Ants, an ecologically successful and numerically dominant group of animals, play key ecological roles as soil engineers, predators, nutrient recyclers, and regulators of plant growth and reproduction in most terrestrial ecosystems. Further, ants are widely used as bioindicators of the ecological impact of land use. We gathered information of ant species in the Atlantic Forest of South America. The ATLANTIC ANTS data set, which is part of the ATLANTIC SERIES data papers, is a compilation of ant records from collections (18,713 records), unpublished data (29,651 records), and published sources (106,910 records; 1,059 references), including papers, theses, dissertations, and book chapters published from 1886 to 2020. In total, the data set contains 153,818 ant records from 7,636 study locations in the Atlantic Forest, representing 10 subfamilies, 99 genera, 1,114 ant species identified with updated taxonomic certainty, and 2,235 morphospecies codes. Our data set reflects the heterogeneity in ant records, which include ants sampled at the beginning of the taxonomic history of myrmecology (the 19th and 20th centuries) and more recent ant surveys designed to address specific questions in ecology and biology. The data set can be used by researchers to develop strategies to deal with different macroecological and region‐wide questions, focusing on assemblages, species occurrences, and distribution patterns. Furthermore, the data can be used to assess the consequences of changes in land use in the Atlantic Forest on different ecological processes. No copyright restrictions apply to the use of this data set, but we request that authors cite this data paper when using these data in publications or teaching events.

BirdNET is a popular machine learning tool for automated recognition of bird sounds. However, evidence on how to optimize its settings for accurate bird monitoring remains limited. Here, we evaluate how BirdNET settings influence model performance in identifying bird vocalizations and characterizing bird communities, using 4224 1‐min recordings from 67 recording locations worldwide. Giving equal importance to recall and precision, a low confidence score threshold (0.1–0.3) appears optimal for detecting bird vocalizations, whereas higher thresholds (around 0.5) are more suitable for characterizing bird communities. Based on our findings, we recommend increasing the Overlap parameter from its default value of 0 to 2 s, as this consistently improves BirdNET performance in detecting both bird vocalizations and species presence. The effect of the Sensitivity parameter varied across regions. However, a value of 0.5 maximizes global performance for community‐level analyses across all confidence thresholds, and a value of 1.5 generally yields better results for vocalization‐level studies, particularly at low confidence thresholds. Our findings offer practical guidance for selecting BirdNET settings in passive acoustic bird surveys, enhancing both the identification of bird vocalizations and the characterization of bird communities.

Under the current global biodiversity crisis, there is a need for automated and noninvasive monitoring techniques that can gather large amounts of data cost‐effectively at various ecological scales, from local to large spatial scales. These data can then be analyzed to inform stakeholders and decision‐makers. One such technique is passive acoustic monitoring, which is commonly coupled with automatic identification of animal species based on their sound. Automated sound analyses usually require the training of sound detection and identification algorithms. These algorithms are based on annotated acoustic datasets which mark the occurrence of sounds of species inside sound recordings. However, compiling large annotated acoustic datasets is time‐consuming and requires experts, and therefore, they normally cover reduced spatial, temporal, and taxonomic scales. This data paper presents WABAD, the World Annotated Bird Acoustic Dataset for passive acoustic monitoring. WABAD is designed to provide the public, the research community, and conservation managers with a novel and globally representative annotated acoustic dataset. This database includes 5047 min of audio files annotated to species‐level by local experts with the start and end time and the upper and lower frequencies of each identified bird vocalization in the recordings. The database has a wide taxonomic and spatial coverage, including information on 91,931 vocalizations from 1192 bird species recorded at 72 recording sites in 29 recording locations (mainly countries) and distributed across 13 biomes. WABAD can be used, for example, for developing and/or validating automatic species detection algorithms, answering ecological questions, such as assessing geographical variations on bird vocalizations, or comparing acoustic diversity indices with species‐based diversity indices. The dataset is published under a Creative Commons Attribution 4.0 International license that permits redistribution and reuse on the condition that the original work is properly credited.