How phenological tracking shapes species and communities in non‐stationary environments
Bibliographic record
Abstract
Climate change alters the environments of all species. Predicting species responses requires understanding how species track environmental change, and how such tracking shapes communities. Growing empirical evidence suggests that how species track phenologically - how an organism shifts the timing of major biological events in response to the environment - is linked to species performance and community structure. Such research tantalizingly suggests a potential framework to predict the winners and losers of climate change, and the future communities we can expect. But developing this framework requires far greater efforts to ground empirical studies of phenological tracking in relevant ecological theory. Here we review the concept of phenological tracking in empirical studies and through the lens of coexistence theory to show why a community-level perspective is critical to accurate predictions with climate change. While much current theory for tracking ignores the importance of a multi-species context, basic community assembly theory predicts that competition will drive variation in tracking and trade-offs with other traits. We highlight how existing community assembly theory can help understand tracking in stationary and non-stationary systems. But major advances in predicting the species- and community-level consequences of climate change will require advances in theoretical and empirical studies. We outline a path forward built on greater efforts to integrate priority effects into modern coexistence theory, improved empirical estimates of multivariate environmental change, and clearly defined estimates of phenological tracking and its underlying environmental cues.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".