Functional diversity and management mediate aboveground carbon stocks in small forest fragments
Bibliographic record
Abstract
Improved landscape connectivity is increasingly considered a viable management strategy to maintain biodiversity, ecosystem functions, and services. How landscape structure affects biodiversity, ecosystem services, and their relationship, however, is still unclear in many cases, including the service of climate regulation. The effects of forest fragmentation on carbon storage remain largely unknown, compounded by uncertainty in both the direction and magnitude of the relationship between carbon storage and biodiversity. We investigated the effects of forest fragmentation and management on carbon stocks and biodiversity in the Montérégie, QC. We quantified total aboveground carbon stocks in 24 small forest fragments of two sizes (∼10 ha, ∼100 ha), and two levels of connectivity, using a combination of satellite data, field‐based methods, and allometry. We correlated this data with both woody plant species richness and functional dispersion to determine the relationship between biodiversity and carbon stocks in these forest fragments. We found functional dispersion was a significant predictor of aboveground carbon stocks, interacting with forest management and connectivity in this fragmented forest system. Both synergies and tradeoffs between biodiversity and carbon stocks were observed. Unmanaged forest stands stored less carbon on average than managed, but demonstrated a significant positive relationship between functional dispersion and aboveground carbon stocks, corroborating the results of biodiversity‐ecosystem function experiments. The slope of the relationship was significantly greater in connected fragments than isolated, suggesting improved forest connectivity may strengthen the relationship between biodiversity and aboveground carbon stocks in this region. Managed stands exhibited a significant negative relationship, demonstrating that anthropogenic influence can alter the link between biodiversity and carbon stocks in natural systems. Our results suggest that considering management, connectivity, and functional diversity may increase accuracy in estimating landscape level carbon stocks. Additionally, the significant contributions of small forest fragments to regional diversity and service provision emphasizes the important role these fragments can play in conservation efforts.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".