Monitoring small and/or threatened animal populations using methodologies that provide accurate information is critical for effective and timely conservation decision-making. In recent years, “unmarked” methods that combine passive sampling of unidentifiable individuals with statistical models that purport to estimate demographic parameters (e.g., abundance) have proliferated. We highlight the risks of using such methods for small populations and the considerable value of individual-based monitoring.

Abstract Population monitoring of at‐risk species is fundamental to directing effective and timely conservation actions. For wide‐ranging threatened species, such as the wolverine ( Gulo gulo ), monitoring across multiple jurisdictions is necessary to track their distribution to inform collaborative regional and national management and policy. We repeated a landscape‐scale, multi‐jurisdictional remote camera survey first completed in 2017 to evaluate wolverine occupancy throughout the western United States in 2022. Our objectives were to 1) expand the sampling frame to include areas of potential recolonization (Colorado, Oregon, Utah, USA), 2) evaluate whether overall and state‐specific occurrence has changed from 2017 in the common sampling frame of Idaho, Montana, Washington, and Wyoming, USA, and 3) evaluate spatial drivers of wolverine occurrence in 2022. From a sampling frame of 770 sites (15 × 15‐km cells) identified as potential wolverine habitat, 229 sites were surveyed in 2022; no wolverines were detected in Colorado, Oregon, or Utah. We predicted wolverine occupancy and 95% highest posterior density intervals (HPDI) in states with detections to be 0.47 (0.37–0.58) in Idaho, 0.35 (0.21–0.52) in Montana, 0.29 (0.09–0.54) in Washington, and 0.35 (0.27–0.47) in Wyoming. We found weak to moderate evidence of a small decline in overall occupancy () from the common sampling frame (2017: 0.44 [0.37–0.52]; 2022: 0.39 [0.30–0.49]; P() = 0.81), driven by lower occurrences in Montana and Washington. However, this finding is complicated by a change in detection methodology that may have contributed to lower occurrences in Montana and Washington. Lastly, we found strong support (probability of an effect >0.95) that within areas of predicted wolverine habitat, increasing habitat connectivity and the proportion of a site that was previously burned (2001–2015) were associated with increased wolverine occupancy, while increasing normalized difference vegetation index (NDVI) was associated with decreased wolverine occupancy. Our expanded sampling frame provides the means to track wolverine occupancy and range extent—including recolonizations and contractions—across the western United States, providing insights at both the state and national scales.

Theory predicts that the strength and direction of species interactions can shift from being competitive in benign environments toward being facilitative in stressful environments. However, the environmental context dependency of species interactions has rarely been tested in animal communities. We capitalized on a 15‐year, landscape‐scale dataset, collected annually in a relatively stable old‐growth forest environment, to test the long‐held hypothesis that the strength and direction of species interactions might be mediated by climatic conditions. It is generally accepted that competitive and facilitative interactions drive the distributions of many species, but are rarely included in climate envelope models, and may partly explain poor predictive performance of species distribution models in the face of climate change. Using multi‐species dynamic occupancy models applied to long‐term data, we tested whether annual settlement by bird species could affect either the persistence or settlement by other phylogenetically related species, and whether these interactions are mediated by microclimate. We found that species interactions were influenced by microclimate for a high proportion of species pairs (80%). Related species pairs more often showed settlement dynamics that were indicative of attraction rather than repulsion (55 versus 30%, respectively). In some cases, competitive interactions in colder microclimates flipped to become facilitative in warmer ones (15%) and vice versa (5%). Furthermore, species pairs that were closely related were more likely to exhibit competitive relationships along at least part of the microclimatic gradient. Our results highlight the importance of using long‐term data to incorporate competitive and facilitative interactions into species distribution models, and support the notion that the strength and direction of species interactions can be dependent on microclimatic conditions.

