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

Abstract

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.

Keywords: capture‐recapture; ecological modelling; hierarchical models; imperfect detection; population dynamics; wildlife monitoring.

FIGURE 1

Ecological insights (left) and focal parameters (right) in published spatial capture–recapture (SCR) literature. Focal parameters of interest in SCR framework include density and abundance (closed‐ or open‐population models), and survival, recruitment, growth rate, and movement in an open‐population SCR model. Parameters of detectability depend on choice of detection model; assuming a half‐normal detection function, the spatial scale parameter of detection function may inform about space use and may be the focus of analysis besides frequency or probability of detection (the magnitude parameter). Variation in focal parameters is modeled in association with spatial, temporal, or individual level covariates in a regression formula and the effect sizes or β coefficients are reported as parameters of interest in analysis of covariate effects

FIGURE 2

Taxonomic groups targeted in spatial capture–recapture studies according to a literature search of journal articles through ISI Web of Science in March 2020. From left to right: Class, Order, and Family based on the IUCN Red List assessments in 2020

FIGURE 3

Terrestrial zoogeographic realms of the world based on Holt et al. (2013), excluding Antarctica, showing the geographic distribution of spatial capture–recapture studies by March 2020 in ISI Web of Science. Number of studies is shown per Class in each region (see the corresponding animal silhouettes in Figure 2)

FIGURE 4

Approximate geographic distribution of published spatial capture–recapture studies by March 2020 accessed through ISI Web of Science. The size of circles indicates the relative size of study areas

FIGURE 5

Data collection methods used in published spatial capture–recapture studies, including noninvasive (a. acoustic, b. camera trap, c. direct observation, d. DNA sampling of hair, e. other noninvasive DNA sampling, f. scat DNA, g. sign survey) and invasive sampling methods (h. telemetry and i. trapping). Log of number of studies is shown in bar graphs for each continent (top panel from left to right: North America, South America, Africa, Europe, Asia, and Oceania). Asia, Africa, and South America have the highest number of studies with noninvasive data collection methods (94%, 92%, and 88% of studies in these continents, respectively)