Occurrence datasets, model outputs, and R script for 12 termite species used for niche modeling

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.