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The McKay's Bunting (Plectrophenax hyperboreus) is endemic to Alaska, breeds solely on the remote and uninhabited St. Matthew and Hall islands (332 km2) in the central Bering Sea, and is designated as a species of high conservation concern due to its small population size and restricted range. A previous hypothesized population estimate (~2,800 to 6,000 individuals) was greatly increased (~31,200 individuals) after systematic surveys of the species' entire breeding range in 2003, establishing McKay's Bunting as one of the rarest passerines in North America. In 2018, we replicated the 2003 surveys and used density surface models to estimate breeding season densities, distributions, and population change over the intervening time period. Our results indicate that the McKay's Bunting population declined by 38% (95% CI: 27 to 48%) from ~31,560 to 19,481 individuals since 2003. Spatial model predictions showed no areas with an increase of birds on either St. Matthew or Hall islands but revealed declines across 13% (42 km2) of St. Matthew Island. Declines disproportionately occurred both in marginal habitats with reduced rocky nesting substrate and in high-density hotspots along the coast of St. Matthew Island. The total area occupied by breeding adults decreased by 8%, and high-density hotspots shifted inland from the coast of St. Matthew Island to higher elevations on both islands, the latter potentially responses to exceptionally warm weather and reduced spring snow cover in 2018. Additionally, we observed low numbers of predators and interspecific competitors in 2018, suggesting that these did not cause the decline. Our findings indicate that McKay's Bunting meets international standards for elevating its conservation status from Least Concern to Endangered based on the International Union for Conservation of Nature Red List of Threatened Species ranking criteria. Additional population monitoring and studies to identify the causal mechanisms of the recent population decline of this rare species could assist future population assessments. [ABSTRACT FROM AUTHOR]

Anthropogenic resource subsidization across western ecosystems has contributed to widespread increases in generalist avian predators, including common ravens (Corvus corax; hereafter, raven). Ravens are adept nest predators and can negatively impact species of conservation concern. Predation effects from ravens are especially concerning for greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse), which have experienced prolonged population decline. Our objectives were to quantify spatiotemporal patterns in raven density, evaluate sage‐grouse nest success concurrent with fluctuating raven densities, and demonstrate a spatially explicit decision support tool to guide management applications to appropriate conflict areas. We combined ~28,000 raven point count surveys with data from more than 900 sage‐grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage‐grouse nest survival using a hierarchical frailty model as a function of raven density and other environmental covariates that influence the risk of nest failure. Raven density commonly exceeded 0.5 ravens km−2 and increased at low elevations with more anthropogenic development and/or agriculture. Reduced sage‐grouse nest survival was strongly associated with elevated raven density (e.g., >0.5 ravens km−2) and varied with topographic ruggedness, shrub cover, and burned areas. For conservation application, we developed a spatially explicit planning tool that predicts nest survival under current and reduced raven numbers within the Great Basin to help direct management actions to localized areas where sage‐grouse nests are at highest risk of failure. Our modeling framework can be generalized to multiple species where spatially registered abundance and demographic data are available. [ABSTRACT FROM AUTHOR]

Mortality risk to imperiled animal populations from anthropogenic hazards can be managed by spatial limits on human behavior if exposure and vulnerability of the animals are known. To provide this information for sea turtles in waters near their nesting beaches, we mapped exposure to vessel strikes, which are a major, lethal hazard to these endangered animals. During the 2021 and 2022 nesting seasons, we carried out standardized transect line surveys off Florida’s Atlantic coast, focusing on Loggerhead and Green Sea Turtles. Using Distance Sampling and Density Surface Modeling, we estimated the distribution of turtles near the surface (thus vulnerable to vessel strikes) based on several covariates. Our results revealed a clustering of vulnerable turtles near the shore and within hotspots identified by adjacent nesting beach density over tens of kilometers. Contrary to risk assessments based on stranding data, our findings present a new perspective on potential strike risk. We propose that our methodology and data can significantly contribute to initiating human behavioral changes required to reduce widespread vessel strikes on sea turtles.

