Optimal planning to mitigate the impacts of roads on multiple species
Bibliographic record
Abstract
Abstract Common worldwide and encroaching on even the most remote locations, roads negatively affects wildlife through habitat loss, fragmentation and direct mortality. Reducing these effects requires mitigation, including wildlife crossing structures and fencing. However, mitigation measures are expensive and vary in their success level, especially when constructed to meet the needs of several species. Moreover, mitigation planning rarely considers the needs of multiple species. As funds are limited, deciding where and how to act for the greatest return on investment is crucial. Combining decision theory with a metapopulation model, we determined the most cost‐effective actions mitigating the effects of roads on multiple species. The model is illustrated with two sets of species with varying of life‐history traits, from a diversity of taxonomic groups. We tested the cost‐effectiveness of spatially explicit combinations of three management options for each road section: (a) no mitigation, (b) fences without wildlife crossings, and (c) fences combined with wildlife crossings. We explored the trade‐offs between each population's probability of persistence and total mitigation cost, first on a per‐species basis and then considering all species. We then tested the cost‐effectiveness of different planning strategies: (a) single species, (b) two types of focal species based on different life‐history traits, and (c) comprehensive multispecies planning. Planning for the needs of all species at the same time (multispecies strategy) maximizes the number of persisting species and provides the most robust and cost‐effective planning strategy, while single‐species strategies were found to be inefficient. However, basing decisions on the focal species with the largest home range can provide reasonably cost‐effective results, but should be considered only when there is not enough time or money to collect the necessary information to perform a multispecies analysis. Synthesis and applications . Our model can be adapted to most road mitigation problems. It illustrates that the needs of multiple species should be considered to plan a cost‐effective road mitigation system. However, when resources are limited to plan for all species, those with larger home ranges should be used as reasonable proxies for other species.
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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) | 0.002 | 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".