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1. Landscape‐level environmental stressors contributing to the decline of Poweshiek skipperling ( Oarisma poweshiek )

   https://doi.org/10.1111/icad.12399  

   Belitz, Michael W.; Monfils, Michael J.; Cuthrell, David L.; Monfils, Anna K. (December 2019, Insect Conservation and Diversity)

   Abstract 1. The Poweshiek skipperling [Oarisma poweshiek(Parker, 1870; Lepidoptera: Hesperiidae)] is a federally endangered butterfly that was historically common in prairies of the upper Midwestern United States and Southern Manitoba, Canada. Rapid declines over the last 20 years have reduced the population numbers to four verified extant sites. The causes of Poweshiek skipperling decline are unknown. 2. We aggregated all known Poweshiek skipperling occurrence records to examine the spatiotemporal patterns of Poweshiek skipperling decline. Ecological niche models were developed for five time frames (1985, 1990, 1995, 2000 and 2005) and three spatial extents (eastern occupied range, western occupied range and total occupied range). We used a backward elimination method to investigate the effects of climate and land use on the ecological niche of Poweshiek skipperling. 3. Predictors of occurrence changed over time and across the geographical extent of Poweshiek skipperling. Land use covariates were retained in east models. In the west and total extent, climate variables contributed the most to model predictive power for the 1985, 1990 and 1995 models; land use variables contributed the most to model predictive power in the 2000 and 2005 models. 4. During the rapid decline in Poweshiek skipperling population numbers occurring at the turn of the century, probability of Poweshiek skipperling presence was being driven by proportion of natural land cover and distance to nearest grassland/wetland. Our results suggest that these land use variables are important landscape‐level variables to consider when developing risk assessments of extant populations and potential reintroduction sites. 

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2. Making Parasite-Host Associations Visible using Global Biotic Interactions

   https://doi.org/10.3897/biss.4.58985  

   Seltmann, Katja; Poelen, Jorrit; Sullivan, Kathryn; Zaspel, Jennifer (October 2020, Biodiversity Information Science and Standards)

   null (Ed.)

   A wealth of information about how parasites interact with their hosts already exists in collections, scientific publications, specialized databases, and grey literature. The US National Science Foundation-funded Terrestrial Parasite Tracker Thematic Collection Network (TPT) project began in 2019 to help build a comprehensive picture of arthropod ectoparasites including the evolution of these parasite-host biotic associations, distributions, and the ecological interactions of disease vectors. TPT is a network of biodiversity collections whose data can assist scientists, educators, land managers, and policymakers to better understand the complex relationship between hosts and parasites including emergent properties that may explain the causes and frequency of human and wildlife pathogens. TPT member collections make their association information easier to access via Global Biotic Interactions (GloBI, Poelen et al. 2014), which is periodically archived through Zenodo to track progress in the TPT project. TPT leverages GloBI's ability to index biotic associations from specimen occurrence records that come from existing management systems (e.g., Arctos, Symbiota, EMu, Excel, MS Access) to avoid having to completely rework existing, or build new, cyber-infrastructures before collections can share data. TPT-affiliated collection managers use collection-specific translation tables to connect their verbatim (or original) terms used to describe associations (e.g., "ex", "found on", "host") to their interpreted, machine-readable terms in the OBO Relations Ontology (RO). These interpreted terms enable searches across previously siloed association record sets, while the original verbatim values remain accessible to help retain provenance and allow for interpretation improvements. TPT is an ambitious project, with the goal to database label data from over 1.2 million specimens of arthropod parasites of vertebrates coming from 22 collections across North America. In the first year of the project, the TPT collections created over 73,700 new records and 41,984 images. In addition, 17 TPT data providers and three other collaborators shared datasets that are now indexed by GloBI, visible on the TPT GloBI project page. These datasets came from collection specimen occurrence records and literature sources. Two TPT data archives that capture and preserve the changes in the data coming from TPT to GloBI were published through Zenodo (Poelen et al. 2020a, Poelen et al. 2020b). The archives document the changes in how data are shared by collections including the biotic association data format and quantity of data captured. The Poelen et al. 2020b report included all TPT collections and biotic interactions from Arctos collections in VertNet and the Symbiota Collection of Arthropods Network (SCAN). The total number of interactions included in this report was 376,671 records (500,000 interactions is the overall goal for TPT). In addition, close coordination with TPT collection data managers including many one-on-one conversations, a workshop, and a webinar (Sullivan et al. 2020) was conducted to help guide the data capture of biotic associations. GloBI is an effective tool to help integrate biotic association data coming from occurrence records into an openly accessible, global, linked view of existing species interaction records. The results gleaned from the TPT workshop and Zenodo data archives demonstrate that minimizing changes to existing workflows allow for custom interpretation of collection-specific interaction terms. In addition, including collection data managers in the development of the interaction term vocabularies is an important part of the process that may improve data sharing and the overall downstream data quality. 

