All wet or dried up? Real differences between aquatic and terrestrial food webs
Bibliographic record
Abstract
Ecologists have greatly advanced our understanding of the processes that regulate trophic structure and dynamics in ecosystems. However, the causes of systematic variation among ecosystems remain controversial and poorly elucidated. Contrasts between aquatic and terrestrial ecosystems in particular have inspired much speculation, but only recent empirical quantification. Here, we review evidence for systematic differences in energy flow and biomass partitioning between producers and herbivores, detritus and decomposers, and higher trophic levels. The magnitudes of different trophic pathways vary considerably, with less herbivory, more decomposers and more detrital accumulation on land. Aquatic-terrestrial differences are consistent across the global range of primary productivity, indicating that structural contrasts between the two systems are preserved despite large variation in energy input. We argue that variable selective forces drive differences in plant allocation patterns in aquatic and terrestrial environments that propagate upward to shape food webs. The small size and lack of structural tissues in phytoplankton mean that aquatic primary producers achieve faster growth rates and are more nutritious to heterotrophs than their terrestrial counterparts. Plankton food webs are also strongly size-structured, while size and trophic position are less strongly correlated in most terrestrial (and many benthic) habitats. The available data indicate that contrasts between aquatic and terrestrial food webs are driven primarily by the growth rate, size and nutritional quality of autotrophs. Differences in food-web architecture (food chain length, the prevalence of omnivory, specialization or anti-predator defences) may arise as a consequence of systematic variation in the character of the producer community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".