Over the top: do thermal barriers along elevation gradients limit biotic similarity?
Bibliographic record
Abstract
Organismal dispersal through mountain passes should be more constrained by temperature‐related differences between lowland and highland sites in montane environments. This may lead to higher rates of diversification through isolation of existing lineages toward the tropics. This mechanism, proposed by Janzen, could influence broad‐scale patterns of biodiversity across mountainous regions and more broadly across latitudinal gradients. We constructed two complementary analyses to test this hypothesis. First, we measured topographically‐derived thermal gradients using recently‐developed climatic data across the Americas, reviewing the main expectations from Janzen's climatic model. Then, we evaluated whether thermal barriers predict assemblage similarity for amphibians and mammals along elevational gradients across most of their latitudinal extent in the Americas. Thermal barriers between low and high elevation areas, initially proposed to be unique to tropical environments, are comparably strong in some temperate regions, particularly along the western slopes of North American dividing ranges. Biotic similarity for both mammals and amphibians decreases between sites that are separated by elevation‐related thermal barriers. That is, the stronger the thermal barrier separating pairs of sites across the latitudinal gradient, the lower the similarity of their species assemblages. Thermal barriers explain 10–35% of the variation in latitudinal gradients of biotic similarity, effects that were stronger in comparisons of sites at high elevations. Mammals' stronger dispersal capacities and homeothermy may explain weaker effects of thermal barriers on gradients of assemblage similarity than among amphibians. Understanding how temperature gradients have shaped gradients of montane biological diversity in the past will improve understanding of how changing environments may affect them in the future.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".