Dr. Katie Banner (Dept. of Mathematical Sciences, MSU)

Abstract: 

Bats in North America face emerging threats due to the spread of the bat disease white-nose-syndrome (WNS), the expanding footprint of the wind energy industry, and the effects of global change on suitable bat habitats. One goal of the North American monitoring program (NABat) is to monitor status and trend indicators for bat species assemblages at varying spatial extents. Bats are both cryptic and nocturnal, making them difficult to study. Autonomous recording units can efficiently gather data from bats, but also necessitate the use of auto-classifiers to assign species labels to large numbers of observations, which introduces misclassification error. Statistical models that account for misclassification error require additional information to estimate misclassification rates. Observation-level verification designs can reduce impractical efforts required by verification designs, but can also impact parameter estimation (i.e., introduce bias). In this talk, I will discuss incremental steps my research team has made to accommodate the use of the most realistic models and verification designs for monitoring occurrence and relative activity of North American bats. I will highlight statistical frameworks that afford reductions in validation effort while still producing unbiased and reasonably precise estimates of parameters of interest. These approaches and associated software tools provide practical ways forward for NABat partner researchers to balance validation costs with programmatic goals.