Spatial Capture-recapture Methods Research Articles

Low signs of territorial behavior in the Eurasian otter during low-water conditions in a Mediterranean river

The Eurasian otter Lutra lutra is a territorial semi-aquatic carnivore usually found at low densities in rivers, coastal areas, and wetlands. Its diet is based on prey associated with aquatic environments. Mediterranean rivers are highly seasonal, and suffer reduced flow during the summer, resulting in isolated river sections (pools) that sometimes can be left with a minimal amount of water, leading to concentrations of food for otters. To our knowledge, this process, which was known to field naturalists, has not been accurately described, nor have otter densities been estimated under these conditions. In this study, we describe the population size and movements of an aggregation of otters in an isolated pool in the Guadiana River in the Tablas de Daimiel National Park (central Spain), which progressively dried out during the spring–summer of 2022, in a context of low connectivity due to the absence of circulating water in the Guadiana and Gigüela rivers. Using non-invasive genetic sampling of 120 spraints collected along 79.4 km of sampling transects and spatial capture-recapture methods, we estimated the otter density at 1.71 individuals/km of river channel length (4.21 individuals/km2) in a progressively drying river pool, up to five times higher than previously described in the Iberian Peninsula. The movement patterns obtained with the spatial capture-recapture model are not quite different from those described in low density, which seems to indicate a wide home range overlap, with low signs of territoriality.

Abundance estimation of plains zebras via search–encounter sampling

Spatial capture–recapture (SCR) using search–encounter methods estimate population abundance from encounters of individually recognisable animals along an a priori-designated search path. We applied search–encounter SCR methods and photographic sampling to estimate the abundance of plains zebras (Equus quagga) at Telperion and Ezemvelo nature reserves, South Africa. We analysed encounter data by comparing four hazard-function models for the detection process. The abundance estimate under three models was just above 1000 animals (95% credible intervals c. 960, 1220) versus 811 (719, 917) for the remaining model. The former estimates were broadly similar to aerial counts conducted around the same time. Standard deviation in locations around individual activity centres (σmove\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _{move}$$\\end{document}) was c. 0.8 km, with little difference between models. In situations where structured surveys are not possible, the approach presented here has the potential to estimate abundance from opportunistic animal encounters (e.g. generated via citizen science schemes) within an SCR framework.

That's not the Mona Lisa! How to interpret spatial capture-recapture density surface estimates.

Spatial capture-recapture methods are often used to produce density surfaces, and these surfaces are often misinterpreted. In particular, spatial change in density is confused with spatial change in uncertainty about density. We illustrate correct and incorrect inference visually by treating a grayscale image of the Mona Lisa as an activity center intensity or density surface and simulating spatial capture-recapture survey data from it. Inferences can be drawn about the intensity of the point process generating activity centers, and about the likely locations of activity centers associated with the capture histories obtained from a single survey of a single realization of this process. We show that treating probabilistic predictions of activity center locations as estimates of the intensity of the process results in invalid and misleading ecological inferences, and that predictions are highly dependent on where the detectors are placed and how much survey effort is used. Estimates of the activity center density surface should be obtained by estimating the intensity of a point process model for activity centers. Practitioners should state explicitly whether they are estimating the intensity or making predictions of activity center location, and predictions of activity center locations should not be confused with estimates of the intensity.

Distance sampling and spatial capture-recapture for estimating density of Northern Bobwhite

Obtaining accurate density estimates is critical for managers making decisions regarding wildlife populations. A variety of methods have been used to estimate population density, with two of the most common being distance sampling (DS) and spatial capture recapture (SCR). We evaluated precision and bias of density estimators through a simulation study of DS using data collected from either human observer point counts or automated recording units (ARUs), and SCR using trapping data or trapping data augmented with telemetry data. We then fit each of the four models to empirical data collected during autumn 2016–2018 for a population of northern bobwhite Colinus virginianus on Di-Lane Wildlife Management Area in Georgia. Density estimates in our simulation study were relatively unbiased using all four methods but were most accurate for the two SCR methods. Empirical density estimates were similar across methods and years, with annual averages of 0.41 birds per ha in 2016, 0.44 birds per ha in 2017 and 0.31 birds per ha in 2018. In general, both SCR methods were also the most precise using the empirical data, although ARU distance sampling did also produce comparable density estimates and precision values. Although SCR methods performed the best overall, cost and increased labor of these monitoring programs should be evaluated in relation to the relative ease of ARU deployment or point count surveys, which also provided adequate, and often similar, density estimates. Integrating these constraints into a structured decision-making framework will aid managers weighing decisions regarding how to monitor, and ultimately make management decisions.

