A pseudo time‐series reveals the rapid recovery and high variability of benthic macroinvertebrate populations following catchment wildfire
Bibliographic record
Abstract
Abstract The increasing risk of wildfire has focused attention on the timescale of the impact and recovery of river ecosystems and methods for their bioassessment. An 18‐year pseudo time‐series was exploited to document patterns in benthic macroinvertebrate impact and recovery and evaluate the efficacy of alternative metrics to assess fire damage. Macroinvertebrates were surveyed by kick‐sampling and data were collected on river habitats. Details of river catchments and wildfire were collated as a GIS database. Macroinvertebrate richness and abundance recovered rapidly, marked by a phase of dynamic increase, followed by relative stability (0–2 years and 3–18 years, respectively). Across sites, richness and abundance were best explained by time since fire. A biotic index of general river quality was ineffective as an indicator of fire damage. While a metric ofK‐selected taxa (Odonata richness) was generally indicative of fire‐affected assemblages, a contrasting metric ofr‐selected taxa (percentage of chironomids, baetids, and simuliids) was not. Ordination analysis revealed time as a significant determinant of community structure across sites; however, its overall statistical importance was eclipsed by habitat characteristics (water quality, shade, altitude, and latitude) that were associated with ecological variation across both recently affected sites and the putatively recovered communities. These results highlight the stochastic processes – environmental and ecological – that frame the macroinvertebrate response to wildfire. This probabilistic context emphasizes the difficulties of developing indicator taxa for wildfire bioassessment and reinforces the importance of standardized survey protocols and the use of contrasting metrics in the assessment of wildfire impact on the ecological quality of rivers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".