Integrating carbon stocks and wildlife connectivity for nature-based climate solutions
Bibliographic record
Abstract
Abstract Actions to protect against biodiversity loss and climate change will require a framework that addresses synergies between these interrelated issues. In this study we present methods for identifying areas important for the implementation of nature-based climate solutions and biodiversity conservation by intersecting high resolution spatial data for carbon storage and terrestrial connectivity. We explored the spatial congruence of carbon and connectivity in Ontario, Canada and examined effectiveness of current protected areas coverage. We found a weak positive relationship between carbon stocks and terrestrial connectivity; however, our maps revealed large hotspots, with high values of both indices, throughout the boreal forest and northern peatlands and smaller, isolated hotspots in the settled landscapes of the south. Location of hotspots varied depending on whether we considered forest or soil carbon. Further, our results show that current protected and conserved areas in Ontario only cover 13% of landscapes with the highest values for both carbon storage and connectivity. Protection or restoration of areas that maximize the co-benefits of carbon storage and connectivity would make significant contributions towards ambitious national targets to reduce greenhouse gas emissions and conserve biodiversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".