Effects of catchment perturbations by logging and wildfires on zooplankton species richness and composition in Boreal Shield lakes
Bibliographic record
Abstract
SUMMARY 1. Forest logging and wildfires are important perturbations of the boreal forest, but their effects on lake biota remain largely unknown. Here, we test whether zooplankton species richness and species assemblages differed among three groups of lakes in Eastern Canada characterised by different catchment conditions: logged in 1995 ( n =9); burnt in 1995 ( n =9); unperturbed ( n =20). Lakes were sampled in June, July and September 1 year after catchment perturbations. 2. Cumulative species richness in reference lakes averaged 46 (33–60) of which 63% were rotifers. Mean cumulative species richness and mean diversity in logged and burnt lakes did not differ from those in reference lakes. 3. Lake species assemblages were described by the density of 62 species (41 rotifers and 21 crustaceans). Among‐group differences in species assemblages were not significant. Eighteen per cent of the total variability in species assemblages could be explained by 13 environmental factors, among which dissolved oxygen concentration and cyanobacteria biovolume were the most important. About 5% of species assemblage variability was attributed to covariation between environmental factors and time of sampling, while 4.1% was attributed to temporal variation. 4. Variations in zooplankton species richness and assemblages in Boreal Shield lakes are important, both among lakes and among sampling dates. They seem to depend on environmental factors unrelated to catchment‐based perturbations, at least on the short‐term of 1 year.
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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) | 0.000 | 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".