Will a Fish's Perspective Improve the Ecological Relevance of River Connectivity Metrics?
Bibliographic record
Abstract
ABSTRACT Structural indices of aquatic connectivity typically focus on the spatial arrangement of barriers to fish movement. Here, we describe a method that adapts the widely applied structural Dendritic Connectivity Index (DCI) into a functional index (DCI F ) that constrains connectivity measurements to biologically relevant scales (e.g., based on fish movement behavior). We compare fish communities in five Ontario watersheds to empirically test the hypothesis that they are better explained when connectivity is measured using the DCI F . We test the response of fish abundance grouped by swimming abilities and morphology, along with fish community assemblage as a whole. We expected the DCI F to better explain the abundance of the weakest swimmer groups and overall community assemblage than the structural index. At the spatial scales examined, the DCI F provided modest improvements in explaining the abundance of some fish functional groups. Conversely, we found little to no improvement over the structural index in explaining community structure. Regardless, our paper illustrates a new approach that explicitly incorporates ecologically relevant scales into connectivity measures within watersheds. These scale effects can be significant, and the approach presented here can be applied to aquatic systems at various spatial extents. Our case study suggests that managers may not always need to gather data on fish movement or other biological parameters, and instead can focus on variables that may better explain the effect of barriers on fish community patterns.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".