Sampling variables and their thresholds for the precise estimation of wild felid population density with camera traps and spatial capture–recapture methods

ABSTRACT Robust monitoring, providing information on population status, is fundamental for successful conservation planning. However, this can be hard to achieve for species that are elusive and occur at low densities, such as felids. These are often keystones of functioning ecosystems and are threatened by habitat loss and human persecution. When elusive species can be individually identified by visible characteristics, for example via camera‐trapping, observations of individuals can be used in combination with capture–recapture methods to calculate demographic parameters such as population density. In this context, spatial capture–recapture (SCR) outperforms conventional non‐spatial methods, but the precision of results is inherently related to the sampling design, which should therefore be optimised. We focussed on territorial felids in different habitats and investigated how the sampling designs implemented in the field affected the precision of population density estimates. We examined 137 studies that combined camera‐trapping and SCR methods for density estimation. From these, we collected spatiotemporal parameters of their sampling designs, monitoring results, such as the number of individuals captured and the number of recaptures, as well as SCR detection parameters. We applied generalised linear mixed‐effects models and tree‐based regression methods to investigate the influence of variables on the precision of population density estimates and provide numerical thresholds. Our analysis shows that the number of individuals, recapture frequency, and capture probability play the most crucial roles. Surveys yielding over 20 captured individuals that were recaptured on average at least once obtain the most precise population density estimates. Based on our findings, we provide practical guidelines for future SCR studies that apply to all territorial felids. Furthermore, we present a standardised reporting protocol for study transparency and comparability. Our results will improve reporting and reproducibility of SCR studies and aid in setting up optimised sampling designs.

An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data

AbstractNon‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions. We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space‐use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois (Rupicapra rupicapra), red deer (Cervus elaphus), and wild boar (Sus scrofa). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of unaccounted individual heterogeneity in space use. The degree to which abundance estimates were underestimated depended mostly on the amount of variation in space use and detectability among age classes. This bias was also accompanied by a reduction in precision and coverage probability of the SCR estimators. We discuss the implications of these findings, possible approaches to identify problematic violations in available data sets (goodness‐of‐fit tests), and potential further developments of SCR models to ensure reliable abundance estimates for ungulate populations from NGS data.

The Use of Camera Traps and Auxiliary Satellite Telemetry to Estimate Jaguar Population Density in Northwestern Costa Rica.

Simple SummaryWe used data from a grid of camera traps, combined with satellite telemetry data from one female jaguar, to estimate jaguar population density in northwestern Costa Rica. Our estimate of 2.6 females and 5.0 males per 100 km2 was influenced by camera placement and sex of the jaguar, and indicated the importance of telemetry data to improve field design and parameter estimation. We recommend population assessments of at least 3 consecutive months, expanding the survey area to one several times the size of an individual’s home range, and including sex and camera placement considerations to reduce bias in jaguar density estimates.Regular evaluation of jaguar (Panthera onca) population status is an important part of conservation decision-making. Currently, camera trapping has become the standard method used to estimate jaguar abundance and demographic parameters, though evidence has shown the potential for sex ratio biases and density overestimates. In this study, we used camera trap data combined with satellite telemetry data from one female jaguar to estimate jaguar population density in the dry forest of Santa Rosa National Park in the Guanacaste Conservation Area of northwestern Costa Rica. We analyzed camera trap data collected from June 2016 to June 2017 using spatial capture-recapture methods to estimate jaguar density. In total, 19 individual jaguars were detected (11 males; 8 females) with a resulting estimated population density of 2.6 females (95% [CI] 1.7–4.0) and 5.0 male (95% [CI] 3.4–7.4) per 100 km2. Based on telemetry and camera trap data, camera placement might bias individual detections by sex and thus overall density estimates. We recommend population assessments be made at several consecutive 3-month intervals, that larger areas be covered so as not to restrict surveys to one or two individual home ranges, as in our case, and to carry out long-term camera monitoring programs instead of short-term studies to better understand the local population, using auxiliary telemetry data to adjust field designs and density estimations to improve support for jaguar conservation strategies.

