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.

中文翻译：

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在空间和时间上智能分配恢复资金

自然区域管理者面临的一个挑战是确保用于土地恢复的公共支出具有成本效益、高效和透明，但这在实践中很难实现，尤其是在多年内有许多可能的项目时。在这里，我们制定了土地恢复投资的“路线图”。它明确地考虑了与生态结果和恢复成本（以及它们在时间和空间上的变化）相关的空间、时间及其相互作用。在效益成本核算框架中使用整数线性规划优化，路线图包含：在多种生态系统类型的空间镶嵌中生态恢复不同阶段之间的过渡；与随着时间的推移管理这些过渡相关的成本计划；开始管理和取得成果之间的时间差；与各种因素相关的限制和目标的变化，包括场地可及性、特定的保护优先事项（例如受威胁的物种或生态系统）；和背景环境趋势。这种方法使土地管理者能够：（1）预测长期管理战略的景观规模结果；(2) 解决实现具体成果需要多长时间和成本的问题；(3) 探索替代管理策略之间的潜在权衡结果。我们通过一个地方政府当局在澳大利亚的森林恢复案例研究来说明其应用，该案例研究包括 765 个不同大小的土地单元，总面积约 13,000 公顷，涵盖五种不同的植物区系植被类型，年度预算为约 AU 500 万美元，预计超过 50 年的时间范围。这些模拟揭示了寻求增加原生森林总覆盖率或优质森林数量的管理策略之间的权衡：在选择短期（20-30 年）时间框架的情况下，基于质量的策略受到青睐一开始，但如果最初的目标是更长的时间范围，那么基于覆盖的策略会受到青睐。预计的结果也受到植被衰退或恢复的假设背景率的强烈影响。此恢复路线图中的许多问题是可概括的（尽管具体结果和权衡可能因案例研究而异），并且该方法既可扩展又可转移到其他地区和生态系统。这些模拟揭示了寻求增加原生森林总覆盖率或优质森林数量的管理策略之间的权衡：在选择短期（20-30 年）时间框架的情况下，基于质量的策略受到青睐一开始，但如果最初的目标是更长的时间范围，那么基于覆盖的策略会受到青睐。预计的结果也受到植被衰退或恢复的假设背景率的强烈影响。此恢复路线图中的许多问题是可概括的（尽管具体结果和权衡可能因案例研究而异），并且该方法既可扩展又可转移到其他地区和生态系统。这些模拟揭示了寻求增加原生森林总覆盖率或优质森林数量的管理策略之间的权衡：在选择短期（20-30 年）时间框架的情况下，基于质量的策略受到青睐一开始，但如果最初的目标是更长的时间范围，那么基于覆盖的策略会受到青睐。预计的结果也受到植被衰退或恢复的假设背景率的强烈影响。此恢复路线图中的许多问题是可概括的（尽管具体结果和权衡可能因案例研究而异），并且该方法既可扩展又可转移到其他地区和生态系统。在开始时选择较短（20-30 年）时间框架的情况下，基于质量的策略受到青睐，但如果最初针对更长的时间范围，则基于覆盖的策略受到青睐。预计的结果也受到植被衰退或恢复的假设背景率的强烈影响。此恢复路线图中的许多问题是可概括的（尽管具体结果和权衡可能因案例研究而异），并且该方法既可扩展又可转移到其他地区和生态系统。在开始时选择较短（20-30 年）时间框架的情况下，基于质量的策略受到青睐，但如果最初针对更长的时间范围，则基于覆盖的策略受到青睐。预计的结果也受到植被衰退或恢复的假设背景率的强烈影响。此恢复路线图中的许多问题是可概括的（尽管具体结果和权衡可能因案例研究而异），并且该方法既可扩展又可转移到其他地区和生态系统。