The positive carbon stocks–biodiversity relationship in forests: co‐occurrence and drivers across five subclimates
Bibliographic record
Abstract
Carbon storage in forests and its ability to offset global greenhouse gas emissions, as well as biodiversity and its capacity to support ecosystem functions and services, are often considered separately in landscape planning. However, the potential synergies between them are currently poorly understood. Identifying the spatial patterns and factors driving their co-occurrence across different climatic zones is critical to more effectively conserve forest ecosystems at the regional level. Here, we integrated information of National Forest Inventories and Breeding Bird Atlases across Europe and North America (Spain and Quebec, respectively), covering five subclimates (steppe, dry Mediterranean, humid Mediterranean, boreal, and temperate). In particular, this study aimed to (1) determine the spatial patterns of both forest carbon stocks and biodiversity (bird richness, tree richness, and overall biodiversity) and the factors that influence them; (2) establish the relationships between forest carbon stocks and biodiversity; and (3) define and characterize the areas of high (hotspots) and low (coldspots) values of carbon and biodiversity, and ultimately quantify their spatial overlap. Our results show that the factors affecting carbon and biodiversity vary between regions and subclimates. The highest values of carbon and biodiversity were found in northern Spain (humid Mediterranean subclimate) and southern Quebec (temperate subclimate) where there was more carbon as climate conditions were less limiting. High density and structural diversity simultaneously favored carbon stocks, tree, and overall biodiversity, especially in isolated and mountainous areas, often associated with steeper slopes and low accessibility. In addition, the relationship between carbon stocks and biodiversity was positive in both regions and all subclimates, being stronger where climate is a limiting factor for forest growth. The spatial overlap between hotspots of carbon and biodiversity provides an excellent opportunity for landscape planning to maintain carbon stocks and conserve biodiversity. The variables positively affecting carbon and biodiversity were also driving the hotspots of both carbon and biodiversity, emphasizing the viability of "win-win" solutions. Our results highlight the need to jointly determine the spatial patterns of ecosystem services and biodiversity for an effective and sustainable planning of forest landscapes that simultaneously support conservation and mitigate climate change.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".