Simulating potential effects of climate change scenarios on the succession of temperate tree species in eastern Canadian forests
Bibliographic record
Abstract
There is ample relevant literature on the potential effects of climate change on forest ecosystems. However, the majority of studies have focused on analyzing the effects of increase in temperature or atmospheric CO 2 on specific processes over short periods of time. This may be explained by the difficulty of implementing long-term field experiments to monitor changes that occur very slowly in forest ecosystems. Forest simulation models may contribute to evaluating long-term changes in forest dynamics under different scenarios of climate change. However, as models are continually developed, there is a need to evaluate their biological consistency and realism of their predictions. The gap model ZELIG-CFS was used to simulate the long-term effects of climate change scenarios Representative Concentration Pathways (RCP) 4.5 and 8.5 on the dynamics of seventeen temperate tree species in Nova Scotia, eastern Canada. A dataset of 454 permanent sample plots was assembled, which consisted mostly of mixed stands. The simulation results indicated that the effects of climate change differed among species. Some species, such as balsam fir ( Abies balsamea (L.) Mill.), were negatively affected under RCP 4.5 and 8.5 scenarios by showing a decrease in mean basal area, stand density and diameter at breast height. In contrast, other species, such as trembling aspen ( Populus tremuloides Michx.), increased their abundance. The simulated responses of the species were discussed in the light of their autecology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".