The experimental evolution of specialists, generalists, and the maintenance of diversity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Environmental heterogeneity may be a general explanation for both the quantity of genetic variation in populations and the ecological niche width of individuals. To evaluate this hypothesis, I review the literature on selection experiments in heterogeneous environments. The niche width usually – but not invariably – evolves to match the amount of environmental variation, specialists evolving in homogeneous environments and generalists evolving in heterogeneous environments. The genetics of niche width are more complex than has previously been recognized, particularly with respect to the magnitude of costs of adaptation and the putative constraints on the evolution of generalists. Genetic variation in fitness is more readily maintained in heterogeneous environments than in homogeneous environments and this diversity is often stably maintained through negative frequency-dependent selection. Moreover environmental heterogeneity appears to be a plausible mechanism for at least two well-known patterns of species diversity at the landscape scale. I conclude that environmental heterogeneity is a plausible and possibly very general explanation for diversity across the range of scales from individuals to landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it