White-tailed deer population dynamics in a multipredator landscape shaped by humans.

Large terrestrial mammals increasingly rely on human-modified landscapes as anthropogenic footprints expand. Land management activities such as timber harvest, agriculture, and roads can influence prey population dynamics by altering forage resources and predation risk via changes in habitat, but these effects are not well understood in regions with diverse and changing predator guilds. In northeastern Washington state, USA, white-tailed deer (Odocoileus virginianus) are vulnerable to multiple carnivores, including recently returned gray wolves (Canis lupus), within a highly human-modified landscape. To understand the factors governing predator-prey dynamics in a human context, we radio-collared 280 white-tailed deer, 33 bobcats (Lynx rufus), 50 cougars (Puma concolor), 28 coyotes (C. latrans), and 14 wolves between 2016 and 2021. We first estimated deer vital rates and used a stage-structured matrix model to estimate their population growth rate. During the study, we observed a stable to declining deer population (lambda = 0.97, 95% confidence interval: 0.88, 1.05), with 74% of Monte Carlo simulations indicating population decrease and 26% of simulations indicating population increase. We then fit Cox proportional hazard models to evaluate how predator exposure, use of human-modified landscapes, and winter severity influenced deer survival and used these relationships to evaluate impacts on overall population growth. We found that the population growth rate was dually influenced by a negative direct effect of apex predators and a positive effect of timber harvest and agricultural areas. Cougars had a stronger effect on deer population dynamics than wolves, and mesopredators had little influence on the deer population growth rate. Areas of recent timber harvest had 55% more forage biomass than older forests, but horizontal visibility did not differ, suggesting that timber harvest did not influence predation risk. Although proximity to roads did not affect the overall population growth rate, vehicle collisions caused a substantial proportion of deer mortalities, and reducing these collisions could be a win-win for deer and humans. The influence of apex predators and forage indicates a dual limitation by top-down and bottom-up factors in this highly human-modified system, suggesting that a reduction in apex predators would intensify density-dependent regulation of the deer population owing to limited forage availability.

Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring.

Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018-2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.

Integrating multiple data sources in species distribution modeling: a framework for data fusion.

The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

The ecology of religious beliefs.

Although ecological forces are known to shape the expression of sociality across a broad range of biological taxa, their role in shaping human behavior is currently disputed. Both comparative and experimental evidence indicate that beliefs in moralizing high gods promote cooperation among humans, a behavioral attribute known to correlate with environmental harshness in nonhuman animals. Here we combine fine-grained bioclimatic data with the latest statistical tools from ecology and the social sciences to evaluate the potential effects of environmental forces, language history, and culture on the global distribution of belief in moralizing high gods (n = 583 societies). After simultaneously accounting for potential nonindependence among societies because of shared ancestry and cultural diffusion, we find that these beliefs are more prevalent among societies that inhabit poorer environments and are more prone to ecological duress. In addition, we find that these beliefs are more likely in politically complex societies that recognize rights to movable property. Overall, our multimodel inference approach predicts the global distribution of beliefs in moralizing high gods with an accuracy of 91%, and estimates the relative importance of different potential mechanisms by which this spatial pattern may have arisen. The emerging picture is neither one of pure cultural transmission nor of simple ecological determinism, but rather a complex mixture of social, cultural, and environmental influences. Our methods and findings provide a blueprint for how the increasing wealth of ecological, linguistic, and historical data can be leveraged to understand the forces that have shaped the behavior of our own species.

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model.

