Lindsey N. Rich

Determining Occurrence Dynamics when False Positives Occur: Estimating the Range Dynamics of Wolves from Public Survey Data

Large-scale presence-absence monitoring programs have great promise for many conservation applications. Their value can be limited by potential incorrect inferences owing to observational errors, especially when data are collected by the public. To combat this, previous analytical methods have focused on addressing non-detection from public survey data. Misclassification errors have received less attention but are also likely to be a common component of public surveys, as well as many other data types. We derive estimators for dynamic occupancy parameters (extinction and colonization), focusing on the case where certainty can be assumed for a subset of detections. We demonstrate how to simultaneously account for non-detection (false negatives) and misclassification (false positives) when estimating occurrence parameters for gray wolves in northern Montana from 2007–2010. Our primary data source for the analysis was observations by deer and elk hunters, reported as part of the state’s annual hunter survey. This data was supplemented with data from known locations of radio-collared wolves. We found that occupancy was relatively stable during the years of the study and wolves were largely restricted to the highest quality habitats in the study area. Transitions in the occupancy status of sites were rare, as occupied sites almost always remained occupied and unoccupied sites remained unoccupied. Failing to account for false positives led to over estimation of both the area inhabited by wolves and the frequency of turnover. The ability to properly account for both false negatives and false positives is an important step to improve inferences for conservation from large-scale public surveys. The approach we propose will improve our understanding of the status of wolf populations and is relevant to many other data types where false positives are a component of observations.

Comparing capture-recapture, mark-resight, and spatial mark-resight models for estimating puma densities via camera traps

Abstract Camera-trapping surveys, in combination with traditional capture–recapture or spatially explicit capture–recapture techniques, have become popular for estimating the density of individually identifiable carnivores. When only a portion of the population is uniquely identifiable, traditional and spatial mark–resight models provide a viable alternative. We reanalyzed a data set that used photographic capture–recapture methods to estimate the densities of pumas (Puma concolor) across 3 study sites in Belize, Argentina, and Bolivia using newer, more-advanced modeling including spatial and nonspatial mark–resight techniques. Additionally, we assessed how photo identification influenced density estimates by comparing estimates based on capture histories constructed by 3 independent investigators. We estimated the abundances of pumas using mark–resight models in program MARK and then estimated densities ad hoc. We also estimated densities directly using spatial mark–resight models implemented in a Bayesian framework. Puma densities did not vary substantially among observers but estimates generated from the 3 statistical techniques did differ. Density estimates (pumas/100 km2) from spatial mark–resight models were lower (0.22–7.92) and had increased precision compared to those from nonspatial capture–recapture (0.50–19.35) and mark–resight techniques (0.54–14.70). Our study is the 1st to estimate the density of a population of carnivores, where only a subset of the individuals are naturally marked, using camera-trapping surveys in combination with spatial mark–resight models. The development of spatial mark–resight and spatially explicit capture–recapture techniques creates the potential for using a single camera-trapping array to estimate the density of multiple, sympatric carnivores, including both partially marked and uniquely marked species. Resumen Los relevamientos con trampas-cámara en combinación con modelos tradicionales o espacialmente explícitos de captura–recaptura, se han convertido en metodologías muy utilizadas para estimar la densidad de carnívoros que pueden ser identificados individualmente. Cuando sólo una porción de la población puede ser identificada inequívocamente, los modelos de marcado–revisualización tradicionales y espacialmente explícitos proveen una alternativa viable. Reanalizamos un conjunto de datos, que se utilizó para estimar la densidad de pumas (Puma concolor) mediante el método fotográfico de captura–recaptura en 3 sitios de estudio en Belice, Argentina y Bolivia, utilizando modelos más novedosos y avanzados incluyendo técnicas de marcado–revisualización tradicionales y espacialmente explicitas. Adicionalmente, evaluamos cómo la identificación de fotografías influyó en las estimaciones de densidad, comparando estimaciones basadas en las historias de captura construidas por 3 investigadores independientes. Estimamos la abundancia de pumas usando modelos de marcado–revisualización en el programa MARK y luego estimamos las densidades ad hoc. También estimamos densidades usando modelos espaciales de marcado–revisualización espacialmente explícitos implementados en un marco Bayesiano. La densidad de pumas no varió sustancialmente entre observadores, pero las estimaciones generadas mediante los 3 modelos estadísticos fueron diferentes. Las densidades de pumas (pumas/100 km2) de modelos de marcado–revisualización espacialmente explícitos fueron más bajas (0.22–7.92) y aumentaron en precisión comparadas con aquellas de captura–recaptura (0.50–19.35) y técnicas de marcado–revisualización no espacialmente explícitos (0.54–14.70). Nuestro estudio es el primero en estimar la densidad mediante la utilización de datos de trampas-cámara en combinación con modelos marcado–revisualización espacialmente explícitos de una población de carnívoros donde sólo un subconjunto de individuos está marcado naturalmente. El desarrollo de técnicas de marcado–revisualización y captura–recaptura espacialmente explícitos ofrece la oportunidad de utilizar un mismo diseño de trampas-cámara para estimar la densidad de múltiples carnívoros simpátricos, incluyendo especies parcial o totalmente identificables individualmente.

