Role of ambient light in structuring north-temperate fish communities: potential effects of increasing dissolved organic carbon concentration with a changing climate
Bibliographic record
Abstract
The vertical light environment in freshwater lakes is sensitive to changes in dissolved organic carbon (DOC) concentrations, and DOC concentrations in most boreal lakes appear to be increasing as the climate changes. Understanding how DOC dynamics in lakes and watersheds are then linked to fish habitat is therefore critical to assessing the effects of climate change on fish communities. This paper reviews the evidence for climate-induced alterations to DOC in north-temperate watersheds and assesses the potential consequences of the resultant decreases in water clarity on fish production and community structure in small boreal lakes (<500 ha). Although DOC increases are forecast for most boreal lakes, complex interactions between local climate, rates of terrestrial organic matter decomposition and runoff, existing water quality, species shifts in forest communities, and changing season lengths can all combine to affect the magnitude of water clarity declines that will occur in individual lakes. Elevated DOC concentrations will affect the availability of coldwater versus warmwater habitat and niche characteristics that are dependent on lake depth, water clarity, and the preferences and physiological tolerances of the individual species. The shifts in availability of suitable thermal habitat will in turn affect fish growth, production, and community composition. Range expansions for warmwater species and range reductions for coldwater species will likely occur in warmer, darker environments, with adverse consequences for some native species and likely advantages for invasive species such as smallmouth bass. Likewise, major shifts in trophic dynamics would accompany the darkening of boreal lakes as species-specific feeding efficiencies change and primary and secondary production are altered. From a beneficial perspective, elevated DOC concentrations will likely relieve some physiological stress in biota via protection against contaminant toxicity and ultraviolet radiation (UVR) exposure. As with many other disturbances, we need to view climate change impacts on fish communities as a multiple stressor problem and the potential impacts of the changing light environment needs to be given as much prominence as the study of stressors such as eutrophication and trace contaminants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".