Artificial intelligence (BirdNET) supplements manual methods to maximize bird species richness from acoustic data sets generated from regional monitoring

Abstract

Processing methods that maximize species richness from acoustic recordings obtained from regional monitoring programs can increase detections of uncommon, rare, and cryptic species and provide key information on species status and distribution. Using data from regional bird monitoring in Yukon, Canada, we (1) compared the number of bird species detected (species richness) and the cost associated with four acoustic processing methods ( Listening, Visual Scanning, Recognizer, and Recognizer with Validation) and (2) combined Listening and Recognizer with Validation information to increase detections of all bird species at the ecoregion scale. We used comprehensive Visual Scanning to detect all bird species on the recordings. We processed ∼1% of the recordings using Listening and detected 56% of the bird community with 71.5 h of human effort. We used Recognizer (multispecies recognizer BirdNET) with Validation and detected 89% of the bird community with ∼22% of the effort required for Visual Scanning (56 and 257 h, respectively). As an application of our approach, we combined Listening and Recognizer with Validation to process recordings from five northern ecoregions and found a 23%–63% increase in the number of bird species detected with little additional effort. Combining Listening and Recognizer with Validation can maximize species detections from large passive acoustic monitoring data sets.