Validating species sensitivity distributions using salinity tolerance of riverine macroinvertebrates in the southern Murray–Darling Basin (Victoria, Australia)
Bibliographic record
Abstract
Species sensitivity distributions (SSDs) are commonly used in risk assessment and in setting water quality guidelines, yet their predictions have not been validated against loss of species with increasing pollutant concentrations in nature. We used a rapid toxicity testing method to determine the acute salinity tolerance (72 h LC 50 values (concentration of salinity lethal to 50% of individuals)) of 110 macroinvertebrate taxa from the southern Murray–Darling Basin in central Victoria, Australia, and construct an SSD. This SSD was compared with loss of riverine macro invertebrates species from increasing salinity in Victoria. Macroinvertebrate species richness per individual sample, when salinity was <9.9 mS·cm –1 , was invariant of salinity. However, when species richness was calculated across multiple samples above about 0.3–0.5 mS·cm –1 , it declined with increasing salinity. This decline was predicted from the SSD after application of a variable safety factor calculated from an exponential or quadratic equation. Our findings confirm that SSDs can predict the loss of freshwater macroinvertebrate species from increases in salinity. This suggests that SSDs may be useful more generally for other aquatic organisms, other stressors, and toxicants.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".