Apparent evolutionary maladaptation and inference from reciprocal transplants
Bibliographic record
Abstract
In rapidly changing environments populations and species face a challenge to remain adapted and avoid extinction or replacement by fitter types. If evolutionary adaptation cannot keep pace with the speed of environmental change populations will exhibit varying degrees of maladaptation with respect to the current environmental state. Reciprocal transplant experiments are an established method for comparatively assessing the relative fitness of multiple populations in their respective environments. Here we use a quantitative-genetics model to show that inference from reciprocal transplants can be misleading when applied to populations that are in the process of adapting to environmental change. Specifically, we analyze (a) the case of two populations adapting to two different fitness optima starting from a suboptimal initial state and (b) the case of two populations attempting to adapt to changing trait targets that move at different speeds. We find that, in both scenarios, populations can undergo transitional fitness states that, if reciprocal transplant experiments were performed, would lead to the conclusion of (local) non-adaptation or maladaptation. This signature of apparent maladaptation occurs although both populations strictly follow an evolutionary trajectory dictated by the principle of fitness increase over time. Our results have implications for potential patterns of latitudinal replacement of populations/species with ongoing global change and might help shed light on the surprising finding (based on reciprocal transplants) that many populations in the wild fail to show a strong signature of adaptation to their local environments.
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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 | 0.000 | 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".