Abstract Anthropogenic resource subsidization across western ecosystems has contributed to widespread increases in generalist avian predators, including common ravens (Corvus corax; hereafter, raven). Ravens are adept nest predators and can negatively impact species of conservation concern. Predation effects from ravens are especially concerning for greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse), which have experienced prolonged population decline. Our objectives were to quantify spatiotemporal patterns in raven density, evaluate sage‐grouse nest success concurrent with fluctuating raven densities, and demonstrate a spatially explicit decision support tool to guide management applications to appropriate conflict areas. We combined ~28,000 raven point count surveys with data from more than 900 sage‐grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage‐grouse nest survival using a hierarchical frailty model as a function of raven density and other environmental covariates that influence the risk of nest failure. Raven density commonly exceeded 0.5 ravens km−2 and increased at low elevations with more anthropogenic development and/or agriculture. Reduced sage‐grouse nest survival was strongly associated with elevated raven density (e.g., >0.5 ravens km−2) and varied with topographic ruggedness, shrub cover, and burned areas. For conservation application, we developed a spatially explicit planning tool that predicts nest survival under current and reduced raven numbers within the Great Basin to help direct management actions to localized areas where sage‐grouse nests are at highest risk of failure. Our modeling framework can be generalized to multiple species where spatially registered abundance and demographic data are available.

Spatio‐temporal maps of organismal density or relative abundance are fundamental to many applications in conservation and ecology. There are often a number of data sources to inform species maps, including citizen science monitoring programs, remote sensing, geolocation data from satellite‐tagged animals and formal scientific surveys. In these cases, it may be desirable to come up with a single map integrating all data sources.We introduce a novel two‐step method for combining inference about relative abundance maps using multiple data sources. Log‐scale relative abundance surfaces are first estimated from individual data sets, and resultant surfaces are then treated as 'data' within a generalized linear mixed model framework with a spatially autocorrelated mean process. We also introduce generalizations allowing data misalignment and predictive processes to decrease computational burden.Using simulation, we show that our approach frequently outperfoms other approaches, including basing inference on a single surface, or taking a simple arithmetic mean (although the arithmetic mean performs well when there are few surfaces). We then demonstrate our method using citizen science and satellite‐tracking data of Steller sea lions in Alaska. In this case, relative abundance surfaces consisted of an effort‐adjusted map developed from platform‐of‐opportunity sightings and a utilization distribution developed from geolocation records. The resulting combined surface represented a compromise between single‐data source predictions.Our approach should be useful for ecologists seeking to reconcile alternative species distribution maps, particularly when individual surfaces are prone to bias, when there is no obvious common currency (e.g. point process), or when computational demands preclude a fully integrated analysis. [ABSTRACT FROM AUTHOR]

Maximising survey efficiency can help reduce the tradeoff between spending limited conservation resources on identifying population changes and responding to those changes through management. Burrow‐nesting seabirds are particularly challenging to survey because nests cannot be counted directly. We evaluated a stratified random survey design for generating unbiased population estimates simultaneously for four petrel species nesting on Macquarie Island, Australia, where the survey cue, burrow entrances, is similar for all species. We also compared the use of design‐based and model‐based analyses for minimising uncertainty in estimates. We recorded 2845 Antarctic prion burrows, 306 white‐headed petrel burrows and two blue petrel burrows while distance‐sampling along 154 km of transects. For blue petrels and grey petrels, we completed nocturnal searches along a further 71 km and searched 249 km of tracks during follow‐up ground searches. We failed to generate unbiased population estimates for two rare and localised species, blue and grey petrels, from our stratified random survey. Only for the most widespread and abundant species, Antarctic prion, did the estimate have reasonable power to detect a rapid population change. Model‐based analyses of the stratified random survey data did not improve upon traditional design‐based analyses in terms of uncertainty in population estimates, but they did provide useful spatial representation of current populations. Models that used the targeted survey data did not reflect current population sizes and distributions of the two rare and localised species. We found that when species ecologies, distributions and abundances vary, a multi‐method approach to surveys is needed. Species with low abundance that occur patchily across large islands are likely to be best estimated using targeted surveys, whereas widespread and abundant species can be accurately and precisely estimated from randomised surveys using informative model‐based analyses. [ABSTRACT FROM AUTHOR]

