Rare species generally require specific survey methods to optimize their detection. Both these results translate into better ecological understanding of the systems in question and, consequently, an improved ability to make effective management decisions. In turn, that precision directly increases our ability to track temporal changes in occupancy in surveyed areas and create refined habitat suitability models. As detection probability increases, estimated occupancy becomes more precise (i.e. Accounting for detection probability increases the performance of models estimating occupancy, especially for rare or difficult to detect species. Including estimates of detectability in occupancy analyses is crucial because false negatives can lead to a biased and imprecise estimation of habitat characteristics and their relative influence on the occurrence of the target species. Survey methods and conditions can alter the behavior of animals or affect surveyor performance, further reducing the ability to distinguish between a true negative, where a species is absent from the survey area, and a false negative, when a species is present but undetected during the survey. However, even when species are fairly easy to observe and identify, detection is often imperfect. Imperfect detection is a frequent sampling problem when animals are rare, populations are small, or individuals are difficult to observe. Occupancy modeling is a logistic regression-based framework that estimates the probability of a species occupying sampled sites (occupancy) while accounting for the probability of detecting the species using the given sampling methods (detectability). Identifying such critical habitats often relies on occupancy modeling, which has become a common tool to monitor wildlife populations when insufficient captures prevent the use of mark-recapture analysis or when individual identification is impossible. Rapid and global environmental change increases the need to identify and conserve critical habitats for at-risk and endangered species. Increasing detection probability of rare components of a community can improve the results and understanding of future studies. Our results show that simple changes to standard small-mammal trapping methods can dramatically increase the detectability of rare and elusive small mammals. We were also able to demonstrate that by deploying a combination of different traps and baits it is possible to overcome the potential effect of non-target species (e.g., deer mice, Peromyscus maniculatus) on the detection probability of pocket mice. Increasing grid size, while maintaining a similar trapping effort, resulted in higher detection probability, although our analyses showed that effective grids can be about three-quarters of the size we use to achieve similar results. We found that bait and trap type selection varied significantly by species, with pocket mice showing strongest selection for Havahart traps baited with bird seed. Regardless of species, trap success was higher for Havaharts. We also assessed the effect of captures of non-target species on detection probability of pocket mice. We used three trap and bait types and trapped an area 4.4 times larger than the standard grid. Our goal was to create a new small mammal trapping protocol that improved detection of rare species, such as the olive-backed pocket mouse ( Perognathus fasciatus). Improving detection probabilities for rare species is critical when assessing presence or habitat associations.
0 Comments
Leave a Reply. |