Outdoor recreation is expanding across public lands, yet managers lack evidence for how long wildlife responses to recreation persist and whether response duration depends on disturbance type and frequency. Using a field-based acoustic playback experiment in the Bridger–Teton National Forest, Wyoming, USA, we isolated recreation noise from other components of human presence and quantified how long mammal detections took to return to baseline. Across 12 sites over two field seasons, we broadcast motorized and non-motorized recreation sounds at rates mimicking high- and low-use trails, paired with nature-sound controls. We quantified treatment effects as deviations from expected weekly detections at our control sites, defining time-to-return as the first week when deviations overlapped zero. Response duration differed between disturbance types. High-rate motorized playbacks produced the longest suppression: all species except mule deer did not return to baseline within our 15-week season, whereas mule deer recovered by six weeks. For high-rate non-motorized playbacks, all species except for mule deer had suppressed detections until seven weeks while mule deer detections were not different from baseline. For low-rate non-motorized playbacks, mule deer detections returned to baseline at nine weeks while all other species returned to baseline at seven weeks. Species differences indicate that apparent overall recovery can mask prolonged avoidance by less tolerant taxa. Recreation effects persisted for weeks to months and varied with disturbance type and exposure frequency, highlighting how temporal patterns of use influence wildlife responses and where recreational planning of spatial refugia or seasonal restrictions could reduce impacts on sensitive species.

Long‐term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad‐scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi‐structured eBird community science data. Moreover, weather surveillance radar data of swallow and martin roosting populations yield a third complementary source of trend information. Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022. We found a high probability of agreement between all three sources about observed declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives. Synthesis and applications . Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long‐term monitoring data of different species, at different stages of their annual cycle, in any geographic location.

Translocations are an established element of restoration plans for threatened species, but success in establishing new populations is often limited, highlighting the need for careful evaluation of translocation efforts. Variation among individuals may contribute to poorly placed translocations, particularly when there is variation in the spatial ecology of target species. As a test of this we investigated the spatial ecology of imperiled New England cottontail ( Sylvilagus transitionalis (Bangs, 1895)) in Rhode Island, USA. We combined telemetry data with remotely-sensed vegetation data to evaluate the home ranges, resource selection, and survival of translocated cottontails at three sites, including one where we also tracked resident cottontails. Despite instances of alignment among individuals, we found a wide span of home range estimates and high individual variability on resource selection. Both of these results suggest that population-level inferences of translocated individuals may fail to capture important aspects of animal ecology at the individual level. Further, we found lower survival compared to residents at one of our sites and literature values for other resident populations. Our results suggest that there are benefits to considering variation among individuals when designing management plans to support translocations.

Motion-activated cameras ('camera-traps') have become indispensable for wildlife monitoring. Data from camera-trap surveys can be used to make inferences about animal behaviour, space use and population dynamics. Occupancy modelling is a statistical framework commonly used to analyse camera-trap data, which estimates species occurrence while accounting for imperfect detection. Including covariates in models enables the investigation of relationships between occupancy and the environment. Survey design studies help practitioners decide the number of cameras to deploy, deployment duration and camera positioning. However, existing assessments have generally assumed constant occupancy and detectability (i.e. no covariates were considered), which is unrealistic for most real-world scenarios. We investigated the effects of covariates on the relationship between survey effort and the combination of accuracy and precision (i.e. error) of occupancy models. Camera-trap data for a 'virtual' species were simulated as a function of randomly generated, site- and survey-specific covariates (e.g. habitat type/quality and temperature, respectively). We then assessed how varying survey design and total effort influenced estimation error with and without covariate information. Increasing the number of cameras consistently reduced error, while longer deployments were only beneficial when the covariate influenced occupancy. When both parameters were affected by covariates, omitting effects on detectability had limited impact on model performance. However, failing to account for effects on occupancy significantly increased error, and none of the predefined thresholds (root mean squared error = 0.15, 0.10 and 0.075) were achievable, even with the maximum survey effort of 9000 camera-days. These results suggest that increasing survey effort is unlikely to improve model performance unless site-level conditions are appropriately modelled. Thus, robust study design should consider total effort and the monitoring of covariates across sites to ensure efficient use of time and financial resources.

