Competition and the Effect of Spatial Resource Heterogeneity on Evolutionary Diversification
Bibliographic record
Abstract
A model is presented to explore how the form of selection arising from competition for resources is affected by spatial resource heterogeneity. The model consists of a single species occupying two patches connected by migration, where the two patches can differ in the type of resources that they contain. The main goal is to determine the conditions under which competition for resources results in disruptive selection (i.e., selection favoring a polymorphism) since it is this form of selection that will give rise to the evolutionary diversification of resource exploitation strategies. In particular, comparing the conditions giving rise to disruptive selection when the two patches are identical to the conditions when they contain different resources reveals the effect of spatial resource heterogeneity. Results show that when the patches are identical, the conditions giving rise to disruptive selection are identical to those that give rise to character displacement in previous models. When the patches are different, the conditions giving rise to disruptive selection can be either more or less stringent depending upon demographic parameters such as the intrinsic rate of increase and the migration rate. Surprisingly, spatial resource heterogeneity can actually make forms of evolutionary diversification such as character displacement less likely. It is also found that results are dependent on how the resource exploitation strategies and the spatial resource heterogeneity affect the population dynamics. One robust conclusion however, is that spatial resource heterogeneity always has a disruptive effect when the migration rate between patches is low.
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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 | 0.001 |
| 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 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".