Indirect Effects of Temperature Drive Gradients in Fish Food Web Properties
Bibliographic record
Abstract
ABSTRACT Aim Understanding the direct (e.g., on biological rates) and indirect (e.g., through changes in species richness) effects of temperature on food web properties, in the context of latitudinal gradients and climate warming. We focus on species interactions and predict variations in two metrics of food web properties: trophic control and temporal variability. Location Global oceans. Time Period 2001–2018. Major Taxa Studied Marine fish species. Methods We use a modelling approach coupled with a global dataset of fish food webs. Species occurrences are obtained from data sources, while trophic interactions are predicted by a size‐based niche model calibrated with a global interaction dataset. Interaction strengths are constrained by allometric scaling laws for predation and biomass. We investigate how predictors varying with latitude (temperature, species richness, productivity, food web structure) drive latitudinal variations in trophic regulation and variability. Results Our results suggest a latitudinal gradient in two metrics of community dynamics, with both trophic feedback strength (underlying phenomena such as cycles and cascades) and temporal stability increasing with latitude. In our model, this variation is tied directly and indirectly to temperature, and we find that direct effects of temperature are weaker than (or at most equal to) indirect effects. The direct effect on interaction rates decreases trophic feedbacks yet increases variability. The organism‐level temperature–size rule is found to increase both feedback and variability. Finally, community‐level indirect effects (species richness and connectance) impact trophic control but not variability. Climate warming moderately affects trophic control, variability and total biomass, but more strongly alters individual species biomass. Main Conclusions Our study improves understanding of the drivers of latitudinal variation in food web properties and helps disentangle the direct and indirect effects of temperature. Indirect effects are predicted to drive biogeographic variation in food web properties, while direct effects such as short‐term warming could have stronger consequences at the species level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".