Challenges and opportunities for comparative studies of survival rates: An example with male pinnipeds.

Survival rates are a central component of life-history strategies of large vertebrate species. However, comparative studies seldom investigate interspecific variation in survival rates with respect to other life-history traits, especially for males. The lack of such studies could be due to the challenges associated with obtaining reliable datasets, incorporating information on the 0-1 probability scale, or dealing with several types of measurement error in life-history traits, which can be a computationally intensive process that is often absent in comparative studies.

Evidence of region-wide bat population decline from long-term monitoring and Bayesian occupancy models with empirically informed priors.

Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long-term encounter surveys with multi-season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model-based understanding about at-risk species with new data.

Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification.

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species.

Considerations for assessing model averaging of regression coefficients.

Model choice is usually an inevitable source of uncertainty in model-based statistical analyses. While the focus of model choice was traditionally on methods for choosing a single model, methods to formally account for multiple models within a single analysis are now accessible to many researchers. The specific technique of model averaging was developed to improve predictive ability by combining predictions from a set of models.