Abstract Long‐term monitoring of biodiversity in protected areas (PAs) is critical to assess threats, link conservation action to species outcomes, and facilitate improved management. Yet, rigorous longitudinal monitoring within PAs is rare. In Southeast Asia (SEA), there is a paucity of long‐term wildlife monitoring within PAs, and many threatened species lack population estimates from anywhere in their range, making global assessments difficult. Here, we present new abundance estimates and population trends for 11 species between 2010 and 2020, and spatial distributions for 7 species, based on long‐term line transect distance sampling surveys in Keo Seima Wildlife Sanctuary in Cambodia. These represent the first robust population estimates for four threatened species from anywhere in their range and are among the first long‐term wildlife population trend analyses from the entire SEA region. Our study revealed that arboreal primates and green peafowl (Pavo muticus) generally had either stable or increasing population trends, whereas ungulates and semiarboreal primates generally had declining trends. These results suggest that ground‐based threats, such as snares and domestic dogs, are having serious negative effects on terrestrial species. These findings have important conservation implications for PAs across SEA that face similar threats yet lack reliable monitoring data.

Assessing the distribution, density, and abundance of organisms is essential for conservation and management of imperiled species. Simple counts of sampled individuals are often inadequate to make such estimates. This is especially true for highly mobile marine animals like green sea turtles (Chelonia mydas). We used distance sampling and density surface model techniques to generate estimates of green turtle abundance on foraging grounds at the Eastern Quicksands, located west of Key West, Florida, USA. From 2006 to 2018, we conducted 18 surveys of six standardized transect lines from which we estimated abundances, plotted spatial distributions, and identified spatial segregation of life stages using density surface models and null model analysis. The Eastern Quicksands represent one of the densest foraging aggregations of green turtles worldwide. Spatial segregation of Large Juvenile and Adult turtles was evident, which we hypothesize may be due to benthic habitat preferences and differing predator detection and avoidance strategies among the sea turtle life stages. Given the high green turtle densities in this foraging area, and recent increases in Florida green turtle nesting, combined with anthropogenic stressors to seagrass pastures, we foresee management concerns for long-term sustainability of seagrass at the Eastern Quicksands and recommend that this area be given Critical Habitat designation. [ABSTRACT FROM AUTHOR]

Long‐term monitoring of biodiversity in protected areas (PAs) is critical to assess threats, link conservation action to species outcomes, and facilitate improved management. Yet, rigorous longitudinal monitoring within PAs is rare. In Southeast Asia (SEA), there is a paucity of long‐term wildlife monitoring within PAs, and many threatened species lack population estimates from anywhere in their range, making global assessments difficult. Here, we present new abundance estimates and population trends for 11 species between 2010 and 2020, and spatial distributions for 7 species, based on long‐term line transect distance sampling surveys in Keo Seima Wildlife Sanctuary in Cambodia. These represent the first robust population estimates for four threatened species from anywhere in their range and are among the first long‐term wildlife population trend analyses from the entire SEA region. Our study revealed that arboreal primates and green peafowl (Pavo muticus) generally had either stable or increasing population trends, whereas ungulates and semiarboreal primates generally had declining trends. These results suggest that ground‐based threats, such as snares and domestic dogs, are having serious negative effects on terrestrial species. These findings have important conservation implications for PAs across SEA that face similar threats yet lack reliable monitoring data. [ABSTRACT FROM AUTHOR]

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

Identification of species' Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence‐only data are increasingly being used to develop SDMs to aid the conservation decision‐making process. The application of presence‐only SDMs for marine species' is particularly attractive due to often logistical and economic costs of obtaining systematic species' distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species' occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross‐validation (CV) approach to validate a MaxEnt presence‐only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003–2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line‐transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost‐effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non‐calf groups) was obtained that the presence‐only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence‐only models within certain conservation management applications, such as spatial risk assessments. [ABSTRACT FROM AUTHOR]

