A spatial open‐population capture‐recapture model

Toxicological effects assessment for wildlife in the 21st century: Review of current methods and recommendations for a path forward

Model species (e.g., granivorous gamebirds, waterfowl, passerines, domesticated rodents) have been used for decades in guideline laboratory tests to generate survival, growth, and reproductive data for prospective ecological risk assessments (ERAs) for birds and mammals, while officially adopted risk assessment schemes for amphibians and reptiles do not exist. There are recognized shortcomings of current in vivo methods as well as uncertainty around the extent to which species with different life histories (e.g., terrestrial amphibians, reptiles, bats) than these commonly used models are protected by existing ERA frameworks. Approaches other than validating additional animal models for testing are being developed, but the incorporation of such new approach methodologies (NAMs) into risk assessment frameworks will require robust validations against in vivo responses. This takes time, and the ability to extrapolate findings from nonanimal studies to organism- and population-level effects in terrestrial wildlife remains weak. Failure to adequately anticipate and predict hazards could have economic and potentially even legal consequences for regulators and product registrants. In order to be able to use fewer animals or replace them altogether in the long term, vertebrate use and whole organism data will be needed to provide data for NAM validation in the short term. Therefore, it is worth investing resources for potential updates to existing standard test guidelines used in the laboratory as well as addressing the need for clear guidance on the conduct of field studies. Herein, we review the potential for improving standard in vivo test methods and for advancing the use of field studies in wildlife risk assessment, as these tools will be needed in the foreseeable future. Integr Environ Assess Manag 2024;20:699-724. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Comparative spatially explicit approach for testing effects of soil chemicals on terrestrial wildlife bioindicator demographics

Wildlife species are often used as bioindicators to evaluate the extent and severity of environmental contamination and the effectiveness of remediation practices. A common approach for investigating population- or community-level impacts on bioindicators compares demographic parameter estimates (e.g., population size or density) between sites that were subjected to different levels of contamination. However, the traditional analytical method used in such studies is nonspatial capture-recapture, which results in conclusions about potential relationships between demographics and contaminants being inferred indirectly. Here, we extend this comparative approach to the spatially explicit framework, allowing direct estimation of said relationships and comparisons between study areas, by applying spatial capture-recapture (SCR) models to bioindicator (deer mice [Peromyscus spp.]) detection data from two study areas that were subjected to different industrial activities and remediation practices. Bioindicator density differed by 178% between the neighboring study areas, and the area with the highest soil concentrations of polychlorinated biphenyls, chromium, and zinc had the highest bioindicator density. Under the traditional nonspatial approach, we might have concluded that soil chemical levels had negligible influences on demographics. However, by modeling density as a spatial function of select chemical concentrations using SCR models, we found strong support for a positive relationship between density and soil chromium concentrations in one study area (β = 0.82), which was not masked by or associated with habitat-related metrics. To obtain reliable inferences about potential effects of environmental contamination on bioindicator demographics, we contend that a comparative spatially explicit approach using SCR ought to become standard.

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.

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.

Pushing the limits: ship rat (Rattus rattus) population dynamics across an elevational gradient in response to mast seeding and supplementary feeding

Understanding marginal habitat use by invasive species is important for predicting how distributions may change under future climates. We investigated the influence of food availability and temperature on ship rat (Rattus rattus) distribution and density across a forested elevational gradient in New Zealand by measuring ship rat demographics following a beech (Nothofagaceae) mass seeding event (‘mast’) at three elevation bands (20–80, 400–500, 800–900 m asl). We tested whether declining food availability limits rat populations at the highest elevation band post-mast by experimentally increasing food abundance above baseline food availability. When our study started 4 months post-seedfall, rats at mid- and low- elevations were at high densities (11.4–16.5 ha⁻¹). Rats at higher elevations were barely detectable, but densities peaked (9.4 ha⁻¹) 10 months post-seedfall, with the initial increase possibly driven by immigration from lower elevations. All populations declined sharply over the next year. Supplementary feeding at high elevation increased survival, recruitment, and density of rats through winter, 16 months post-seedfall, relative to unfed grids, suggesting food limitation. However, both fed and non-fed populations declined to zero by the following spring, perhaps due to stoat (Mustela erminea) predation. Our results suggest that low food availability plays a significant role in restricting rats from cool, high elevation environments. The variation in the timing and magnitude of ship rat responses to the pulsed resource across the gradient also highlights the importance of initial population size and spatial processes as factors modulating ship rat responses to pulsed resources across a landscape.

