Abstract
Simple SummaryFeral cats are detrimental to native biodiversity worldwide. In New Zealand, feral cats are well established across much of the pastoral landscape, including forested areas. Feral cats, like many carnivore species, are elusive in their nature, and often occur at low densities, making them difficult to detect. Camera traps are a useful, non-invasive monitoring device, capable of ‘capturing’ feral cats as they pass by. Although cameras provide a wealth of information about animals within their field of view; there remains much to be learned about optimal camera trap placement within a landscape, if maximizing detection probability is the objective. Here, we report the results of two methods of camera trap deployment within similar sites: (1) systematic deployment on a grid and (2) strategic deployment, predominantly favoring habitats with assumed higher cat activity. Using the Royle–Nichols abundance-induced heterogeneity model (RN), which assumes detection probability and animal abundance are linked, we found that more cats were detected by cameras at forest margins than in mixed scrub or open farmland (but only slightly more than in forest locations). If maximizing cat detections is the aim, we recommend that cameras should be placed at the edges of forests (including forest fragments) whenever feasible. We deploy camera traps to monitor feral cat (Felis catus) populations at two pastoral sites in Hawke’s Bay, North Island, New Zealand. At Site 1, cameras are deployed at pre-determined GPS points on a 500-m grid, and at Site 2, cameras are strategically deployed with a bias towards forest and forest margin habitat where possible. A portion of cameras are also deployed in open farmland habitat and mixed scrub. We then use the abundance-induced heterogeneity Royle–Nichols model to estimate mean animal abundance and detection probabilities for cameras in each habitat type. Model selection suggests that only cat abundance varies by habitat type. Mean cat abundance is highest at forest margin cameras for both deployment methods (3 cats [95% CI 1.9–4.5] Site 1, and 1.7 cats [95% CI 1.2–2.4] Site 2) but not substantially higher than in forest habitats (1.7 cats [95% CI 0.8–3.6] Site 1, and 1.5 cats [95% CI 1.1–2.0] Site 2). Model selection shows detection probabilities do not vary substantially by habitat (although they are also higher for cameras in forest margins and forest habitats) and are similar between sites (8.6% [95% CI 5.4–13.4] Site 1, and 8.3% [5.8–11.9] Site 2). Cat detections by camera traps are higher when placed in forests and forest margins; thus, strategic placement may be preferable when monitoring feral cats in a pastoral landscape.
Highlights
Accurate and precise population estimates are necessary to understand the distribution and relative abundance of a target species for wildlife management
The number of cameras deployed in each habitat type at each site and the average nightly detections of cats can be found in Cameras (Site 1)
Estimates for cat abundance at both sites were highest in forest margin and forest habitats for cameras in both deployment arrays
Summary
Accurate and precise population estimates are necessary to understand the distribution and relative abundance of a target species for wildlife management. Often the problem is low rates of detection, i.e., the probability of an individual being detected is much less than 1, and this problem has inspired a variety of different statistical models and sampling designs for estimating population abundance and dynamics over time, such as occupancy modeling [5]. Heterogeneity in detection probability at the individual monitoring station level is another issue to account for in estimating abundance [1,6]. Failure to adjust for heterogeneity in detection probabilities assumes uniform abundance throughout the sites, which is often incorrect [1,7]. Animals will usually be detected more where they are more abundant [1]. The abundance-induced heterogeneity Royle–Nichols (RN) model extends the traditional occupancy modelling approach [3]
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