In spring 2019, we conducted a comprehensive abundance and distribution survey for Peary caribou (Rangifer tarandus pearyi) and muskox (Ovibos moschatus) on Axel Heiberg Island, Nunavut, Canada. Although much of Axel Heiberg Island is rugged and extensively glaciated, areas east of the Princess Margaret mountain range have high productivity given the latitude and have supported relatively large numbers of Peary caribou and muskoxen. This region of the island has been previously identified as a potential High Arctic refugium. The last island-wide survey, in 2007, estimated 4237 muskoxen (95% confidence interval [CI] [3371:5325]) and 2291 Peary caribou (95% CI [1636:3208]); based on our 2019 results, it appears that muskox numbers have been stable on Axel Heiberg Island since then. Using distance sampling and density surface models, we estimated 3772 muskoxen (95% CI [3001:4742]) on Axel Heiberg Island during our 2019 survey. In contrast, Peary caribou, which is listed as an endangered species under the Canadian Species at Risk Act, appear to have declined dramatically from the 2007 estimate. During the 2019 survey, we observed only six Peary caribou and could not generate an island-wide estimate. Abrupt declines in numbers are characteristic of the species and are usually related to poor winter conditions such as dense snowpack or extreme weather events that result in widespread ground-fast icing. However, the limited monitoring information available at the northern extent of Peary caribou range presents major challenges to our understanding of the mechanisms leading to this near total absence of approximately 20% of range-wide Peary caribou numbers. [ABSTRACT FROM AUTHOR]

European roe deer (Capreolus capreolus L.) are important given their economic, recreational and ecological value. However, uncontrolled roe deer numbers can result in negative impacts on forest regeneration and agricultural crops, disease transmission and occurrences of deer-vehicle collisions. Information on the abundance and distribution is needed for effective management. We combined distance sampling (DS) of roe deer dung pellet groups with multiple variables to develop a density surface model (DSM) in the federal state of Bavaria in south-eastern Germany. We used the estimates of pellet group density as a proxy for roe deer relative abundance. We extrapolated our best DSM, conducted a quantitative evaluation and contrasted relative abundance along climate and land-use gradients. Relative abundance of roe deer was influenced by a combination of habitat type, climate and wildlife management variables, which differed between seasons and which reflected changes in food and shelter availability. At the landscape scale, the highest abundance was observed in agriculture-dominated areas and the lowest in urban areas. Higher abundance was also observed in areas with intermediate temperatures compared to the warmest areas. Our results provide information on possible future changes in the distribution of relative abundance due to changes in climate and land-use.

Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (

Background: Citizen science is increasingly popular and has the potential to collect extensive datasets at lower costs than traditional surveys conducted by professional scientists. Ferries have been used to collect data on cetacean populations for decades, providing long-term time series for monitoring of cetacean populations. One cetacean species of concern is the common dolphin, which has been found stranded around the north-east Atlantic in recent years, with high numbers on French coasts being attributed to fisheries bycatch. We estimate common dolphin densities in the north-east Atlantic and investigate the ability of citizen science data to identify changes in marine mammal densities and areas of importance. Materials and Methods: Data were collected by citizen scientists on ferries between April and October in 2006-2017. Common dolphin sightings data from two ferry routes across three regions, Bay of Biscay (n = 569); south-west United Kingdom to the Isles of Scilly in the Celtic Sea (n = 260); and English Channel (n = 75), were used to estimate density across ferry routes. Two-stage Density Surface Models accounted for imperfect detection, and tested the influence of environmental (chlorophyll a, sea surface temperature, depth, and slope), spatial (latitude and longitude) and temporal terms (year and Julian day) on occurrence. Results: Overall detection probability was highest in the areas sampled within the English Channel (0.384) and Bay of Biscay (0.348), and lowest on the Scilly's route (0.158). Common dolphins were estimated to occur in higher densities on the Scilly's route (0.400 per km2) and the Bay of Biscay (0.319 per km2), with low densities in the English Channel (0.025 per km2). Densities on the Scilly's route appear to have been relatively stable since 2006 with a slight decrease in 2017. Densities peaked in the Bay of Biscay in 2013 with lower numbers since. Densities in the English Channel appear to have increased over time since 2009. Discussion: This study highlights the effectiveness of citizen science data to investigate the distribution and density of cetaceans. The densities and temporal changes shown by this study are representative of those from wider-ranging robust estimates. We highlight the ability of citizen science to collect data over extensive periods of time which complements dedicated, designed surveys. Such long-term data are important to identify changes within a population; however, citizen science data may, in some situations, present challenges. We provide recommendations to ensure high-quality data which can be used to inform management and conservation of cetacean populations. [ABSTRACT FROM AUTHOR]

