Mapping Evidence to Theory in Ecology

Mapping Evidence to Theory in Ecology

Addressing the Challenges of Generalization and Causality

September 2024 - February 2025

Ecology as a scientific discipline is expected to provide the knowledge needed for solving the biodiversity crisis. Accordingly, ecological research generates a steeply increasing amount of data and empirical findings, aimed at increasing knowledge about ecological systems. In principle, this rise in information should lead to a steady improvement of understanding of these systems, and thus also to a steady increase in information that can directly be used for improving biodiversity protection and management.

However, the translation of knowledge gained in single empirical studies into more general knowledge about ecological systems and into practically useful knowledge is not straightforward. Ecological systems are highly complex, and ecological processes are strongly context dependent. This leads to the fact that empirical results from single case studies are hard to extrapolate to other systems, and hard to translate into meaningful instructions for management. The core problem is how to synthesize the results of large amounts of case studies that are highly diverse with respect to research approach (e.g. field surveys or controlled lab experiments) as well as study systems (e.g. plants or insects, dry grasslands or oceans).

Meaningful synthesis needs to take ecological complexity into account and needs to assure that important information on the respective context of the study is not lost. What is needed, thus, are tools and workflows that allow developing 'case-specific generalizations'. Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context
dependence. Especially promising seems the idea to bring together advanced AI based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.