Understanding tradeoffs and synergies in spatial prioritization for conservation in the conterminous US

Varsha Vijay, National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, Paul R. Armsworth, Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN and Jonathan R.B. Fisher, Conservation Science, The Pew Charitable Trusts, Washington, DC

Background/Question/Methods
Targets like 30x30, which call for expanding US protected areas, require conservation frameworks that address various conservation goals, from ecological to socio-economic, within land protection portfolios. Here we create a ROI modeling framework to understand the tradeoffs and synergies between prioritization goals, through the patterns of covariation between costs and benefit functions. Our model uses a hierarchical structure in which we compare multiple indices within distinct “classes” of objective (e.g., area, biodiversity, carbon storage, recreational use, etc.) and then carry out comparisons between classes of objectives. As part of this work, we also examine how current protected areas address different classes of conservation goals with respect to the distribution of values in the landscape.
Results/Conclusions
By partitioning the components of ROI, we see that the inclusion of cost and intactness can reinforce some goals, while for others, the inclusion of cost pulls prioritizations away from a given goal. While synergies exist between some ecological and socio-economic goals (e.g. endemic species richness and recreational use in parts of the Southeastern US), the amount of agreement varies based on the scale and quantification of benefits. For example, we show how inclusion of soil carbon at multiple depths can influence the agreement between sites prioritized for biodiversity and carbon storage in this framework. We also observed differences in how various types of protected areas sample these distributions, protecting habitat which addresses each conservation goal. These analyses provide generalizable insights into trade-offs between particular types of user-generated conservation goals and their sensitivity to assumptions about ecological and socio-economic processes.