Title: Unlocking tropical forest complexity: How tree assemblages in secondary forests boost biodiversity conservation Show Chinese title

Title: 解锁热带森林的复杂性：次生林中的树群如何促进生物多样性保护

Abstract: Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems and untangling biodiversity complexity. Using national inventories, we identified in situ clusters through hierarchical clustering based on dominant species abundance dissimilarity, determined using the Importance Variable Index. These clusters were characterised by analysing species assemblages and their interactions. We then applied object-oriented Random Forest modelling, segmenting the national forest cover using NDVI to identify the forest ecosystems derived from in situ clusters. Freely available spectral (Sentinel-2) and environmental data were used in the model to delineate and characterise key forest ecosystems. We finished with an assessment of distribution of secondary and old-growth forests within ecosystems. In Costa Rica, 495 dominant tree species defined 10 in situ clusters, with 7 main clusters successfully modelled. The modelling (F1-score: 0.73, macro F1-score: 0.58) and species-based characterisation highlighted the main ecological trends of these ecosystems, which are distinguished by specific species dominance, topography, climate, and vegetation dynamics, aligning with local forest classifications. The analysis of secondary forest distribution provided an initial assessment of ecosystem vulnerability by evaluating their role in forest maintenance and dynamics. This approach also underscored the major challenge of in situ data acquisition Show Chinese abstract

Abstract: 次生森林现在主导了热带景观，并在实现COP15保护目标中发挥着关键作用。 本研究开发了一种可重复的国家方法，用于识别和描述森林生态系统，重点研究次生森林的作用。 我们假设森林冠层中的优势树种可以作为划分森林生态系统的可靠指标，并理清生物多样性复杂性。 利用国家调查数据，我们通过基于优势物种丰度差异的层次聚类来识别现场集群，该差异使用重要变量指数确定。 这些集群通过分析物种组合及其相互作用进行表征。 然后我们应用面向对象的随机森林建模，使用NDVI分割全国森林覆盖，以识别来自现场集群的森林生态系统。 免费提供的光谱（哨兵-2）和环境数据用于模型中，以划分和描述关键森林生态系统。 我们最后评估了次生林和原始林在生态系统中的分布情况。 在哥斯达黎加，495种优势树种定义了10个现场集群，其中7个主要集群成功建模。 建模（F1分数：0.73，宏F1分数：0.58）和基于物种的特征分析突出了这些生态系统的主导生态趋势，这些生态系统由特定物种优势、地形、气候和植被动态所区分，与当地森林分类一致。 对次生林分布的分析通过评估其在森林维持和动态中的作用，提供了生态系统脆弱性的初步评估。 这种方法还强调了现场数据获取的主要挑战。