PondNet – towards a global network of experiments on the effects of climate change on aquatic ecosystems

Global change is reshaping the distribution of biodiversity and the functioning of ecosystems. Predicting the long‐term consequences of such changes remains a challenge due to a need for a clear understanding of the mechanisms underpinning ecosystem‐level responses, as well as the role of geographical and environmental contingencies. We propose that these gaps can be addressed for freshwater ecosystems using a globally distributed experiment with standardized observations and disturbances. Specifically, this paper outlines the structure of PondNet – a globally distributed network of pond mesocosm experiments – designed to investigate how aquatic food webs respond to environmental change across broad geographical gradients. Pond mesocosms are affordable, low maintenance and easily replicated model ecosystems, with broadly predictable trophic architectures and community size‐structure. PondNet would implement state‐of‐the‐art environmental DNA biodiversity assessments for standardized taxonomic identification across biogeographical regions. A major operational bottleneck for developing a global understanding of environmental change effects on ecosystem functioning is the current lack of standardized experiments across coordinated infrastructures. We propose that by building on existing distributed experiments, we can assemble a modular participation scheme that ensures broad biogeographical coverage whilst accounting for varying levels of resource commitments from local hosts. PondNet aims to answer two overarching questions: 1) how general are community, food web and ecosystem‐level responses to climate change across scales (i.e. local environmental gradients to biogeographical regions)?, and 2) to what extent are such responses contingent on local climate, environment, and regional species pools? PondNet will contribute to developing predictive models that can be adaptively improved from testing with data from globally replicated experiments and monitoring programmes.