Intraspecific competition drives increased resource use diversity within a natural population
Bibliographic record
Abstract
Resource competition is thought to play a major role in driving evolutionary diversification. For instance, in ecological character displacement, coexisting species evolve to use different resources, reducing the effects of interspecific competition. It is thought that a similar diversifying effect might occur in response to competition among members of a single species. Individuals may mitigate the effects of intraspecific competition by switching to use alternative resources not used by conspecific competitors. This diversification is the driving force in some models of sympatric speciation, but has not been demonstrated in natural populations. Here, we present experimental evidence confirming that competition drives ecological diversification within natural populations. We manipulated population density of three-spine sticklebacks (Gasterosteus aculeatus) in enclosures in a natural lake. Increased population density led to reduced prey availability, causing individuals to add alternative prey types to their diet. Since phenotypically different individuals added different alternative prey, diet variation among individuals increased relative to low-density control enclosures. Competition also increased the diet-morphology correlations, so that the frequency-dependent interactions were stronger in high competition. These results not only confirm that resource competition promotes niche variation within populations, but also show that this increased diversity can arise via behavioural plasticity alone, without the evolutionary changes commonly assumed by theory.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".