The adaptive capacity of lake food webs: from individuals to ecosystems
Bibliographic record
Abstract
Abstract Aquatic ecosystems support size structured food webs, wherein predator‐prey body sizes span orders of magnitude. As such, these food webs are replete with extremely generalized feeding strategies, especially among the larger bodied, higher trophic position taxa. The movement scale of aquatic organisms also generally increases with body size and trophic position. Together, these body size, mobility, and foraging relationships suggest that organisms lower in the food web generate relatively distinct energetic pathways by feeding over smaller spatial areas. Concurrently, the potential capacity for generalist foraging and spatial coupling of these pathways often increases, on average, moving up the food web toward higher trophic levels. We argue that these attributes make for a food web architecture that is inherently ‘adaptive’ in its response to environmental conditions. This is because variation in lower trophic level dynamics is dampened by the capacity of predators to flexibly alter their foraging behavior. We argue that empirical, theoretical, and applied research needs to embrace this inherently adaptive architecture if we are to understand the relationship between structure and function in the face of ongoing environmental change. Toward this goal, we discuss empirical patterns in the structure of lake food webs to suggest that ecosystems change consistently, from individual traits to the structure of whole food webs, under changing environmental conditions. We then explore an empirical example to reveal that explicitly unfolding the mechanisms that drive these adaptive responses offers insight into how human‐driven impacts, such as climate change, invasive species, and fisheries harvest, ought to influence ecosystem structure and function (e.g., stability, secondary productivity, maintenance of major energy pathways). We end by arguing that such a directed food web research program promises a powerful across‐scale framework for more effective ecosystem monitoring and management.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".