Individual animal’s perception of risk can alter how it navigates a landscape altered by anthropogenic and natural disturbances. As perception depends on experience, we should expect habitat selection to be context dependent and individualistic. We hypothesized that: (i) fine-scale habitat selection of fisher (Pekania pennanti) in a human dominated landscape is driven by multiple interacting spatio-temporal factors; and (ii) an individual’s response to these factors depend on their exposure to anthropogenic disturbance within their home range (i.e., functional response). We used fine-scale GPS location data of fisher in step-selection functions to make inference on the effects of human development, habitat loss, and road risk on fisher habitat selection. We found fisher habitat selection is individualistic, spatio-temporally dependent and a function of their exposure to anthropogenic disturbance in their home range. Fisher selected areas of lower road risk more frequently relative to availability, particularly during daylight hours. Higher road risk areas were only used more frequently when they were available at night. With a higher human land use in their home ranges fisher selected space near roads at night only, however when the extent of human use in their home range was lower, they selected areas further from roads at all times. Our study shows how individual variability allows fisher to adapt their diel activity to utilize resources in areas of high human land use. This further emphasizes the importance of accounting for individuality and multiple interacting spatio-temporal factors in habitat selection, particularly in highly human modified landscapes.

Monitoring wildlife populations through the collection of abundance and distribution data across climatic seasons and multiple years is critical to understanding wildlife spatiotemporal dynamics. This is especially important in landscapes faced with natural and anthropogenic disturbances, which include the state of Rhode Island, USA. Rhode Island is the second most densely populated state in the United States, yet the landscape remains highly forested. Similar to many areas in the region, land cover change and conversion to non-habitat cover types continue to be an issue as a result of increased anthropogenic disturbance, in addition to recent natural disturbance including forest structural changes from the spongy moth caterpillar (Lymantria dispar). These changes in land cover types and landscape patterns have the potential to positively or negatively affect wildlife communities, and thus, it is increasingly important to monitor wildlife populations. Camera traps provide an efficient way to inventory and monitor a large spatial area and record detections of a wide variety of terrestrial vertebrates. We began surveying the state of Rhode Island as part of a focal study on bobcats (Lynx rufus, 2018-2020) and later fishers (Pekania pennanti, 2020-2023) while documenting all species of terrestrial vertebrates detected at camera survey locations. We placed cameras in areas with land cover appropriate for the original target species-primarily forests and forested wetlands-and avoided placing cameras directly along hiking trails or roads. The state was divided into two sections-west and east-to maximize study area coverage with limited equipment. Cameras were deployed for at least six weeks in each survey period and section. In total, we monitored 249 survey sites in the state over 12 survey periods (six winter seasons, five summer seasons, and one spring season). We collected 244,013 unique detections from 39 terrestrial vertebrate species (25 mammal species, 13 bird species, and non-personnel humans) throughout the study. These data provide spatial and temporal detection information that is useful for investigating the changes in wildlife populations over time and varying degrees of development through analyses including single species, multi-species, dynamic, and diel occupancy modeling. Results of these analyses can be used to understand how a changing landscape impacts wildlife species. The data are openly available for reuse, and please cite this data paper when these data are used in other materials.

Circadian rhythms are a mechanism by which species adapt to environmental variability and fundamental to understanding species behavior. However, we lack data and a standardized framework to accurately assess and compare temporal activity for species during rapid ecological change. Through a global network representing 38 countries, we leveraged 8.9 million mammalian observations to create a library of 14,587 standardized diel activity estimates for 445 species. We found that less than half the species' estimates were in agreement with diel classifications from the reference literature and that species commonly used more than one diel classification. Species diel activity was highly plastic when exposed to anthropogenic change. Furthermore, body size and distributional extent were strongly associated with whether a species is diurnal or nocturnal. Our findings provide essential knowledge of species behavior in an era of rapid global change and suggest the need for a new, quantitative framework that defines diel activity logically and consistently while capturing species plasticity.

