Intraguild predation enhances biodiversity and functioning in complex food webs
Bibliographic record
Abstract
Intraguild predation (IGP), that is, feeding interaction between two consumers that share the same resource species, is commonly observed in natural food webs. IGP expands vertical niche space and slows down energy flows from lower to higher trophic levels, which potentially affects the diversity and dynamics of food webs. Here, we use food-web models to investigate the effects of IGP on species diversity and ecosystem functioning. We first simulate a five-species food-web module with different strengths of IGP at the herbivore and/or carnivore level. Results show that as the strength of IGP within a trophic level increases, the biomass of its resource level increases because of predation release; this increased biomass in turn alters the energy fluxes and biomass of other trophic levels. These results are then extended by subsequent simulations of more diverse food webs. As the strength of IGP increases, simulated food webs maintain (1) higher species diversity at different trophic levels, (2) higher total biomasses at different trophic levels, and (3) larger energy fluxes across trophic levels. Our results challenge the intuitive hypothesis that food-web structure should maximize the efficiency of energy transfer across trophic levels; instead, they suggest that the assembly of food webs should be governed by a balance between efficiency (of energy transfer) and persistence (i.e., the maintenance of species and biomasses). Our simulations also show that the relationship between biodiversity and ecosystem functioning (e.g., total biomass or primary production) is much stronger in the presence of IGP, reconciling the contrast from recent studies based on food-chain and food-web models. Our findings shed new light on the functional role of IGP and contribute to resolving the debate on structure, diversity and functioning in complex food webs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".