Diamondback Terrapin (Malaclemys terrapin terrapin) Density and Space Use in Dynamic Tidal Systems: Novel Insights from Spatial Capture–Recapture

The status, size, and density of Malaclemys terrapin (Diamondback Terrapin) populations along the Atlantic coast have been reported by most states as unknown or declining. Robust demographic or population data are lacking, with even less information available on their spatial ecology. Spatial capture–recapture (SCR) methods explicitly incorporate spatial processes, providing a formal link between encounter data and space use. Despite the widespread adoption of SCR across ecological disciplines, it has yet to be applied to turtle populations. We present the first application of SCR methods to Diamondback Terrapins by analyzing data collected from two known activity areas in the tidal marsh systems of Wellfleet Bay, Massachusetts. We found that Terrapin detection was positively associated with survey effort at both sites. Detection was also influenced by day of season, tide cycle, the time of tide, survey time relative to the tide, cloud cover, and windspeed. Density and space use differed markedly between the two sites: the estimated density in The Run was 9 individuals/ha with a space use parameter of 309 m, compared to 59 individuals/ha and a space use parameter of 107 m in The Cove. Sex structure was female-biased, with a sex ratio of 0.34 and 0.18 males in The Run and The Cove, respectively. We demonstrate the utility in using SCR methods in turtles, specifically Diamondback Terrapins, to produce comparable estimates of detection and population size and density, while simultaneously providing inference on differential space-use and detection resulting from variation in both behavior and sampling conditions.

A review of spotted hyaena population estimates highlights the need for greater utilisation of spatial capture-recapture methods

As apex predators with a regulating effect on interspecific competitors and prey demographics, monitoring of spotted hyaena (Crocuta crocuta) population trends can provide a reliable indicator of ecosystem health. However, the ability of current survey techniques to monitor carnivore densities effectively are increasingly questioned. This has led recent studies to advocate increased application of spatial capture-recapture (SCR) methods to estimate population density for large carnivores. We reviewed the literature regarding methods used to estimate population density for spotted hyaena since 2000. Our review found that SCR methods are underutilised for estimating spotted hyaena density, with only eight published studies (13% of articles assessed) using an SCR approach. Call-in surveys were the most frequently used method, featuring in 47% of studies. However, 63% of studies that used call-in surveys could not estimate a site-specific calibration index. The calibration index estimates the distance and rate at which the focal species responds to audio lures and, as response rates are impacted by site-specific ecological and environmental factors, studies that could not calibrate this index are likely inaccurate. Further application of SCR techniques will allow more robust estimation of spotted hyaena density, reducing uncertainty and potential overestimation that limit inference from existing survey methods.

Evaluating the efficacy of reintroducing fishers (Pekania pennanti) to a landscape managed for timber production

Understanding the role of landscapes managed for timber production in the conservation of forest-obligate species is a priority for preserving ecological integrity and fostering socioeconomic wellbeing. The forest characteristics generally associated with the survival, reproduction, and persistence of forest-obligate species (e.g., large-diameter trees, standing dead trees, understory vegetation, downed logs) are often believed to be at odds with timber production. One such species is the fisher (Pekania pennanti), a mesocarnivoran (member of the order Carnivora) associated with mature forest characteristics whose range has decreased substantially since the mid-1800s. Fishers exemplify the perceived conflict between forest-obligate species and timber production because they generally require areas exhibiting complex forest structure including multiple canopy layers, old trees and standing dead trees with cavities, logs, and understory vegetation that provide sufficient prey, escape cover, and structures to support reproduction and parturition. Consequently, understanding if fishers can persist in landscapes managed for timber production can provide a critical test of the compatibility among forest-obligate species and forest management. We reintroduced 40 fishers (24 females, 16 males) between November 2009 and December 2011 onto a landscape managed for timber production to establish a new fisher population and to evaluate the viability of fisher populations on a forest managed for timber production. We studied this reintroduced population of fishers for 8 years following the reintroduction using annual live-captures and year-round tracking with radio telemetry. Using population modeling with spatial capture-recapture methods, we estimated this population of fishers to be growing during the 7-year study period. The density of the reintroduced fisher population in 2017 (10.8 fishers/100 km2) was within the reported range of fisher densities across the western United States. The reintroduction of fishers to previously occupied portions of their range is an important component of fisher conservation and will play a role in the recovery of the species in western portions of the fisher's range. Our results suggest that forests managed for timber production with landscape conditions similar to our study area may be important for future fisher reintroductions and species recovery.

