Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort Drum Army Installation, New York

Nocera, Tomás; Ford, W. Mark; Silvis, Alexander; Dobony, Christopher A.

doi:10.1016/j.gecco.2019.e00633

Cited by 33 publications

(43 citation statements)

References 31 publications

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“…Rodhouse et al, 2019). Alternatively, estimating relative activity helps address questions at smaller scales, such as how bat populations are affected by disease (Nocera et al, 2019), habitat alteration (Johnson et al, 2008), or artificial light (Straka, Wolf, Gras, Buchholz, & Voigt, 2019). In both cases, species-specific estimates are important to account for species-specific responses (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…In both cases, species-specific estimates are important to account for species-specific responses (e.g. Nocera et al, 2019). Within a single framework, our approach provides inferences for both occupancy and relative activity while accounting for errors in automated species classifications.…”

Section: Discussionmentioning

confidence: 99%

“…acoustic data, bats, false positives, imperfect detection, misidentification, occupancy models, passive acoustic monitoring, species classification al., 2019) or model patterns in activity (e.g. Nocera et al, 2019). We introduce a comprehensive framework for modelling acoustic data which accounts for species classification errors while simultaneously estimating species-specific occupancy and relative activity.…”

Section: Introductionmentioning

confidence: 99%

“…Another characteristic of ARU data is detection counts are available for each visit to a site and allow for ecological inferences beyond species occurrence. For instance, anuran surveys collect calling rates that are used as an index for abundance (Royle, 2004;Royle & Link, 2005) and bat acoustic surveys monitor activity using the number of recorded call files (Johnson, Gates, & Ford, 2008;Nocera, Ford, Silvis, & Dobony, 2019). Estimates of relative activity are useful for addressing questions at smaller scales and provide a more sensitive metric for monitoring populations.…”

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confidence: 99%

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Modelling misclassification in multi‐species acoustic data when estimating occupancy and relative activity

et al. 2019

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1. Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) can efficiently gather community data for a variety of taxa, but generally require software algorithms to classify each recorded call to a species. Species classification Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) efficiently gather community data by passively recording animal vocalizations (Gibb, Browning, Glover-Kapfer, & Jones, 2019), generally for multiple time periods ('visits') at each surveyed location ('site'). These data, including counts of call recordings and corresponding species classifications, can be used to investigate various ecological questions and are applicable for surveying multiple taxa (e.g. anurans, bats, birds). However, due to the large volumes of data typically collected, most studies using acoustic surveys require classification software to identify the species of each call recording (Gibb et al., 2019). This automated process includes species classification errors that lead to both false-negative and false-positive detections. For instance, when a species is present, false-negative detections can result from successfully recording its calls but misclassifying them as alternative species. These errors are in addition to false negatives from failing to record any of its calls. False-positive detections at sites where a species is absent are often due to misclassifying recorded calls from another species. Estimating the ecological parameters of interest, while addressing these errors is an important consideration when analysing ARU data. Occupancy models (MacKenzie et al., 2002) are a natural framework for analysing ARU data when visits are summarized to detection/non-detection observations for each species (e.g. Banner et al., 2018; Rodhouse et al., 2019). Originally developed to account for false negatives, standard occupancy models assume that all false positives are removed (MacKenzie et al., 2002). Completely eliminating false positives from ARU data is generally cost prohibitive because it requires manually confirming at least one recording for every visit. False positives are an important source of errors in many

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling misclassification in multi‐species acoustic data when estimating occupancy and relative activity

et al. 2019

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“…Lesssteep slopes in 2017 suggest that (i) the number of passes counted for that species was lower in 2017 and (ii) they remained lower throughout the entire 2017 season. Intriguingly, similarly reduced activity over the season may also indicate lower overall reproductive output in a given species, at least at the local population level (see Nocera et al 2019).…”

Section: Reduced Competition Apparently But Niche Expansion?mentioning

confidence: 99%

Potential foraging niche release in insectivorous bat species relatively unaffected by white-nose syndrome?

