Abstract
AbstractA central goal in camera‐trapping (CT) studies is to maximize detection probability and precision of occupancy estimates while minimizing the number of CTs to reduce equipment and labor costs. Few studies, however, have examined the effect of CT number on detection probability. Moreover, historically, most studies focused on a specific species and the design could be tailored toward maximizing detection of this target species. Increasingly, however, such studies use data for all captured, non‐target, species (by‐catch data) for animal community‐level analyses. It remains unclear if, and how, the targeting of CTs toward one species affects the detection of non‐target species. We paired CTs from a permanent camera‐trapping grid (with 38 CTs) targeted at monitoring Eurasian lynx (Lynx lynx) in Innlandet County, Norway, with additional randomly placed CTs at two spatial scales (38 CTs within the same habitat patch and 38 CTs within the same 50‐km2 grid cell as the lynx‐targeted CTs) for three months. We combined multi‐scale occupancy models that enable the separation of large‐scale occupancy, CT‐scale site use, and detection probability with single‐scale occupancy models. This allowed us to study the effects of targeted placement and CT number on the detection probability of the target species (lynx) and seven non‐target mammal species (four carnivores, three herbivores, and one rodent). We found that all species, except moose (Alces alces), had the highest detection probability at lynx‐targeted CTs. Moose had equal detection probabilities at all three placement types. Adding extra CTs generally increased detection probabilities. Consequently, for all species, combining a lynx‐targeted CT with one or more randomly placed CTs, increased the accuracy and precision of occupancy estimates for 50‐km2 grid cells compared to single CT estimates. The placement of single CTs underestimated grid‐cell occupancy compared to known minimum occupancy and were similar to site‐use probability estimates of multi‐scale models. It is, however, uncertain to which spatial extent these site‐use probabilities refer. We therefore recommend the use of multiple (targeted) CTs to estimate occupancy in large grid cells and to interpret occupancy estimates from single CTs as site use of an, as of yet undefined, area surrounding the CT.
Highlights
Remote cameras activated by a passive infrared (PIR) sensor, known as camera traps or trail cameras, are increasingly used to study wildlife (Burton et al 2015)
As the PIR sensor is relatively unselective in which species it detects, CT surveys targeted at large carnivores can potentially be used to look at non-target species or even whole mammal communities (Tobler et al 2008, Rich et al 2016, Mazzamuto et al 2019)
Low detection probabilities can result in biased estimates of occupancy (MacKenzie et al 2002), while the precision of the occupancy estimate is determined by the combination of detection probability and the number of surveys (Mackenzie and Royle 2005)
Summary
Remote cameras activated by a passive infrared (PIR) sensor, known as camera traps or trail cameras (from here on referred to as CTs), are increasingly used to study wildlife (Burton et al 2015). As the PIR sensor is relatively unselective in which species it detects (but see, e.g., Hofmeester et al 2017 for the effect of body size on detectability), CT surveys targeted at large carnivores can potentially be used to look at non-target species or even whole mammal communities (Tobler et al 2008, Rich et al 2016, Mazzamuto et al 2019). In these scenarios, biases in detection of non-target species, for example, due to differences in movement ecology, habitat preferences, or inter-species interactions, are crucial to know, but are poorly studied (but see, e.g., Harmsen et al 2010).
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