Analysis of diversity effects on above-ground productivity in forests: advancing the mechanistic understanding of spatio-temporal dynamics in canopy space filling using mobile laser scanning

林木生物量储存和生产力是森林生态系统的重要功能，树木多样性对它们有积极的影响。最近的工作表明，改善冠层空间填充在不同的森林，通过两种不同的机制实现：种间建筑互补的冠和种内冠的可塑性。然而，这是否会导致更高水平的生态系统功能，以及它如何通过空间和时间变化仍然不清楚。该研究项目的主要目的是利用移动的激光扫描（MLS）数据分析天然林地上木材体积和生产力的多样性效应的潜在机制。MLS是一种非破坏性的连续测量技术，可提供观察对象的高分辨率三维（3D）点云。虽然只有少数研究MLS记录植被采样存在，他们表现出强大的潜力，用于森林生物多样性生态系统功能（BEF）的研究。我们在北方德国的一个结构复杂、物种丰富的森林中进行了一项试点研究，这有力地支持了这一点。MLS数据提供了非常详细和精确的信息，单树生物量分配和树冠结构，有效地记录了大面积。在森林BEF研究中广泛使用MLS的主要限制是缺乏自动个体树分割和物种识别的方法，以及冠层空间填充和树-树相互作用空间的时空动态的精确量化。因此，我们提出的项目有两个主要目标：解决目前的方法限制，防止广泛使用的MLS在森林BEF研究和开发的方法，MLS数据可以用来直接梳理除了在森林中的多样性效应的机制。

Woody biomass storage and productivity are key ecosystem functions in forests, and it has often been shown that they are positively influenced by tree diversity. Recent work suggests an improved canopy space filling in diverse forests, achieved by means of two different mechanisms: interspecific architectural complementarity of crowns and intraspecific crown plasticity. However, whether or not this results in greater levels of ecosystem functioning, and how it changes through space and time remains unclear. Main aim of the research project is to analyse the underlying mechanisms of diversity effects on above-ground wood volume and productivity in natural forests using data from mobile laser scanning (MLS). MLS is a non-destructive sequential measurement technique that delivers high-resolution three-dimensional (3D) point clouds of the observed objects. Although only few studies of MLS recording for vegetation sampling exist, they demonstrate a strong potential for the use in forest biodiversity-ecosystem functioning (BEF) research. This is strongly supported by a pilot study which we conducted in a structurally complex and species-rich forest in northern Germany. MLS data provide very detailed and precise information on individual-tree biomass allocation and crown architecture, effectively recorded over large areas. The main limitation for a widespread use of MLS in forest BEF research is the lack of methods for automatic individual-tree segmentation and species identification, and for the precise quantification of canopy space filling and spatio-temporal dynamics in tree-tree interactions for space in the canopy. Thus, our proposed project has two main goals: to resolve current methodological constraints preventing a widespread use of MLS in forest BEF research and to develop methods by which MLS data can be used to directly tease apart the mechanisms underlying diversity effects in forests.