Toward a unified approach to modelling adaptation among demographers and evolutionary ecologists
Bibliographic record
Abstract
Abstract Demographic and evolutionary modelling approaches are critical to understanding and projecting species responses to global environmental changes. Population matrix models have been a favoured tool in demography, but until recently, they failed to account for short‐term evolutionary changes. Evolutionary‐explicit demographic models remain computationally intensive, difficult to use and have yet to be widely adopted for empirical studies. Researchers focusing on short‐term evolution often favour individual‐based simulations, which are more flexible but less transferable and computationally efficient. Limited communication between fields has led to differing perspectives on key issues, such as how life‐history traits affect adaptation to environmental change. We develop a new EvoDemo hyperstate matrix population model (EvoDemo‐Hyper MPM) that incorporates the genetic inheritance of quantitative traits, enabling fast computation of evolutionary and demographic dynamics. We evaluate EvoDemo‐Hyper MPM against individual‐based simulations and provide analytical approximations for adaptation rates across six distinct scales in response to selection. We show that different methods yield equivalent results for the same biological scenario, although semantic differences between fields may obscure these similarities. Our results demonstrate that EvoDemo‐Hyper MPM provides accurate, computationally efficient solutions, closely matching outcomes from individual‐based simulations and analytical approximations under similar biological conditions. Adaptation rates per generation remain constant across species when selection acts on fertility but vary with other vital rates. Adaptation per time decreases with generation time unless selection targets adult survival, where intermediate life histories adapt fastest. Rates per generation, defined as the relative change in individual fitness, remain constant across species and vital rates. We discuss that no general prediction emerges about which species or life‐history traits yield higher adaptation rates, as outcomes depend on life cycles, vital rates and the definition used. We provide Matlab and R code to support the application of our EvoDemo‐Hyper MPM.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".