An empirical evaluation of camera trap study design: How many, how long and when?

Abstract Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters. We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals. We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Data from: A multispecies occupancy model for two or more interacting species

Species occurrence is influenced by environmental conditions and the presence of other species. Current approaches for multispecies occupancy modelling are practically limited to two interacting species and often require the assumption of asymmetric interactions. We propose a multispecies occupancy model that can accommodate two or more interacting species. We generalize the single-species occupancy model to two or more interacting species by assuming the latent occupancy state is a multivariate Bernoulli random variable. We propose modelling the probability of each potential latent occupancy state with both a multinomial logit and a multinomial probit model and present details of a Gibbs sampler for the latter. As an example, we model co-occurrence probabilities of bobcat (Lynx rufus), coyote (Canis latrans), grey fox (Urocyon cinereoargenteus) and red fox (Vulpes vulpes) as a function of human disturbance variables throughout 6 Mid-Atlantic states in the eastern United States. We found evidence for pairwise interactions among most species, and the probability of some pairs of species occupying the same site varied along environmental gradients; for example, occupancy probabilities of coyote and grey fox were independent at sites with little human disturbance, but these two species were more likely to occur together at sites with high human disturbance. Ecological communities are composed of multiple interacting species. Our proposed method improves our ability to draw inference from such communities by permitting modelling of detection/non-detection data from an arbitrary number of species, without assuming asymmetric interactions. Additionally, our proposed method permits modelling the probability two or more species occur together as a function of environmental variables. These advancements represent an important improvement in our ability to draw community-level inference from multiple interacting species that are subject to imperfect detection

Listeria monocytogenes at the human–wildlife interface: black bears (Ursus americanus) as potential vehicles for Listeria

Listeria monocytogenes is the causative agent of the foodborne illness listeriosis, which can result in severe symptoms and death in susceptible humans and other animals. L. monocytogenes is ubiquitous in the environment and isolates from food and food processing, and clinical sources have been extensively characterized. However, limited information is available on L. monocytogenes from wildlife, especially from urban or suburban settings. As urban and suburban areas are expanding worldwide, humans are increasingly encroaching into wildlife habitats, enhancing the frequency of human–wildlife contacts and associated pathogen transfer events. We investigated the prevalence and characteristics of L. monocytogenes in 231 wild black bear capture events between 2014 and 2017 in urban and suburban sites in North Carolina, Georgia, Virginia and United States, with samples derived from 183 different bears. Of the 231 captures, 105 (45%) yielded L. monocytogenes either alone or together with other Listeria. Analysis of 501 samples, primarily faeces, rectal and nasal swabs for Listeria spp., yielded 777 isolates, of which 537 (70%) were L. monocytogenes. Most L. monocytogenes isolates exhibited serotypes commonly associated with human disease: serotype 1/2a or 3a (57%), followed by the serotype 4b complex (33%). Interestingly, approximately 50% of the serotype 4b isolates had the IVb-v1 profile, associated with emerging clones of L. monocytogenes. Thus, black bears may serve as novel vehicles for L. monocytogenes, including potentially emerging clones. Our results have significant public health implications as they suggest that the ursine host may preferentially select for L. monocytogenes of clinically relevant lineages over the diverse listerial populations in the environment. These findings also help to elucidate the ecology of L. monocytogenes and highlight the public health significance of the human–wildlife interface

Data from: Does hunting or hiking affect wildlife communities in protected areas?

