Variation in litterfall‐climate relationships between coniferous and broadleaf forests in Eurasia
Bibliographic record
Abstract
ABSTRACT Aim The objectives of this study were to determine the relationships between climatic factors and litterfall in coniferous and broadleaf forests in Eurasia and to explore the difference in litterfall between coniferous and broadleaf forests as related to climate at a continental scale. Location We have used data from across Eurasia. Methods The relationships between litterfall and climatic factors were examined using linear regression analysis of a compilation of published data from coniferous and broadleaf forests in Eurasia. Results The relationships between litterfall and climatic factors show that in the temperate, subtropical, and tropical areas, broadleaf forests had higher litterfall than coniferous ones, whilst the opposite was found for boreal forests. Combining all climatic zones, a multiple regression analysis using annual mean temperature (T) and annual precipitation (P) as independent variables gave an adjusted R2 ( ) of 0.272 for total litterfall in coniferous forests (n = 199, P < 0.001), 0.498 for broadleaf litterfall (n = 240, P < 0.001), and 0.535 for combined coniferous and broadleaf litterfall (n = 439, P < 0.001). The linear models for broadleaf stands have significantly higher coefficients for T and P than those for coniferous ones but the intercepts were similar. Thus, litterfall in broadleaf forests increased faster with T and P than that in coniferous forests. Further, a transformation of temperature and precipitation to relative units showed that a relative‐unit change in T had a larger impact than P on total litterfall in broadleaf forests. The results indicate that at a continental scale, climatic controls over litterfall differ between coniferous and broadleaf forests. Conclusions A relative unit change in annual mean temperature has a greater effect on litterfall compared to the same change in annual precipitation across the Eurasian forests. Further, the higher response to T for broadleaf forests indicates a difference in climate control between coniferous and broadleaf forests at a continental scale, and consequently different litterfall responses to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".