Managing aquatic habitat structure for resilient trophic interactions
Bibliographic record
Abstract
Structural habitat (the three-dimensional arrangement of physical matter, abiotic and biotic, at a location) is a foundational element for the resilience and maintenance of biodiversity, yet anthropogenic development is driving the global simplification of aquatic environments. Resource managers regularly seek to conserve aquatic food webs by increasing structural habitat complexity with expected benefits to fisheries; however, the global effectiveness of such actions is unclear. Our synthesis and theoretical analyses found that the response of a consumer-resource interaction (predatory sportfish and forage fish prey) to the addition of prey refuge habitat differed among systems with low and high rates of biomass transfer from resource to consumer (i.e., biomass potential); stabilization was not the rule. Greater prey refuge habitat availability tended to stabilize systems characterized by high biomass potential while simultaneously increasing consumer densities. In contrast, increasing prey refuge habitat availability in systems characterized by low biomass potential tended to mute energy transfer and moved consumer densities toward local extinction. Importantly, biomass potential and prey refuge can have antagonistic effects on stability and relative consumer densities, and it is therefore important to consider the local conditions of a system when using habitat manipulation as a management measure. Further development of our context-dependent perspective to whole food webs, and across different environments, may help to guide structural habitat management to better restore and protect aquatic ecosystems.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".