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 4 / 22 Materials and methods Study area The New World is divided into three regions: the Nearctic, Neotropical, and Patagonian regions, with two transition zone: (i) Mexican transition zone comprise mountainous areas of central and southern Mexico and northern Central America, serves as a pathway connecting elements of the Nearctic and Neotropical fauna; and (ii) South American transition zone comprise highlands of the Andes between western Venezuela and northern Chile and central western Argentina [48]. Following these delimitations and using the distribution of Trichoptera as a proxy de Moor & Ivanov [20] divided Nearc- tic region into three regions: (i) West Nearctic (=Western subregion), (ii) East Nearctic (=Alleghany subregion), and (iii) Beringian “in part” (=Artic subregion) [14]. In addition to two regions covering part of Mexico, Central and South America, the Neotropical region (=Neotropical region) which includes Antillean, Brazilian, and Chacon subregions [sensu 48,49], and Patagonian region (=Andean or Chilean region) which includes Central Chilean, Subantarctic and Patagonian subre- gions [sensu 50]. Apart from these, all other proposals for bioregionalization for aquatic insects have been made with too large divisions [e.g., 51], making it difficult to use. Here we’ll follow the gradients of de Moor & Ivanov’ proposal [20] and when necessary subregions of Morrone and Escalante [48–50]. Distributional data and richness estimates

The distribution data basis was compiled through the primary literature (description and record of the occurrence of spe- cies), the database of Global Biodiversity Information Facility (GBIF, https://www.gbif.org), and SpeciesLink (http://www. splink.org.br/). Original data were obtained from collections at the Museu de História Natural da Bahia of the Universidade Federal da Bahia (UFBA), the Laboratório de Citotaxonomia e Insetos Aquáticos of the Instituto Nacional de Pesquisas da Amazônia (LACIA-INPA), the Museum of Comparative Zoology at Harvard University (MCZ), the University of Minne- sota Insect Collection (UMSP), and the National Museum of Natural History of the Smithsonian Institution (USNM). As the study was based on previously collected and curated material from these museums and institutional collections, no additional permits were required for the collection or transportation of biological specimens. We use gazetteers and Google Maps© to register localization without coordinates; the centroid of the less comprehen- sive location was used. After the data compilation, two-stage filtering process was performed, (i) manual selection of the data, discarding points without coordinate information, generic data of locality (e.g., only state), or with the indeterminacy of the species (e.g., identification only to the genus level), and (ii) selection in R environment, discarding points that can generate an analysis bias (e.g., points localized at the centroid of the capital areas and/or with same coordinates or in

marine areas). Species distribution map and heatmap (Kernel density) were prepared using QGIS v. 3.10.10. Distribu- tional records of 146 species, of which 17 species are H. (Cochliopsyche) and 129 species are H. (Feropsyche) (Table 1; S1 Table) and five undescribed species of H. (Cochliopsyche) (being described in another paper) were catalogued. For estimating the number of unknown species of H. (Feropsyche) in NA-NT-PA and H. (Cochliopsyche) in NT, the distri- butional records and bioregions delimited here are used, using non-parametric estimators. Estimators were calculated based on incidence data (presence-absence only), using freshwater ecoregions (Abell et al. 2008) as sampling unities, counting the occurrences in each one, with the function ‘specpool’ from the ‘vegan’ package [52] in the R environment. This function calculates two estimators of species richness: CHAO2, second-order jackknife (JACK2) (more adequate for incidence data) [53]. These non-parametric estimators are useful to estimate a potential number of unobserved species based on incidence data as those available here, and they have shown better performance than model-based or asymptotic estimators [54,55]. Environmental gradients To obtain the standards of distribution and distribution range regarding temperature, precipitation, elevation, and char- acteristics of freshwater environments, a dataset was produced consisting of distribution data, biogeoclimatic data (i.e.,