et al. 2020

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White-nose syndrome (WNS) has rendered four of Ontario’s species endangered, while leaving the other four species relatively unaffected. The causes and extent of the declines have been widely studied. The influence on remaining bat species has not. Comparing acoustic data recorded ∼10 years apart, we evaluated how species in southeastern Ontario, Canada, use different foraging habitats pre- and post-WNS detection. We observed activity declines in now-endangered species over open fields (small-footed myotis, Myotis leibii (Audubon and Bachman, 1842); little brown bat, Myotis lucifugus (Le Conte, 1831); northern myotis, Myotis septentrionalis (Trouessart, 1897); tricolored bat, Perimyotis subflavus (F. Cuvier, 1832)) and speculate that the reduction of the once most common species (M. lucifugus) may have resulted in other species searching for prey in habitat once dominated by M. lucifugus. That is, these changes may have allowed greater presence in open field and clutter or edge environments by the big brown bat (Eptesicus fuscus (Palisot de Beauvois, 1796)) and three migratory species (silver-haired bat, Lasionycteris noctivagans (Le Conte, 1831); red bat, Lasiurus borealis (Müller, 1776); hoary bat, Lasiurus cinereus (Palisot de Beauvois, 1796)). However, our results also suggest that (i) while the decline of most resident bat species due to WNS may have relaxed competition for relatively unaffected species in some, but not all habitats, that (ii) sensory and biomechanical constraints may limit prey exploitation by these less-affected bat species in these habitats.

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Species‐specific responses to white‐nose syndrome in the Great Lakes region

Mallinger

Goodwin

Kirschbaum

et al. 2023

Ecology and Evolution

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White‐nose syndrome is a fungal disease that is threatening bat populations across North America. The disease primarily affects cave‐hibernating bats by depleting fat reserves during hibernation and causing a range of other physiological consequences when immune responses are suppressed. Since it was first detected in 2006, the disease has killed millions of bats and is responsible for extensive local extinctions. To better understand the effects of white‐nose syndrome on various bat species, we analyzed summer acoustic survey data collected from 2016 to 2020 at nine US National Parks within the Great Lakes region. We examined the effect that white‐nose syndrome, time of the year relative to pup volancy, habitat type, and regional variation (i.e., park) have on the acoustic abundance (i.e., mean call abundance) of six bat species. As expected, little brown bat (Myotis lucifugus) and northern long‐eared bat (Myotis septentrionalis), both hibernating species, experienced a significant decline in acoustic abundance following white‐nose syndrome detection. We observed a significant increase in acoustic abundance as white‐nose syndrome progressed for hoary bats (Lasiurus cinereus) and silver‐haired bats (Lasionycteris noctivagans), both migratory species that are not impacted by the disease. Contrary to our predictions, we observed an increase in big brown bat (Eptesicus fuscus; hibernating) acoustic abundance and a decrease in eastern red bat (Lasiurus borealis; migratory) acoustic abundance following the detection of white‐nose syndrome. We did not observe any significant changes after the onset of white‐nose syndrome in the seasonal patterns of acoustic activity related to pup volancy, suggesting that production or recruitment of young may not be affected by the disease. Our results suggest that white‐nose syndrome is affecting the acoustic abundance of certain species; however, these changes may not be a result of reduced reproductive success caused by the disease. In addition, species population dynamics may be indirectly affected by white‐nose syndrome as a result of reduced competition or a foraging niche release. We also found that for parks located at higher latitudes, little brown bat and northern long‐eared bat were more likely to experience greater declines in acoustic abundance as a result of white‐nose syndrome. Our work provides insight into species‐specific responses to white‐nose syndrome at a regional scale and examines factors that may facilitate resistance or resiliency to the disease.

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Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort Drum Army Installation, New York

Cited by 33 publications

References 31 publications

Modelling misclassification in multi‐species acoustic data when estimating occupancy and relative activity

Modelling misclassification in multi‐species acoustic data when estimating occupancy and relative activity

Potential foraging niche release in insectivorous bat species relatively unaffected by white-nose syndrome?

Species‐specific responses to white‐nose syndrome in the Great Lakes region

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