State space and movement specification in open population spatial capture-recapture models.

Beth Gardner,K Ullas Karanth,Devcharan Jathanna,Rahel Sollmann,N Samba Kumar

doi:10.1002/ece3.4509

Abstract

With continued global changes, such as climate change, biodiversity loss, and habitat fragmentation, the need for assessment of long‐term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture–recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture–recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models, and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state‐space definition and between‐primary‐period movement models on demographic parameter estimation. We demonstrate the implications on a 10‐year camera‐trap study of tigers in India. The results of our simulation study show that movement biases survival estimates in open SCR models when little is known about between‐primary‐period movements of animals. The size of the state‐space delineation can also bias the estimates of survival in certain cases.We found that both the state‐space definition and the between‐primary‐period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71 to 0.89). In general, we suggest that open SCR models can provide an efficient and flexible framework for long‐term monitoring of populations; however, in many cases, realistic modeling of between‐primary‐period movements is crucial for unbiased estimates of survival and density.

Highlights

Knowledge of animal population size and dynamics is essential to assess a species’ status, inform conservation strategies, and advance ecological understanding
To investigate the sensitivity of the open population Spatial capture (SCR) model to changes in the size of the state space under different movement specifications, we performed a simulation study using the formulation of the open model described above
The low positive bias in density increased slightly with increasing state-space sizes (Table A4 in Appendix S2). Survival estimates under this model were constant and only slightly (1%) negatively biased; in the case study, we found that the posterior mean survival estimates increased as the state-space size was increased when holding activity centers constant across primary periods

Summary

Introduction

Knowledge of animal population size and dynamics is essential to assess a species’ status, inform conservation strategies, and advance ecological understanding. Capture–recapture methods (Otis, Burnham, White, & Anderson, 1978; Pollock, Nichols, Brownie, & Hines, 1990) have traditionally been widely used to estimate these population parameters based on repeated observations of individually identifiable animals. These models produce unbiased estimates of population size and vital rates by correcting for imperfect detection. Models for open populations estimate vital rates such as survival and recruitment between sampling periods when the population under study is allowed to change (Cormack, 1964; Jolly, 1965; Pollock et al, 1990; Seber, 1965). Developments have been made to these models to improve parameter estimates including the development of the robust design (Kendall, Nichols, & Hines, 1997; Pollock, 1982), which provides a flexible framework for combining open and closed models

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