A critical review of successional dynamics in boreal forests of North America
Bibliographic record
Abstract
Forest succession is a dynamic process of progressive compositional development of ecological communities of species following natural or anthropogenic disturbance. Despite a rich history of conceptual frameworks, models, and empirical advances, the complex interactions among climatic conditions, disturbances, edaphic factors, and silvicultural treatments still challenge our ability to accurately predict forest succession, hindering application to forest management. Our goal was to improve understanding of forest succession in the managed boreal forests of North America by clarifying advances in knowledge and limitations in our understanding. We reviewed 152 peer-reviewed papers to: ( i) document conceptual developments in forest succession; ( ii) summarize drivers of North American boreal forest succession, including changes to forest composition and successional trajectories given climate change; and ( iii) discuss the implications of the synthesized information for boreal forest management. While the element of stochasticity is expected to increase under climate change, successional dynamics are anticipated to remain predominantly deterministic. Southern boreal forests are at increased risk of mortality due to warming-driven drought and increased fires. Following disturbance, regeneration is likely to favor deciduous hardwoods. In boreal mixedwoods, increased fires would promote jack pine, and also black spruce on hydric and xeric sites. Dynamics of the northern boreal will depend on the balance between precipitation and evapotranspiration. Forest management must carefully select prescriptions to promote forest regeneration and composition that consider the long-term effects of changing climate and disturbance regimes. For instance, combining retention cut with mechanical site preparation would maintain site productivity and reverse open black spruce stand development in northern boreal stands. Our work shows that multiple disturbances have compounding effects on forest development, but further work is needed to better define thresholds for synergistic and buffering interactions. Modeling of boreal forest succession can be improved by incorporating more of the influential factors, but this is often limited by the lack of data. This information will guide the development of forest management strategies by exploring combinations of prescribed fire and variable intensity selection cutting systems to reproduce the effects of multiple interacting natural disturbances under climate change on successional dynamics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".