Effective conservation requires an understanding of drivers of a species' distribution as well as long-term changes in their distribution. In recent decades, advances in data collection and analysis have allowed researchers to integrate a wide range of information to model species distributions, particularly by allowing presence-only data and detection-nondetection data to be formally combined in integrated species distribution models (ISDMs). However, these models are rarely used to investigate long-term trends, which are important in evaluating a species' status. Here, we use historical presence-only data of river otters (Lontra canadensis; 366 latrine locations from 1999 to 2007 and 105 locations of road-killed individuals recorded from 1999 to 2020) and 919 detection-nondetection surveys from 230 sites between 2021 and 2023 to understand the current distribution of river otters in Rhode Island, USA, as well as the changes in river otter distribution over the past two decades. We found that river otters were strongly associated with key habitat features such as streams and water, positively associated with urban areas, and tolerant of some contaminants, such as lead. Furthermore, despite uncertainties in historical river otter occurrence, we found clear supporting evidence that river otter intensity of use had declined from 1999 to 2023. This decline occurred despite being protected from harvest and in contrast to range expansions in other parts of the northeastern USA throughout the second half of the 20th century. Our results suggest the utility of this approach to detect declines in species for which historical data are available and a need for better understanding the cause of river otter declines. Where monitoring consists of opportunistically collected data, species conservation could benefit by continuing to collect these data as well as introducing designed surveys, as this would allow better integration of data types, improving trend estimation and reducing the amount of (typically more expensive) designed surveys needed.

Animal habitat selection is the process of how individual organisms disproportionately use habitat compared to what is available to them. Understanding habitat selection is important for the study of ecology and conservation. However, learning the foundations of making inference or prediction on animal habitat selection can be quite challenging. Foremost, the literature is large and highly technical. We summarize important considerations in getting the basics right, pointing to key papers in the modern literature. We also demonstrate many of these considerations in an online vignette and associated code. We hope this work will help jumpstart student and practitioner learning about habitat selection modeling, provide guidance when reviewing analyses, and lead to rigorous ecological studies that help guide the management and conservation of animal populations.

ABSTRACTMotivationSNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns.Main Types of Variables ContainedSNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata.Spatial Location and GrainData were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems.Time Period and GrainData were collected between 1st August and 29th December each year from 2019 to 2023.Major Taxa and Level of MeasurementThe dataset includes a wide range of taxa but is primarily focused on medium to large mammals.Software FormatSNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

Animals alter their diel activity in response to physiological constraints and ecological conditions. Fisher (Pekania pennanti) activity is known to vary through the diel cycle and change in response to cold stress and generally through both the climatic and biological seasons. However, less is known whether thermoregulatory effects impact fisher activity in milder climates and in areas of high human disturbance. We focused on two distinct research objectives to understand the 1) physiological constraints, and 2) ecological components of fisher activity in a highly disturbed landscape with a relatively mild climate. We used accelerometer data from 34 individual live-captured fisher in Rhode Island, USA from 2021 to 2023. We found that fisher activity patterns were primarily driven by diel cycle with higher activity levels at night than during the day. We did not observe any physiological influence of ambient temperature on fisher activity; daily minimum temperatures did not constrain fisher activity in the colder months, nor did daily maximum temperatures in warmer months. We did find that female activity levels differed by breeding status with non-pregnant females having higher activity levels than pregnant females. Considering ecological components, we found fisher decreased activity levels in higher road density areas during warmer months that coincide with higher traffic volumes. For fisher living in areas with lower road densities, we saw higher activity in the breeding season and summer than in winter. In contrast, fisher living in areas with high road densities had lower activity in the breeding season and summer than in winter. We conclude that fisher largely do not shift their activity to mitigate thermoregulatory costs in areas where temperatures do not reach extremes for extended periods of time. However, our findings suggest that behavioral shifts in activity are impacted by human disturbance and fisher minimize activity in risky areas.

AbstractHuman dimensions research is valuable to managing human‐wildlife interactions, especially in urban environments where such interactions are common. Survey data, which commonly contain Likert scales and questions, are useful in this field; however, these data can be difficult to analyze with formal modeling approaches. We demonstrate one approach, based on hierarchical Bayesian ordinal regression, to evaluate human‐coyote relationships in Rhode Island, USA. We implemented a survey to collect demographic and sociocultural characteristics of Rhode Island residents and information related to their knowledge of and experiences with coyotes. Our objectives were to assess how these characteristics affected respondents' valuation of and interactions (sightings and incidents) with coyotes. We analyzed 980 surveys from October to December 2020. We found that respondents who had fear of coyotes or experienced an incident between an owned animal and coyote, had the lowest valuation of coyotes. The same demographic of respondents also reported the highest sightings of and incidents with coyote. These results indicate that fearful residents, in addition to pet and livestock owners, are priority targets for disseminating information or programming about coyotes. Our analyses and findings demonstrate how Bayesian ordinal regression can provide clear and appropriate inference from survey data on how groups of people vary in their relationship with wildlife. These results are important in effectively and efficiently allocating resources towards mitigation, education, and management of human‐wildlife interactions.