Proposed offshore wind energy development on the Atlantic Outer Continental Shelf has brought attention to the need for baseline studies of the distribution and abundance of marine birds. We compiled line transect data from 15 shipboard surveys (June 2012-April 2014), along with associated remotely sensed habitat data, in the lower Mid-Atlantic Bight off the coast of Delaware, Maryland, and Virginia, USA. We implemented a recently developed hierarchical community distance sampling model to estimate the seasonal abundance of 40 observed marine bird species. Treating each season separately, we included six oceanographic parameters to estimate seabird abundance: three static (distance to shore, slope, sediment grain size) and three dynamic covariates (sea surface temperature [SST], salinity, primary productivity). We expected that avian bottom-feeders would respond primarily to static covariates that characterize seafloor variability, and that surface-feeders would respond more to dynamic covariates that quantify surface productivity. We compared the variation in species-specific and community-level responses to these habitat features, including for rare species, and we predicted species abundance across the study area. While several protected species used the study area in summer during their breeding season, estimated abundance and observed diversity were highest for nonbreeding species in winter. Distance to shore was the most common significant predictor of abundance, and thus useful in estimating the potential exposure of marine birds to offshore development. In many cases, our expectations based on feeding ecology were confirmed, such as in the first winter season, when bottom-feeders associated significantly with the three static covariates (distance to shore, slope, and sediment grain size), and surface-feeders associated significantly with two dynamic covariates (SST, primary productivity). However, other cases revealed significant relationships between static covariates and surface-feeders (e.g., distance to shore) and between dynamic covariates and bottom-feeders (e.g., primary productivity during that same winter). More generally, we found wide interannual, seasonal, and interspecies variation in habitat relationships with abundance. These results show the importance of quantifying detection and determining the ecological drivers of a community's distribution and abundance, within and among species, for evaluating the potential exposure of marine birds to offshore development.

An open-population hierarchical distance sampling model.

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for Island Scrub-Jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying numbers of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring.

Use of multispecies occupancy models to evaluate the response of bird communities to forest degradation associated with logging.

Forest degradation is arguably the greatest threat to biodiversity, ecosystem services, and rural livelihoods. Therefore, increasing understanding of how organisms respond to degradation is essential for management and conservation planning. We were motivated by the need for rapid and practical analytical tools to assess the influence of management and degradation on biodiversity and system state in areas subject to rapid environmental change. We compared bird community composition and size in managed (ejido, i.e., communally owned lands) and unmanaged (national park) forests in the Sierra Tarahumara region, Mexico, using multispecies occupancy models and data from a 2-year breeding bird survey. Unmanaged sites had on average higher species occupancy and richness than managed sites. Most species were present in low numbers as indicated by lower values of detection and occupancy associated with logging-induced degradation. Less than 10% of species had occupancy probabilities >0.5, and degradation had no positive effects on occupancy. The estimated metacommunity size of 125 exceeded previous estimates for the region, and sites with mature trees and uneven-aged forest stand characteristics contained the highest species richness. Higher estimation uncertainty and decreases in richness and occupancy for all species, including habitat generalists, were associated with degraded young, even-aged stands. Our findings show that multispecies occupancy methods provide tractable measures of biodiversity and system state and valuable decision support for landholders and managers. These techniques can be used to rapidly address gaps in biodiversity information, threats to biodiversity, and vulnerabilities of species of interest on a landscape level, even in degraded or fast-changing environments. Moreover, such tools may be particularly relevant in the assessment of species richness and distribution in a wide array of habitats.

A spatial mark--resight model augmented with telemetry data.

Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark--resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad hoc approaches. We developed a spatial mark--resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark--resight study of raccoons (Procyon lotor) on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina, USA. We estimated a population of 186.71 +/- 14.81 individuals, which translated to a density of 8.29 +/- 0.66 individuals/km2 (mean +/- SD). The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

Integrated animal movement and spatial capture-recapture models: Simulation, implementation, and inference.

Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies - individual animals observed at specific locations and times - can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional "building blocks" of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.

A spatial capture-recapture model for group-living species.

