespread, but controversial, management practice with many positive and negative effects. Besides the effects on the target species, it can also affect other (non-target) species by modulating interspecific interactions. Previous research showed that the presence of artificial feeding sites for bears and ungulates increases the risk of kleptoparasitism by the brown bear (Ursus arctos) on kills made by Eurasian lynx (Lynx lynx). However, it remains unknown whether lynx adjust their behaviour to the distribution of artificial feeding sites, which also attract potential prey (ungulates) for lynx. Using GPS telemetry data, we explored the spatiotemporal lynx response to such sites and studied how this response varied with experience level, i.e. between adult resident individuals ('experienced lynx') and juveniles and/or translocated individuals ('naïve lynx'). We found that lynx experience played an important role in the use of artificial feeding sites. Specifically, while both experienced and naïve lynx selected feeding sites while moving within their home range, the attraction was stronger among the naïve lynx. Considering the distribution of kill sites, naïve lynx killed prey closer to the artificial feeding sites than expected, while experienced lynx avoided them. Finally, the proximity to artificial feeding sites by experienced lynx showed an annual variation, matching the seasonal kleptoparasitism risk, with overall closer proximity to feeding sites during the winter, when bears are less active, which is also when ungulates are more concentrated around feeding sites. Our study suggests that, despite the relatively recent introduction of artificial feeding in the ecosystems, wildlife can learn to respond to the altered interactions with other species. However, this appears to be a learning process with manifold management and conservation implications. A better understanding of species interactions and space use in the context of resource manipulation is increasingly relevant as the use of anthropogenic food by wildlife is nowadays substantial and widespread across most parts of the world.

Combining species distribution models and moderate resolution satellite information to guide conservation programs for reticulated giraffe

AbstractThe conservation of threatened and rare species in remote areas often presents two challenges: there may be unknown populations that have not yet been documented and there is a need to identify suitable habitat to translocate individuals and help populations recover. This is the case of the reticulated giraffe (Giraffa reticulata), a species of high conservation priority for which: (a) there may be unknown populations in remote areas, and (b) detailed maps of suitable habitat available within its range are lacking. We implemented a species distribution modeling (SDM) workflow in Google Earth Engine, combining GPS telemetry data of 31 reticulated giraffe with Landsat 8 OLI, Advanced Land Observing Satellite Phased Arrayed L‐band Synthetic Aperture Radar, and surface ruggedness layers to predict suitable habitat at 30‐m spatial resolution across the potential range of the species. Models had high predictive power, with a mean AUC‐PR of 0.88 (SD: 0.02; range: 0.86–0.91), mean sensitivity of 0.85 (SD: 0.04; range: 0.80–0.91), and mean precision was 0.81 (SD: 0.02; range: 0.79–0.83). Model predictions were also consistent with two independent validation datasets, with higher predicted suitable habitat values at known occurrence locations than at a random set of locations (P < 0.01). Our model predicted a total of 5519 km2 of potentially suitable habitat in Kenya, 963 km2 in Ethiopia, and 147 km2 in Somalia. Our results indicate that is possible to combine moderate spatial resolution imagery with telemetry data to guide conservation programs of threatened terrestrial species. We provide a free web app where managers can visualize and interact with the 30 m resolution map to help guide future surveys to search for existing populations and to inform future reintroduction assessments. We present all analysis code as a framework that could be adapted for other species across the globe.

Spatial use of non-breeding sites by adult GPS-tracked Ospreys Pandion haliaetus from Germany

Recent analyses of telemetry data on Ospreys Pandion haliaetus predominantly address migratory movements, whereas studies focusing on spatial use are rare, especially concerning the African non-breeding areas. We analysed GPS telemetry data of 15 adult Ospreys breeding in northeast Germany, assigned to 37 non-breeding events at non-breeding sites in Africa and south-western Europe. We calculated seasonal home ranges and investigated daily movements as well as overnight roosts. The females arrived in Africa about one month before the males. The home ranges of males correlated with those reported for their respective breeding seasons and were similar in size to those of the females. Half of the non-breeding sites were established on rivers, 36% on the coast and 14% on lakes, with no difference between sexes. Ospreys tracked for more than one year always returned to the same non-breeding sites. Daily home-range sizes varied during the non-breeding period, with the largest home ranges found in January, in parallel with the dry season. The average number of nights per overnight roost was lowest for river habitats and was related to the overall home-range size of the individuals. We highlight the importance of non-breeding sites for the survival of populations and suggest further studies to detect and mitigate threats to migratory bird species.

