Niche concept scale in space and time: evolutionary perspectives from tropical insectivorous birds
Bibliographic record
Abstract
Ecological niches are pivotal in addressing questions of species richness gradients like the Latitudinal Diversity Gradient (LDG). The Hutchinsonian niche hypervolume model and derivatives are some of the most proven tools. Accordingly, species occupy mathematically convenient spaces in relation to functional, especially trophic, relationships, as well as the physical environment. In one application, the number of species in a community is a function of average niche sizes, overlaps, and total niche volume. Alternatively, the number of coexisting species derives from invasibility criteria in relation to species-interaction modules. The daunting complexity of tropical communities begs the question of how well these ecologically inspired paradigms accommodate present knowledge of species interactions and functional relationships. Recent studies of hyperdiverse tropical insectivorous bird species suggests reevaluating the applicability of such concepts. Here I review Neotropical, arthropod-feeding bird species interactions needed to explain these species’ trophic relationships, including their diets, feeding substrates, and behavioral and morphological traits relevant to resource acquisition. Important emergent generalizations include extraordinary specializations on both prey resource locations (substrates) and behaviors, rather than on particular resources per se, and a preponderance of adaptations to exploit the anti-predator traits of prey, traits evolved in response to other predators. These specializations and implicit arms races necessitate evolutionary approaches to niches necessary to understand the relevant natural history and ecology, how these species compete interspecifically, and even how these predator species interact with prey via evolutionary enhancements. These findings, compared and contrasted with prevailing concepts and findings, suggest expanding niche concepts to accommodate both the large temporal and regional geographic scales to understand the accumulated species richness of the mainland Neotropics. These trophic specializations also highlight why many of these birds are so sensitive to human disturbances, especially habitat loss, fragmentation, and degradation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".