Smart allocation of restoration funds over space and time

Abstract

A challenge for natural area managers is to ensure that public expenditure on land restoration is cost effective, efficient and transparent but this is difficult to achieve in practice, especially when there are many possible projects across multiple years. Here we develop a “roadmap” for investment in land restoration. It explicitly considers space, time and their interaction, in relation to ecological outcomes and restoration costs (and their variation in time and space). Using integer linear programming optimization in a benefit‐cost accounting framework, the roadmap incorporates: transitions between different stages of ecological recovery in a spatial mosaic of multiple ecosystem types; cost schedules associated with managing those transitions over time; time lags between beginning management and achieving outcomes; variations to constraints and goals associated with various factors including site accessibility, specific conservation priorities (such as threatened species or ecosystems); and background environmental trends. This approach enables land managers to: (1) forecast landscape‐scale outcomes of management strategies over long timeframes; (2) address the question of how long it will take and how much it will cost to achieve specific outcomes; and (3) explore potential trade‐offs in outcomes among alternative management strategies. We illustrate its application using a case study of forest restoration in Australia by a local government authority across a public conservation estate comprising 765 land units of varying size, totaling ˜13,000 ha, across five different floristic vegetation types, with an annual budget of ˜AU$5M, projected over a 50‐yr timeframe. These simulations revealed a trade‐off between management strategies that seek to increase either the total cover of native forest or the amount of high quality forest: quality‐based strategies were favored in scenarios in which shorter term (20–30 yr) timeframes were chosen at the outset, but cover‐based strategies were favored if longer time horizons were initially targeted. Projected outcomes were also strongly influenced by assumed background rates of vegetation decline or recovery. Many of the issues in this restoration roadmap are generalizable (even though specific outcomes and trade‐offs are likely to vary among case studies), and the approach is both scalable and transferable to other regions and ecosystems.

Designing an optimal large‐scale reintroduction plan for a critically endangered species

“…Our optimization approach could also be further refined, particularly as regards uncertainty. The ideal extension of our model would be to implement a fully stochastic model to better represent uncertainty in this dynamic system (Shoo et al, 2021). We represented uncertainty as 1000 scenarios, and the algorithm found the optimal strategy for each scenario independently, assuming perfect knowledge of the system: in other words, what we would do if we knew which scenario we were in.…”

Distinctive patterns of soil microbial community during forest ecosystem restoration in southwestern China

“…Ecological systems that are damaged seriously are usually treated by artificial restoration, including land preparation, soil dressing, tree species selection, plant community structure design, and modified biochar that can improve soil properties (Khan et al, 2022; Lu et al, 2017; Mansourian & Vallauri, 2014). Under different restoration years, the forest ecosystem's ability to resist pest invasion, plant and animal diversity, soil organic matter (OM), and other indicators were significantly different (Schiappacasse et al, 2012; Shoo et al, 2021). In addition, the damaged ecosystem caused by different factors could evolve distinctive microbial community structures during the succession process (Banning et al, 2011).…”