Transient dynamics and counterintuitive competitive performance in periodic environments
Bibliographic record
Abstract
Abstract Despite the rapid pace of global change altering temporal environmental patterning, we lack a general understanding of how periodic environments structure ecological communities. In fluctuating environments, nonlinear dynamics associated with temporal trade-offs between competing species can create the potential for both niche differentiation (coexistence) and seemingly unexpected outcomes (exclusion) that deviate from deterministic coexistence theory. Yet, the mechanisms behind these outcomes are not fully understood. Here, we show that periodic fluctuations between times of high and low growth (e.g., seasons), and adaptive temporal trade-offs within and between species, can drive counterintuitive over- and under-performance of competing species. Most notable is the counterintuitive outcome of seasonally-mediated competitive exclusion that would not occur in either season alone, but is rather the direct result of environmental variability itself. We find that seasonal trade-offs in species’ growth rates, seasonal differences in competition strength, and functional similarity between competing species have the potential to drive nonlinear responses in coexistence to changing seasonality under global change. These biological conditions collectively influence our model’s transient dynamics, further explaining the mechanisms behind counterintuitive outcomes and highlighting the importance of non-equilibrium theory for global change ecology. Importantly, the seasonal patterns and species’ trade-offs that magnify these results are biologically realistic, therefore providing important insight into the implications for the maintenance of biodiversity under global change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".