Cryptic eco‐evolutionary dynamics
Bibliographic record
Abstract
Natural systems harbor complex interactions that are fundamental parts of ecology and evolution. These interactions challenge our inclinations and training to seek the simplest explanations of patterns in nature. Not least is the likelihood that some complex processes might be missed when their patterns look similar to predictions for simpler mechanisms. Along these lines, theory and empirical evidence increasingly suggest that environmental, ecological, phenotypic, and genetic processes can be tightly intertwined, resulting in complex and sometimes surprising eco-evolutionary dynamics. The goal of this review is to temper inclinations to unquestioningly seek the simplest explanations in ecology and evolution, by recognizing that some eco-evolutionary outcomes may appear very similar to purely ecological, purely evolutionary, or even null expectations, and thus be cryptic. We provide theoretical and empirical evidence for observational biases and mechanisms that might operate among the various links in eco-evolutionary feedbacks to produce cryptic patterns. Recognition that cryptic dynamics can be associated with outcomes like stability, resilience, recovery, or coexistence in a dynamically changing world provides added impetus for finding ways to study them.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".