Catchment‐scale effects of forestry practices on benthic invertebrate communities in Pacific coastal streams
Bibliographic record
Abstract
Summary 1. Catchment‐scale effects of forestry practices alter stream physical structures and influence composition and abundance of benthic communities. Looking beyond the reach‐scale effects to disturbance impacts that come from upstream catchments is rare. Understanding of effects of large‐scale catchment disturbances on downstream habitats is crucial to better manage the impacts or mitigate the effects of forest management. 2. By comparing catchments with different forestry histories, we examined the impacts of past forestry practices on stream habitats and benthic invertebrate diversity in six reference streams in mature forests and five previously logged streams in young‐growth forests in the Chilliwack River basin, Canada. Principal components analysis separated the reference streams and the previously logged streams by their physical characteristics. Streams in mature forests had coarser substrates than in young‐growth forests. 3. Using partial least squares projection to latent structures (PLS) modelling, we assessed the relationship between multiple environmental variables and benthic communities. Predictive PLS models were developed based on environmental variables and community response variables at reference streams. The observed taxon richness and relative abundance of total benthos, scrapers and predators at previously logged sites were significantly lower than the model predicted. The biomasses of total benthos and shredders at previously logged sites were significantly lower than those at reference sites. 4. Synthesis and applications. This study demonstrates that a legacy of measurable logging impacts on habitats and invertebrate community metrics can be detected up to 40 years after logging on these streams and at a catchment‐scale. This predictive model approach combined with intensive field observations has potential for assessing the catchment‐scale effects of forestry on stream ecosystems. This method also can be used to estimate the time to recovery from harvesting and to assist the evaluation of improving management decisions to reduce the negative impacts of forest harvesting on stream ecosystems.
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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".