Stand‐level drivers most important in determining boreal forest response to climate change
Bibliographic record
Abstract
Abstract Forest ecosystems contain several climate‐sensitive drivers that respond differentially to changes in climate and climate variability. For example, growth and regeneration processes are “stand‐scale” drivers, while natural disturbances operate at “landscape scale”. The relative contributions of these different scale drivers of change in ecosystems create great uncertainty when simulating potential responses of a forest to changes in climate. Here, we assess those contributions, along with harvesting effects, on biomass (both total and of individual species) in the southern boreal forest of Canada under three climate scenarios ( RCP 2.6, RCP 4.5 and RCP 8.5). Projections were performed for three future 30‐year time periods, in four study regions located on an east–west transect, using a forest landscape model ( LANDIS ‐ II ), parameterized using a forest patch model ( PICUS ). Projected future impacts were assessed for each driver of change, and found to vary greatly among regions, species, future period and forcing scenarios. Fire, and stand‐scale climate‐induced impacts, had the strongest effects on forest vegetation, as well as on total and species’ biomass under most RCP scenarios, but the largest impacts occurred mostly after 2050, particularly with the RCP 8.5 scenario. The relative importance and trends in species‐specific impacts varied, both spatially and according to the different RCP scenarios. Western regions were generally more sensitive to stand‐scale climate‐induced changes, whereas eastern regions were more sensitive to changes in fire regime. Our study also highlights the importance of considering the prevalence of species‐level functional traits when assessing the sensitivity of forest landscapes to a given driver of change in the context of increasing anthropogenic climate forcing. Synthesis . Increases in fire activity, and direct impacts of climate change on forest growth and regeneration, will be the most important drivers of future changes in southern boreal forest landscapes.
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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 | 0.002 | 0.001 |
| 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".