Spatial and taxonomic diversification for conservation investment under uncertainty

Abstract

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.

Examining how risk diversification for conservation is influenced by the probability assigned to uncertainty scenarios

“…Our data could support much larger numbers of uncertainty scenarios and counties up to maxima of 486 scenarios and 246 counties, respectively, by further varying the GCM, SRES and timber volume projections as was done in Kang et al (2022). Yet, we reduce the number of scenarios and create multiple samples of counties for both MPT models based on different average pairwise correlations of the variance-covariance matrix to determine the suitability of the MPT application (Ando et al 2018).…”

“…The future annualized forest return for each of the 10 counties is acquired from the results obtained in Kang et al (2022) using the following two-step procedure. In the first step, soil expectation value (SEV) is used to estimate annualized forest return under an infinite series of identical harvest rotations of 50-75 years and a discount rate of 5% with identical timber management practices.…”

“…In the second step, a Brownian motion model is applied to forecast timber price and per hectare harvest volume using annualized forest return and historical timber volume data from the first step based on two Special Report on Emission Scenarios (SRES: B2 and A2; Nakićenović & Swart 2000) derived from the General Circulation Model (GCM). The model forecasts the high, low and moderate timber prices as one standard deviation above and below the mean price and the mean price, respectively, for each state, with two timber volumes based on two pairs of GCM and SRES (CSIRO-MK2 with SRES B2 and CSIRO-MK3.5 with SRES A2; see Kang et al 2022 for more details).…”

“…Conservation resource allocation guided by historical benefit and cost data that ignore future uncertainty will adversely affect its cost efficiency (Shah et al 2016). To address this issue, risk-diversification strategies have been applied in the conservation literature to determine portfolios of target assets (i.e., species, sites and activities) in the context of conservation programmes that face mainly climate uncertainty but also, in part, market uncertainty (Sanchirico et al 2008, Ando & Mallory 2012, Eaton et al 2019, Sierra-Altamiranda et al 2020, Kang et al 2022). Despite progress in focusing on conservation risk management, conservation organizations are reluctant to interface risk-diversification strategies with their decision-making processes (Hunt & Fraschini 2020).…”