Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks.

By Stuart Parsons, Gareth Jones. Journal of Experimental Biology, 2000

352 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9809a49291fdbdd12c96d7373442bb908f3870f0

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Citations in the corpus

Conserving energy at a cost to biodiversity? Impacts of LED lighting on bats (2012, methodology): “A single bat pass was classified when the time between call pulses exceeded four times the pulse interval (as quantified in Parsons & Jones, 2000).”
Dark Matters: The Effects of Artificial Lighting on Bats (2016, background): “hipposideros use different echolocation strategies (Parsons and Jones 2000) but have similar flight and foraging patterns.”
Bat activity and species richness on organic and conventional farms: impact of agricultural intensification (2003, methodology): “…been developed at the University of Bristol (Bristol, UK), where it was ‘trained’ using data sets of calls produced by known species and programmed to classify calls down to genus and then species level with a reported degree of confidence associated with each identification (Parsons & Jones 2000).”
Rapid Acoustic Survey for Biodiversity Appraisal (2008, background): “A first intuitive approach is to try to automatically identify assemblages of singing species like bats [21], birds [22], amphibians or insects [23].”
Estimating animal population density using passive acoustics (2012, methodology): “…detection and statistical classification of sounds will continue to be fertile topics for further research, as these will allow the efficient processing of large quantities of data (e.g. Parsons & Jones, 2000; Acevedo et al., 2009), making methods cheaper and faster, and hence more appealing.”
Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices (2013, unknown): “Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices”
Sampling period, size and duration influence measures of bat species richness from acoustic surveys (2012, methodology): “Identification accuracy was vetted using discriminant function analysis (Parsons & Jones 2000; Russo & Jones 2002; Fukia, Agetsuma &Hill 2004) and tests of interobserver agreement.”
Citizen science reveals trends in bat populations: The National Bat Monitoring Programme in Great Britain (2015, methodology): “A number of quantitative methods for species identification have also been developed (Parsons and Jones, 2000; Obrist et al., 2004; Walters et al., 2012).”
Identification of twenty‐two bat species (Mammalia: Chiroptera) from Italy by analysis of time‐expanded recordings of echolocation calls (2002, methodology): “Models so far devised for European bats have not included > 12 species (Zingg, 1990; Vaughan et al., 1997; Parsons & Jones, 2000).”
Differences in sensory ecology contribute to resource partitioning in the bats Myotis bechsteinii and Myotis nattereri (Chiroptera: Vespertilionidae) (2006, background): “Both species use short, downwards, frequencymodulated echolocation calls of broad bandwidth (Staden 1995; Parsons and Jones 2000; Siemers and Schnitzler 2000, 2004; Siemers and Kerth 2005).”