Co‐occurrence in snake and lizard sister species is unrelated to major ecomorphological traits
Bibliographic record
Abstract
Abstract Environmental factors and interspecific interactions, such as competition and facilitation, can shape species' geographic ranges. Here, we tested the relationship between geographic range overlap of squamate sister species, and their divergence in body size, diel activity, and microhabitat use. Competition theory predicts that sister species with similar traits will overlap less geographically than sister pairs with dissimilar traits. However, similar distributions may present similar selective pressures and favour similar adaptations, such that habitat filtering may result in species with more similar traits in sympatry. Across 1434 sister species contrasts, we found little relationship between range overlap and niche divergence. In some models, the divergence in body size and microhabitat use marginally increased with sympatry, while in other models, sympatric sisters had similar activity times. However, the low R‐squared values of almost all these models lend only weak support to predictions from competition or habitat filtering theories. Sympatric sister species within the same biome showed more similar activity times than expected, lending some support to habitat filtering. Niche divergence in allopatry or sympatry, as calculated using a multi‐trait dissimilarity index, did not show a phylogenetic signal, and niches of sister species from different squamate clades did not deviate significantly from the null expectation. Overall, niche divergence or convergence, across the axes we explored, is not a prerequisite for regional species co‐occurrence. We suggest here that the lack of consistent support for either limiting similarity or habitat filtering may reflect that both these forces act as transient phenomena. More fine‐grained analyses, in space and time, would be needed to detect their fingerprints. Thus, coexistence in the face of competition can arise due to various evolutionary and biogeographic mechanisms, acting concurrently or asynchronously.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".