Regional variability in Atlantic salmon (<i>Salmo salar</i>) riverscapes: a simple landscape ecology model explaining the large variability in size of salmon runs across Gaspé watersheds, Canada
Bibliographic record
Abstract
Kim M, Lapointe M. Regional variability in Atlantic salmon ( Salmo salar ) riverscapes: a simple landscape ecology model explaining the large variability in size of salmon runs across Gaspé watersheds, Canada. Ecology of Freshwater Fish 2011: 20: 144–156. © 2010 John Wiley & Sons A/S Abstract – Atlantic salmon ( Salmo salar ) rivers in the Gaspé Peninsula, Quebec, present a 20 to 1 variability in the average numbers of returning adult salmon per km 2 of watershed area (the ‘specific run size’). These variations are very poorly explained by interbasin differences in total stream length ( R 2 = 0.033, P = 0.533) or in estimates of total area of salmon habitat that fail to take into account complementarity and interconnections across life stage habitats ( R 2 = 0.065, P = 0.448). The relative spatial distribution of three complementary habitat types (adult holding pools, spawning beds, parr habitats) is hypothesised to be an important factor controlling the production of salmon at any given watershed area or given total stream length, and by extension the numbers of adults returning each year to spawn. We developed a simple riverine landscape ecology (or riverscape) model that uses easily accessible topographic map sources to identify optimally productive river segments. These segments were identified based on large‐scale river and valley features and the associated spatial organisation of complementary salmon habitats. We tested the ability of this model to predict salmon run sizes for 14 watersheds in the Gaspé Peninsula. The aggregate length of optimally productive segments, as defined in our model, is a strong predictor of the average size of the annual salmon runs for these watersheds ( R 2 = 0.913, P < 0.0005). Furthermore, specific salmon run sizes were accurately predicted ( R 2 = 0.771, P < 0.0005) after removal of the obvious scale effects of watershed size.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".