Managed public wild areas have dual mandates to protect biodiversity and provide recreational opportunities for people. These goals could be at odds if recreation, ranging from hiking to legal hunting, disrupts wildlife enough to alter their space use or community structure. We evaluated the effect of managed hunting and recreation on 12 terrestrial wildlife species by employing a large citizen science camera trapping survey at 1947 sites stratified across different levels of human activities in 32 protected forests in the eastern USA. Habitat covariates, especially the amount of large continuous forest and local housing density, were more important than recreation for affecting the distribution of most species. The four most hunted species (white-tailed deer, raccoons, eastern grey and fox squirrels) were commonly detected throughout the region, but relatively less so at hunted sites. Recreation was most important for affecting the distribution of coyotes, which used hunted areas more compared with unhunted control areas, and did not avoid areas used by hikers. Most species did not avoid human-made trails, and many predators positively selected them. Bears and bobcats were more likely to avoid people in hunted areas than unhunted preserves, suggesting that they perceive the risk of humans differently depending on local hunting regulations. However, this effect was not found for the most heavily hunted species, suggesting that human hunters are not broadly creating ‘fear’ effects to the wildlife community as would be expected for apex predators. Synthesis and applications. Although we found that hiking and managed hunting have measureable effects on the distribution of some species, these were relatively minor in comparison with the importance of habitat covariates associated with land use and habitat fragmentation. These patterns of wildlife distribution suggest that the present practices for regulating recreation in the region are sustainable and in balance with the goal of protecting wildlife populations and may be facilitated by decades of animal habituation to humans. The citizen science monitoring approach we developed could offer a long-term monitoring protocol for protected areas, which would help managers to detect where and when the balance between recreation and wildlife has tipped

eMammal detections dataset

This file contains the raw species detections from camera-trap data across 33 protected areas in the Southeastern United States from 2012-2013. Each row represents one detection and the associated location, time and species

Appendix A. Detailed description of the Metropolis-within-Gibbs MCMC sampler for a spatial mark-resight model augmented with telemetry location data and with individual identi cation rate of marks < 100 %.

Detailed description of the Metropolis-within-Gibbs MCMC sampler for a spatial mark-resight model augmented with telemetry location data and with individual identi cation rate of marks < 100 %

Supplement 1. R code and data to run spatial mark–resight model for raccoon camera trap and telemetry data.

<h2>File List</h2><div> <p><a href="data files/Raccoon_data.R">Raccoon_data.R</a>: R list object with following slots</p> <blockquote> <p>$n: matrix with unmarked photo-counts for each trap (rows) and sampling period (columns)</p> <p>$yknown: 3-dimensional array with individual photographic encounter histories (counts) of all tagged and radio-collared individuals (rows) at each camera (columns) and each sampling period (3rd dimension)</p> <p>$X: matrix with X and Y coordinates (columns) of each camera trap (rows), in UTM scaled to km</p> <p>$mi: vector with total number of photos of marked individuals that could be identified to individual level for each sampling period</p> <p>$mall: vector with total number of photos of marked individuals, identified and unidentified, for each sampling period</p> <p>$M: size of the augmented data set</p> <p>$Eff: 3-dimensional array with information on how many days each camera trap (column) was functional during each sampling period (3rd dimension), expanded to hold the same information for each individual in the augmented data set (row), for easier array multiplication in the MCMC algorithm</p> <p>$collar: vector of length M with “TRUE” for individuals with radio-collars and “FALSE” for individuals without collars, tagged or unmarked</p> <p>$locs: list of length sum(collar) with 2-dimensional matrices containing X and Y coordinates of radio telemetry locations for each of the collard individuals</p> </blockquote> <p><a href="data files/SSp.dbf">SSp.dbf</a>, <a href="data files/SSp.shp">SSp.shp</a>, <a href="data files/SSp.shx">SSp.shx</a>: Shapefile with outline of South Core Banks (which comprises the state-space for the raccoon analysis)</p> <p><a href="data files/Rscript_Raccoons.R">Rscript_Raccoons.R</a>: R code to load data and run raccoon analysis</p> <p><a href="data files/MCMC_algorithm.txt">MCMC_algorithm.txt</a>: R code for MCMC algorithm for spatial mark-resight analysis of raccoon data</p> </div><h2>Description</h2><div> <p>Data files and code are set up to repeat the spatial mark–resight analysis of raccoon camera trapping and telemetry data from South Core Bank, NC, as presented in the Application example in the manuscript. <u></u></p> </div