Addressing biodiversity knowledge shortfalls in New World Helicopsychidae (Insecta, Trichoptera): Potential distribution, environmental gradients, and identification of conservation and research priority areas

PLOS One | https://doi.org/10.1371/journal.pone.0327580 July 28, 2025 19 / 22 Sampling efforts across the New World remain uneven, with the majority of data derived from historically surveyed regions (e.g., the Antilles) or areas with established research institutions (e.g., Central America, the Amazon, and the Atlantic Forest). These regions, due to their high species richness, should be prioritized in conservation planning, especially those identified as biodiversity hotspots. However, vast areas remain poorly explored, including the Amazon lowlands, northern and central Atlantic Forest, high-altitude environments in the Caatinga and Cerrado domains, and tran- sitional zones between major phytogeographic domains. Targeted research in these underrepresented areas is essential. This study represents a foundational step toward a more comprehensive database of Trichoptera diversity in the New World. It provides critical tools for delineating biogeographic patterns and identifying knowledge gaps, thereby enhancing our understanding of Helicopsychidae diversity. The compilation presented herein establishes a valuable baseline for future taxonomic, ecological, and conservation-oriented studies on this family. and may inspire future research proposals for this family or other Trichoptera taxa using similar approaches. Supporting information S1 Table. Summary of georeferenced distribution data for all New World Helicopsychidae species. (XLSX) S2 Table. Summary of distribution in Freshwater Ecoregion (sensu Abell et al. 2015) and limits data for biogeocli-

matic variables of all New World Helicopsychidae species. (XLSX) S3 Table. Pearson correlation between environmental variables. (XLSX) S1 Fig. Boxplot with the result of Pearson correlation between environmental variables. (TIFF) S2 Fig. Boxplot with result of evaluation of distribution modelling algorithms using the area under curve (AUC) method. (TIF) Acknowledgments We extend our sincere thanks to Dr. Neusa Hamada, Dr. Ana Maria Pês, and Dr. Gleison R. Desidério for kindly providing data and permitting the analysis of material from the Laboratório de Citotaxonomia e Insetos Aquáticos of the Instituto Nacional de Pesquisas da Amazônia (LACIA-INPA). We are also grateful to the Museum of Comparative Zoology (MCZ) at Harvard and to Dr. Crystal A. Maier for granting access to, and assisting with the analysis of, biological material from the MCZ collection. Our appreciation further extends to Dr. Ralph Holzenthal and Dr. Robin E. Thomson at the University of Minnesota, and to Dr. Dikow Torsten of the Smithsonian Institution for kindly loaning specimens from institutional collec- tions for analysis at the MCZ. Author contributions Conceptualization: Rafael Pereira, Adolfo Calor. Data curation: Rafael Pereira. Formal analysis: Rafael Pereira. Funding acquisition: Adolfo Calor.