A spatial capture-recapture model for group-living species.

Group living in species can have complex consequences for individuals, populations, and ecosystems. Therefore, estimating group density and size is often essential for understanding population dynamics, interspecific interactions, and conservation needs of group-living species. Spatial capture-recapture (SCR) has been used to model both individual and group density in group-living species, but modeling either individual-level or group-level detection results in different biases due to common characteristics of group-living species, such as highly cohesive movement or variation in group size. Furthermore, no SCR method currently estimates group density, individual density, and group size jointly. Using clustered point processes, we developed a cluster SCR model to estimate group density, individual density, and group size. We compared the model to standard SCR models using both a simulation study and a data set of detections of African wild dogs (Lycaon pictus), a group-living carnivore, on camera traps in northern Botswana. We then tested the model's performance under various scenarios of group movement in a separate simulation study. We found that the cluster SCR model outperformed a standard group-level SCR model when fitted to data generated with varying group sizes, and mostly recovered previous estimates of wild dog group density, individual density, and group size. We also found that the cluster SCR model performs better as individuals' movements become more correlated with their groups' movements. The cluster SCR model offers opportunities to investigate ecological hypotheses relating group size to population dynamics while accounting for cohesive movement behaviors in group-living species.

Tourist photographs as a scalable framework for wildlife monitoring in protected areas.

Protected areas are critical to conservation efforts in the face of rapid biodiversity declines [1]. Yet the resources for conservation are often limited and shared amongst many competing priorities [2]. As a consequence, even basic monitoring surveys are absent within most protected areas [3]. Although a range of wildlife monitoring methods exist, considerable focused survey effort is often required to yield accurate and precise estimates [4]. This makes monitoring difficult to sustain or replicate, limiting access to the data required for evidence-based conservation decisions. Citizen-scientists have been proposed as an important complement to the finite resources available for basic monitoring within protected areas [5]; however, the full potential of this approach has yet to be realised. Wildlife tourists and guides are especially focussed on encountering and photographing fauna and flora, yet the data collected in these efforts is rarely harnessed for conservation monitoring within protected areas. A detailed understanding of photographic tourism's potential role in wildlife monitoring has been lacking, but is essential for the development of new tools to harness the data being collected through tourism. Here, we demonstrate that tourist-contributed data can aid wildlife monitoring in protected areas by providing population estimates of large carnivores comparable to those from traditional survey methods. Our approach could capitalize upon the immense number of wildlife photographs being taken daily as part of the global > 30-billion USD, wildlife-based tourism industry.

Ecological correlates of the spatial co-occurrence of sympatric mammalian carnivores worldwide.

The composition of local mammalian carnivore communities has far-reaching effects on terrestrial ecosystems worldwide. To better understand how carnivore communities are structured, we analysed camera trap data for 108 087 trap days across 12 countries spanning five continents. We estimate local probabilities of co-occurrence among 768 species pairs from the order Carnivora and evaluate how shared ecological traits correlate with probabilities of co-occurrence. Within individual study areas, species pairs co-occurred more frequently than expected at random. Co-occurrence probabilities were greatest for species pairs that shared ecological traits including similar body size, temporal activity pattern and diet. However, co-occurrence decreased as compared to other species pairs when the pair included a large-bodied carnivore. Our results suggest that a combination of shared traits and top-down regulation by large carnivores shape local carnivore communities globally.

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.