A review of supervised learning methods for classifying animal behavioural states from environmental features

Abstract Accurately predicting behavioural modes of animals in response to environmental features is important for ecology and conservation. Supervised learning (SL) methods are increasingly common in animal movement ecology for classifying behavioural modes. However, few examples exist of applying SL to classify polytomous animal behaviour from environmental features especially in the context of millions of animal observations. We review SL methods (weighted k‐nearest neighbours; neural nets; random forests; and boosted classification trees with XGBoost) for classifying polytomous animal behaviour from environmental predictors. We also describe tuning parameter selection and assessment strategies, approaches for visualizing relationships between predictors and class outputs, and computational considerations. We demonstrate these methods by predicting three categories of risk to bald eagles from colliding with wind turbines using, as predictors, 12 environmental state features associated with 1.7 million GPS telemetry data points from 57 eagles. Of the SL methods we considered, XGBoost yielded the most accurate model with 86.2% classification accuracy and pairwise‐averaged area under the ROC curve of 90.6. Computational time of XGBoost scaled better to large data than any other SL method. We also show how SHAP values integrated in the R package (xgboost) facilitate investigation of variable relationships and importance. For big data applications, XGBoost appears to provide superior classification accuracy and computational efficiency. Our results suggest XGBoost should be considered as an early modelling option in situations where the intent is to classify millions of animal behaviour observations from environmental predictors and to understand relationships between those predictors and movement behaviours. We also offer a tutorial to assist researchers in implementing this method.

Radiation Belt Daily Average Electron Flux Model (RB‐Daily‐E) From the Seven‐Year Van Allen Probes Mission and Its Application to Interpret GPS On‐Orbit Solar Array Degradation

AbstractWe use NASA's Van Allen Probes data to build a 3‐dimensional Radiation Belt Daily Average Electron flux model (RB‐Daily‐E) covering 25 differential energies (33–7,700 keV), 17 pitch angles, and a variable number of L shells from 2 to 7. RB‐Daily‐E can be used to deduce the fluences observed by any satellite that flew within Van Allen Probes' 7‐year mission lifetime. We supplement Van Allen Probes fluxes with THEMIS average fluxes to cover higher L shells when applying the model to a secondary satellite. RB‐Daily‐E agrees with both the Arase high‐energy electron experiment (XEP) flux data and the GPS Combined X‐ray Dosimeter (CXD) electron flux approximately within a factor of two or better, and comparisons with AE9 are as expected. The RB‐Daily‐E Model has applications for when actual fluxes on day‐timescales are required for post‐anomaly investigations, including long‐term radiation environment effects such as solar cell and solar array degradation. We therefore applied the model to two GPS satellites to determine electron fluence inputs to the Equivalent Flux (EQFLUX) solar cell degradation model. The modeled voltage degradation well‐matched the voltage degradation trends identified in the GPS telemetry data, suggesting that the radiation environment is the primary cause of the voltage degradation. The current degradation was largely underestimated, suggesting that current degradation is influenced by other sources.

Understanding spatially explicit capture–recapture parameters for informing invasive animal management

AbstractSpatially explicit capture–recapture modeling is used to estimate population density to enhance our ecological understanding and management of wildlife populations. The two primary parameters estimated in the capture process of these models are σ and g0. The σ parameter is the standard deviation of a bivariate normal home range kernel (indicating home range size), while g0 is the probability of capture by a device placed at the home range center. These parameters are being increasingly generalized and used in simulation models to predict the detection or capture rates of invasive animals to inform management strategies. Given the sensitivity of simulation model predictions to parameter values, we performed an analysis of preexisting GPS telemetry and trapping data of invasive brushtail possums (Trichosurus vulpecula) across New Zealand to address the following three questions. First, how does σ vary with population density, habitat, and age–sex class? Second, how is g0 influenced by home range size (i.e., σ) and trap type? Third, how does the predicted probability of capture or detection of individuals vary with σ, different trap types and trap densities? We used data from 180 possums across 18 sites to develop a Bayesian hierarchical model. Results showed that σ decreased with increasing population density and increasing area of productive grassland. There was a strong negative relationship between σ and g0, and g0 was highest for cage traps and lowest for raised leg‐hold traps. Despite the potential compensatory inverse effect of g0 with σ, the probability of capturing a randomly located possum by a large array of traps increased with increasing σ. Results show that selection of σ for predictive simulation modeling should begin with an estimated population density. The associated g0 should then be identified as a function of σ, and stochasticity should be incorporated to account for inter‐individual variability. The approach employed here provides a methodology for estimating density, σ, and g0 using trapping and telemetry data, and our results will help guide careful consideration of density, σ, and g0 to improve the accuracy of simulation studies across species.

