The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Tracking and forecasting ecosystem interactions in real time
Open accessAbstract
Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time—a requirement for managing resources in a nonlinear, non-equilibrium world.
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Citations
Cites
Detecting Causality in Complex Ecosystems
Sugihara, G., May, R., Ye, H., Hsieh, C.-H., Deyle, E., Fogarty, M., & Munch, S. (2012) · Science · doi:10.1126/science.1227079 · Cited by 2,539
Self-citationNonlinear forecasting as a way of distinguishing chaos from measurement error in time series
Sugihara, G., & May, R. M. (1990) · Nature · doi:10.1038/344734a0 · Cited by 1,932
Self-citationA gentle introduction to quantile regression for ecologists
Cade, B. S., & Noon, B. R. (2003) · Frontiers in Ecology and the Environment · doi:10.1890/1540-9295(2003)001[0412:agitqr]2.0.co;2 · Cited by 1,855
Chaos in a Three‐Species Food Chain
Hastings, A., & Powell, T. (1991) · Ecology · doi:10.2307/1940591 · Cited by 1,086
Species packing and competitive equilibrium for many species
MacArthur, R. (1970) · Theoretical Population Biology · doi:10.1016/0040-5809(70)90039-0 · Cited by 1,070
THE ECOLOGICAL CONSEQUENCES OF SHARED NATURAL ENEMIES
Holt, R. D., & Lawton, J. H. (1994) · Annual Review of Ecology and Systematics · doi:10.1146/annurev.es.25.110194.002431 · Cited by 994
Rising variance: a leading indicator of ecological transition
Carpenter, S. R., & Brock, W. A. (2006) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00877.x · Cited by 963
The interaction between predation and competition: a review and synthesis
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Why fishing magnifies fluctuations in fish abundance
Anderson, C. N. K., Hsieh, C.-H., Sandin, S. A., Hewitt, R., Hollowed, A., Beddington, J., May, R. M., & Sugihara, G. (2008) · Nature · doi:10.1038/nature06851 · Cited by 686
Self-citationESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME-SERIES DATA
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Cited by
Haltuch, M. A., Brooks, E. N., Brodziak, J., Devine, J. A., Johnson, K. F., Klibansky, N., Nash, R. D. M., Payne, M. R., Shertzer, K. W., Subbey, S., et al. (2019) · Fisheries Research · doi:10.1016/j.fishres.2018.12.016 · Cited by 83
Underwater dual-magnification imaging for automated lake plankton monitoring
Merz, E., Kozakiewicz, T., Reyes, M., Ebi, C., Isles, P., Baity-Jesi, M., Roberts, P., Jaffe, J. S., Dennis, S. R., Hardeman, T., et al. (2021) · Water Research · doi:10.1016/j.watres.2021.117524 · Cited by 46
Metadata
- Authors
- Deyle, E. R., May, R. M., Munch, S. B., & Sugihara, G.
- Venue
- Proceedings of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2016-01-13
- Volume
- 283
- Issue
- 1822
- Pages
- 20152258
- ISSN
- 0080-4649 (ISSN-L)
- 0962-8452
- 1471-2954
- OpenAlex
- W2258834670
- Cited by
- 284
- References
- 32
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Deyle, E. R., May, R. M., Munch, S. B., & Sugihara, G. (2016). Tracking and forecasting ecosystem interactions in real time. Proceedings of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rspb.2015.2258
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
