Quantifying site-level usage and certainty of absence for an invasive species through occupancy analysis of camera-trap data

Abstract

Efficient implementation of management programs for invasive species depends on accurate surveillance for guiding prioritization of surveillance and control resources in space and time. Occupancy probabilities can be used to determine where surveillance should occur. Conversely, knowledge of the certainty of site-level absence is of special interest in situations where the objective is to completely remove populations despite substantial risk of reinvasion. Indeed, the decision to shift from emphasizing control activities over the full range to emphasizing reinvasion prevention, surveillance, and response near the borders, depends on accurate knowledge of absence across space. We used a dynamic occupancy model to monitor changes in the distribution of an invasive species, feral swine (Sus scrofa), based on camera-trap data collected as part of a management program from June 2014 to January 2016 in San Diego County, California. Site usage of feral swine declined overall. The most informative predictors of site usage were spatial (latitude and longitude). Site-level nonusage rates increased over time and in response to management removal efforts; and site-level usage rates were heavily impacted by having neighboring sites that were used. Combining the detection probability estimated from the occupancy model and Bayes Theorem, we demonstrated how certainty of local (site-level) absence can be estimated iteratively in time in areas with negative surveillance (no detections) data. Our framework provides a means for using management-based surveillance data to quantify certainty of site-level absence of an invasive species, allowing for adaptive prioritization of surveillance and control resources. Our approach is flexible for application to other species and types of surveillance (e.g., track-plates, eDNA).

Reassessing the success of experts and nonexperts at correctly differentiating between closely related species from camera trap images: A reply to Gooliaff and Hodges

“…We note that recent features on virtually all trail camera models include a burst of images when the camera is triggered, and the resulting series of images allows different views of individuals which may improve classification (Rovero, Zimmermann, Berzi, & Meek, ). Increasingly, burst or video settings (which would also provide multiple image angles) are being used in camera‐trapping studies and are quickly becoming standard practice among researchers (Comer et al, ; Davis et al, ; Hedwig et al, ; Ladle, Steenweg, Shepherd, & Boyce, ; McCarthy et al, ). Likewise, Gooliaff and Hodges note that the use of multiple images may result in an improved classification, but this added realism was not included in their analysis.…”

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

“…We 334 examined two levels of uncertainty in abundance (Uo: low and high). We assumed that the 335 process for observing abundance is biased low (underestimation of the true abundance) 336 because studies that have estimated detection probability during management-based monitoring 337 in populations have found that it is well below 1 (e.g., (Davis et al 2018b), although in the 338 supplementary information we also present results for instances where abundance estimation is 339 imperfect but unbiased; Appendix 1: Fig. S10).…”

Optimal invasive species surveillance in the real world: practical advances from research

“…Even when detection is highly reliable, it is inefficient to survey everywhere across all areas at risk with the same degree of effort, since invasions rarely progress across a landscape in uniform fashion [39,41]. Moreover, surveillance is merely one element of post-border response to invasions; regularly, decision-makers must assess the trade-offs of devoting limited resources to surveillance instead of other management efforts [47][48][49][50].…”