Using fish guilds to assess community responses to temperature and flow regimes in unregulated and regulated Canadian rivers
Bibliographic record
Abstract
Summary Hydropower currently accounts for 63% of Canada's total electricity generation and is bound to increase with the energy demands of a growing population. With damming and flow regulation known as major threats to aquatic biodiversity and river and floodplain habitats, an improved understanding of the specific impacts of river regulation is needed for the proper management of these systems. Although interactions among river flow and thermal regimes have been described in the literature, their concurrent influence on fish guild responses has yet to be analysed for temperate rivers. Such an analysis may be used to identify the ecological traits linked with the flow and thermal variables reflecting river regulation. Extensive field surveys were conducted across 25 unregulated and regulated rivers to estimate fish species density and biomass. Fish guild models were developed to characterise morphologic, trophic, reproductive, habitat preferences and behavioural traits, as well as phylogenetic associations. To characterise ecologically relevant components of the flow and thermal regimes of rivers, we calculated indices based on the magnitude, frequency, duration, timing and rate of change in each driver. Model relationships between fish biomass and density estimates were then run using redundancy analyses ( RDA ) on each type of guild and dominant patterns of flow and thermal variability. Variables representing the magnitude of summer temperatures and intra‐annual flow variability were consistently selected as independent drivers of fish guild responses (>86% of RDA models), clearly showing the importance of integrating thermal regimes in current river hydro‐ecological studies. Fish guild density and biomass were significantly explained ( R 2 Adj = 25–44%) and predicted ( R 2 CV = 35–76%) by flow and thermal variables characterising regimes across unregulated and regulated rivers, whereas total fish density and biomass were not. Fish guild models based on trait–environmental relationships performed better than those based on phylogeny. Our results also showed that the models describing habitat and trophic guilds had the greatest explanatory power ( R 2 Adj = 0.44 and R 2 Adj = 0.41 respectively). This study identified differences in guild trait–environment relationships across rivers and the guilds most susceptible to changes in flow and temperature conditions resulting from river regulation. In particular, more constant summer temperatures and lower flow variability favoured habitat and trophic guilds over morphologic, reproductive and behavioural guilds. Our results showed that maintaining particular aspects of the flow and thermal regime may be important for ensuring the presence of certain guilds in temperate rivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".