Long-term functional group recovery of lotic macroinvertebrates from logging disturbance
Bibliographic record
Abstract
Clear-cut logging rapidly affects stream macroinvertebrates through substantial alteration of terrestrial–aquatic resource linkages; however, lesser known are the long-term influences of forest succession on benthic macroinvertebrate assemblages, which play key roles in stream ecosystem function. We compared secondary production and standing crops of detritus in two mountain headwater streams within Coweeta Hydrologic Laboratory, North Carolina, USA, as part of a long-term, paired-watershed investigation of macroinvertebrate recovery from whole-catchment logging. Mean annual habitat-weighted abundance and biomass in the disturbed stream (catchment entirely logged 26 years prior) did not differ from that of the reference stream, and secondary production was only 0.8 g ash-free dry mass (AFDM)·m–2·year–1 greater in the disturbed stream (disturbed, 10.1; reference, 9.3). Taxonomic composition, shredder–scraper ratios, and North Carolina biotic index scores were more similar between streams than in previous years. However, mean annual standing crops of benthic organic matter (BOM) were much lower in the disturbed stream (167 g AFDM·m–2) than in the reference stream (339 g AFDM·m–2), and the disturbed stream supported greater macroinvertebrate biomass per unit BOM. We suggest that despite similarities in macroinvertebrate structure and function, past logging activity continues to influence consumer–resource relationships in the disturbed stream through enhanced resource quality.
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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 | 0.001 |
| 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 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".