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AIandML_results_SHARE.csv (1.07 kB)

Data associated with: Han, B.A., Varshney, K.R., LaDeau, S., Subramaniam, A., Weathers, K.C., Zwart, J. A synergistic future for AI and ecology. PNAS 120 (38) (2023).

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Version 2 2023-12-08, 16:11
Version 1 2023-03-21, 20:32
posted on 2023-12-08, 16:11 authored by Barbara HanBarbara Han, Kush Varshney, Shannon LaDeauShannon LaDeau, Ajit Subramanian, Kathleen C. Weathers, Jacob Zwart

The file is associated with:

Han, B.A., Varshney, K.R., LaDeau, S., Subramaniam, A., Weathers, K.C., Zwart, J. A synergistic future for AI and ecology. PNAS 120 (38) (2023).


Research in both ecology and artificial intelligence (AI) strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of advances built on a staggered cycle of computational development and ecological adaptation, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change.

The unpredictability of systems-level phenomena and associated challenges in understanding resilience dynamics are critical challenges on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a synergistic convergence research paradigm between ecology and AI. The systems studied in ecology are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behavior that should inspire new, robust AI architectures and methodologies. We share several examples of how challenges in ecological systems modeling will require advances in AI techniques that are themselves inspired by the systems they seek to model.

Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. Here we emphasize the need for more purposeful synergy to accelerate understanding of ecological resilience whilst building the resilience currently lacking in modern AI. There are persistent epistemic barriers that require attention in both disciplines, yet the implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence -- they are critical for both persisting and thriving in an uncertain future.

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AIandML_results_SHARE.csv - contains literature search results from Clarivate Web of Science.


Conference: Frontiers in artificial intelligence and machine learning for ecology: 17-20 October 2022

Directorate for Biological Sciences

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Geographic description

Literature search data (global)

Time period

1 September 2022 - 31 August 2023


We conducted a literature search for the number of papers per year for the following query on Web of Science: ((TS=("artificial intelligence" OR "machine learning"))) AND WC=(Ecology OR "environmental sciences"). Note: search query may yield slightly different numbers due to regular database updates.

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