Combining expert‐based and computational approaches to design protected river networks under climate change
Bibliographic record
Abstract
Abstract Aim Estimate the current and future distribution of brown trout and identify priority areas for conservation of the species. Location Rhône River basin and Mediterranean streams. Methods We first developed a spatially explicit species distribution model to estimate the current and future distribution of brown trout for three time horizons (2030, 2055 and 2080) and two climate change scenarios (RCP 4.5 and RCP 8.5). We then performed a prioritization analysis to identify priority areas for brown trout conservation, accounting for: (a) spatial dependencies along the riverine system, (b) several sources of uncertainty arising from climate‐related forecasts and (c) different protected area scenarios by comparing hypothetical, optimal protected networks to an actual protected network designed by regional fish experts. Results Future projections of brown trout densities exhibited a general trend towards a gradual range contraction, with a significant risk of extirpation across mountainous regions of low to mid‐elevation. Overall, the projected current and future distributions were well‐covered by the existing protected network. In addition, up to 70% of the river reaches included in this expert‐based protection network were also priorities in the optimal priority set (e.g. the best set of areas to maximize biodiversity protection). Finally, a large proportion of these reaches were invariably identified regardless of climate change scenarios and uncertainties or spatial dependencies. Main conclusions Our analytical approach highlighted priority areas for brown trout conservation which were robust to a set of climate and connectivity assumptions. This core priority network could be further refined by taking into account key fine‐scale processes like thermal refugia. Therefore, we advocate for combining computational and expert‐based approaches in conservation planning of riverine ecosystems to achieve a relevant consensus between regional‐scale management and fine‐grain ecological knowledge.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 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 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".