Acoustic monitoring for conservation in tropical forests: examples from forest elephants. Issue 10 (9th February 2017)

• Record Type: Journal Article
• Authors: Wrege, Peter H.; Rowland, Elizabeth D.; Keen, Sara; Shiu, Yu
• Editors: Matthiopoulos, Jason
• Is Part Of: Methods in ecology and evolution. Volume 8:Issue 10(2017)
• Page Start: 1292
• Page End: 1301
• Publication Date: 2017-02-09
• Subjects: anthropogenic impacts -- anti‐poaching -- bioacoustics -- cryptic species -- density estimate -- law enforcement monitoring -- Loxodonta cyclotis -- passive acoustic monitoring -- signal detection -- smart; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.12730
• Languages: English
• ISSNs: 2041-210X

Abstract

Summary: The accelerating loss of biodiversity worldwide demands effective tools for monitoring animal populations and informing conservation action. In habitats where direct observation is difficult (rain forests, oceans), or for cryptic species (shy, nocturnal), passive acoustic monitoring (PAM) provides cost‐effective, unbiased data collection. PAM has broad applicability in terrestrial environments, particularly tropical rain forests. Using examples from studies of forest elephants in Central African rain forest, we show how PAM can be used to investigate cryptic behaviour, mechanisms of communication, estimate population size, quantify threats, and assess the efficacy of conservation strategies. We discuss the methodologies, requirements, and challenges of obtaining these data using acoustics. Where applicable, we compare these methods to more traditional approaches. While PAM methods and associated analysis are maturing rapidly, mechanisms are needed for processing the dense raw data efficiently with standard computer hardware, speeding development of detection algorithms, and harnessing communication networks to move data from the field to research facilities. Passive acoustic monitoring is a viable and cost‐effective tool for conservation and should be incorporated in monitoring schemes much more broadly. The capability to quickly assess changes in behaviour, population size, and landscape use, simultaneously over large geographical areas, makes this approach attractive for detecting human‐induced impacts and for assessing the success of conservation strategies.