Climate change mitigation through adaptation: the effectiveness of forest diversification by novel tree planting regimes
Bibliographic record
Abstract
Abstract Climate change is projected to have negative implications for forest ecosystems and their dependent communities and industries. Adaptation studies of forestry practices have focused on maintaining the provisioning of ecosystem services; however, those practices may have implications for climate change mitigation as well by increasing biological sinks or reducing emissions. Assessments of the effectiveness of adaptation strategies to mitigate climate change are therefore needed; however, they have not been done for the world's northern coniferous forests. Diversifying the forest by planting tree species more likely suited to a future climate is a potential adaptation strategy to increase resilience. The efficacy of this strategy to reduce the risks of climate change is uncertain, and other ecosystem services provided by the forest are also likely to be affected. We used a spatially explicit forest landscape modeling framework ( LANDIS ‐ II ) to simulate the effects of planting a range of native tree species in colder areas than where they are currently planted in a managed temperate coniferous forest landscape in British Columbia, Canada. We investigated impacts on carbon pools, fluxes, tree species diversity, and harvest levels under different climate scenarios for 100 yr (2015–2115) and found that the capacity of our forest landscape to sequester carbon would largely depend on the precipitation rates in the future, rather than on temperature. We further found that, irrespective of the climate prediction model, current planting standards led to relatively low levels of resilience as indicated by carbon fluxes and stocks, net primary productivity ( NPP ), and species diversity. In contrast, planting a mix of alternative tree species was generally superior in increasing the resilience indicators: carbon stocks and fluxes, NPP , and tree species diversity, but not harvest rates. The second best novel planting regime involved adding Pinus contorta to the stocking standard in three ecoregions; however, that species is susceptible to a high number of insects and pathogens. We conclude that although the capacity of temperate coniferous forest landscapes to sequester carbon in the future is largely dependent on the precipitation regime, negative effects may be counteracted to some extent by increasing resilience through tree species diversity in forests.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".