Passive acoustic surveys and the BirdNET algorithm reveal detailed spatiotemporal variation in the vocal activity of two anurans-Reference-Cited by-同舟云学术

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Passive acoustic surveys and the BirdNET algorithm reveal detailed spatiotemporal variation in the vocal activity of two anurans

Published:2023-05-15 Issue:5 Volume:32 Page:532-543
ISSN:0952-4622
Container-title:Bioacoustics
language:en
Short-container-title:Bioacoustics

Author:

Wood Connor M.¹^ORCID,Kahl Stefan¹,Barnes Stephanie²,Van Horne Rachel³,Brown Cathy⁴

Affiliation:

1. K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA

2. USDA Forest Service, Sierra National Forest, Prather, CA, USA

3. USDA Forest Service, Humboldt-Toiyabe National Forest, Sparks, NV, USA

4. USDA Forest Service, Stanislaus National Forest, Sonora, CA, USA

Funder

Cornell Lab of Ornithology

U.S. Forest Service

Publisher

Informa UK Limited

Subject

Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://www.tandfonline.com/doi/pdf/10.1080/09524622.2023.2211544

Reference30 articles.

1. Explainable automated anuran sound classification using improved one-dimensional local binary pattern and Tunable Q Wavelet Transform techniques

2. Species detection framework using automated recording units: a case study of the Critically Endangered Jerdon's courser

3. Species tree estimation of North American chorus frogs (Hylidae: Pseudacris) with parallel tagged amplicon sequencing

4. Amphibian Breeding and Climate Change

5. Occupancy modelling reveals a highly restricted and fragmented distribution in a threatened montane frog (Philoria kundagungan) in subtropical Australian rainforests

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1. A post‐processing framework for assessing BirdNET identification accuracy and community composition;Ibis;2024-09-12

2. Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET;Sensors;2024-09-05

3. Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings;Ecosphere;2024-08

4. Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data;Ecological Indicators;2024-07

5. Effective ecological monitoring using passive acoustic sensors: Recommendations for conservation practitioners;Conservation Science and Practice;2024-05-10

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