Multi‐scale analysis of responses of stream macrobenthos to forestry activities and environmental context
Bibliographic record
Abstract
Summary 1. Forestry activities can greatly modify the structure and function of invertebrate communities in streams, but the ability to detect effects of forestry may depend on the spatial scale considered, the choice of response metric and the environmental context. In this study, a multi‐scale, multi‐metric approach was used to compare the usefulness of proximate and larger‐scale measurements of forestry activity for understanding the impacts of forestry on stream macrobenthos. 2. Site‐specific responses of macrobenthic communities to forestry activities measured at four spatial scales (sub‐basin and 8‐, 2‐ and 0.5‐km radii upstream of study sites) were examined for 90 riffle sites distributed among 22 tributary streams (Strahler order 1–5) of the Cascapedia River basin, Quebec, Canada. 3. Multiple regression models and canonical correspondence analysis were used to relate six biological metrics (taxonomic richness, numerical density, biomass density, normalised biomass spectrum, individual body mass and community structure) to variables quantifying logging 1–19 years prior to the study and road density. Environmental predictors (variables quantifying local habitat or landscape features) were included in all analyses to statistically account for environmental context and increase the likelihood of detecting potentially subtle forestry impacts. 4. Forestry activities measured at the larger (sub‐basin and 8 km) scales were linked to decline in taxonomic richness, increase in numerical and biomass densities and shift in size structure of benthic macroinvertebrates, indicating that analyses encompassing larger areas, up to the full basin, may allow for more sensitive detection of effects than those of more limited span. 5. These responses primarily reflected marked increases in the abundance of chironomids and decline in the number of trichopteran taxa with increasing areal coverage of recent (≤2–4 years) cuts, suggesting that larger, longer‐lived and possibly more specialised taxa were more vulnerable to forestry impacts than smaller, multivoltine, generalist invertebrates. After partialling out the influence of other variables, rapid decline in richness occurred even when <1% of the basin had been clear cut in the year prior to the study. 6. Effects of forestry were detected after statistically accounting for natural environmental variability, which may have otherwise concealed those effects. The combined use of multiple biological metrics, partialling out of environmental effects and measurement of impacts at multiple spatial scales may be a broadly applicable approach for enhancing sensitivity and facilitating interpretation in studies of anthropogenic effects on macroinvertebrate communities.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".