INFLUENCE OF SPRUCE BUDWORM DEFOLIATION ON STREAM MICROBIOMES
Bibliographic record
Abstract
Insect pests are the most widespread disturbance in Canadian forests, but resulting impacts of forest defoliation on stream ecosystem functions are poorly understood. This study investigated the effects of a spruce budworm outbreak on water quality and the structure and function of microbial communities in streams of 12 catchments across a gradient of cumulative defoliation severity in the Gaspésie Peninsula, Québec, Canada. Bi-weekly stream habitat sampling was conducted spring to fall 2019-2021, with stream flow rates measured and water samples collected and analyzed for water chemistry parameters, nutrients, and dissolved organic matter (DOM) structure and quality. Algal communities were assessed at the same time by measuring in-situ biomass. Bacteria and fungi communities on leaf packs were assessed by incubating six leaf packs for five weeks (mid-August- late September) in one stream reach per watershed. Microbial community composition of leaf packs was determined using metabarcoding of 16S and ITS rRNA genes, and functions were examined using extracellular enzyme assays, leaf litter decomposition rates, and taxonomic functional assignments. This study determined that cumulative defoliation increased stream temperatures, flow rates, and SUVA (DOM aromaticity), but not nutrients. It increased algal biomass and altered microbial community composition, with a stronger influence on bacteria than fungi. The observed increases in SUVA and algal biomass corresponded with changes to bacteria carbon cycling functions, which indicated that microbes were preferentially selecting carbohydrates produced by algae rather than the aromatic compounds from increased terrestrial inputs. There were no changes to other bacteria or fungi functions and no changes to taxonomic or functional diversity. Overall, results indicate that forest pest outbreaks alter carbon inputs to streams and the structure and function of stream microbial communities associated with carbon cycling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".