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A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

Methods in Ecology and Evolution

By:
, , , ORCID iD , and ORCID iD
https://doi.org/10.1111/2041-210X.12910

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Abstract

  1. The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.
  2. We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.
  3. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.
  4. When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.
  5. Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing
Series title:
Methods in Ecology and Evolution
DOI:
10.1111/2041-210X.12910
Edition:
Online First
Year Published:
2017
Language:
English
Publisher:
British Ecological Society
Contributing office(s):
Wetland and Aquatic Research Center