Safeguarding Wetlands and Their Connections within Wetlandscapes to Improve Conservation Outcomes for Threatened Amphibian Species
Bibliographic record
Abstract
Abstract Wetlands should not be considered as independent objects but as dynamically connected objects, collectively known as wetlandscapes. We developed a framework that analyzes the influences of wetland suitability and connectivity on amphibian distributions. We defined two indices: a Wetland Suitability Index describing wetland quality and a Movement Permeability Index characterizing wetland connectivity for amphibian population dynamics. These indices were calculated from raster datasets and time‐varying inundation estimates. The indices were used to define a wetlandscape and an amphibian model was used to simulate population dynamics within the wetlandscape. The framework was applied to the Nose Creek watershed, a highly modified wetlandscape in Alberta, Canada. Two amphibian species were selected with different habitat preferences: the Northern Leopard Frog that prefers wet habitats and has high mobility over land, and the Great Plains Toad that prefers terrestrial habitats and has low mobility over land. We found each amphibian species had a “preferred” wetlandscape, reflecting their life cycle traits and migration strategies which in turn were dependent on the hydrological and ecological connections within the wetlandscape. This study highlights the importance of investigating both individual wetlands and the wetlandscape and considering both wetland habitat quality and connectivity as non‐substitutable properties that act jointly, but differently, on population dynamics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".