Considering behavioral state when predicting habitat use: Behavior-specific spatial models for the endangered Tasmanian wedge-tailed eagle

Effective planning for species conservation often requires an understanding of habitat use. The resources an animal selects within the landscape relate to its behavioral state and, therefore, incorporating behavior into habitat selection analyses can help inform management of threatened species. Here we present an approach for developing behavior-specific spatial habitat-use models using large quantities of GPS telemetry data. Using hidden Markov models, we first characterize 231,478 GPS fixes from 22 recently fledged endangered Tasmanian wedge-tailed eagles (Aquila audax fleayi) as reflective of either perching, short-distance flight, or long-distance flight. We then use a multivariate habitat selection ratio to develop spatial models predicting where these behavioral states occur. Recently fledged Tasmanian wedge-tailed eagles selected for areas close to forest edges during perching and short distance flights, whereas they selected more strongly for areas with steep topography (slopes > 15°) and further from forest edges for longer flights. Models using distance to forest edge and topographic slope effectively predicted where eagles engaged in long flights (R2 > 0.91, rs > 0.90) in each of six regions, whereas the performance varied by region for models describing perching (R2 = 0.43–0.97, rs = 0.80–0.97) and short flights (R2 = 0.34–0.93, rs = 0.63–1.00). Our study provides a detailed understanding of habitat use by young Tasmanian wedge-tailed eagles, which has multiple applications in the ongoing conservation of the population. Our method illustrates a framework for spatially explicit and behavior-specific habitat selection analyses that can be applied to other species of conservation concern.

Displacement Effects of Conservation Grazing on Red Deer (Cervus elaphus) Spatial Behaviour

Conservation grazing uses semi-feral or domesticated herbivores to limit encroachment in open areas and to promote biodiversity. However, we are still unaware of its effects on wild herbivores. This study investigates the influence of herded sheep and goats on red deer (Cervus elaphus) spatial behavior by testing three a-priori hypotheses: (i) red deer are expected to avoid areas used by livestock, as well as adjacent areas, when livestock are present, albeit (ii) red deer increase the use of these areas when sheep and goats are temporarily absent and (iii) there is a time-lagged disruption in red deer spatial behavior when conservation grazing practice ends. Using GPS-telemetry data on red deer from a German heathland area, we modelled their use of areas grazed by sheep and goats, using mixed-effect logistic regression. Additionally, we developed seasonal resource selection functions (use-availability design) to depict habitat selection by red deer before, during, and after conservation grazing. Red deer used areas less during conservation grazing throughout all times of the day and there was no compensatory use during nighttime. This effect mostly persisted within 21 days after conservation grazing. Effects on habitat selection of red deer were detectable up to 3000 meters away from the conservation grazing sites, with no signs of either habituation or adaption. For the first time, we demonstrate that conservation grazing can affect the spatio-temporal behavior of wild herbivores. Our findings are relevant for optimizing landscape and wildlife management when conservation grazing is used in areas where wild herbivores are present.

Understanding the spatial distribution and hot spots of collared Bornean elephants in a multi-use landscape

In the Kinabatangan floodplain, Sabah, Malaysian Borneo, oil palm and settlements have reduced and fragmented lowland tropical forests, home to around 200 endangered Bornean elephants (Elephas maximus borneensis). In this region, elephants range within forests, oil palm and community areas. The degree to which elephants are using these areas remains unclear. We used GPS telemetry data from 2010 to 2020 for 14 collared elephants to map their entire known ranges and highly used areas (hot spots) across four land use categories and estimate time spent within these. The use of land use types across elephants varied significantly. Typically, females had strong fidelity to forests, yet many of these forests are threatened with conversion. For the three males, and several females, they heavily used oil palm estates, and this may be due to decreased landscape permeability or foraging opportunities. At the pooled level, the entire range and hot spot extents, constituted 37% and 34% for protected areas, respectively, 8% and 11% for unprotected forests, 53% and 51% for oil palm estates, and 2% for community areas. Protecting all forested habitats and effectively managing areas outside of protected areas is necessary for the long-term survival of this population.

Assessment of appropriate species-specific time intervals to integrate GPS telemetry data in ecological niche models

Ecological niche models use presence-only data, which is often affected by lack of true absences leading to sampling bias. Over the last decade, there has been an uptick in the integration of occurrence data from global positioning systems telemetry data in ecological niche models and/or species distribution models. These data types can be affected by serial autocorrelation at high relocation frequencies yet have been used in ecological niche models using geographic filters and subsampling techniques. Yet, no study to date has attempted to discern a method to identify the appropriate time interval for a particular species if integrating GPS telemetry occurrence data in a MaxEnt framework. We demonstrate a rigorous spatial technique using a robust contemporary dataset from ocelots (Leopardus pardalis) to assess the appropriate time intervals to use in a species-specific ecological niche model. We assessed a range of daily time intervals (every 0.5, 1–4, 6, 8, and 12 h) commonly used in teresstrial mammalian carnivore studies. We observed the predictive performance of shorter time intervals every 2 h was comparable to much longer intervals every 12 h. These shorter intervals under/overestimated the least amount of data compared to 12 h. This study demonstrates that by accounting for serial autocorrelation and conducting rigorous spatial analyses, scientists can identify the appropriate time interval to integrate GPS telemetry data use in ecological niche models in MaxEnt. These results can also be transferable across highly mobile terrestrial taxa at different spatial scales, which can help inform species management or conservation strategies.