Spatial heterogeneity in the local densities of terrestrial carnivores is driven by multiple interacting biotic and abiotic factors. Space-use patterns of large carnivores reflect the competing demands of resource selection (e.g., exploitation of habitats with abundant prey) and minimization of risks arising from human interactions. Estimating the relative strength of these drivers is essential to understand spatial variation in densities of large carnivores and there are still key knowledge gaps for many large carnivore populations. To better understand the relative roles of environmental and human drivers of spatial variation in tiger (Panthera tigris) densities, we surveyed a 3000 km2 landscape in North India using camera trap data. Over two years, we photo-captured 92 unique adult tigers. Associating spatial covariates with patterns of detection allowed us to test hypotheses about the relative influence of prey abundance, habitat structure and extent, and proximity to habitat edges on spatial variation in tiger densities across a gradient of anthropogenic disturbance. We documented extensive variation in tiger density within and across management units and protected areas. Spatial variation in prey abundance and proximity to grassland habitats, rather than human use (e.g. extent of human-dominated edge habitat and protection status), explained most of the spatial variation in tiger density in two of the five surveyed sites. The region’s largest tiger population occurred in a multi-use forest beyond protected area boundaries, where wild ungulates were abundant. Our results suggest that tigers can occur at high densities in areas with extensive human use, provided sufficiently high prey densities, and tracts of refuge habitats (eg. areas with dense vegetation with low human use). We argue that tiger conservation portfolio can be expanded across multi-use landscapes with a focus on areas that are adaptively managed as “zones of coexistence” and “refuge habitats”. Advancing this conservation strategy is contingent on greatly strengthening systems to effectively and equitably redress human–wildlife conflict and leveraging existing policies to strengthen local participation in conservation planning and forest stewardship. Our insights into the environmental drivers of spatial heterogeneity in tiger populations can inform both local management and guide to species recovery in working landscapes.

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010-2021. Major Taxa Studied: Twenty-five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east-west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.

The proliferation of forest edges and invasive predators have been identified as two primary threats to carnivore populations globally. These threats often occur in unison, facilitated by anthropogenic activities (e.g., fragmentation), and together may pose a greater influence than when they occur separately. Targeted conservation actions for forest carnivores, including Madagascar carnivores, have been hindered by a failure to understand the relative contributions of these factors in driving species declines. To fill this gap, we conducted an extensive camera survey along the edge of intact, continuous protected rainforests in eastern Madagascar to evaluate the extent invasive predators and forest edge separately and in combination affect native carnivore space use. We hypothesized that structural vegetation changes at the forest edge interact with invasive predator trap success and occurrence to reduce native carnivore space use near the forest edge and separately have less influence than when combined. In contrast to findings in fragmented and degraded forests of Madagascar, we found hard forest edge and invasive predators alone do not indiscriminately reduce native carnivore space use in continuous intact forest. Instead, we found free-roaming dogs and cats interact with their surrounding environment (i.e., forest edge) in unique ways that shape species response differently than within interior forest. At the forest edge, vegetational changes of increasing shrub cover and the occurrence of dogs reduce space use of three of four native carnivores. However, we found greater effects of proximity to villages, especially with high invasive predator activity (free-roaming cats). Ultimately, native carnivores showed variable sensitivities to pressures we examined, providing support for species-specific management actions to maximize conservation outcomes. We encourage future studies to consider evaluating the magnitude of separate and combined threats to carnivores. In doing so, conservationists can better identify when threats can be managed in isolation and when they require simultaneous mitigation. Simultaneous pressures from invasive species and habitat degradation should be considered under both independent and interactive scenarios to better understand effects on native species. We found Madagascar carnivores are variable in response with some species showing larger negative effects under a interactive scenario and were dependent on the severity of invasive predator presence. Photo credit: Nick Garbutt.image

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.