Biology subjects

Wilcke, W.

Publications and source records attributed to Wilcke, W..

3 recordsLinked to original sources

bioRxiv2025

Accounting for spatial interactions in the upscaling of ecosystem services

O_LIMaps of ecosystem service (ES) supply are frequently used to guide spatial planning, policy making, and ecosystem management. However, these are typically based upon coarse land-cover proxies. This approach lacks a strong mechanistic basis, and neglects spatial biodiversity dynamics and interactions among landscape properties that can modify ES provision. C_LIO_LIWe present an analytical framework for ES upscaling that incorporates spatial interactions between landscape properties to determine ES supply. The resulting models can be viewed as a spatially informed ES production function. The approach comprises seven steps that include several elements absent from most existing approaches, notably a procedure for identifying geodata variables that represent the true mechanistic drivers, the inclusion of spatial interactions in the upscaling model, and modification following expert feedback on the selected model. C_LIO_LIWe demonstrate the approach using two example ES from German grasslands: biodiversity conservation and water supply. We show that the inclusion of spatial interactions in the upscaling model improved model predictions from 15% to 33% depending on the ES evaluated. In addition, inclusion of spatial interactions led to reduced error associated with the upscaled estimates. C_LIO_LIBy overcoming several shortcomings of existing, upscaling approaches we generate resulting maps of ES supply that can more reliably inform spatial planning Further, the underlying models allow for simulation of changes in the drivers of ES supply and estimation of respective outcomes. These advantages have the potential to better link detailed local-scale ecological understanding and land management with large-scale ES supply mapping, and thus better inform decision making and spatial planning. C_LI

ecology↗

bioRxiv2019

Plant traits are poor predictors of long-term ecosystem functioning

Earth is home to over 350,000 vascular plant species1 that differ in their traits in innumerable ways. Yet, a handful of functional traits can help explaining major differences among species in photosynthetic rate, growth rate, reproductive output and other aspects of plant performance2-6. A key challenge, coined "the Holy Grail" in ecology, is to upscale this understanding in order to predict how natural or anthropogenically driven changes in the identity and diversity of co-occurring plant species drive the functioning of ecosystems7, 8. Here, we analyze the extent to which 42 different ecosystem functions can be predicted by 41 plant traits in 78 experimentally manipulated grassland plots over 10 years. Despite the unprecedented number of traits analyzed, the average percentage of variation in ecosystem functioning that they jointly explained was only moderate (32.6%) within individual years, and even much lower (12.7%) across years. Most other studies linking ecosystem functioning to plant traits analyzed no more than six traits, and when including either only six random or the six most frequently studied traits in our analysis, the average percentage of explained variation in across-year ecosystem functioning dropped to 4.8%. Furthermore, different ecosystem functions were driven by different traits, with on average only 12.2% overlap in significant predictors. Thus, we did not find evidence for the existence of a small set of key traits able to explain variation in multiple ecosystem functions across years. Our results therefore suggest that there are strong limits in the extent to which we can predict the long-term functional consequences of the ongoing, rapid changes in the composition and diversity of plant communities that humanity is currently facing.

ecology↗

bioRxiv2019

The results of biodiversity-ecosystem functioning experiments are realistic

A large body of research shows that biodiversity loss can reduce ecosystem functioning, thus providing support for the conservation of biological diversity1-4. Much of the evidence for this relationship is drawn from biodiversity-ecosystem functioning experiments (hereafter: biodiversity experiments), in which biodiversity loss is simulated by randomly assembling communities of varying species diversity, and ecosystem functions are measured5-9. This random assembly has led some ecologists to question the relevance of biodiversity experiments to real-world ecosystems, where community assembly may often be non-random and influenced by external drivers, such as climate or land-use intensification10-18. Despite these repeated criticisms, there has been no comprehensive, quantitative assessment of how experimental and real-world plant communities really differ, and whether these differences invalidate the experimental results. Here, we compare data from two of the largest and longest-running grassland biodiversity experiments globally (Jena Experiment, Germany; BioDIV, USA) to related real-world grassland plant communities in terms of their taxonomic, functional, and phylogenetic diversity and functional-trait composition. We found that plant communities of biodiversity experiments have greater variance in these compositional features than their real-world counterparts, covering almost all of the variation of the real-world communities (82-96%) while also containing community types that are not currently observed in the real world. We then re-analysed a subset of experimental data that included only ecologically-realistic communities, i.e. those comparable to real-world communities. For ten out of twelve biodiversity-ecosystem functioning relationships, biodiversity effects did not differ significantly between the full dataset of biodiversity experiments and the ecologically-realistic subset of experimental communities. This demonstrates that the results of biodiversity experiments are largely insensitive to the inclusion/exclusion of unrealistic communities. By bridging the gap between experimental and real-world studies, these results demonstrate the validity of inferences from biodiversity experiments, a key step in translating their results into specific recommendations for real-world biodiversity management.

ecology↗