Precipitation is the most crucial factor determining the distribution of moso bamboo in Mainland China
Bibliographic record
Abstract
Moso bamboo is widespread in natural forests and is cultivated over large areas in China. This study investigated how climate controls its distribution, about which little is known. We collected moso bamboo presence-absence data from 674 sites with long-term climate data in Mainland China. Generalized additive models that included location and climate variables were used to test the effects of these predictors on the species’ occurrence. We identified the best model as the one with the lowest Akaike’s Information Criterion value that contained only statistically significant predictors. We found precipitation, especially the mean (APRE) and interannual standard deviation (SDPRE) of the annual precipitation at each site, rather than temperature, to be the main factors determining the distribution of moso bamboo in Mainland China. In addition, we found that there was a significant power law relationship between the mean and interannual variance of precipitation, which made it possible to make long-term predictions. The SDPRE in climate scenarios of changes in the APRE could then be calculated using the fitted power law relationship. We simulated six climate scenarios, in which the APRE increased/decreased by 25, 50, and 75%. We used the 0.5 and 0.9 probability contour lines of model predictions to represent the suitable and core distributions, respectively, of moso bamboo under each scenario. The current core distribution of moso bamboo in Mainland China predicted by our model agreed with actual observations. Our model suggested that the middle and lower reaches of the Huaihe River Plain in eastern China should be climatically suitable for the growth of moso bamboo; it seems likely that its current absence there has resulted from intensive land use. Our model predicted that changes in APRE can strongly alter the distribution of moso bamboo. Increased APRE would expand the core distribution of moso bamboo into southern Shandong Province and over all of Chongqing and most of Guizhou Provinces, which are areas not currently in the species’ core distribution. Conversely, decreased APRE would shrink the core distribution of moso bamboo to the junction of Anhui, Fujian, Jiangxi, and Zhejiang Provinces. We showed that the current distribution of moso bamboo is mainly determined by annual precipitation rather than temperature. The deviations between the moso distributions predicted by the climate model and the current distribution in some plain areas might have resulted from human activities. Future changes in annual precipitation will probably change the distribution of moso bamboo considerably.
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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 | 0.000 | 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".