Simulated decline of a northern forest due to anthropogenic controls on the regeneration-mortality balance
Bibliographic record
Abstract
The population structure of forests is shaped by balancing the opposing forces of regeneration and mortality, each of which influence C turnover rates and are sensitive to climate. Regeneration underlies the migrational potential of forests to climatic change and remains underserved in modeling studies. Our objective was to test the hypothesis that warming may reduce tree regeneration rates while amplifying fire regimes, producing forest loss. Absent sites within dispersal limits, trees may fail to track the velocity of warming, producing a decline in forested area. Long-term implications include changes to biogeochemical and energetic balances, species composition, and evolutionary trajectories. We performed hybrid model simulations to assess the resilience of forests to past-century conditions over the next fifty years in western Canada. We conducted simulations at a species-level taxonomic resolution to capture genotypic/phenotypic variability in response to climate. A recent shift toward small, frequent, human-caused fires and warming-reduced regeneration diminished species migration potential. The simulated rate of forest migration lagged behind temperature equilibria by 319 m yr-1. Understanding species migrational potential is particularly critical for northern forests, which have warmed at a rate twice the global mean. Our findings highlight the effect of diminished regeneration due to climatic change, a process neglected in current global-scale terrestrial biosphere models used in climate studies. We suggest that future terrestrial biosphere model studies incorporate these demographic rates in their findings on global change, as they carry substantial climatic and evolutionary implications.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".