Complex ecological pathways drive boreal forest successional dynamics
Bibliographic record
Abstract
Pervasive shifts in forest successional dynamics are accelerating in response to global environmental change. However, the relative importance of variables driving successional transitions and how these are altered by climate change remain unclear, in part because the ecological pathways among variables can be complex. To untangle the web of interactions driving successional transitions between consecutive catastrophic disturbances, we utilized a long-term forest inventory database repeatedly measured over 43 years, encompassing 3465 boreal forest plots in central Canada and covering stand ages 1–261 years. We developed a hybrid analytical approach that combines boosted regression tree (BRT) and structural equation modelling (SEM). The BRT assessed the relative importance of variables among 37 potentially influential variables. The SEM examined multiple causal pathways of 14 top-ranking (relative importance > 1 %) drivers. Overall, we found an average 4.6 % probability of transitioning to a different forest type over consecutive censuses with a mean interval of 6.8 years. By ranking the relative importance of variables in BRT and SEM, we show that multiple, simultaneously occurring within-community dynamics, rather than climate variations or site and soil conditions, primarily drive successional transitions. In particular, the compositional proportion of the most dominant species was the most influential driver. As it increased from a minimum of 0.24 to monoculture (1.00) in the plot, the likelihood of transition decreased from 41.1 % to 0.2 %, emphasizing slow successional transitions in the mono-species dominated boreal forest. Our empirical findings, spanning the course of secondary succession, suggest that the widely predicted climate-driven transitions in boreal forests may be context-dependent and highly variable than previously thought.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".