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3. A global database of butterfly species native distributions

   https://doi.org/10.1002/ecy.4462  

   Daru, Barnabas H (November 2024, Ecology)

   Butterflies represent a diverse group of insects, playing key ecosystem roles such as pollination and their larval form engage in herbivory. Despite their importance, comprehensive global distribution data for butterfly species are lacking. This lack of comprehensive global data has hindered many large‐scale questions in ecology, evolutionary biology, and conservation at the regional and global scales. Here, I use an integrative workflow that combines occurrence records, alpha hull polygons, species' dispersal capacity, and natural habitat and environmental variables within a framework of species distribution models to generate species‐level native distributions for butterflies at a global scale in the contemporary period. The database releases native range maps for 10,372 extant species of butterflies at a spatial grain resolution of 5 arcmin (~10 km). This database has the potential to allow unprecedented large‐scale analyses in ecology, biogeography, and conservation of butterflies. The maps are available in the WGS84 coordinate reference system (EPSG:4326 code) and stored as vector polygons in the GEOPACKAGE format for maximum compression, allowing easy data manipulation using a standard computer. I additionally provide each species' spatial raster. All maps and R scripts are open access and available for download in Dryad and Zenodo, respectively, and are guided by FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. By making these data available to the scientific community, I aim to advance the sharing of biological data to stimulate more comprehensive research in ecology, biogeography, and conservation of butterflies. 

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4. A globally synthesised and flagged bee occurrence dataset and cleaning workflow

   https://doi.org/10.1038/s41597-023-02626-w  

   Dorey, James_B; Fischer, Erica_E; Chesshire, Paige_R; Nava-Bolaños, Angela; O’Reilly, Robert_L; Bossert, Silas; Collins, Shannon_M; Lichtenberg, Elinor_M; Tucker, Erika_M; Smith-Pardo, Allan; et al (November 2023, Scientific Data)

   Abstract Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We presentBeeBDC, a newRpackage, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducibleBeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and, we added record-level flags for a series of potential quality issues. These data are provided in two formats, “cleaned” and “flagged-but-uncleaned”. TheBeeBDCpackage with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducibleRworkflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation. 

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5. Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?

   https://doi.org/10.1002/ecs2.4419  

   Gaier, Andrew_G; Resasco, Julian (March 2023, Ecosphere)

   Abstract Understanding the ranges of rare and endangered species is central to conserving biodiversity in the Anthropocene. Species distribution models (SDMs) have become a common and powerful tool for analyzing species–environment relationships across geographic space. Although evaluating the distribution of rare species is integral to their conservation, this can be difficult when limited distribution data are available. Community science platforms, such as iNaturalist, have emerged as alternative sources for species occurrence data. Although these observations are often thought to be of lower quality than those of natural history collections, they may have potential for improving SDMs for species with few occurrence records from collections. Here, we investigate the utility of iNaturalist data for developing SDMs for a rare high‐elevation plant,Telesonix jamesii. Because methods for modeling rare species are limited in the literature, five different modeling techniques were considered, including profile methods, statistical models, and machine learning algorithms. The inclusion of iNaturalist data doubled the number of usable records forT. jamesii.We found that a random forest (RF) model using ensemble training data performed the highest of any model (area under curve = 0.98). We then compared the performance of RF models that use only natural history training data and those that use a combination of natural history (herbarium specimens) and iNaturalist training data. All models heavily relied on climate data (mean temperature of driest quarter, and precipitation of the warmest quarter), indicating that this species is under threat as climate continues to change. Validation datasets affected model fits as well. Models using only herbarium data performed slightly poorer when evaluated with cross‐validation than when validated externally with iNaturalist data. This study can serve as a model for future SDM studies of species with similar data limitations. 

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