The functional diversity of food webs: linking ecology, physiology and biogeography
Bibliographic record
Abstract
Trait-based ecology recasts community ecology’s central question about species coexistence as: which processes determine the functional trait composition of ecological communities? Spatial scale is implicit in this question, as different processes are expected to act at different scales. Community ecology has struggled to provide predictive models that link environmental drivers with the structure of biological communities. Greater progress could be made by focussing on the functional traits of species (their physiological, biological and ecological attributes), rather than on their identities. We are specifically missing analyses of trait diversity at large spatial scales where dispersal between sites is rare, so that we cannot determine if functional diversity in general is constrained local resources or limited by dispersal, evolution, or biogeography. The FRB-CESAB FUNCTIONALWEBS focal system (the invertebrates inhabiting water-filled bromeliad leaves) has been sampled from 22 neotropical locations, and the dataset (850 taxa; 1750 bromeliads; 12 traits; environmental variables) has been collated in an SQL database. The working group’s fundamental question was: which processes determine functional community structure at different spatial scales? This document summarizes in a few pages the group’s context and objectives, the methods and approaches used, the main findings, as well as the impact for science, society, and both public and private decision-making. ----------------------------------- Les écologistes peinent à établir des modèles prédictifs reliant environnement et structure des communautés. Des progrès seraient réalisés en mettant l’accent sur les traits fonctionnels, plutôt que sur l’identité des espèces. Nous manquons d’analyses à de vastes échelles spatiales où la dispersion inter-sites est rare, donc nous ne pouvons déterminer si la diversité fonctionnelle est régie par les niches écologiques ou limitée par la dispersion, l’évolution ou la biogéographie. Le groupe FRB-Cesab FunctionalWebs a échantillonné le réseau trophique des broméliacées remplies d’eau de pluie dans 12 régions néotropicales. La base de données est en libre accès et regroupe les données de plus de 1750 broméliacées et 12 traits. Le groupe FRB-Cesab FunctionalWebs a cherché à savoir quels processus déterminent la diversité fonctionnelle à différentes échelles spatiales. Ce document synthétise en quelques pages le contexte et les objectifs du groupe, les méthodes et approches utilisées, les principales conclusions ainsi que l'impact pour la science, la société, la décision publique et privée.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".