Postfire Succession of Ants (Hymenoptera: Formicidae) Nesting in Dead Wood of Northern Boreal Forest
Bibliographic record
Abstract
Dead wood decomposition begins immediately after tree death and involves a large array of invertebrates. Ecological successions are still poorly known for saproxylic organisms, particularly in boreal forests. We investigated the use of dead wood as nesting sites for ants along a 60-yr postfire chronosequence in northeastern coniferous forests. We sampled a total of 1,625 pieces of dead wood, in which 263 ant nests were found. Overall, ant abundance increased during the first 30 yr after wildfire, and then declined. Leptothorax cf. canadensis Provancher, the most abundant species in our study, was absent during the first 2 yr postfire, but increased steadily until 30 yr after fire, whereas Myrmica alaskensis Wheeler, second in abundance, was found at all stages of succession in the chronosequence. Six other species were less frequently found, among which Camponotus herculeanus (Linné), Formica neorufibarbis Emery, and Formica aserva Forel were locally abundant, but more scarcely distributed. Dead wood lying on the ground and showing numerous woodborer holes had a higher probability of being colonized by ants. The C:N ratio was lower for dead wood colonized by ants than for noncolonized dead wood, showing that the continuous occupation of dead wood by ants influences the carbon and nitrogen dynamics of dead wood after wildfire in northern boreal forests.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".