Global and local drivers of the relative importance of allochthonous and autochthonous energy sources to freshwater food webs
Bibliographic record
Abstract
Resource quantity (i.e. organic matter; OM) is a main driver of the prevailing energy pathway in freshwater food webs. The OM pool is mainly composed of allochthonous material, a primary resource for freshwater consumers. Contrastingly, small amounts of autochthonous OM (i.e. algae) can subsidize aquatic communities due to its higher nutritional quality. To date, there is no consensus about the relative importance of allochthonous and autochthonous OM for freshwater food webs or the environmental factors driving their relative importance. We fill this gap by evaluating the relative importance of allochthonous and autochthonous OM sources for freshwater food webs on a global scale through a meta‐analytical approach. We gathered the outcome of stable isotope mixing models of 2789 cases from 58 published studies and calculated a response ratio between the mean contributions of allochthonous and autochthonous OM for freshwater consumers. Using mixed‐effect models and a multimodel inference approach, we tested the influence of latitude, habitat type, ecosystem size, climate and terrestrial productivity over the response ratio. The relative contribution of autochthonous OM was higher in lotic systems. In lentic systems, increasing terrestrial productivity increased the relative contribution of autochthonous OM, while increasing precipitation and temperature seasonality reduced this relative contribution. We suggested that factors increasing terrestrial productivity might also boost autochthonous OM in these systems, while precipitation increases the transport of allochthonous OM to freshwater habitats. We did not find any relationship between environmental factors and the relative contribution of autochthonous OM for lotic systems. We concluded that the relative contribution of allochthonous and autochthonous energy sources to freshwater food webs differs between lotic and lentic ecosystems and it is dependent on multiple environmental factors.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".