Food‐web structure and ecosystem function in the Laurentian Great Lakes—Toward a conceptual model
Bibliographic record
Abstract
Abstract The relationship between food‐web structure (i.e., trophic connections, including diet, trophic position, and habitat use, and the strength of these connections) and ecosystem functions (i.e., biological, geochemical, and physical processes in an ecosystem, including decomposition, production, nutrient cycling, and nutrient and energy flows among community members) determines how an ecosystem responds to perturbations, and thus is key to understanding the adaptive capacity of a system (i.e., ability to respond to perturbation without loss of essential functions). Given nearly ubiquitous changing environmental conditions and anthropogenic impacts on global lake ecosystems, understanding the adaptive capacity of food webs supporting important resources, such as commercial, recreational, and subsistence fisheries, is vital to ecological and economic stability. Herein, we describe a conceptual framework that can be used to explore food‐web structure and associated ecosystem functions in large lakes. We define three previously recognised broad habitat compartments that constitute large lake food webs (nearshore, pelagic, and profundal). We then consider, at three levels, how energy and nutrients flow: (a) into each basal resource compartment; (b) within each compartment; and (c) among multiple compartments (coupling). Flexible shifts in food‐web structures (e.g., via consumers altering their diet or habitat) that sustain these flows in the face of perturbations provide evidence for adaptive capacity. We demonstrate the conceptual framework through a synthesis of food‐web structure and ecosystem function in the Laurentian Great Lakes, with emphasis on the upper trophic levels (i.e., fishes). Our synthesis showed evidence of notable adaptive capacity. For example, fishes increased benthic coupling in response to invasion by mussels and round gobies. However, we also found evidence of loss of adaptive capacity through species extirpations (e.g., widespread collapse in the abundance and diversity of ciscoes, Coregonus spp., except in Lake Superior). In large freshwater lakes, fishery managers have traditionally taken a top‐down approach, focusing on stocking and harvest policy. By contrast, water quality managers have focused on nutrient effects on chemical composition and lower trophic levels of the ecosystem. The synthesised conceptual model provides resource managers a tool to more systematically interpret how lower food‐web dynamics influence harvestable fish populations, and vice versa, and to act accordingly such that sustainable resource practices can be achieved. We identify key gaps in knowledge that impede a fuller understanding of the adaptive capacities of large lakes. In general, we found a greater uncertainty in our understanding of processes influencing energy and nutrient flow within and among habitats than flows into the system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".