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Moving ecological tree-ring big data forwards: Limitations, data integration, and multidisciplinarity

Published Article

Global

Publication date: December 10, 2024

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Tree‑ring databases offer powerful ecological insights but face bias and representativeness concerns. Authors propose three improvements: routine bias analyses and community‑driven data, integration with other ecological datasets, and more theory‑driven, mechanistic research—guidance relevant to many large ecological databases.

Subject Tags

  • Data Science and Artificial Intelligence
  • Life Sciences
  • Forest

Abstract

In recent years, tree-ring databases have emerged as a remarkable resource for ecological research, allowing us to address ecological questions at unprecedented temporal and spatial scales. However, concerns regarding big tree-ring data limitations and risks have also surfaced, leading to questions about their potential to be representative of long-term forest responses. Here, we highlight three paths of action to improve on tree-ring databases in ecology: 1) Implementing consistent bias analyses in large dendroecological databases and promoting community-driven data to address data limitations, 2) Encouraging the integration of tree-ring data with other ecological datasets, and 3) Promoting theory-driven, mechanistic dendroecological research. These issues are increasingly important for tackling pressing cross-disciplinary research questions. Finally, although we focus here on tree ring databases, these points apply broadly across many aggregative databases in ecology.

Citation

Manzanedo, R.D., Chin, A.R., Ettinger, A.K., Pederson, N., Pradhan, K., Guiterman, C.H., Su, J., Baumgarten, F. and Lambers, J.H.R., 2024. Moving ecological tree-ring big data forwards: Limitations, data integration, and multidisciplinarity. Science of The Total Environment, 955, p.177244. https://doi.org/10.1016/j.scitotenv.2024.177244

TNC Authors