Best practices to account for capture probability and viewable area in camera‐based abundance estimation

Abstract

A suite of recently developed statistical methods to estimate the abundance and density of unmarked animals from camera traps require accurate estimates of the area sampled by each camera. Although viewshed area is fundamental to achieving accurate abundance estimates, there are no established guidelines for collecting this information in the field. Furthermore, while the complexities of the detection process from motion sensor photography are generally acknowledged, viewable area (the common factor between motion sensor and time lapse photography) on its own has been underemphasized. We establish a common set of terminology to identify the component parts of viewshed area, contrast the photographic capture process and area measurements for time lapse and motion sensor photography, and review methods for estimating viewable area in the field. We use a case study to demonstrate the importance of accurate estimates of viewable area on abundance estimates. Time lapse photography combined with accurate measurements of viewable area allow researchers to assume that capture probability equals 1. Motion sensor photography requires measuring distances to each animal and fitting a distance sampling curve to account for capture probability of <1.

Assessing spatiotemporal patterns of mesocarnivores along an urban‐to‐rural gradient

“…Thus, future research should assess if red foxes may adjust their activity patterns in urbanized settings as a response to human activity, apex predators, prey abundance, or other ecological variables. Lastly, camera detection distance increased red fox detection likelihood, further underscoring the importance of this variable in future studies (Moeller et al 2022).…”

“…Lastly, camera detection distance increased red fox detection likelihood, further underscoring the importance of this variable in future studies (Moeller et al 2022). more likely to use settings with higher elevations, which may suggest that coyotes still prefer rural areas with larger amounts of natural cover and less human disturbance (Atwood et al 2004, Poessel et al 2013, Ellington and Gehrt 2019, Franckowiak et al 2019, Kellner et al 2020 or a potential change in neophobia at increased distances from human developments (Mettler and Shivik 2007).…”

Efficacy of machine learning image classification for automated occupancy‐based monitoring

“…Empty images result from wind‐blown vegetation, animals triggering a camera from outside the field of view (Moeller et al., 2022), or animals passing through the detection zone too quickly (Norouzzadeh et al., 2021). Empty images can permeate camera‐based datasets (e.g., ~75% of 3.2 million images from Snapshot Serengeti; Swanson et al., 2015), but the proportion of images that are empty will likely vary among environments.…”

Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data

“…One way to achieve this is by estimating the effective detection area . This area correlates with the detection probability of animals ( P ) within the camera viewshed and can be affected by many factors including camera height, distance to animal and animal speed (Hofmeester et al., 2019; Moeller et al., 2023; Palencia et al., 2021; Rowcliffe et al., 2011), as well as periods where the camera is unable to record photos (e.g., recovery time between two triggers). Hence, data analysis requires either accounting for P (Hofmeester et al., 2017; Howe et al., 2017; Rowcliffe et al., 2011) or choosing a maximum distance from the camera, short enough to assume perfect detection within this range (Nakashima et al., 2018).…”