Paying colonization credit with forest management could accelerate the range shift of temperate trees under climate change
Bibliographic record
Abstract
• We test the effect of sylvicultural practices in a forest model under climate change. • We assess how planting and harvesting can increase forest resilience and range shift. • Planting temperate trees is more efficient at increasing response rate to climate change. • Enrichment planting boreal stands is more efficient than planting empty stands. • Plantation increases resilience and coldward range shift to keep up with climate change. The northward migration of several tree species ranges is likely to lag behind climate change due to slow demography, competitive interactions, and dispersal limitations. These will result in a colonization credit, where suitable climate envelopes are left unoccupied, and extinction debt, where tree stands persist at unsuitable climatic locations. While the underlying mechanisms explaining the delayed range shift of forest trees have been investigated, few studies have focused on how management could overcome this lag. Here we extend a forest community state model derived from the metapopulation theory and validated with over 40,000 forest inventory plots, to formulate how forest management can accelerate the response of the boreal-temperate ecotone under warming temperature. We first complete the model equations to represent how four types of forest management may affect the transitions between four forest states: Boreal, Temperate, Mixed and Regeneration. We then simulated the potential of forest management to reduce colonization credit and extinction debt using two complementary approaches to measure the resilience and range shift of the boreal-temperate ecotone in response to warming temperature. Our simulations reveal that paying the colonization credit by planting temperate trees in a stand in Regeneration or Boreal state are likely to i) reduce the return time to equilibrium, ii) increase forest resilience, and iii) move the ecotone towards colder temperatures. Surprisingly, harvesting boreal trees in stands in Boreal or Mixed state were not effective to reduce extinction debt and provide colonization opportunities for temperate trees. Our results suggest that forest management related to planting actions could help the boreal-temperate ecotone keep pace with climate change. Future experiments are required to test these theoretical expectations and make operational recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".