The effect of range overlap on ecological niche divergence depends on spatial scale in monkeyflowers
Bibliographic record
Abstract
Patterns of niche divergence and geographical range overlap of closely related species provide insights into the evolutionary dynamics of ecological niches. When ranges overlap, shared selective pressures may preserve niche similarity along coarse-scale macrohabitat axes (e.g., bioclimates). Alternatively, competitive interactions may drive greater divergence along local-scale microhabitat axes (e.g., micro-topographical features). We tested these hypotheses in 16 species pairs of western North American monkeyflowers (Erythranthe and Diplacus, formerly Mimulus) with estimations of species' niches, geographic ranges, and a robust phylogeny. We found that macrohabitat niche divergence decreased with increasing range overlap, consistent with convergent selection operating at a coarse scale. No significant relationship was detected for microhabitat niches. Additionally, niche divergence was greater for recently diverged pairs along all macrohabitat niche axes, but greater for distantly diverged pairs along one microhabitat axis related to vegetation cover. For species pairs with partially overlapping ranges, greater microhabitat divergence was detected in sympatry than in allopatry for at least one niche axis for three of four pairs, consistent with character displacement in sympatry. Thus, coarse- and local-scale niche divergence show dissimilar patterns in relation to range overlap and divergence time, perhaps because the relative importance of convergent versus divergent selection depends on spatial scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".