Abstract
AbstractEcosystem restoration requires choosing among potential interventions which differ in cost, and the time required to achieve outcomes of varying quality. Managers have different preferences for timeframes, certainty, and quality of outcomes, which can influence the choice of investment strategy. Here we develop a probabilistic approach to quantify expected restoration outcomes from alternative investment strategies, given operational constraints or alternative preferences. We apply the approach to a tropical forest restoration case study in which managers seek to allocate future resources between active planting and self‐organized regrowth. We find that the best strategy depends on the desired forest attributes and the time required for outcomes to be achieved. We quantify the trade‐off for three key forest attributes between restoring large areas of vegetation to low quality and restoring smaller areas to a higher quality. Explicit consideration of preferences and trade‐offs will enhance the likelihood that projects deliver desired outcomes.
Highlights
Direct payments schemes are an important tool used by natural resource managers to achieve conservation of biodiversity (Ferraro & Kiss 2002) and promote activities that protect or recover ecosystem services (Wunder 2013)
The probability of exceeding minimum levels of vegetation quality increased with time and in response to more intensive restoration intervention
Probabilities differed among the three forest attributes used to measure restoration outcomes
Summary
Direct payments schemes are an important tool used by natural resource managers to achieve conservation of biodiversity (Ferraro & Kiss 2002) and promote activities that protect or recover ecosystem services (Wunder 2013). A challenge of direct payment schemes, is how to allocate scarce financial resources among competing projects to maximize return on investment. Environmental restoration schemes are challenging in this regard. The desired outcomes can differ among stakeholders (e.g., carbon storage versus habitat for wildlife) and their measurement is often unclear (Wortley et al 2013). Restoration of degraded systems involves uncertainty and time lags over several decades (Holl & Aide 2011). Restoration involves a range of potential actions, each with their own costs, time frame and likelihood of success (Wilson et al 2011)
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