The extent, condition and connectedness of ecosystems has been significantly impacted by human activities globally, leading to a widespread appeal of and demand for ecological restoration. Maximising the ecological outcomes and cost effectiveness of restoration requires that plans be optimised by indexing the distribution, abundance and condition of habitat-forming species, and that target ecosystem conditions be developed in concert with restoration site attributes. This type of quantitative approach for creating malleable ecological targets which are informed by local environmental conditions is, however, uncommon. In this study, we surveyed the composition and coverage of understorey plants, and the composition and size of trees in coastal dunes at five transects at each of 20 sites (for n = 100) on the Sunshine Coast, central eastern Australia, and use this information to prioritise restoration sites and create optimised planting regimes for degraded sites. Each of the identified indicator species (six understorey and four tree species) had unique preferred conditions and was affected by multiple environmental and spatial variables at varying spatial scales, with most species affected by urbanisation. Species distribution models (SDMs) were used to identify target/reference ecosystems and optimal planting mixes for potential restoration sites given the site’s environmental attributes. Our approach of integrating data on the distribution, abundance and condition of habitat-forming species into multiple SDMs can be used to optimise planting regimes at restoration sites and provides a framework for setting dynamic restoration targets across landscapes.

全球人类活动对生态系统的范围、状况和连通性产生了重大影响，导致对生态恢复的广泛吸引力和需求。最大化恢复的生态结果和成本效益需要通过索引栖息地形成物种的分布、丰度和条件来优化计划，并且目标生态系统条件的制定与恢复地点的属性相一致。然而，这种根据当地环境条件创建可延展生态目标的定量方法并不常见。在这项研究中，我们调查了澳大利亚中东部阳光海岸 20 个地点（n = 100）的 5 个样带的下层植物的组成和覆盖率，以及沿海沙丘中树木的组成和大小，并使用这些信息来确定恢复地点的优先级，并为退化地点创建优化的种植制度。每种已确定的指示物种（六种林下和四种树种）都有独特的偏好条件，并受到不同空间尺度的多种环境和空间变量的影响，其中大多数物种受到城市化的影响。考虑到场地的环境属性，物种分布模型 (SDM) 用于确定潜在恢复场地的目标/参考生态系统和最佳种植组合。我们将栖息地形成物种的分布、丰度和状况的数据整合到多个 SDM 中的方法可用于优化恢复地点的种植制度，并为设置跨景观的动态恢复目标提供框架。