Anthropogenic mortality, intraspecific competition, and prey availability influence territory sizes of wolves in Montana

Abstract Territoriality in animals is of both theoretical and conservation interest. Animals are territorial when benefits of exclusive access to a limiting resource outweigh costs of maintaining and defending it. The size of territories can be considered a function of ecological factors that affect this benefit–cost ratio. Previous research has shown that territory sizes for wolves (Canis lupus) are largely determined by available biomass of prey, and possibly pack size and density of neighboring wolf packs, but has not been interpreted in a benefit–cost framework. Such a framework is relevant for wolves living in the Northern Rocky Mountains where conflicts with humans increase mortality, thereby potentially increasing costs of being territorial and using prey resources located near humans. We estimated territory sizes for 38 wolf packs in Montana from 2008 to 2009 using 90% adaptive kernels. We then created generalized linear models (GLMs) representing combinations of ecological factors hypothesized to affect the territory sizes of wolf packs. Our top GLM, which had good model fit (R2  =  0.68, P < 0.0005), suggested that territory sizes of wolves in Montana were positively related to terrain ruggedness, lethal controls, and human density and negatively related to number of surrounding packs relative to the size of the territory. We found that the top GLM successfully predicted territory sizes (R2  =  0.53, P < 0.0005) using a jackknife approach. Our study shows that territory sizes of group-living carnivores are influenced by not only intraspecific competition and availability of limiting resources, but also by anthropogenic threats to the groups survival, which could have important consequences where these territorial carnivores come into conflict with humans.

On the Right Track? Comparing Concurrent Spoor and Camera-Trap Surveys in Botswana

A diverse range of techniques have been used to survey mammals. Spoor counts and camera trapping are increasingly common survey tools used to detect the presence of species of interest in an area (occupancy). Given the significant time and financial investments in such surveys, and the management decisions based on their conclusions, it is imperative that confidence can be assigned to the results. It is therefore important to increase our understanding of the accuracy and constraints of each technique to allow managers and researchers to select the most suitable method for each situation. Here we compare results collected simultaneously using spoor and camera-trap surveys at a human—wildlife interface in northern Botswana. While our spoor survey and camera-trap surveys detected a similar number of mammal species (17 and 15, respectively), the species detected by each method differed. Of the 21 species detected overall, only about half (52.4%) were detected by both methods, and these co-detected species had significantly higher occupancy estimates than those species detected by only one method. Moreover, the direct comparison showed that some tracks were missed or misidentified by the spoor survey. Our results suggest that over short time frames, neither method is ideal for detecting species at low densities, and that researchers should consider combining multiple methods in such circumstances.