The functional role of biodiversity in ecosystems: incorporating trophic complexity
Bibliographic record
Abstract
Understanding how biodiversity affects functioning of ecosystems requires integrating diversity within trophic levels (horizontal diversity) and across trophic levels (vertical diversity, including food chain length and omnivory). We review theoretical and experimental progress toward this goal. Generally, experiments show that biomass and resource use increase similarly with horizontal diversity of either producers or consumers. Among prey, higher diversity often increases resistance to predation, due to increased probability of including inedible species and reduced efficiency of specialist predators confronted with diverse prey. Among predators, changing diversity can cascade to affect plant biomass, but the strength and sign of this effect depend on the degree of omnivory and prey behaviour. Horizontal and vertical diversity also interact: adding a trophic level can qualitatively change diversity effects at adjacent levels. Multitrophic interactions produce a richer variety of diversity-functioning relationships than the monotonic changes predicted for single trophic levels. This complexity depends on the degree of consumer dietary generalism, trade-offs between competitive ability and resistance to predation, intraguild predation and openness to migration. Although complementarity and selection effects occur in both animals and plants, few studies have conclusively documented the mechanisms mediating diversity effects. Understanding how biodiversity affects functioning of complex ecosystems will benefit from integrating theory and experiments with simulations and network-based approaches.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".