Exploring the role of physiology and biotic interactions in determining elevational ranges of tropical animals
Bibliographic record
Abstract
Tropical mountains contain some of the world’s richest animal communities as a result of high turnover of species along elevational gradients. We describe an approach to study the roles of biotic and abiotic factors in establishing elevational ranges, and to improve our ability to predict the effects of climate change on these communities. As a framework we use Hutchinson’s concept of the fundamental niche (determined by the match between the physical environment and the organism’s physiological and biophysical characteristics) and realized niche (the subset of the fundamental niche determined by biotic interactions). Using tropical birds as an example, we propose a method for estimating fundamental niches and discuss five biotic interactions that we expect to influence distributions of tropical montane animals: predation, competition, parasites and pathogens, mutualisms, and habitat associations. The effects of biotic factors on elevational ranges have been studied to some extent, but there is little information on physiological responses of tropical montane animals. It will be necessary to understand all of these ecological constraints in concert to predict current and future elevational ranges and potential threats to montane species. Given the importance of tropical mountains as global biodiversity hotspots, we argue that this area of research requires urgent attention.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".