Fish Assemblage Structure of Two Contrasting Stream Catchments of the Mahaweli River Basin in Sri Lanka: Hallmarks of Human Exploitation and Implications for Conservation
Bibliographic record
Abstract
Patterns of fish community composition in the Mahaweli ichthyological region of Sri Lanka were examined in agricultural tributaries of the Uma-oya catchment of the upper Mahaweli River in comparison to more pristine streams in a nature reserve in the Amban-ganga catchment.The Uma-oya catchment shows characteristics commonly observed in extensive agricultural exploitation such as impaired water quality and altered riparian vegetation.The most abundant fish species in the two regions were Garra ceylonensis, Devario malabaricus, and Rasbora daniconius, although their relative abundance differed between sites.G. ceylonensis and Neomacheilus notostigma were the only endemic fish species in common but the latter has been extremely depauperate.Endemism is higher in the reference sites (62.5%) than agricultural sites (ca.25%); some of the reference streams showed greater diversity with unique fish species and a few species that have not been recorded previously in the catchment.The ichthyofaunal similarity between two catchments was 39% and fish species diversity was negatively correlated with stream gradients (Pearson correlation (-0.630); r 2 = 39.6% p = 0.028).Species density and biomass of G. ceylonensis and D. malabaricus were positively associated with some water quality parameters.The presence of exotic fish species were amongst the primary discriminants between human exploited and least-exploited (reference) streams.The possible negative impact of exotic Oreochromis niloticus (Nile tilapia) on indigenous G. ceylonensis is highlighted.This paper provides impetus for making several hypotheses for sustainability of stream fish assemblages in highly exploited agricultural catchments in Sri Lanka.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".