A comparison of three methods for estimating call densities of migrating bowhead whales using passive acoustic monitoring
Abstract
Various methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have recently been adapted for estimating call density using passive acoustic monitoring (PAM) data, e.g., recordings of animal calls. Here we summarize three methods available for passive acoustic density estimation: plot sampling, DS, and SECR. The first two require distances from the sensors to calling animals (which are obtained by triangulating calls matched among sensors), but SECR only requires matching (not localizing) calls among sensors. We compare via simulation what biases can arise when assumptions underlying these methods are violated. We use insights gleaned from the simulation to compare the performance of the methods when applied to a case study: bowhead whale call data collected from arrays of directional acoustic sensors at five sites in the Beaufort Sea during the fall migration 2007–2014. Call detections were manually extracted from the recordings by human observers simultaneously scanning spectrograms of recordings from a given site. The large discrepancies between estimates derived using SECR and the other two methods were likely caused primarily by the manual detection procedure leading to non-independent detections among sensors, while errors in estimated distances between detected calls and sensors also contributed to the observed patterns. Our study is among the first to provide a direct comparison of the three methods applied to PAM data and highlights the importance that all assumptions of an analysis method need to be met for correct inference.
Citation
Oedekoven , C S , Marques , T A , Harris , D , Thomas , L , Thode , A M , Blackwell , S B , Conrad , A S & Kim , K H 2021 , ' A comparison of three methods for estimating call densities of migrating bowhead whales using passive acoustic monitoring ' , Environmental and Ecological Statistics . https://doi.org/10.1007/s10651-021-00506-3
Publication
Environmental and Ecological Statistics
Status
Peer reviewed
ISSN
1352-8505Type
Journal article
Rights
Copyright © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Description
TAM thanks partial support by Centro de Estatistica e Aplicações, Universidade de Lisboa (funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).Collections
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