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Spatial modeling, parameter uncertainty, and precision of density estimates from line-transect surveys: a case study with Western Arctic bowhead whales
Megan C. Ferguson, David L. Miller, Janet T. Clarke, Amelia A. Brower, Amy L. Willoughby and Audrey D. Rotrock
Spatially-explicit models of animal density, such as density surface models (DSMs), are diverse, 15 flexible, and powerful tools for investigating spatial patterns in animal density, examining 16 associations between animal density and environmental covariates, and estimating abundance. 17 Advances in spatial modeling methods and subsequent incorporation into widely accessible software 18 allow the non-specialist to add these tools to their analytical toolbox. However, limitations in some 19 software may prevent a thorough treatment of uncertainty. We expanded the functionality of tools 20 for constructing DSMs from line-transect survey data to derive a population abundance estimate 21 that honestly accounts for multiple sources of detection bias and associated uncertainty. As an 22 illustrative case study, we used data collected during an aerial line-transect survey for Western Arctic 23 bowhead whales (Balaena mysticetus) over their summering grounds in the Beaufort Sea and 24 Amundsen Gulf during August 2019. Using spatially explicit hierarchical generalized additive models 25 that incorporated correction factors and associated uncertainty for perception and availability bias, 26 we estimated the abundance of the Western Arctic bowhead whale population to be 17,175 whales 27 (CV-hat = 0.237; 95% confidence interval = [10,793, 27,330]). This model-based abundance estimate is 28 similar in magnitude to the two most recent estimates for this population based on data from ice-29 based surveys in 2011 and 2019 . Additionally, our abundance estimate is sufficiently precise to 30 inform management decisions for this protected species. The enhanced precision of our abundance 31 estimate over the estimate derived using design-based analytical methods applied to the same data is 32 due to explicit modeling of the spatial correlation in whale density. Applying the power of DSMs to 33 the aerial line-transect survey data made this survey methodology a viable alternative to ice-based 34 surveys, which are facing obstacles due to climate change, for updating abundance estimates for 35 Western Arctic bowhead whales in the future. Our analytical developments can easily be applied to 36 other line-transect datasets with similar and common challenges due to multiple survey platforms, 37 spatial heterogeneity in animal density and environmental conditions, and habitat partitioning among 38 groups (e.g., defined by age, sex, activity state) in the target population.