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.

On spatial variation in the detectability and density of social media user protest supporters

Although much has been published regarding street protests on social media, few works have attempted to characterize social media users’ spatial behavior in such events. The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users’ spatial detectability and density during a protest in Mexico City. The best-obtained model, together with explaining the spatial density of users, shows that there is high variability in the probability of detection of social media user protest supporters and that the collective posting rhythm and the day of observation are significant explanatory factors. The implication is that studies of collective spatial behavior would benefit by focussing on users’ activity centres and their urban environment, rather than their physical proximity to the protest location, the latter being unable to adequately explain spatial variations in users’ probability of detection and density during the protest event.

Spatial partial identity model reveals low densities of leopard and spotted hyaena in a miombo woodland

AbstractDecline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture–recapture (SCR) is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two‐flank spatial partial identity model (SPIM) offers a cost‐effective approach, which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We used SPIM to estimate density for leopard (Panthera pardus) and spotted hyaena (Crocuta crocuta) and compared estimates with conventional SCR methods. Density estimates were low across survey years, when compared to estimates from sub‐Saharan Africa, for both leopard (1.9 ± 0.19 sd adults/100 km2) and spotted hyaena (1.15 ± 0.42 sd adults/100 km2). Estimates from SPIM improved precision compared with analytical alternatives. Lion (Panthera leo) and wild dog (Lycaon pictus) were absent from the 2016 survey, but lone dispersers were recorded in 2017 and 2018, and both species appear limited to transient individuals from within the wider transfrontier conservation area. Low densities may reflect low carrying capacity in miombo woodlands or be a result of reduced prey availability from intensive poaching. We provide the first leopard density estimates from Malawi and a miombo woodland habitat, whilst demonstrating that SPIM is beneficial for density estimation in surveys where only one camera trap per location is deployed. The low density of large carnivores requires urgent management to reduce the loss of the carnivore guild in Kasungu National Park and across the wider transfrontier landscape.

Population density of sitatunga in riverine wetland habitats

Estimates of population density of mammals are critical data for effective management. Estimating density is complicated if the species of interest has cryptic markings and occupies dense habitat. Sitatunga is such a species, specially adapted to the dense swamps and marshes of sub-Saharan Africa, where traditional population survey techniques have been ineffective. In this study, we used camera traps to estimate density of sitatunga in central Uganda using both spatial capture-recapture methods and time in front of the camera (TIFC). We collected data in three years, 2015–2017. The TIFC model resulted in density estimates similar to the spatial capture-recapture models, without needing information on movement or individual identification. However, spatial capture-recapture models provide an estimate of movement and home range, which is of interest to management. For sitatunga, spatial capture-recapture models revealed higher movement parameters and higher heterogeneity in movement than previously reported. These results illustrate the utility of camera traps for a cryptic species in dense habitats, and provide a potential alternative to spatial capture-recapture methods.

Trends in cheetah Acinonyx jubatus density in north‐central Namibia

AbstractAssessing trends in abundance and density of species of conservation concern is vital to inform conservation and management strategies. The remaining population of the cheetah (Acinonyx jubatus) largely exists outside of protected areas, where they are often in conflict with humans. Despite this, the population status and dynamics of cheetah outside of protected areas have received relatively limited attention across its range. We analyzed remote camera trapping data of nine surveys conducted from 2005 to 2014 in the Waterberg Conservancy, north‐central Namibia, which included detections of 74 individuals (52 adult males, 7 adult females and 15 dependents). Using spatial capture–recapture methods, we assessed annual and seasonal trends in cheetah density. We found evidence of a stable trend in cheetah density over the study period, with an average density of 1.94/100 km2 (95% confidence interval 1.33–2.84). This apparent stability of cheetah density is likely the result of stable and abundant prey availability, a high tolerance to carnivores by farmers and low turnover rates in home range tenure. This study highlights the importance of promoting long‐term surveys that capture a broad range of environmental variation that may influence species density and the importance of nonprotected areas for cheetah conservation.

