Ecosystem memory of wildfires affects resilience of boreal mixedwood biodiversity after retention harvest
Bibliographic record
Abstract
The extent to which past states influence present and future ecosystem characteristics (ecosystem memory (EM)) is challenging to assess because signals of past ecological conditions fade with time. Using data about seven different taxa, we show that ecological gradients initiated by wildfires up to three centuries earlier affect biotic recovery after variable retention harvest in the boreal mixedwood forest. First, we show that fire history over the last 300 years is reflected in pre‐harvest species‐specific stand basal area (BA), with longer times since high severity fire associated with proportionally higher BA of shade‐tolerant softwood species than shade‐intolerant hardwoods. Second, using patterns in the BA of pre‐harvest tree species we link fire history to species composition of pre‐harvest assemblages of bryophytes, herbs, shrubs, regenerated trees, songbirds, spiders and carabid beetles. Finally, we use variance partitioning to compare the importance of species‐specific pre‐ versus post‐harvest BA for explaining the structure of these seven biotic assemblages two, five and ten years after harvest. We detected persistent significant effects of pre‐harvest BA in all post‐harvest biotic assemblages up to ten years after harvest. Pre‐harvest BA was more strongly associated with early post‐harvest understory plant and carabid beetle assemblages than was post‐harvest BA, but the opposite was true for spiders, songbirds and regenerated trees. EM effects were detected two, five and ten years after harvest but temporal patterns varied according to taxa. Thus, EM of fire history can persist at least ten years after variable retention harvest and such effects appear to be stronger for understory plants than for animals. We conclude that management of biological legacies to increase post‐disturbance EM will increase overall resilience and sustainability of these mixedwood forests.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".