Revealing unseen dynamical regimes of ecosystems from population time-series data
Bibliographic record
Abstract
Many dynamical systems can exist in alternative regimes for which small changes in an environmental driver can cause sudden jumps between regimes. In ecology, predicting the regime of population fluctuations under unobserved levels of an environmental driver has remained an unsolved challenge with important implications for conservation and management. Here, we show that integrating time-series data and information on a putative driver into a Gaussian Process regression model for the system's dynamics allows us to predict dynamical regimes without the need to specify the equations of motion of the system. As a proof of concept, we demonstrate that we can accurately predict fixed-point, cyclic, or chaotic dynamics under unseen levels of a control parameter for a range of simulated population dynamics models. For a model with an abrupt population collapse, we show that our approach goes beyond an early warning signal by characterizing the regime that follows the tipping point. We then apply our approach to data from an experimental microbial food web and from a lake planktonic food web. We find that we can reconstruct transitions away from chaos in the microbial food web and anticipate the dynamics of the oligotrophic regime in the planktonic food web. These results lay the groundwork for making rational decisions about preventing, or preparing for, regime shifts in natural ecosystems and other dynamical systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".