Response of stream habitat and microbiomes to spruce budworm defoliation: New considerations for outbreak management
Bibliographic record
Abstract
Defoliation by eastern spruce budworm is one of the most important natural disturbances in Canadian boreal and hemi-boreal forests with annual area affected surpassing that of fire and harvest combined, and its impacts are projected to increase in frequency, severity, and range under future climate scenarios. Deciding on an active management strategy to control outbreaks and minimize broader economic, ecological, and social impacts is becoming increasingly important. These strategies differ in the degree to which defoliation is suppressed, but little is known about the downstream consequences of defoliation and, thus, the implications of management. Given the disproportionate role of headwater streams and their microbiomes on net riverine productivity across forested landscapes, we investigated the effects of defoliation by spruce budworm on headwater stream habitat and microbiome structure and function to inform management decisions. We experimentally manipulated a gradient of defoliation among 12 watersheds during a spruce budworm outbreak in the Gaspésie Peninsula, Québec, Canada. From May through October of 2019-2021, stream habitat (flow rates, dissolved organic matter [DOM], water chemistry, and nutrients), algal biomass, and water temperatures were assessed. Bacterial and fungal biofilm communities were examined by incubating six leaf packs for five weeks (mid-August to late September) in one stream reach per watershed. Microbiome community structure was determined using metabarcoding of 16S and ITS rRNA genes, and community functions were examined using extracellular enzyme assays, leaf litter decomposition rates, and taxonomic functional assignments. We found that cumulative defoliation was correlated with increased streamflow rates and temperatures, and more aromatic DOM (measured as specific ultraviolet absorbance at 254 nm), but was not correlated to nutrient concentrations. Cumulative defoliation was also associated with altered microbial community composition, an increase in carbohydrate biosynthesis, and a reduction in aromatic compound degradation, suggesting that microbes are shifting to the preferential use of simple carbohydrates rather than more complex aromatic compounds. These results demonstrate that high levels of defoliation can affect headwater stream microbiomes to the point of altering stream ecosystem productivity and carbon cycling potential, highlighting the importance of incorporating broader ecological processes into spruce budworm management decisions.
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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 | 0.000 | 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) | 0.001 | 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 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".