Group living in species can have complex consequences for individuals, populations, and ecosystems. Therefore, estimating group density and size is often essential for understanding population dynamics, interspecific interactions, and conservation needs of group-living species. Spatial capture-recapture (SCR) has been used to model both individual and group density in group-living species, but modeling either individual-level or group-level detection results in different biases due to common characteristics of group-living species, such as highly cohesive movement or variation in group size. Furthermore, no SCR method currently estimates group density, individual density, and group size jointly. Using clustered point processes, we developed a cluster SCR model to estimate group density, individual density, and group size. We compared the model to standard SCR models using both a simulation study and a data set of detections of African wild dogs (Lycaon pictus), a group-living carnivore, on camera traps in northern Botswana. We then tested the model's performance under various scenarios of group movement in a separate simulation study. We found that the cluster SCR model outperformed a standard group-level SCR model when fitted to data generated with varying group sizes, and mostly recovered previous estimates of wild dog group density, individual density, and group size. We also found that the cluster SCR model performs better as individuals' movements become more correlated with their groups' movements. The cluster SCR model offers opportunities to investigate ecological hypotheses relating group size to population dynamics while accounting for cohesive movement behaviors in group-living species.

An integrated path for spatial capture-recapture and animal movement modeling.

Ecologists and conservation biologists increasingly rely on spatial capture-recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.

Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals.

Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat (Felis silvestris) from cheek-rub lure sticks, extracted DNA from the samples, and identified each animals' genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture-recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km² (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home-range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap- and individual-level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture-recapture models will improve population assessments, especially for rare and elusive animals.

Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies.

We develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture-recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual-based formulation of a Jolly-Seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.

Modeling spatial variation in avian survival and residency probabilities.

The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both phi and pi among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush phi and pi that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.

Distribution patterns of wintering sea ducks in relation to the North Atlantic Oscillation and local environmental characteristics.

Twelve species of North American sea ducks (Tribe Mergini) winter off the eastern coast of the United States and Canada. Yet, despite their seasonal proximity to urbanized areas in this region, there is limited information on patterns of wintering sea duck habitat use. It is difficult to gather information on sea ducks because of the relative inaccessibility of their offshore locations, their high degree of mobility, and their aggregated distributions. To characterize environmental conditions that affect wintering distributions, as well as their geographic ranges, we analyzed count data on five species of sea ducks (black scoters Melanitta nigra americana, surf scoters M. perspicillata, white-winged scoters M. fusca, common eiders Somateria mollissima, and long-tailed ducks Clangula hyemalis) that were collected during the Atlantic Flyway Sea Duck Survey for ten years starting in the early 1990s. We modeled count data for each species within ten-nautical-mile linear survey segments using a zero-inflated negative binomial model that included four local-scale habitat covariates (sea surface temperature, mean bottom depth, maximum bottom slope, and a variable to indicate if the segment was in a bay or not), one broad-scale covariate (the North Atlantic Oscillation), and a temporal correlation component. Our results indicate that species distributions have strong latitudinal gradients and consistency in local habitat use. The North Atlantic Oscillation was the only environmental covariate that had a significant (but variable) effect on the expected count for all five species, suggesting that broad-scale climatic conditions may be directly or indirectly important to the distributions of wintering sea ducks. Our results provide critical information on species-habitat associations, elucidate the complicated relationship between the North Atlantic Oscillation, sea surface temperature, and local sea duck abundances, and should be useful in assessing the impacts of climate change on seabirds.

Hierarchical models for estimating density from DNA mark-recapture studies.

Genetic sampling is increasingly used as a tool by wildlife biologists and managers to estimate abundance and density of species. Typically, DNA is used to identify individuals captured in an array of traps (e.g., baited hair snares) from which individual encounter histories are derived. Standard methods for estimating the size of a closed population can be applied to such data. However, due to the movement of individuals on and off the trapping array during sampling, the area over which individuals are exposed to trapping is unknown, and so obtaining unbiased estimates of density has proved difficult. We propose a hierarchical spatial capture-recapture model which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to (via movement) and detection by traps. Detection probability is modeled as a function of each individual's distance to the trap. We applied this model to a black bear (Ursus americanus) study conducted in 2006 using a hair-snare trap array in the Adirondack region of New York, USA. We estimated the density of bears to be 0.159 bears/km2, which is lower than the estimated density (0.410 bears/km2) based on standard closed population techniques. A Bayesian analysis of the model is fully implemented in the software program WinBUGS.

