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Data from: Estimating population density of insectivorous bats based on stationary acoustic detectors: a case study

Citation

Milchram, Markus; Suarez-Rubio, Marcela; Schröder, Annika; Bruckner, Alexander (2020), Data from: Estimating population density of insectivorous bats based on stationary acoustic detectors: a case study, Dryad, Dataset, https://doi.org/10.5061/dryad.hx3ffbg9m

Abstract

1. Automated recording units are commonly used by consultants to assess environmental impacts and to monitor animal populations. Although estimating population density of bats using stationary acoustic detectors is key for evaluating environmental impacts, estimating densities from call activity data is only possible through recently developed numerical methods, as the recognition of calling individuals is impossible.
2. We tested the applicability of generalized random encounter models (gREMs) for determining population densities of three bat species (Common pipistrelle Pipistrellus pipistrellus, Northern bat Eptesicus nilssonii,, and Natterer’s bat Myotis nattereri) based on passively collected acoustical data. To validate the results, we compared them to (i) density estimates from the literature and to (ii) Royle-Nichols (RN) models of detection/non-detection data.
3. Our estimates for M. nattereri matched both the published data and RN-model results. For E. nilssonii, the gREM yielded similar estimates to the RN-models, but the published estimates were more than twice as high. This discrepancy might be because the high-altitude flight of E. nilssonii is not accounted for in gREMs. Results of gREMs for P. pipistrellus were supported by published data but were approximately 10 times higher than those of RN-models. RN-models use detection/non-detection data and this loss of information probably affected population estimates of very active species like P. pipistrellus.
4. gREM models provided realistic estimates of bat population densities based on automatically recorded call activity data. However, the average flight altitude of species should be accounted for in future analyses. We suggest including flight altitude in the calculation of the detection range to assess the detection sphere more accurately and to obtain more precise density estimates.

Methods

Bat calls were recorded using automatic recording units (file bats_harz.csv). They were analyzed using bcAdmin, bcAnalyze, and batIdent. The habitatparameters (habitatparameters.csv) were collected in field surveys and using ArcGIS.

Description of variables (bats_harz.csv): sunset, sunrise, species (automatically identified species or Operational Taxonomic Unit (OTU) using batIdent), recordingTime (time when the sequence was recorded), filename (ID of the recorded file), speciesVerified (manually verified species), site (ID of sample point).

For the description of variables (habitatparameters.csv) we refer to Appendix S1 of Milchram et al. (2019).