Data from: Collection methods and distribution modeling for Strepsiptera in the United States
Creators: name: Millena, Rebecca Jean
name: Eichert, Anna
name: Ware, Jessica
Publisher: name: Zenodo
Year: 2024
Subjects: Strepsiptera
twisted-wing parasite
Black Rock Forest
Species distribution model
ecological niche model
Vespidae
Sphecidae
Cicadellidae
Endoparasites
modeling
sampling
Parasitology/Microparasites

description: The twisted-wing parasite order (Strepsiptera Kirby, 1813) is difficult to study due to the complexity of strepsipteran life histories, small body sizes, and a lack of accessible distribution data for most species. Here, we present a review of the strepsipteran species known from New York State. We also demonstrate successful collection methods and a survey of species carried out in an old-growth deciduous forest dominated by native New York species (Black Rock Forest, Cornwall, NY) and a private site in the Catskill Mountains (Shandaken, NY). Additionally, we model suitable habitat for Strepsiptera in the United States with species distribution modeling. We base our models on host distributions and climatic variables to inform predictions of where these twisted-wing parasites are likely to be found. With this work, we hope to provide a useful reference for the future collection of Strepsiptera.
description: Funding provided by: Black Rock Forest ConsortiumROR ID: https://ror.org/02qczxe62Award Number:
description: Our specimens were collected in Black Rock Forest (BRF), Cornwall, New York over the course of six trips in July and August of 2022 and 2023. BRF is an old growth forest protected and maintained by a namesake scientific organization dedicated to its study—as such, this forest provides a uniquely mature and native environment in which to collect ecological data. We sampled six areas: native growth by the Black Rock Forest (BRF) Science Center (41.41408°, -74.011919°), a patch of wild growth in the parking lot (41.413249°, -74.011421°), the meadow of the Upper Reservoir (41.411015°, -74.007048°), Aleck Meadow (41.406405°, -74.014587°), meadows of Jim's Pond (41.387490°, -74.020348°), and brush near the Stone House (41.397177°, -74.021423°) (Figure S1). In addition to the BRF sites, we sampled one privately owned site in the Catskill Mountains, Shandaken, New York in June and July 2023 (42.129425°, -74.377613°).

To generate predictive models of host and Strepsiptera ranges, we gathered occurrence data for each host-parasite pair for which collection coordinates were available from the Global Biodiversity Information Facility (GBIF) and combined it with the locality data from our collection efforts. Of the 78 strepsipteran species documented in the United States, only a subset had occurrence data. Of these, 51 species included specific coordinate data, and only 15 species had multiple unique coordinates. If hosts of these strepsipterans did not have occurrence data, we excluded these host species from the predictive analyses as well. Since our models require at least 5 occurrence datapoints to run, we ran models on genera instead of species to ensure that our predictions were robust. Our list was based on a checklist of strepsipteran species and their hosts in the United States from Kathirithamby, 2005, plus a United States checklist (Zabinski & Cook, 2023) and world checklist of the genus Stylops (Straka et al., 2015). Our GBIF search parameters specified human observation and preserved specimens as basis of record, data with coordinates, and the United States as an administrative area to restrict the search. When necessary for lessening computational time, we thinned the data by specifying coordinate uncertainty between 0-1 meters.

We took a species distribution modeling approach with the R package "wallace" and its modeling application Wallace v2.0 (Kass et al., 2018, 2023), using the algorithm MaxEnt (Maximum Entropy) (Phillips et al., 2004) and incorporating Bioclim environmental data (Booth et al., 2014) as explanatory variables driving species presence. For each species of Strepsiptera, we incorporated its host presence-absence prediction (10 percentile training presence threshold visualization) as a categorical variable. We standardized our models by specifying their region of study to a shapefile of the 48 contiguous United States, which we generated in QGIS using publicly available data (United States Government, 2023). We chose each model based on corrected Akaike information criterion (AICc), average omission rate when applying a 10-percentile training presence threshold to withheld validation data (OR.10p), and area under the curve of a receiver operating characteristic plot (auc.val.avg) (Kass et al., 2021; Peterson et al., 2011). Our R scripts for each model are openly available at Dryad. We visualized all data resulting from our models in QGIS v3.2.6 (Flenniken et al., 2020), and generated our host-parasite and species richness maps by using the QGIS Raster Calculator addition function.