DOI: 10.5061/dryad.n5tb2rc34
title: Data from: Collection methods and distribution modeling for Strepsiptera in the United States

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: 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 <em>Stylops</em>
 (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.
description: # Supplemental data for "Collection methods and distribution modeling
 for Strepsiptera in the United States"
 [https://doi.org/10.5061/dryad.n5tb2rc34](https://doi.org/10.5061/dryad.n5tb2rc34) These files are supplemental information related to surveys of the twisted-wing parasites (Strepsiptera) in New York State and species distribution modeling for Strepsiptera in the United States. The latest version (8) is for organizational changes: the names of two .MOV files in the supplemental files hosted by Zenodo have been changed to reflect their citations in the related manuscript. Version 7 features organizational changes: the names of the two .xlsx files have been changed to reflect their citations in the related manuscript. ## Data and file structure * Video_S1_Xenos_pull.MOV (renamed as of version 8 to reflect manuscript) * A video file of a female *Xenos peckii* being pulled from a female *Polistes fuscatus* host wasp * This video is included to demonstrate how the wasp was immobilized with wire and foam for the purpose of isolating the parasite. * Video_S2_Eupathocera_pull.MOV (renamed as of version 8 to reflect manuscript) * A video file of a gravid female *Eupathocera auripedis* being pulled from host wasp *Isodontia mexicana* * This video is included as visual accompaniment to the description of the gravid female *E. auripedis* and her larvae. * Map_BRF.jpeg * A map displaying the collection sites for this study in Black Rock Forest as an inset over a map of New York State * Twisted-winged Insect (Pseudoxenos sp.) larvae hatching from female in Eumeninae host 20230720_5072.jpg * An image of a gravid *Pseudoxenos tigridis* cephalothorax protruding from the sclerites of its host wasp *Ancistrocerus adiabatus*, with larvae visible in the brood canal * Taken by John and Kendra Abbott, original image, 2023, used with permission of Abbott Nature Photography. This image is not covered by the terms of the license of this dataset. For permission to reuse, please contact the rights holder. * Table_S1_NY_strepsiptera_survey.xlsx (Renamed as of version 7 to reflect manuscript) * An Excel spreadsheet of the full collection data for the survey of Strepsiptera in some sites of New York State conducted in this study * Metadata for this dataset can be found in the second tab. * Table_S2_GBIF_strepsiptera_US_occurrences.xlsx (Renamed as of version 7 to reflect manuscript) * An Excel spreadsheet of the full GBIF and host information used to inform Table 2 and all models featured in the manuscript related to these supplemental data * Metadata for the first dataset in this spreadsheet (GBIF_data) can be found at [https://doi.org/10.15468/dl.q43zd3](https://doi.org/10.15468/dl.q43zd3) * Metadata for the second dataset in this spreadsheet (host_species) can be found in the third tab. * US_shapefiles.zip * A compressed folder containing all files related to a border shapefile of the contiguous United States * Files: US_contiguous_final.cpg, US_contiguous_final.dbf, US_contiguous_final.prj, US_contiguous_final.qmd, US_contiguous_final.shp, US_contiguous_final.shx; the .shp file can be opened for use in QGIS and the .shp, .shx, and .dbf files are for use in species distribution modeling with Wallace. * final_scripts.zip * A compressed folder containing all scripts for the species distribution models featured in this manuscript as R markdown files * The models and their corresponding files are as follows: * Predicted Range of Xenos peckii and Polistes sp.: xenos_peckii_prediction.Rmd, polistes_fuscatus_prediction.Rmd * Predicted Range of Elenchus sp. and Delphacidae: elenchus_prediction.Rmd, elenchus_host_prediction.Rmd * Predicted Range of Halictophagus sp. and Cicadellidae: halictophagidae_prediction.Rmd, cicadellidae_prediction.Rmd * Predicted Range of Eupathocera sp. and Sphecidae: eupathocera_prediction.Rmd, eupathocera_host_prediction.Rmd * Predicted Range of Pseudoxenos sp. and Eumeninae: pseudoxenos_prediction.Rmd, pseudoxenos_host_prediction.Rmd * Predicted Range of Xenos sp. and Polistes sp.: xenos_prediction.Rmd, polistes_prediction.Rmd * Predicted Range of Stylops sp. and Andrena sp.: stylops_prediction.Rmd, andrena_prediction.Rmd * plots_with_reference_data.zip * A compressed folder containing all plots of strepsipteran SDMs in the main body of the manuscript, with the GBIF/survey reference data for each model plotted to contextualize their accuracy * Files: elenchus_references.pdf, eupathocera_references.pdf, halictophagus_references.pdf, pseudoxenos_references.pdf, stylops_references.pdf, xenospeckii_references.pdf, xenosspp_references.pdfp ## Sharing/Access information Data was derived from the following sources: * GBIF.org (30 December 2023) GBIF Occurrence Download [https://doi.org/10.15468/dl.q43zd3](https://doi.org/10.15468/dl.q43zd3) * User, G. O. (2023). *Occurrence Download* [dataset]. The Global Biodiversity Information Facility. [https://doi.org/10.15468/DL.WVPJE9](https://doi.org/10.15468/DL.WVPJE9) * United States Government. (2023, May 23). *Cartographic Boundary Files*. United States Census Bureau. [https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html](https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html) ## Code/Software US shapefiles were generated with QGIS v3.2.6. * Flenniken, J. M., Stuglik, S., & Iannone, B. V. (2020). Quantum GIS (QGIS): An introduction to a free alternative to more costly GIS platforms: FOR359/FR428, 2/2020. *EDIS*, *2020*(2), 7–7. [https://journals.flvc.org/edis/article/download/108810/120175](https://journals.flvc.org/edis/article/download/108810/120175) All R markdown scripts were generated 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. * Kass, J. M., Pinilla-Buitrago, G. E., Paz, A., Johnson, B. A., Grisales-Betancur, V., Meenan, S. I., Attali, D., Broennimann, O., Galante, P. J., Maitner, B. S., Owens, H. L., Varela, S., Aiello-Lammens, M. E., Merow, C., Blair, M. E., & Anderson, R. P. (2023). *wallace* 2: A shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions. *Ecography*. [https://doi.org/10.1111/ecog.06547](http://dx.doi.org/10.1111/ecog.06547) Kass, J. M., Vilela, B., Aiello-Lammens, M. E., Muscarella, R., Merow, C., & Anderson, R. P. (2018). Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. *Methods in Ecology and Evolution / British Ecological Society*, *9*(4), 1151–1156. [https://doi.org/10.1111/2041-210x.12945](http://dx.doi.org/10.1111/2041-210x.12945) * Phillips, S. J., Dudík, M., & Schapire, R. E. (2004). A maximum entropy approach to species distribution modeling. *Proceedings of the Twenty-First International Conference on Machine Learning*, 83. [https://doi.org/10.1145/1015330.1015412](http://dx.doi.org/10.1145/1015330.1015412) * Booth, T. H., Nix, H. A., Busby, J. R., & Hutchinson, M. F. (2014). bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. *Diversity & Distributions*, *20*(1), 1–9. [https://doi.org/10.1111/ddi.12144](http://dx.doi.org/10.1111/ddi.12144)