Using partial aggregation in spatial capture recapture

Cyril Milleret,Pierre Dupont,Richard Bischof,Henrik Brøseth,Jonas Kindberg,J Andrew Royle,Nigel Yoccoz

doi:10.1111/2041-210x.13030

Abstract

Abstract Spatial capture–recapture (SCR) models are commonly used for analysing data collected using noninvasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g., centre of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations. Using simulations, we explored the impact that spatial aggregation of detections has on a trade‐off between computing time and parameter precision and bias, under a range of biological conditions. We used three different observation models: the commonly used Poisson and Bernoulli models, as well as a novel way to partially aggregate detections (Partially Aggregated Binary model [PAB]) to reduce the loss of information after aggregating binary detections. The PAB model divides detectors into K subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of K. Finally, we demonstrate the consequences of aggregation and the use of the PAB model using NGS data from the monitoring of wolverine (Gulo gulo) in Norway. Spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. SCR models estimated abundance with a low bias (<10%) even at high degree of aggregation, but only for the Poisson and PAB models. Overall, the cost of aggregation is mitigated when using the Poisson and PAB models. At the same level of aggregation, the PAB observation model out‐performs the Bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count‐based model. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale‐parameter of the detection function in order to limit bias. We recommend the use of the PAB observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the Bernoulli model.

Highlights

Explicit capture–recapture models (SCR; Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle, Chandler, Sollmann, & Gardner, 2014) are rapidly growing in popularity for ecological data analysis
We aggregated detections consistently with the observation process represented in Figure 1: (a) for each individual, we summed all detections that occurred within aggregated grid cells for the Poisson model (Figure 1, top row); (b) we recorded whether an individual was detected at least once within an aggregated grid cell for the application of the Bernoulli observation model ( called “proximity detector” model (Efford, 2017), Figure 1, middle row); and (c) we introduced a novel way of aggregating detections for the application the partially aggregated binary (PAB) observation model (Figure 1, bottom row)
Aggregation led to a decrease of the precision and coverage (Figure 4, Supporting Information S3, Table S3.2.A) but the magnitude and rate at which bias increased with aggregation was larger for the Bernoulli compared to the Poisson and PAB models (Figure 4, Supporting Information S3, Table S3.2.A)

Summary

Introduction

Explicit capture–recapture models (SCR; Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008; Royle, Chandler, Sollmann, & Gardner, 2014) are rapidly growing in popularity for ecological data analysis. SCR models utilize the information contained in the spatial configuration of detections and nondetections to yield spatially explicit estimates of abundance. At their core, SCR models describe the distribution of latent activity centres (AC; centroid of an individual’s activity during the time of sampling) of individuals in a population from the spatial configuration of individual detections and nondetections. In SCR, spatial detections of individuals can be derived from a multitude of methods, from physical capture and marking, to genetic, acoustic or visual/photographic detections. These detections occur at so-called traps or, more generally, detectors. Depending on the data collection methods, detections may be associated with the point locations of physical detectors or detection devices, but could refer to transects, irregular or gridded search areas (Efford, Borchers, & Byrom, 2009; Efford, Dawson, & Borchers, 2009; Royle, Kéry, & Guélat, 2011; Royle et al, 2014)

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Conclusion