Estimates Of Occupancy Probabilities Research Articles

Evaluating temporal turnover in avian species richness in a Mediterranean semiarid region: Different responses to elevation and forest cover

AbstractAimWhen studying the effects of global change on biodiversity, it is far more common for the effects of climate change and land‐use changes to be assessed separately rather than jointly. However, the effects of land‐use changes in recent decades on species richness in areas affected by climate change have been less studied. We assess the temporal turnover in species richness of an avian community between a historical period and a modern one as a consequence of global change.LocationSemiarid Mediterranean ecosystem (southeastern Spain).MethodWe fitted a hierarchical multispecies occupancy model for each period (1991–1992, and 2012–2017), obtaining avian species‐specific estimates of occupancy probability in relation to environmental covariates (elevation and forest cover). We analyse the relationships between changes in the bird community and environmental variables, analysing the temporal turnover of the species richness and the richness‐based species‐exchange ratio.ResultsThe estimated species richness accounting for detectability was higher than observed species richness, and decreased in the more recent period. Following our hypotheses, we observed a dual pattern of species richness increase associated with different elevations, showing different species turnover rates due to the joint effects of climate change and land‐use change. There is a trend towards greater species richness with higher elevations that is associated with climate change, where the species turnover rate is low. Also, species richness increased towards lower elevations, but with a high turnover rate. The latter can be due to species expansions throughout new habitat configurations in bordering forest systems associated with anthropic land‐use changes.ConclusionsOur study is of great interest to understand the temporal turnover of avian species richness associated with areas experiencing both climate and land‐use change.

A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Sites

ABSTRACTReliable and efficient population estimates are a critical need for effective management of invasive wild pigs (Sus scrofa). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture–recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions. © 2020 The Wildlife Society.

Spatial Occupancy and Abundance Trends of Endangered Florida Grasshopper Sparrows at Three Lakes Wildlife Management Area

Abstract We analyzed spatiotemporal variations in point counts of Ammodramus savannarum floridanus (Florida Grasshopper Sparrow) at Three Lakes Wildlife Management Area (WMA) during 2003–2012 to provide a detailed report of population changes during that period. There were significant increases in estimates of occupancy probability and abundance of Florida Grasshopper Sparrows at count points in some parts of Three Lakes WMA from 2003 to 2008 followed by significant reductions in these estimates from 2008 to 2012. Inconsistent, finer-scale population fluctuations appeared to be occurring within these time periods. From 2003 to 2012, estimates of overall change in occupancy probability and abundance of the Florida Grasshopper Sparrows at count points were largely negative throughout the area, but a region in the northeast portion of the WMA may offer the greatest chance for population persistence. The count points characterized by most persistent occurrence and abundance were ≥600 m from the edge of non-pr...

Maxent is not a presence–absence method: a comment on Thibaud et al.

Summary Thibaud et al. (Methods in Ecology and Evolution 2014) present a framework for simulating species and evaluating the relative effects of factors affecting the predictions from species distribution models (SDMs). They demonstrate their approach by generating presence–absence data sets for different simulated species and analysing them using four modelling methods: three presence–absence methods and Maxent, which is a presence‐background modelling tool. One of their results is striking: that their use of Maxent performs well in estimating occupancy probabilities and even outperforms the other methods on small sample sizes. This result is of concern to us, because it suggests that Maxent directly offers a useful alternative for modelling presence–absence data, which may prompt widespread adoption of this use of Maxent. In this paper, we explore why this would be a mistake. We draw on the theory underlying how the Maxent model operates and on simulations to discover: (i) why Maxent appears to fare as well as it does in their evaluation and (ii) why the best‐suited presence–absence method for data analysis (the generating model; a GLM) does not perform as well as we would expect. We demonstrate that (i) the good performance observed for Maxent is largely a coincidence; the simulated species match well the arbitrary default parameter that Maxent applies to map its relative output into a 0–1 scale, but errors are much larger for other species we simulate; (ii) the performance of the GLM is poorer than expected because Thibaud et al. do not use model selection and fit a model that is too complex for the amount of data available. Maxent is a presence‐background method and only provides estimates of relative suitability regardless of how the background sample is specified. When presence–absence data are available, one can transform Maxent's relative estimates into estimates of occupancy probability, and we provide methods to do so. However, this requires the user to post‐process Maxent's output. Proper PA methods such as GLMs can perform well under small sample sizes, provided care is taken during modelling to avoid overfitting. We demonstrate an effective method using regularisation with the R package glmnet.