Accounting for spatial autocorrelation improves the estimation of climate, physical environment and vegetation’s effects on boreal forest’s burn rates
Bibliographic record
Abstract
CONTEXT: Wildfires play a crucial role in maintaining ecological and societal functions of North American boreal forests. Because of their contagious way of spreading, using statistical methods dealing with spatial autocorrelation has become a major challenge in fire studies analyzing how environmental factors affect their spatial variability. OBJECTIVES: We aimed to demonstrate the performance of a spatially explicit method accounting for spatial autocorrelation in burn rates modelling, and to use this method to determine the relative contribution of climate, physical environment and vegetation to the spatial variability of burn rates between 1972 and 2015. METHODS: territory located in the coniferous boreal forest of eastern Canada, we built and compared burn rates models with and without accounting for spatial autocorrelation. The relative contribution of climate, physical environment and vegetation to the burn rates variability was identified with variance partitioning. RESULTS: Accounting for spatial autocorrelation improved the models' performance by a factor of 1.5. Our method allowed the unadulterated extraction of the contribution of climate, physical environment and vegetation to the spatial variability of burn rates. This contribution was similar for the three groups of factors. The spatial autocorrelation extent was linked to the fire size distribution. CONCLUSIONS: Accounting for spatial autocorrelation can highly improve models and avoids biased results and misinterpretation. Considering climate, physical environment and vegetation altogether is essential, especially when attempting to predict future area burned. In addition to the direct effect of climate, changes in vegetation could have important impacts on future burn rates.
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".