Statistical assessment on determining local presence of rare bat species-Reference-Cited by-同舟云学术

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Statistical assessment on determining local presence of rare bat species

Published:2022-06 Issue:6 Volume:13 Page:
ISSN:2150-8925
Container-title:Ecosphere
language:en
Short-container-title:Ecosphere

Author:

Irvine Kathryn M.¹^ORCID,Banner Katharine M.²,Stratton Christian²,Ford William M.³,Reichert Brian E.⁴^ORCID

Affiliation:

1. Northern Rocky Mountain Science Center U.S. Geological Survey Bozeman Montana USA

2. Department of Mathematical Sciences Montana State University Bozeman Montana USA

3. Virginia Cooperative Fish and Wildlife Unit U.S. Geological Survey Blacksburg Virginia USA

4. Fort Collins Science Center U.S. Geological Survey Fort Collins Colorado USA

Funder

U.S. Geological Survey

Publisher

Wiley

Subject

Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.4142

Reference54 articles.

1. Automated classification of bird and amphibian calls using machine learning: A comparison of methods

2. Bat Detective—Deep Learning Tools for Bat Acoustic Signal Detection;Aodha O. M.;PLoS Computational Biology,2018

3. Banner K. M.2022.“kbanner14/SuppS2_IrvineEtAl‐ecosphere: Zenodo Release (v1.0.0).” Zenodo.https://doi.org/10.5281/zenodo.6422770.

4. Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification

5. Accounting for automated identification errors in acoustic surveys

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