A large‐scale assessment of ant diversity across the Brazilian Amazon Basin: integrating geographic, ecological and morphological drivers of sampling bias. Issue 9 (16th June 2022)

• Record Type: Journal Article
• Authors: Andrade‐Silva, Joudellys; Baccaro, Fabrício B.; Prado, Lívia P.; Guénard, Benoit; Warren, Dan L.; Kass, Jamie M.; Economo, Evan P.; Silva, Rogério R.
• Is Part Of: Ecography. Volume 2022:Issue 9
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-16
• Subjects: Amazonian ants -- ant morphology -- neotropical biodiversity -- sampling bias -- vertical stratification; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.06295
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Tropical ecosystems are often biodiversity hotspots, and invertebrates represent the main underrepresented component of diversity in large‐scale analyses. This problem is partly related to the scarcity of data widely available to conduct these studies and the lack of systematic organization of knowledge about invertebrates' distributions in biodiversity hotspots. Here, we introduce and analyze a comprehensive data compilation of Amazonian ant diversity. Using records from 1817 to 2020 from both published and unpublished sources, we describe the diversity and distribution of ant species in the Brazilian Amazon Basin. Further, using high‐definition images and data from taxonomic publications, we build a comprehensive database of morphological traits for the ant species that occur in the region. In total, we recorded 1067 nominal species in the Brazilian Amazon Basin, with sampling locations strongly biased by access routes, urban centers, research institutions and major infrastructure projects. Large areas where ant sampling is non‐existent represent about 52% of the basin and are concentrated mainly in the northern, southeastern and western Brazilian Amazon. We found that distance to roads is the main driver of ant sampling in the Amazon. Contrary to our expectations, morphological traits had lower predictive power in predicting sampling bias than purely geographic variables. However, when geographic predictors were controlled, habitat stratum and traits contribute to explain the remaining variance. More species were recorded in better‐sampled areas, but species richness estimation models suggest that areas in southern Amazonian edge forests are associated with especially high species richness. Our results represent the first trait‐based, large‐scale study for insects in Amazonian forests and a starting point for macroecological studies focusing on insect diversity in the Amazon Basin.