Evaluating Snake Density Using Passive Integrated Transponder (PIT) Telemetry and Spatial Capture–Recapture Analyses for Linear Habitats

Many snake species are elusive and difficult to study in field settings. As such, little is known about their population ecology despite conservation needs for many species. Advances in field techniques and statistical methods can improve our understanding of snake ecology. We used passive integrated transponder (PIT) telemetry to track Nerodia sipedon (Northern Watersnakes, n = 94) and Regina septemvittata (Queensnakes, n = 119) in six low-order streams in central Kentucky, USA from June to October 2016. We assessed snake density, spatial scale of detection, and detection probability using PIT tag relocations and spatial capture–recapture methods for linear habitats. Specifically, we modeled population density as a function of individual stream and land cover type, spatial scale of detection as a function of sex, and detection probability as a function of sex and time-varying covariates. Individual streams were a better predictor of snake density than land cover type; density estimates ranged from 6 ± 3 N. sipedon/km (mean ± standard error) to 107 ± 17 N. sipedon/km and 6 ± 5 R. septemvittata/km to 63 ± 10 R. septemvittata/km. Female R. septemvittata had a larger spatial scale of detection (55 ± 4 m) than male R. septemvittata snakes (40 ± 4 m). Spatial scale of detection did not differ between sexes for N. sipedon (females: 40 ± 4 m; males: 35 ± 3 m). The combination of PIT telemetry and spatial capture–recapture analyses can effectively estimate population densities and other population parameters for snakes and other reptiles and amphibians associated with linear habitats.

Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains.

Obtaining estimates of animal population density is a key step in providing sound conservation and management strategies for wildlife. For many large carnivores however, estimating density is difficult because these species are elusive and wide‐ranging. Here, we focus on providing the first density estimates of the Eurasian lynx (Lynx lynx) in the French Jura and Vosges mountains. We sampled a total of 413 camera trapping sites (with two cameras per site) between January 2011 and April 2016 in seven study areas across seven counties of the French Jura and Vosges mountains. We obtained 592 lynx detections over 19,035 trap days in the Jura mountains and 0 detection over 6,804 trap days in the Vosges mountains. Based on coat patterns, we identified a total number of 92 unique individuals from photographs, including 16 females, 13 males, and 63 individuals of unknown sex. Using spatial capture–recapture (SCR) models, we estimated abundance in the study areas between 5 (SE = 0.1) and 29 (0.2) lynx and density between 0.24 (SE = 0.02) and 0.91 (SE = 0.03) lynx per 100 km2. We also provide a comparison with nonspatial density estimates and discuss the observed discrepancies. Our study is yet another example of the advantage of combining SCR methods and noninvasive sampling techniques to estimate density for elusive and wide‐ranging species, like large carnivores. While the estimated densities in the French Jura mountains are comparable to other lynx populations in Europe, the fact that we detected no lynx in the Vosges mountains is alarming. Connectivity should be encouraged between the French Jura mountains, the Vosges mountains, and the Palatinate Forest in Germany where a reintroduction program is currently ongoing. Our density estimates will help in setting a baseline conservation status for the lynx population in France.

OSCR: a spatial capture–recapture R package for inference about spatial ecological processes

Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.

Large-scale variation in density of an aquatic ecosystem indicator species

Monitoring indicator species is a pragmatic approach to natural resource assessments, especially when the link between the indicator species and ecosystem state is well justified. However, conducting ecosystem assessments over representative spatial scales that are insensitive to local heterogeneity is challenging. We examine the link between polychlorinated biphenyl (PCB) contamination and population density of an aquatic habitat specialist over a large spatial scale using non-invasive genetic spatial capture-recapture. Using American mink (Neovison vison), a predatory mammal and an indicator of aquatic ecosystems, we compared estimates of density in two major river systems, one with extremely high levels of PCB contamination (Hudson River), and a hydrologically independent river with lower PCB levels (Mohawk River). Our work supports the hypothesis that mink densities are substantially (1.64–1.67 times) lower in the contaminated river system. We demonstrate the value of coupling the indicator species concept with well-conceived and spatially representative monitoring protocols. PCBs have demonstrable detrimental effects on aquatic ecosystems, including mink, and these effects are likely to be profound and long-lasting, manifesting as population-level impacts. Through integrating non-invasive data collection, genetic analysis, and spatial capture-recapture methods, we present a monitoring framework for generating robust density estimates across large spatial scales.

Unifying population and landscape ecology with spatial capture–recapture

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio‐temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture–recapture (SCR) represents an individual‐based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non‐invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.