SYMP 3-2 - Integrating theory and big data to advance ecological understanding

Background/Question/Methods
New opportunities for deepening our understanding of ecology are emerging, stemming from the rapidly expanding scale of ecological and environmental data. Big data offers several advantages for ecologists, including: (i) enhanced power to detect subtle effects and better quantify uncertainties, (ii) sufficient data to study processes operating across systems and scales, (iii) opportunities to tackle new, emergent questions, and (iv) improved ecological management, drawing on real-time data streams and enhanced forecasting. However, making the most of these opportunities requires the thoughtful integration of theory and data. We will synthesize the results of a series of discussions on achieving this goal, emerging from an NSF-funded workshop on the future of theory in ecology. In particular, we will consider several challenges posed by big data. These range from perennial (yet often overlooked) issues inherent in data-theory integration generally, to novel challenges posed by big data in particular. Complementing this, we will articulate areas where theory can help overcome these challenges, and where big data creates novel opportunities for advancing theory. Neither big data nor theory alone are sufficient to address pressing ecological concerns. However, their integration offers important chances to advance ecological understanding.

Results/Conclusions
Applications of big data in ecology face critical challenges, besides immediate logistical issues associated with storage, access, and management. Information that is easy to collect in large volumes often arises from opportunistic, rather than hypothesis-driven sources. Available data may be hard to relate to fundamental variables of interest, especially those most theory is built upon (e.g., rates, functional forms, and state variables). Analyses of big data often rely on correlative approaches; absent a mechanistic foundation, these can lead to predictions that fail badly, especially in times of widespread and non-stationary changes. Theory can help with several of these challenges, including identifying important questions to address, guiding the collection of (useful) big data, and supporting the development of process-based analysis that improve understanding and forecasting. It also provides ways of better accounting for critical observation- and process-based uncertainties. In turn, big data creates opportunity for theory, ranging from identifying phenomena that existing theory cannot explain, to creating chances to interrogate previously untestable theories, and providing methods theorists can use to synthesize and interpret large model outputs. Together, these considerations highlight the importance of integrating theory with big data, for their mutual benefit, and to advance the study of ecology.