Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset

Abstract

Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1–10 cameras), and (2) by total season length (1–365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40–128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species.

Monitoring the mammalian fauna of urban areas using remote cameras and citizen science

“…We highlighted how citizen scientists can efficiently classify vast amounts of data, the main constrain of remote camera technology. However, vandalism, privacy issues, study design and camera specifications and deployment should be taken into consideration before using remote cameras in urban environments (Anton, Hartley, and Wittmer 2018;O'Connor et al 2017).…”

Quantifying daily activity patterns of the spotted-tailed quoll (Dasyurus maculatus) using camera trap data from a stronghold population in south-eastern New South Wales

“…To avoid use of repeated records of the same individual due to the close proximity of camera traps (100 m spacing), we used the first record per hour per camera trap as a detection event for a 24 h period and all other records within the hour were ignored in subsequent analyses (Porfirio et al 2016). Further, latency to initial detection (the number of days between camera trap placement and first detection; Foresman and Pearson 1998) and detection probability (p) at each camera (the number of days out of total survey effort; Mackenzie et al 2002;O'Connor et al 2017) were calculated.…”

“…Here we present the results of a year-long pilot study using camera traps to estimate the daily activity levels of spotted-tailed quolls in a recognised stronghold population on the south coast of New South Wales (NSW). With results providing information useful to optimise camera traps efficiency for detecting quolls within a study area (Bischof et al 2014;O'Connor et al 2017).…”

Spatial capture-recapture models for the ecological study of common voles (Microtus arvalis) in NW Spain

“…Variants to CMR can be used which do not require physical capture or manipulation of individuals Greenwood & Robinson 2006;Jareño et al, 2014;Fauteux et al, 2018). Non-invasive "trapping" methods are increasingly popular, including the use of camera trappings (Karanth & Nichols, 1998;O'Connell et al, 2011), acoustic recording devices (Dawson & Efford, 2009) and methods based on fecal or hair samples, DNA extraction and genotyping in order to identify individuals Sabino-Marques et al, 2018). While these methods could be considered to be easier than traditional CMR, they still require a substantial time and expensive investment.…”

“…Estimation of abundance is often been based on the use of descriptive indices (Schwarz & Seber, 1999;Wilson & Delahay, 2001;O'Connell et al, 2011), with the main assumption that the index, scalar or ordinal, is directly proportional to the population density of the study species.…”