Evaluating Multi-Level Models to Test Occupancy State Responses of Plethodontid Salamanders

Abstract

Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

Optimizing monitoring schemes to detect trends in abundance over broad scales

“…Detection probability and mean abundance of species also were extremely important. As expected, detecting declines was particularly challenging in rare species with low detectability (Guillera‐Arroita, ; Kroll et al ., ). In the most extreme scenario (7.5 individuals/site, p = 0.05), models failed to detect weak declines, and their performance remained limited even with strong declines (Fig.…”

“…This may violate statistical assumptions (pseudorepliction), and it is even possible that the same individual is counted in different transects (Chandler & Royle, ). It is thus important to ensure the independence of transects, or to adopt analytical frameworks allowing to take spatial dependence into account, such as multi‐scale models (Chandler & Royle, ; Kroll et al ., ). Combining data from target and non‐target sites: We first assumed that, for each species, several sites where the species is relatively frequent were monitored. In the real world, multiple species can be detected in most of sites.…”

“…A few studies tried to identify under which conditions (number of sites, number of surveys) it is possible to successfully detect population declines (e.g. Guillera‐Arroita, ; Sewell et al ., ; Kroll et al ., ), but these analyses mostly focused on occupancy models. Occupancy models do not provide explicit estimates of abundance (but see Royle & Nichols, ; Ellis, Ivan & Schwartz, ), and thus mostly deal with changes in number of populations, instead of estimating trends in abundance.…”

Disentangling elevational richness: a multi‐scale hierarchical Bayesian occupancy model of Colorado ant communities

“…The data required to fit a complex model such as this necessitated several simplifying assumptions, highlighting areas for improvement in future efforts. First, we assumed that detection probability was constant for each species, though it could be allowed to vary across space or through time according to local conditions (Nichols et al 2008, Kroll et al 2015. Second, our sampling occurred across three years, with different sites and transects sampled each year (Supplementary material Appendix 1 Table A1).…”

“…canopy cover ( Fig. 1c; Nichols et al 2008, Kroll et al 2015. However, a species may not be detected at a trap or site even though it is present (Fig.…”