Bottom‐up influences on tropical freshwater food web structure support the “environmental filtering” hypothesis
Bibliographic record
Abstract
Abstract Studies of ecosystem functions are gaining traction in the scientific community along with a growing consensus that losses in ecosystem functions have widespread consequences. Food webs, which are networks comprising all trophic interactions (represented by links) between taxa present in a community (represented by nodes), are important aspects of ecosystem functioning, yet a clear understanding of the factors and mechanisms influencing their assembly and structure is lacking. In our study, we addressed this fundamental question by investigating the respective roles of (1) environmental filtering and (2) biotic filtering, in governing food web structure. We did this by assessing the relationship between the network structure of five high‐resolution empirical tropical food webs and associated environmental and biotic covariates. Our data suggest that only environmental filtering is important in shaping food webs. Further, we found that the underlying ecological mechanism is a function of bottom‐up influences comprising resource levels, and to a lesser degree, resource type (i.e., terrestrial organic matter) available. Specifically, our data suggest high‐nutrient environments favor greater food web complexity. In the general context of community assembly, our findings add to existing knowledge of the process by demonstrating that environmental conditions previously shown to influence species assemblages can also drive trends in prevailing species interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".