State space and movement specification in open population spatial capture-recapture models.

With continued global changes, such as climate change, biodiversity loss, and habitat fragmentation, the need for assessment of long-term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture-recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture-recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models, and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state-space definition and between-primary-period movement models on demographic parameter estimation. We demonstrate the implications on a 10-year camera-trap study of tigers in India. The results of our simulation study show that movement biases survival estimates in open SCR models when little is known about between-primary-period movements of animals. The size of the state-space delineation can also bias the estimates of survival in certain cases.We found that both the state-space definition and the between-primary-period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71 to 0.89). In general, we suggest that open SCR models can provide an efficient and flexible framework for long-term monitoring of populations; however, in many cases, realistic modeling of between-primary-period movements is crucial for unbiased estimates of survival and density.

Co-occurrence dynamics of endangered Lower Keys marsh rabbits and free-ranging domestic cats: Prey responses to an exotic predator removal program.

The Lower Keys marsh rabbit (Sylvilagus palustris hefneri) is one of many endangered endemic species of the Florida Keys. The main threats are habitat loss and fragmentation from sea-level rise, development, and habitat succession. Exotic predators such as free-ranging domestic cats (Felis catus) pose an additional threat to these endangered small mammals. Management strategies have focused on habitat restoration and exotic predator control. However, the effectiveness of predator removal and the effects of anthropogenic habitat modifications and restoration have not been evaluated. Between 2013 and 2015, we used camera traps to survey marsh rabbits and free-ranging cats at 84 sites in the National Key Deer Refuge, Big Pine Key, Florida, USA. We used dynamic occupancy models to determine factors associated with marsh rabbit occurrence, colonization, extinction, and the co-occurrence of marsh rabbits and cats during a period of predator removal. Rabbit occurrence was positively related to freshwater habitat and patch size, but was negatively related to the number of individual cats detected at each site. Furthermore, marsh rabbit colonization was negatively associated with relative increases in the number of individual cats at each site between survey years. Cat occurrence was negatively associated with increasing distance from human developments. The probability of cat site extinction was positively related to a 2-year trapping effort, indicating that predator removal reduced the cat population. Dynamic co-occurrence models suggested that cats and marsh rabbits co-occur less frequently than expected under random conditions, whereas co-detections were site and survey-specific. Rabbit site extinction and colonization were not strongly conditional on cat presence, but corresponded with a negative association. Our results suggest that while rabbits can colonize and persist at sites where cats occur, it is the number of individual cats at a site that more strongly influences rabbit occupancy and colonization. These findings indicate that continued predator management would likely benefit endangered small mammals as they recolonize restored habitats.

Estimating wildlife activity curves: comparison of methods and sample size.

Camera traps and radiotags commonly are used to estimate animal activity curves. However, little empirical evidence has been provided to validate whether they produce similar results. We compared activity curves from two common camera trapping techniques to those from radiotags with four species that varied substantially in size (~1 kg-~50 kg), diet (herbivore, omnivore, carnivore), and mode of activity (diurnal and crepuscular). Also, we sub-sampled photographs of each species with each camera trapping technique to determine the minimum sample size needed to maintain accuracy and precision of estimates. Camera trapping estimated greater activity during feeding times than radiotags in all but the carnivore, likely reflective of the close proximity of foods readily consumed by all species except the carnivore (i.e., corn bait or acorns). However, additional analyses still indicated both camera trapping methods produced relatively high overlap and correlation to radiotags. Regardless of species or camera trapping method, mean overlap increased and overlap error decreased rapidly as sample sizes increased until an asymptote near 100 detections which we therefore recommend as a minimum sample size. Researchers should acknowledge that camera traps and radiotags may estimate the same mode of activity but differ in their estimation of magnitude in activity peaks.