Monitoring partially marked populations using camera and telemetry data

Long-term monitoring is an important component of effective wildlife conservation. However, many methods for estimating density are too costly or difficult to implement over large spatial and temporal extents. Recently developed spatial mark-resight (SMR) models are increasingly being applied as a cost-effective method to estimate density when data include detections of both marked and unmarked individuals. We developed a generalized SMR model that can accommodate long-term camera data and auxiliary telemetry data for improved spatiotemporal inference in monitoring efforts. The model can be applied in two stages, with detection parameters estimated in the first stage using telemetry data and camera detections of instrumented individuals. Density is estimated in the second stage using camera data, with all individuals treated as unmarked. Serial correlation in detection and density parameters is accounted for using time-series models. The two-stage approach reduces computational demands and facilitates the application to large data sets from long-term monitoring initiatives. We applied the model to 3 years (2015-2017) of white-tailed deer (Odocoileus virginianus) data collected in three study areas of the Big Cypress Basin, Florida, USA. In total, 59 females marked with ear tags and fitted with GPS-telemetry collars were detected along with unmarked females on 180 remote cameras. Most of the temporal variation in density was driven by seasonal fluctuations, but one study area exhibited a slight population decline during the monitoring period. Modern technologies such as camera traps provide novel possibilities for long-term monitoring, but the resulting massive data sets, which are subject to unique sources of observation error, have posed analytical challenges. The two-stage spatial mark-resight framework provides a solution with lower computational demands than joint SMR models, allowing for easier implementation in practice. In addition, after detection parameters have been estimated, the model may be used to estimate density even if no synchronous auxiliary information on marked individuals is available, which is often the case in long-term monitoring.

Agent-based models reveal limits of mark-release-recapture estimates for the rare butterfly, Bhutanitis thaidina (Lepidoptera: Papilionidae)

Insect diversity and abundance are in drastic decline worldwide, but quantifying insect populations to better conserve them is a difficult task. Mark-release-recapture (MRR) is widely used as an ecological indicator for insect populations, but the accuracy of MRR estimates can vary with factors such as spatial scale, sampling effort and models of inference. We conducted a 3-year MRR study of B. thaidina in Yanzigou valley, Mt. Gongga but failed to obtain sufficient data for a robust population estimate. This prompted us to integrate B. thaidina life history information to parameterize agent-based models and evaluate the conditions under which successful MRR studies could be conducted. We evaluated: (1) the performance of MRR models under different landscape types, and (2) the influence of experimental design on the accuracy and variance of MRR-based estimates. Our simulations revealed systematic underestimates of true population parameters by MRR models when sampling effort was insufficient. In a total of 2772 simulations, subjective decisions in sampling protocol (e.g., frequency, number of sampling locations, use of spatially explicit models, type of estimands) accounted for nearly half of the variation in estimates. We conclude that MRR-based estimates could be improved with the addition of more field-specific parameters.

Orientation and emigration of larval and juvenile amphibians: selected topics and hypotheses

Most amphibians have a complex life cycle with an aquatic larval and an adult (semi-) terrestrial stage. However, studies concerning spatial behaviour and orientation mainly focus on either the aquatic larvae or the adult animals on land. Consequently, behavioural changes that happen during metamorphosis and the consequences for emigration and population distribution are less understood. This paper aims to summarize the knowledge concerning specific topics of early amphibian life history stages and proposes several testable hypotheses within the following fields of research: larval and juvenile orientation, influences of environmental and genetic factors on juvenile emigration, their habitat choice later in life as well as population biology. I argue that studying larval and juvenile amphibian spatial behaviour is an understudied field of research, however, could considerably improve our understanding of amphibian ecology.

A review of spatial capture-recapture: Ecological insights, limitations, and prospects

First described by Efford (2004), spatial capture-recapture (SCR) has become a popular tool in ecology. Like traditional capture-recapture, SCR methods account for imperfect detection when estimating ecological parameters. In addition, SCR methods use the information inherent in the spatial configuration of individual detections, thereby allowing spatially explicit estimation of population parameters, such as abundance, survival, and recruitment. Paired with advances in noninvasive survey methods, SCR has been applied to a wide range of species across different habitats, allowing for population- and landscape-level inferences with direct consequences for conservation and management. I conduct a literature review of SCR studies published since the first description of the method and provide an overview of their scope in terms of the ecological questions answered with this tool, taxonomic groups targeted, geography, spatio-temporal extent of analyses, and data collection methods. In addition, I review approaches for analytical implementation and provide an overview of parameters targeted by SCR studies and conclude with current limitations and future directions in SCR methods.

Density and habitat use of one of the last jaguar populations of the Brazilian Atlantic Forest: Is there still hope?

The jaguar (Panthera onca) plays an important role in maintaining biodiversity and ecological processes. We evaluated the status of a jaguar population in one of the last stronghold habitats for its conservation in the Atlantic Forest, the Rio Doce State Park (RDSP). We used a random survey design from 2016/17 to estimate jaguar abundance and density as well as its occupancy and detection probabilities in the entire Park's area. To monitor for temporal fluctuations in density and abundance, we used a systematic survey design in the southern portion of the Park where jaguars were more recorded when using the random approach. We then conducted two surveys in 2017/18 and 2020. Our 2016/17 random survey revealed that jaguar density (0.11 ± SE 0.28 individuals/100 km2) was the lowest obtained for the species across the Atlantic Forest. We noticed that jaguar density increased three times from 2017/18 (0.55 ± SE 0.45 individuals/100 km2) to 2020 (1.61 ± SE 0.6 individuals/100 km2). Jaguar occupancy and detection probability were 0.40 and 0.08, respectively. The low jaguar occupancy probability was positively associated with smaller distances from lakes and records of potential prey. The detection probability was positively associated with prey detection, the rainy season, and smaller distances from lakes. Our work contributes to a growing awareness of the potential conservation value of a protected area in a human-dominated landscape as one of the last strongholds for jaguars across the Atlantic Forest.

Movement-assisted localization from acoustic telemetry data

BACKGROUND

Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., "localization"). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays.

METHODS

In this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies.

RESULTS

Simulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by >50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models.

CONCLUSIONS

Movement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.