A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models

RESEARCH ARTICLE A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models Jesse Whittington1*, Michael A. Sawaya2 1 Parks Canada, Banff National Park Resource Conservation, Banff, Alberta, Canada, 2 Sinopah Wildlife Research Associates, Missoula, Montana, United States of America * Abstract OPEN ACCESS Citation: Whittington J, Sawaya MA (2015) A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models. PLoS ONE 10(7): e0134446. doi:10.1371/journal. pone.0134446 Editor: Mark S. Boyce, University of Alberta, CANADA Received: April 19, 2015 Accepted: July 10, 2015 Published: July 31, 2015 Copyright: © 2015 Whittington, Sawaya. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: Funding for this project was provided by Parks Canada, the Western Transportation Institute at Montana State University, the Woodcock Foundation, the Henry P. Kendall Foundation, and the Wilburforce Foundation. Additional support was provided by the National Fish and Wildlife Foundation, Alberta Conservation Association, Calgary Foundation, and the Mountain Equipment Cooperative. Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capturerecapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal’s home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNAbased data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786–1.071) for females, 0.844 (0.703–0.975) for males, and 0.882 (0.779–0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758–1.024) for females, 0.825 (0.700–0.948) for males, and 0.863 (0.771– 0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth rates suggest that Banff National Park’s population of grizzly bears requires continued conservation-oriented management actions. PLOS ONE | DOI:10.1371/journal.pone.0134446 July 31, 2015 1 / 17 Grizzly Bear Population Trends Competing Interests: The authors have declared that no competing interests exist. Introduction Increasing human activity throughout the world threatens many species and subsequent ecosystem processes [1]. Basic metrics such as population growth rates are required to help understand how human activities, ecological conditions, and management actions affect the conservation status of wildlife populations. Capture-recapture techniques are commonly used to estimate abundance, density, and demographic parameters such as population growth, apparent survival, and recruitment. Capture-recapture studies use repeated surveys of identifiable individuals to estimate detection probability and variance around density, apparent survival, recruitment, and population growth rates [2]. Within closed population capture-recapture studies, multiple sampling occasions generate individual capture histories that are then used to estimate detection probability and the number of individuals in the study area that were present but undetected. When surveys are conducted across multiple years or sessions, open population capture-recapture models track individual detections over time to estimate demographic parameters such as apparent survival, per capita recruitment, and population growth rates [3,4]. In its simplest form, non-spatial capture-recapture models determine whether or not each animal was detected within an occasion and use the proportion of occasions each animal was detected to estimate detection probability. Challenges with capture-recapture arise when individuals vary in their exposure to traps. For example, animals with home ranges that occur entirely within a study area may have higher detection probabilities than animals with home ranges that only partially overlap the study area. This variability in detection probability is pronounced for wide ranging carnivores that have large home ranges relative to the size of the study area. Capture-recapture models have included the distance between an animal’s home range center and the edge of the study area as a covariate affecting detection probability [5–8], but this approach assumes a linear relationship between distance to edge and detection probability and does not reflect observation processes. Spatial capture-recapture techniques are a rapidly evolving class of models that directly estimate the effects of distance between an animal’s home range centre and each trap location on probability of detection [9–12]. Comparisons between closed population non-spatial and spatial capture-recapture models have found that spatial models generally provide more robust density estimates with fewer biases [13–16] but can be biased low [17]. Spatial capture-recapture methods have been used to estimate densities of many species including lynx [16], wolverine [18], and black bears [19–21]. However, most studies have focussed on single year models and only a few studies have used open population spatial capture-recapture approaches to estimate population parameters [12,22–24]. Comparisons between open population non-spatial and spatial models found via simulation that the non-spatial models under-estimated mortality rates [23]. No studies to our knowledge have compared populati (...truncated)