Site complementarity between biodiversity and ecosystem services in conservation planning of sparsely-populated regions
Bibliographic record
Abstract
SUMMARY The consequences of considering ecosystem services (ES) in conservation assessment are still widely debated. The degree of success depends on the extent to which biodiversity and ES can be secured under joint conservation actions. Unlike biodiversity, ES conservation is inseparably linked to human beneficiaries. Reconciling biodiversity with ES and conservation can be particularly challenging in sparsely populated areas. This study, in a sparsely-populated region of eastern Canada, focused on freshwater wetland biodiversity and ten ES provided by wetlands. Within a given maximal total area, the results showed that planning for biodiversity underrepresented local flow ES supply by 57% and demand by 61% in conservation networks. Planning for ES alone underrepresented wetland biodiversity surrogates by an average of 34%. Considering both biodiversity and ES simultaneously, all of the biodiversity and ES targets were achieved with only a 6% mean increase in area. Achieving all conservation targets starting from a network that was primarily built for either ES or biodiversity features alone was two to five times less efficient than considering both ES and biodiversity simultaneously in conservation assessment. A better framework is required to translate these spatial synergies into effective joint conservation actions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".