Summary Conservation organizations often need to develop risk-diversification strategies that identify not just what species to protect but also where to protect them. The objective of this research is to identify optimal conservation investment allocations for both target sites and species under conditions of uncertainty. We develop a two-step approach using modern portfolio theory (MPT) to estimate percentages of conservation investment (referred to as ‘portfolio weights’) for counties and taxonomic groups in the central and southern Appalachian region under climate and market uncertainties. The portfolio weights across the counties and taxonomic groups from the two steps entail both spatial and taxonomic diversification strategies. Conservation decisions that allow for selecting sites for risk diversification fit the purpose of the first step. Likewise, conservation investments that benefit the biodiversity of particular taxonomic groups for the selected sites are made based on the relative importance of diversifying risk among species in a given area, fitting the purpose of the second step. The two-step MPT approach as a whole allows the greatest flexibility on where and what to protect for conservation investment under uncertainty, and thus would be applicable for the distribution of general conservation funds without predisposition towards protecting either specific sites or species.

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The purpose of this study is to understand how solutions from single‐ and multiobjective optimization for the conservation of multiple species are different and what impacts these differences. We identify optimal conservation investment allocations maximizing expected species' habitat ranges for multiple pairs of species using two approaches in the central and southern Appalachian region. We find that disparities between the two approaches are affected by differences in the involved species' expected habitat ranges (i.e., contrasting and similar) and their correlation pattern (i.e., positive, negative, and insignificant). Using a single metric by aggregating species' habitats for multiple species to carry out single‐objective optimization is shown to favor the species with a larger habitat distribution more if the involved species' expected habitat distributions are negatively correlated and their distribution difference is larger. Framing multiple metrics of species' habitats separately using multiobjective optimization for the same set of multiple species, in contrast, does not show such a drastic disparity.

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