Hybrid datasets: integrating observations with experiments in the era of macroecology and big data
Benedetti-Cecchi, L.; Bulleri, F.; dal Bello, M.; Maggi, E.; Ravaglioli, C.; Rindi, L. (2018). Hybrid datasets: integrating observations with experiments in the era of macroecology and big data. Ecology 99(12): 2654-2666. https://dx.doi.org/10.1002/ecy.2504
In: Ecology. Ecological Society of America: Brooklyn, NY. ISSN 0012-9658; e-ISSN 1939-9170, more
Understanding how increasing human domination of the biosphere affects life on earth is a critical research challenge. This task is facilitated by the increasing availability of open-source data repositories, which allow ecologists to address scientific questions at unprecedented spatial and temporal scales. Large datasets are mostly observational, so they may have limited ability to uncover causal relations among variables. Experiments are better suited at attributing causation, but they are often limited in scope. We propose hybrid datasets, resulting from the integration of observational with experimental data, as an approach to leverage the scope and ability to attribute causality in ecological studies. We show how the analysis of hybrid datasets with emerging techniques in time series analysis (Convergent Cross-mapping) and macroecology (Joint Species Distribution Models) can generate novel insights into causal effects of abiotic and biotic processes that would be difficult to achieve otherwise. We illustrate these principles with two case studies in marine ecosystems and discuss the potential to generalize across environments, species and ecological processes. If used wisely, the analysis of hybrid datasets may become the standard approach for research goals that seek causal explanations for large-scale ecological phenomena.
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