Drivers, climate sensitivity, and management of functional diversity in northeastern North America
Bibliographic record
Abstract
The functional diversity (FD) represented by plant traits is fundamentally linked to the ecosystem’s capacity to respond to changes in the environment. Thus, there is an ongoing debate about including FD considerations in management plans to safeguard forests and their services to the society under climate change. However, incomplete scientific knowledge and difficulties to understand the concept of FD hinder the implementation of FD-based management approaches. Our study fills these knowledge gaps by (i) mapping the current distribution, (ii) analyzing the drivers, and (iii) testing the sensitivity of FD to projected increases in temperature and precipitation in northeastern North America. Following the stress-dominance hypothesis, we expected the strongest effect on FD from environmental filtering (i.e., climatic conditions) within our study region. We combined a literature and database review of 44 traits for 43 tree species with terrestrial inventory data of 48,426 plots spanning an environmental gradient from northern boreal to temperate biomes. Employing multiple non-parametric models, we evaluated the impacts of 25 covariates on FD. Subsequently, we conducted a climate sensitivity analysis. The effect of rarity and commonness were tested for all outcomes using Hill numbers with different abundance weightings. We identified FD hotspots in temperate forests and the boreal-temperate ecotone east and northeast of the Great Lakes. Forest stand structure explained most of the variation in FD. Elevated temperature increased FD in boreal, but lowered FD in temperate forests. Different species abundance weightings affected trait diversity distributions and drivers only marginally. As environmental filtering was of secondary importance behind forest structure in explaining the trait diversity distribution of tree species in northeastern North America, our study provides only partial support for the stress-dominance hypothesis. Forest management can increase FD by promoting structural complexity. In addition, mixing species from functionally different groups identified in this study can enhance the response diversity of forests to an uncertain future.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".