Density estimation of sound-producing terrestrial animals using single automatic acoustic recorders and distance sampling
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Obtaining accurate information on the distribution, density, and abundance of animals is an important first step toward their conservation. Methodological approaches using automatic acoustic recorders for species that communicate acoustically are gaining increased interest because of their advantages over traditional sampling methods. In this study, we created and evaluated a protocol to estimate population density, which can be used to compute abundance of terrestrial sound-producing animals from single automatic acoustic recorders and using an automatic detection algorithm. The protocol uses cue rates from the target species, environmental conditions, and an estimate of the distance of the individual to the recorder based on the power of the received sound. We applied our protocol to estimate the density of a Hawaiian forest bird species (Hawaiˊi ˊAmakihi [Chlorodrepanis virens]) on the island of Hawaiˊi, USA. We validated our approach by comparing our density estimates with those calculated at the same stations using a traditional point-transect distance sampling method based on human observations. Overall density estimates based on recorded signals were lower than those based on human observations, but 95% confidence intervals of the two density estimates overlapped. This study presents a relatively simple but effective protocol for estimating animal density using single automatic acoustic recorders. Our protocol may easily be adapted to other sound-emitting terrestrial animals.
Sebastián-González , E , Camp , R J , Tanimoto , A M , de Oliveira , P M , Lima , B B , Marques , T A & Hart , P J 2018 , ' Density estimation of sound-producing terrestrial animals using single automatic acoustic recorders and distance sampling ' Avian Conservation and Ecology , vol. 13 , no. 2 , 7 . https://doi.org/10.5751/ACE-01224-130207
Avian Conservation and Ecology
Copyright © 2018 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.
DescriptionFinancial support was provided by the NSF award #1345247 to D. Price, P. Hart, E. Stacy, and M. Takabayashi. ESG is funded by the Juan de la Cierva program from the Spanish Government (IJCI-2015-24947). TAM thanks partial support by CEAUL (funded by FCT - Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013). RJC is partially funded through the U.S. Geological Survey and the University of St. Andrews.
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