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Two species of Psilochalcis wasps (P. minuta and P. quadratis) were recently described from Utah’s eastern Great Basin. The extent of their known distributions is extremely limited, based on few data points. We developed species distribution models (SDMs) using Maxent modeling software for each Psilochalcis species to identify areas of probable suitable habitat for targeted collecting to improve our knowledge of their distributions. We used six occurrence data points for P. minuta and eight occurrence data points for P. quadratis, along with ten environmental variables as inputs into the Maxent modeling software. Model-predicted areas with a potential suitable habitat value greater than 0.69 were mapped using ArcGIS Pro to help select locations for model accuracy assessment. Employing Malaise traps, eighteen sites were sampled to evaluate each SDM’s ability to predict the occurrence of Psilochalcis species. Psilochalcis minuta occurred at eight of nine juniper-dominated sample sites that were predicted as having high suitability by the model for this species. Likewise, P. quadratis occurred at two of four cheatgrass-dominated sample sites predicted by the model. Psilochalcis minuta occurred at three of nine sampled sites that were not predicted by the model, and P. quadratis occurred at seven of fourteen non-predicted sites. The Maxent SDM results yielded an AUC value of 0.70 and p-value of 0.02 for P. minuta and 0.68 and 0.02. for P. quadratis. These results were reflected in our model accuracy assessment. Of the selected environmental variables, aspect, historic fire disturbance, and elevation yielded the greatest percent contributions to both species’ models. Sympatric distributions were observed for P. minuta and P. quadratis. Elevation, vegetation type, NDVI, and soil type are the most important environmental variables in differentiating areas of optimal suitable habitat for the two species.

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.