Birds and Bioenergy: A modeling framework for managed landscapes at multiple spatial scales

This dissertation examines the design and management of bioenergy landscapes at multiple spatial scales given numerous objectives. Objectives include biodiversity outcomes, biomass feedstock yields, and economic value.

Our study examined biodiversity metrics for 25 avian species in Iowa, including subsets of these species related to ecosystem services. We used our species distribution model (SDM) framework to determine the importance of predictors related to switchgrass production on species richness. We found that distance to water, mean diurnal temperature range, and herbicide application rate were the three most important predictors of biodiversity overall. We found that 76% of species responded positively to presence of grassland. We determined that a relationship between ecosystem service provided and potential species occurrence in a landscape does exist, with pollinators specifically benefiting from increased biomass production.

We explored predicted species occurrence under an alternative bioenergy landscape in which clustered corn/soy acres with a low return on investment (ROI) were replaced with grassland. We developed SDMs to predict changes in species occurrence for 28 birds. We compared results for three models: Random forest (RF), Stochastic gradient boosting (GBM), and Neural network (Nnet). Predicted species richness increased by 3.66% (RF), 2.79% (GBM), and 7.51% (Nnet) under the alternative landscape. If harvested, these areas could generate approximately 7.6 million dry tons/year of switchgrass for bioenergy. Unprofitable areas tended to occur along streams, suggesting that incorporating partially harvested riparian buffers can benefit avian biodiversity, while improving water quality and reducing unnecessary costs for farmers.

We explored effects of spatiotemporal harvest strategies at the field scale. We developed an agent-based model (ABM) that simulates ring-necked pheasants, tractors, hunters, and vegetation classes. Using this ABM, we assessed four different landscapes: corn-dominated, CRP-dominated, grassland-dominated, and mixed landscape. We determined that biomass yield and pheasant population size were sensitive to harvesting times, but unaffected by changes in spatial harvesting strategies. Specifically, harvesting in the fall yielded the most value because winter harvest resulted in lower yields and spring harvest resulted in decreases in pheasant population sizes. Trends were consistent across all landscapes. We discovered that corn (~$300,00/km2) and CRP (~$500,000/km2) landscapes had the highest-valued scenarios.