Productivity in the oceans is heightened around oceanographic and bathymetric features such as fronts and islands. This can have a flow‐on effect, providing increased food availability for higher trophic level species. Using data from a 5‐day combined visual and acoustic survey, we examined the hypothesis that higher Antarctic krill (Euphausia superba) density provides a lucrative resource for humpback whales (Megaptera novaeangliae) at a remote Antarctic feeding area, the Balleny Islands (67oS, 164°E). We assessed whale presence at the feeding area in relation to prey (krill), productivity and environmental variables using density surface modeling. We found stark differences between krill swarms near the islands and those in adjacent open water. Swarms were twice as dense and three times more numerous near the Balleny Islands compared to an open water pelagic environment, suggesting that the islands offered a profitable feeding opportunity. At the feeding area, whales were found in deeper and more productive waters with medium krill densities. These relationships, along with the high krill availability around the islands, may be the result of the Island Mass Effect. [ABSTRACT FROM AUTHOR]

Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance components demonstrated that overall detectability as well as surface availability contributed substantial amounts of variance in the final abundance estimates whereas uncertainty in mean group size contributed a negligible amount. Our method provides a Bayesian alternative to DSMs that incorporates much of the flexibility available in the Two-Stage Method. In addition, these results demonstrate the degree to which uncertainty can be underestimated if certain components of a DSM are assumed fixed. [ABSTRACT FROM AUTHOR]

Simple Summary: The European roe deer is the most abundant ungulate in Europe. Information on the number of animals and the factors that influence this are essential for the effective management of this species. We developed a model to estimate the pellet group density using transect surveys of dung pellets in the federal state of Bavaria, south-eastern Germany. We used the pellet group density as a proxy for roe deer relative abundance. Our results show that climate, habitat type and wildlife management approach determine the relative number of roe deer. Influential factors differed between seasons and were expected due to changes in food and shelter availability. Although recognized as a woodland species, the majority of roe deer are expected in agriculture-dominated landscapes, which shows their ability to adapt to a wide range of landscape types, especially those with high food availability. Higher numbers were also predicted in regions with intermediate temperatures. Estimates of relative number do not give the actual absolute number of animals but are useful in determining which conditions will have more or fewer animals and can provide information for broad-scale management recommendations. Our results also provide insights into possible future changes in the distribution of relative numbers of roe deer due to climate and land-use change. European roe deer (Capreolus capreolus L.) are important given their economic, recreational and ecological value. However, uncontrolled roe deer numbers can result in negative impacts on forest regeneration and agricultural crops, disease transmission and occurrences of deer-vehicle collisions. Information on the abundance and distribution is needed for effective management. We combined distance sampling (DS) of roe deer dung pellet groups with multiple variables to develop a density surface model (DSM) in the federal state of Bavaria in south-eastern Germany. We used the estimates of pellet group density as a proxy for roe deer relative abundance. We extrapolated our best DSM, conducted a quantitative evaluation and contrasted relative abundance along climate and land-use gradients. Relative abundance of roe deer was influenced by a combination of habitat type, climate and wildlife management variables, which differed between seasons and which reflected changes in food and shelter availability. At the landscape scale, the highest abundance was observed in agriculture-dominated areas and the lowest in urban areas. Higher abundance was also observed in areas with intermediate temperatures compared to the warmest areas. Our results provide information on possible future changes in the distribution of relative abundance due to changes in climate and land-use. [ABSTRACT FROM AUTHOR]