Mobility explains the response of aerial insectivorous bats to anthropogenic habitat change in the Neotropics

Bader, Elias; Jung, Kirsten; Kalko, Elisabeth K. V.; Page, Rachel A.; Rodríguez, Ruben Dario Cruz; Sattler, Thomas

doi:10.1016/j.biocon.2015.02.028

Cited by 65 publications

(58 citation statements)

References 65 publications

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“…In our study, we observed high correlations between vegetation structure and ultrasound transmission, suggesting that habitats with contrasting vegetation will affect bat detection differently. Indeed, it was shown that vegetation measures correlate with bat activity (Ford et al, 2005), and they have been taken into account in occupancy models recently (Bader et al, 2015). Although it was recognized early that ultrasound transmission differs across habitat types (Patriquin et al, 2003), and it has been observed to affect sampling of bats directly (Kalcounis et al, 1999), such considerations are not systematically taken into account (see for example Frey-Ehrenbold et al, 2013;Jung et al, 2012;Newson et al, 2015).…”

Section: Implications For Ecoacoustic Studiesmentioning

confidence: 98%

“…Passive acoustic monitoring systems are increasingly prevalent for surveying a wide range of sound-emitting animals: ecologists use these systems to record birds (Celis-Murillo et al, 2009), bats (Bader et al, 2015), amphibians (Aide et al, 2013), insects (Lehmann et al, 2014), terrestrial (Mielke and Zuberbühler, 2013) and marine mammals (Wiggins and Hildebrand, 2007), to determine species richness (Wimmer et al, 2013), to record soundscapes, and to construct general biodiversity indices (Sueur et al, 2014). More complex systems using microphone arrays have been proposed for a wider audience of biologists to study a variety of other aspects such as anthropogenic noise, species interactions and social dynamics (reviewed in Blumstein et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Measuring sound detection spaces for acoustic animal sampling and monitoring

Darras

Pütz

Fahrurrozi

et al. 2016

Biological Conservation

112

123

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Section: Implications For Ecoacoustic Studiesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Measuring sound detection spaces for acoustic animal sampling and monitoring

Darras

Pütz

Fahrurrozi

et al. 2016

Biological Conservation

112

123

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“…Automated recording devices (ARD) are utilized to document a large variety of vocalizing animals. Ecologists use these systems to monitor the behavior of birds (Digby, Towsey, Bell, & Teal, 2013), bats (Bader et al, 2015), anurans (Aide et al, 2013;Oseen & Wassersug, 2002), insects (Lehmann, Frommolt, Lehmann, & Riede, 2014;Romer & Lewald, 1992), and both terrestrial (Mielke & Zuberbühler, 2013) and marine mammals (Selby et al, 2016;Wiggins & Hildebrand, 2007). ARDs are commonly applied to determine species richness (Hsu, Kam, & Fellers, 2005;Wimmer, Towsey, Roe, & Williamson, 2013) or construct general biodiversity indices (Sueur, Farina, Gasc, Pieretti, & Pavoine, 2014;Zimmerman, 1994).…”

Section: Introductionmentioning

confidence: 99%

Observer‐free experimental evaluation of habitat and distance effects on the detection of anuran and bird vocalizations

MacLaren

Crump

Royle³

et al. 2018

Ecology and Evolution

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Acoustic surveys of vocalizing animals are conducted to determine density, distribution, and diversity. Acoustic surveys are traditionally performed by human listeners, but automated recording devices (ARD) are becoming increasingly popular. Signal strength decays, or attenuates, with increasing distance between source and receiver and some habitat types may differentially increase attenuation beyond the effects of distance alone. These combined effects are rarely accounted for in acoustic monitoring programs. We evaluated the performance of three playback devices and three ARD models using the calls of six anurans, six birds, and four pure tones. Based on these evaluations, we determined the optimal playback and recording devices. Using these optimal devices, we broadcast and recorded vocalizations in five habitat types along 1,000 m transects. We used generalized linear models to test for effects of habitat, distance, species, environmental, and landscape variables. We predicted detection probabilities for each vocalization, in each habitat type, from 0 to 1,000 m. Among playback devices, only a remote predator caller simulated vocalizations consistently. Differences of ~10 dB were observed among ARDs. For all species, we found differences in detectability between open and closed canopy habitats. We observed large differences in predicted detection probability among species in each habitat type, as well as along 1,000 m transects. Increases in temperature, barometric pressure, and wind speed significantly decreased detection probability. However, aside from differences among species, habitat, and distance, topography impeding a line‐of‐sight between sound source and receiver had the greatest negative influence on detections. Our results suggest researchers should model the effects of habitat, distance, and frequency on detection probability when performing acoustic surveys. To optimize survey design, we recommend pilot measurements among varying habitats.

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“…PAM devices have also been used to increase the number of observations of bats (Bader et al . ), nocturnal birds (Sberze, Cohn‐Haft & Ferraz ) and marine cetaceans (Moore et al . ) that are particularly difficult to detect with traditional sampling techniques.…”

Section: Introductionmentioning

confidence: 99%

Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling

2016

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Running title: Acoustic monitoring and occupancy modeling Summary 1. Conservation of threatened species relies on predictions about their spatial distribution; however, it is often difficult to detect species in the wild. The combination of acoustic monitoring to improve species detectability and statistical methods to account for false negative detections can improve species distribution estimates. 2. Here, we combine a novel automated species-specific identification approach with occupancy models that account for imperfect detectability to provide a more accurate species distribution map of the Elfin-woods warbler Setophaga angelae, a rare, elusive, and threatened bird species. We also compared three automated species identification/validation approaches to determine which approach provided occupancy estimates similar to manual validation of all recordings. Acoustic data were collected along three elevational gradients (95 to 1074 m a.s.l) in El Yunque National Forest, Puerto Rico. The detection matrices acquired through automated species-specific identification models and manual validations of all recordings were used to create occupancy models. 3. Although this species has a wider distribution than previously reported, it depends on Palo Colorado forest cover and it mainly occurs between 600 and 900 m a.s.l. Unbiased and precise occupancy models were developed by using automated species identification models and only manually validating 4% of the recordings. 4. Our approach draws on the strength of two active areas of ecological research: acoustic monitoring and occupancy modeling. Our methods provide an effective and efficient way to translate the enormous amount of acoustic information collected with passive acoustic monitoring devices into meaningful ecological data that can be applied to understand and map the distribution of rare, elusive and threatened species.

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Mobility explains the response of aerial insectivorous bats to anthropogenic habitat change in the Neotropics

Cited by 65 publications

References 65 publications

Measuring sound detection spaces for acoustic animal sampling and monitoring

Measuring sound detection spaces for acoustic animal sampling and monitoring

Observer‐free experimental evaluation of habitat and distance effects on the detection of anuran and bird vocalizations

Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling

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