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), while 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, while 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.

Occupancy-based methods are commonly used to model the distribution, habitat use, and relative abundance of species. In particular, occupancy-based methods are often recommended for monitoring species, allowing researchers to track population trends using detection-non-detection data alone. Occupancy models, however, have proven difficult to apply to rare, highly mobile species. Species' movements outside of sampling areas may lead to violation of the geographic closure assumption of occupancy models and overestimated occupancy probability. Low detection probability may further inflate occupancy probability estimates. We developed a novel continuous-time, multi-scale occupancy model to simultaneously account for closure assumption violations and low detection probability. We used a simulation study to test our model relative to a discrete-time multi-scale model, and we conducted a power analysis to assess the ability of an instantaneous occupancy parameter to detect trends in abundance, relative to standard occupancy alone. The continuous-time model was competitive with the discrete-time model and was generally computationally faster than and outperformed the discrete-time model when detection probability was low. The instantaneous occupancy parameter outperformed occupancy in terms of power to detect trends when we used an implicit (i.e., estimating occupancy independently in each primary occasion) dynamic occupancy model, but performed no better when we used an explicit (i.e., estimating colonization and extinction) dynamic occupancy model. We applied both discrete-time and continuous-time multi-scale occupancy models to a case study of data collected on wolverines (Gulo gulo) in Washington, USA. We found improved precision in estimates with the continuous-time model and that asymptotic occupancy of wolverines was high, but short-term use of any given area was low. Our multi-scale, continuous-time occupancy model can be used to detect trends in abundance of rare, highly mobile species, regardless of how occupancy dynamics are modeled. Furthermore, our model can allow for more efficient data collection and analysis than traditional discrete-time or spatially multi-scale approaches, as our model uses all available detections and requires only one detector per sampling unit by substituting time for space.

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.

The management of North American waterfowl is predicated on long‐term, continental‐scale banding implemented prior to the hunting season (i.e., July–September) and subsequent reporting of bands recovered by hunters. However, single‐season banding and encounter operations have a number of characteristics that limit their application to estimating demographic rates and evaluating hypothesized limiting factors throughout the annual cycle. We designed and implemented a two‐season banding program for American Black Ducks (Anas rubripes), Mallards (A. platyrhynchos), and hybrids in eastern North America to evaluate potential application to annual life cycle conservation and sport harvest management. We assessed model fit and compared estimates of annual survival among data types (i.e., pre‐hunting season only [July–September], post‐hunting season only [January–March], and two‐season [pre‐ and post‐hunting season]) to evaluate model assumptions and potential application to population modeling and management. There was generally high agreement between estimates of annual survival derived using two‐season and pre‐season only data for all age and sex cohorts. Estimates of annual survival derived from post‐season banding data only were consistently higher for adult females and juveniles of both sexes. We found patterns of seasonal survival varied by species, age, and to a lesser extent, sex. Hunter recovered birds exhibited similar spatial distributions regardless of banding season suggesting banded samples were from the same population. In contrast, goodness‐of‐fit tests suggest this assumption was statistically violated in some regions and years. We conclude that estimates of seasonal and annual survival for Black Ducks and Mallards based on the two‐season banding program are valid and accurate based on model fit statistics, similarity in survival estimates across data and models, and similarities in the distribution of recoveries. The two‐season program provides greater precision and insight into the survival process and will improve the ability of researchers and managers to test competing hypotheses regarding population regulation resulting in more effective management.

Climate change poses significant challenges to protected area management globally. Anticipatory climate adaptation planning relies on vulnerability assessments that identify parks and resources at risk from climate change and associated vulnerability drivers. However, there is currently little understanding of where and how protected area assessments have been conducted and what assessment approaches best inform park management. To address this knowledge gap, we systematically evaluated climate-change vulnerability assessments of natural resources in U.S. National Parks. We categorized the spatial scale, resources, methods, and handling of uncertainty for each assessment and mapped which parks have assessments and for what resources. We found that a few broad-scale assessments provide baseline information-primarily regarding physical climate change exposure-for all parks and can support regional to national decisions. However, finer-scale assessments are required to inform decisions for individual or small groups of parks. Only 10% of parks had parkspecific assessments describing key climate impacts and identifying priority resource vulnerabilities, and 37% lacked any regional or park-specific assessments. We identify assessment approaches that match the scale and objectives of different protected area management decisions and recommend a multiscaled approach to implementing assessments to meet the information needs of a large, protected area network like the National Park system.