Data from: Prioritizing landscapes for restoration based on spatial patterns of ecosystem controls and plant-plant interactions

The widespread degradation of natural ecosystems requires cost-efficient restoration techniques that minimize risk and consider context-specific restoration conditions. However, meeting these demands can be difficult because information on ecosystem-level factors controlling vegetation and continuous spatial data on species interactions are often lacking.
Using airborne LiDAR data from a Hawaiian dry forest, we delineated crowns and assessed the 3D structure of more than 700,000 shrubs and trees. We used Random Forest machine learning to assess the relative importance of resource availability, environmental conditions, and disturbance regimes on canopy density. We then modelled and scaled up plant–plant interactions (i.e. potential nursery effects) to landscape units using a LiDAR-derived Canopy Coalescence index (CC). We used the relative importance of ecosystem factors, canopy cover, and CC to prioritize landscapes for restoration.
Here, we demonstrate a methodological framework that prioritizes landscapes in need of restoration (i.e. planting woody species) using two ecological perspectives: (1) ecosystem-level controls on remnant woody vegetation, and (2) potential interactions between established canopies and seedlings (e.g. nursery effect, competition).
Our results highlight the heterogeneous nature of ecosystem-level drivers affecting forest structure along elevation gradients. Consequently, the degree of potential nursery interactions between established canopies and seedlings at the landscape-scale was context-specific.
Synthesis and applications. Our study provides a methodological approach that prioritizes landscapes for restoration by identifying the main controls on tree spatial distribution and by inferring the favourable conditions for seedlings. This approach can guide land managers to define cost-efficient restoration strategies for large ecological areas.

自然生态系统的广泛退化亟需兼具成本效益、风险可控且适配特定生境修复条件的修复技术。然而，满足此类需求往往困难重重，因为调控植被的生态系统级因子数据，以及物种相互作用的连续空间数据时常缺失。
本研究利用夏威夷旱林的机载激光雷达（airborne LiDAR）数据，对超过70万株灌木与乔木的树冠进行了勾勒，并评估了其三维结构。我们采用随机森林（Random Forest）机器学习算法，分析了资源可获得性、环境条件以及干扰制度对冠层密度的相对重要性。随后，我们通过机载激光雷达衍生的树冠融合指数（CC），将植物间相互作用（即潜在保育效应）的模型推广至景观尺度。我们结合生态系统因子的相对重要性、冠层覆盖度与CC指数，对需要优先修复的景观进行了优先级排序。
本研究展示了一套方法论框架，可从两个生态学视角对需要开展修复（即栽植木本植物）的景观进行优先级划分：（1）对残存木本植被起调控作用的生态系统级因子，（2）成熟冠层与幼苗间的潜在相互作用（例如保育效应、竞争作用）。
研究结果揭示了沿海拔梯度分布的森林结构，其生态系统级驱动因子具有显著异质性。相应地，景观尺度下成熟冠层与幼苗间的潜在保育作用程度，同样具有生境特异性。
综合与应用：本研究提出了一套方法论路径，可通过明确树木空间分布的核心调控因子、推断幼苗定植的有利条件，为景观修复优先级划分提供支撑。该方法可指导土地管理者为大面积生态区域制定兼具成本效益的修复策略。