Biodiversity levels can fluctuate with the amount of agriculture present on a landscape. Why do some species do better in agriculture than others? What are predictors of species success? How does planting biomass feedstocks to be used for bioenergy affect biodiversity levels? To address these questions, we conducted a study on biodiversity levels in multifunctional landscapes in Iowa. We gathered environmental, climatic, and species data, and input these data layers into a species distribution model (SDM) called BioEST that uses the target-species method to create pseudo-absences. BioEST quantifies the habitat provided for wildlife by different crops by considering the presence of biomass crops and the pesticides used to manage crops. In addition to standard environmental indicators (e.g. land use, slope, temperature), we included pesticide application information that we downscaled from the county level to 2 km2. We used BioEST to determine the importance of specific predictors on measures of biodiversity (i.e. species occurrence). In addition, we conducted a principal component analysis (PCA) on environmental data and species data to understand correlations among species and predictors.
Results/Conclusions
Based on our preliminary results, we determined that of the 31 environmental predictors that we input into BioEST, only 3 (one climatic, land use, and pesticide) accounted for 93.98% of the variance. Additionally, after completing SDMs on various species, we examined the relative influence of the environmental predictors on the predicted habitat range. From both the PCA and SDM analyses, we determined that pesticide application has a non-trivial impact on species response to a landscape. Of the 7 pesticides we examined (2,4-D, acetochlor, atrazine, dicamba, glyphosate, imidacloprid, and metolachlor), 5 of them were consistently in the top 10 in terms of relative influence over all the species we modeled. By identifying predictors of species success to bioenergy crops, we hope to create guidelines on incorporating bioenergy crops into existing agricultural landscapes without adversely affecting wildlife.