Working 9 to 5: Diurnal Variability in Terrestrial Invertebrate Activity Does Not Compromise Ecosystem Health Assessments in Dry Stream Channels
Bibliographic record
Abstract
ABSTRACT Temporary streams are impacted by climate change and other anthropogenic pressures, but fluctuating water levels complicate ecological assessments. Terrestrial invertebrate communities may enable dry‐phase assessments, but their sampling can be resource intensive. We assessed diurnal variability in the capacity of two methods (hand searching and pitfall trapping) to rapidly characterise terrestrial invertebrate assemblages and their responses to environmental conditions when channels are dry. The methods provided comparable estimates of richness and abundance at any time of day (i.e., morning, midday and evening), and among sites with different dry‐phase durations, air temperatures and proportions of fine sediment. Differences in taxonomic assemblage composition were detected among sites with differing dry‐phase durations, air temperatures and proportions of fine sediment, suggesting that the effects of natural and human‐influenced environmental stressors can be detected despite intermittence. Assemblage composition differed between methods, but not among times of day, suggesting diurnal activity patterns need not hinder assemblage characterisation in dry streams. Taxon‐specific preferences for dry‐phase duration, silt and sand suggest that biomonitoring indices which distinguish the influence of drying from human impacts could be developed. Monitoring over shorter periods may provide managers, regulators and citizen scientists with opportunities to increase the representation of terrestrial assemblages in ecosystem health assessments for temporary streams.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".