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Abstract Groundwater scarcity poses threats to communities across the globe, and effectively managing those challenges requires designing policy that achieves institutional fit. Collective action is well-suited to match rules with local context, and multiple pathways exist for communities to achieve reductions in groundwater use. To better understand how local conditions influence rule design, we examine two groundwater-reliant communities in the Western US that engaged in collective-action to arrive at distinct groundwater management rules. We consider: what drove stakeholders in Northwestern Kansas (NWKS) and San Luis Valley, Colorado (SLV) to adopt local groundwater policies, and why were different management pathways chosen? Further, why is more heterogeneity observed between local management organizations in SLV as compared to NWKS? To investigate these questions, we employ grounded theory to interpret the rules in reference to local hydro-agro-economic statistics and interviews with stakeholders (n= 19) in each region selected by expert sampling. We find that the additional goals of groundwater rules in SLV, partially driven by key contrasts in the resource system compared to NWKS, and higher resource productivity in SLV, creates both the need for and efficacy of a price-centered policy. Furthermore, variation in the resource systems and associated farm characteristics between subdistricts drives higher heterogeneity in rule design between local management districts in SLV compared to NWKS. More generally, we find the local flexibility afforded through the collective-action process as critical, even if it were to arrive at alternative, non-economic based incentives. 

Abstract The advent of community-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 community-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.