Soil Springtail Communities Are Resilient to Forest Tent Caterpillar Defoliation in Quebec Mixed Hardwood Forests
Bibliographic record
Abstract
Outbreaks of defoliator insects are important natural disturbances in boreal forests, but their increasing frequency under warming climate conditions is of concern. Outbreak events can shape ecosystem dynamics with cascading effects through trophic networks. Caterpillar defoliation can alter tree physiology, increase sunlight to the understory, and result in the deposition of large amounts of leaf litter and caterpillar frass to the forest floor. These modifications can thus affect soil organisms through direct (e.g., changes in soil temperature or moisture) or indirect (e.g., changes in detrital and root food webs) mechanisms. We assessed whether a recent (2015 to 2017) outbreak of the forest tent caterpillar (Malacosoma disstria) at the Lake Duparquet Teaching and Research Forest (Abitibi, QC, Canada) affected soil springtail communities, abundant microarthropods in forest soils. In 2018 and 2019, we sampled litter and soil (0–10 cm depth) at eight sites each in aspen-dominated (Populus tremuloides Michx) stands that were undefoliated or had a recent defoliation history. We found no significant difference in springtail abundance (specimens cm−2) or alpha diversity indices between undefoliated sites and those with defoliation history. However, we observed a transient change in springtail community composition 1 year after the outbreak (2018) with the absence of Folsomia nivalis, Anurophorus sp1, and Xenylla christianseni in sites with defoliation history, but no compositional differences were observed in 2019. Certain soil nutrients (P, C, Mg, Mn) were significant predictors of springtail community composition, but soil microbial biomass was not, despite its significant decrease in sites with defoliation history. Our results show that soil springtail communities respond in the short-term to the forest tent caterpillar outbreak with compositional shifts, but seem ultimately resilient to these events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".