DOI: 10.5061/dryad.k6djh9w7v
title: Occurrence datasets, model outputs, and R script for 12 termite species used for niche modeling

description: The advent of citizen-science databases in conjunction with museum
 specimen locality information has exponentially increased the power and
 accuracy of ecological niche modeling (ENM). Increased occurrence data has
 provided colossal potential to understand the distributions of lesser
 known or endangered species, including arthropods. Although niche modeling
 of termites has been conducted in the context of invasive and pest
 species, few studies have been performed to understand the distribution of
 basal termite genera. Using specimen records from the American Museum of
 Natural History (AMNH) as well as locality databases, we generated
 ecological niche models for 12 basal termite species belonging to six
 genera and three families. We extracted environmental data from the
 Worldclim 19 bioclimatic dataset v2, along with SoilGrids datasets and
 generated models using MaxEnt. We chose Optimal models based on partial
 Receiving Operating characteristic (pROC) and omission rate criterion and
 determined variable importance using permutation analysis. We also
 calculated response curves to understand changes in suitability with
 changes in environmental variables. Optimal models for our 12 termite
 species ranged in complexity, but no discernible pattern was noted among
 genera, families, or geographic range. Permutation analysis revealed that
 habitat suitability is affected predominantly by seasonal or monthly
 temperature and precipitation variation. Our findings not only highlight
 the efficacy of largely citizen-science and museum-based datasets, but our
 models provide a baseline for predictions of future abundance of
 lesser-known arthropod species in the face of habitat destruction and
 climate change.
description: We acquired occurrence records of non-Kalotermitidae
 non-neoisopteran species from the GBIF and iNaturalist. We selected
 occurrences possessing preserved museum samples and research grade
 observations which are occurrences possessing verified latitude and
 longitude coordinates, a photograph of the sighting, date, and ⅔ agreement
 on species identification by the community. Further occurrence filtering
 consisted of removing sightings with erroneous localities (middle of the
 ocean, locations of large museums). Additional localities were acquired
 from undatabased occurrences of species housed within the AMNH termite
 collection. We used gazetteers to acquire coordinates for museum specimens
 lacking latitude and longitude data but specific enough locality
 information. We acquired environmental rasters at 2.5 arc-second
 resolution (~5 km at the equator) from the WorldClim 2.0 database, along
 with environmental variables from the Global Soil Information Facilities
 (GSIF) SoilGrids database at 250m resolution at 0-5cm. We omitted four
 layers from the bioclimatic variables (bio08, bio09, bio18, bio19) due to
 their known spatial artifacts (Moo-Llanes et al. 2021).
description: The first zip file (<strong>Species.zip</strong>)
 contains all of the species occurence data, maxent.jar model outputs,
 calibration results, and final model results. 
 Organization of files follows Figure 2 from Cobos et al. 2019
 https://peerj.com/articles/6281/ Data files are structured for after the kuenm R package functions have been executed. The second zip file <strong>(Envs.zip) </strong>contains the enviromental data layers 'ENVS' used in our analyses which are in ascii format. The final file is the R package script for running the analysis.