Potential effects of interaction between CO<sub>2</sub>and temperature on forest landscape response to global warming
Bibliographic record
Abstract
Abstract Projected temperature increases under global warming could benefit southern tree species by providing them the optimal growing temperature and could be detrimental to northern species by exposing them to the supra optimal growing temperatures. This benefit‐detriment trade‐off could increase the competitive advantage of southern species in the northern species range and cause the increase or even dominance of southern species in the northern domain. However, the optimum temperature for photosynthesis of C3 plants may increase due to CO 2 enrichment. An increase in the optimum temperature could greatly reduce the benefit‐detriment effect. In this study, we coupled a forest ecosystem process model (PnET‐II) and a forest GAP model (LINKAGES) with a spatially dynamic forest landscape model (LANDIS‐II) to study how an optimum temperature increase could affect forest landscape response due to global warming. We simulated 360 years of forest landscape change in the Boundary Water Canoe Area (BWCA) in northern Minnesota, which is transitional between boreal and temperate forest. Our results showed that, under the control scenario of continuing the historic 1984–1993 mean climate (mainly temperature, precipitation and CO 2 ), the BWCA will become a spruce‐fir dominated boreal forest. However, under the scenario of predicted climatic change [the 2000–2099 climates are predicted by Canadian Climate Center (CCC), followed by 200 years of continuing the predicted 2090–2099 mean climate], the BWCA will become a pine‐dominated mixed forest. If the optimum temperature increases gradually with [CO 2 ] (the increase in optimum temperature is assumed to change gradually from 0 °C in year 2000 to 5 °C in year 2099 when [CO 2 ] reaches 711 ppm and stabilizes at 5 °C after year 2099), the BWCA would remain a fir‐dominated boreal forest in areas with relatively high water‐holding capacity, but not in areas with relatively low water‐holding capacity. Our results suggest that the [CO 2 ] induced increases in optimum temperature could substantially reduce forest landscape change caused by global warming. However, not all tree species would be able to successfully adapt to future warming as predicted by CCC, regardless of optimum temperature acclimations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".