Using fusion effects to decrease uncertainty in distance sampling models when collating data from different surveys

Abstract

Estimates of population abundance are required to study the impacts of human activities on populations and assess their conservation status. Despite considerable effort to improve data collection, uncertainty around estimates of cetacean densities can remain large. A fundamental concept underlying distance sampling is the detection function. Here we focus on reducing the uncertainty in the estimation of detection function parameters in analyses combining data sets from multiple surveys, with known effects on the precision of density estimates. We developed detection functions using infinite mixture models that can be applied on data collating multiple species and/or surveys. These models enable automatic clustering by fusing the species and surveys with similar detection functions. We present a simulation analysis of a multisurvey data set in a Bayesian framework where we demonstrated that distance sampling models including fusion effects showed lower uncertainty than classical distance sampling models. We illustrated the benefits of this new model using data of line transect surveys from the Bay of Biscay and Iberian Coast. Future estimates of abundance using conventional distance sampling models on large multispecies surveys or on data sets combining multiple surveys could benefit from this new model to provide more precise density estimates.

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