Ecosystem Physiology, University of Freiburg, Georges-Köhler-Allee 53/54, 79110, Freiburg, Germany.
Experimental and Systems Ecology, University of Bielefeld, Universitätsstraße 25, 33615, Bielefeld, Germany.
Sci Rep. 2017 Jun 23;7(1):4118. doi: 10.1038/s41598-017-04480-4.
Spatial heterogeneity of ecosystems crucially influences plant performance, while in return plant feedbacks on their environment may increase heterogeneous patterns. This is of particular relevance for exotic plant invaders that transform native ecosystems, yet, approaches integrating geospatial information of environmental heterogeneity and plant-plant interaction are lacking. Here, we combined remotely sensed information of site topography and vegetation cover with a functional tracer of the N cycle, δN. Based on the case study of the invasion of an N-fixing acacia in a nutrient-poor dune ecosystem, we present the first model that can successfully predict (R = 0.6) small-scale spatial variation of foliar δN in a non-fixing native species from observed geospatial data. Thereby, the generalized additive mixed model revealed modulating effects of heterogeneous environments on invader impacts. Hence, linking remote sensing techniques with tracers of biological processes will advance our understanding of the dynamics and functioning of spatially structured heterogeneous systems from small to large spatial scales.
生态系统的空间异质性对植物的表现至关重要,而植物对其环境的反馈则可能增加异质性模式。这对于改变本地生态系统的外来植物入侵尤其重要,但目前缺乏整合环境异质性和植物-植物相互作用的地理空间信息的方法。在这里,我们将地点地形和植被覆盖的遥感信息与 N 循环的功能示踪剂δN 相结合。基于对一种在营养贫瘠沙丘生态系统中固氮金合欢入侵的案例研究,我们提出了第一个模型,该模型可以成功地预测(R²=0.6)在观察到的地理空间数据中,非固氮本地物种叶片 δN 的小尺度空间变化。因此,广义加性混合模型揭示了异质环境对入侵影响的调节作用。因此,将遥感技术与生物过程示踪剂相结合,将提高我们对从小空间尺度到大空间尺度的空间结构异质系统动态和功能的理解。