Synthesizing Spectral and Field Observations of Post-fire Conifer Recovery in Dry Conifer Forests
Bibliographic record
Abstract
The coniferous forests of the Western Cordillera are particularly affected by recent increases in wildfire extent and severity. After fire, conifer establishment and growth rates are influenced by a wide range of ecological drivers. Understanding the relative influence of ecological drivers on conifer recovery is crucial when modeling landscape dynamics. Past research has examined a wide variety of ecological drivers; however, syntheses of these drivers are rare. This systematic review focuses on forest recovery pathways, which have distinct variability in spatial and temporal measures of conifer establishment and growth. From studies examined, we extracted whether the study identified a recovery pathway and whether field or satellite spectral methods were used. Spectral methods were the most common method to determine the 84 extracted pathways. Among pathways identified, conifer self-replacement was the most common, but the second most common was state change, wherein the forest transitions in landcover type. We also investigated how recovery varied relative to different ecological drivers. Among the > 1000 drivers considered, pre-fire composition and post-fire moisture had consistent positive associations with all recovery metrics, while the association with other drivers varied by metric (stem density versus composition) and/or method (field versus spectral). Our review outlines key gaps for future research, including (1) the accuracy of spectral monitoring to capture structural growth trends, such as stem densities over time, and (2) how the effects of ecological drivers vary across scales, such as post-fire shrub cover at local versus landscape levels. Overall, fusing spectral and field data across spatiotemporal scales improves our understanding of post-wildfire recovery and dynamics, as well as our ability to anticipate the impacts of changing climate and wildfire conditions on recovering forests. Supplementary Information: The online version contains supplementary material available at 10.1007/s10021-025-01029-9.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".