Monitoring the silver carp invasion in Africa: a case study using environmental DNA (eDNA) in dangerous watersheds
Bibliographic record
Abstract
Biodiverse habitats are increasingly subject to an intensification of anthropogenic stressors that may severely diminish species richness. Invasive species pose a dominant threat to biodiversity and biosecurity, particularly in biodiversity hotspots like Kruger National Park, South Africa. The invasive silver carp, Hypophthalmichthys molitrix, was introduced into the Olifants River and may experience range spread owing to favorable environmental conditions. Intensive monitoring protocols are necessary to effectively manage invasions of species like silver carp. Unfortunately, tropical and sub-tropical aquatic systems are difficult to monitor using conventional methods (e.g., netting, electrofishing and snorkeling) owing to a range of factors including the presence of dangerous megafauna. Conservation of such systems may be advanced by the adoption of novel methods, including environmental DNA (eDNA) detection. Here, we explore the utility of environmental DNA (eDNA) to conduct safe, reliable and repeatable surveys in dangerous watersheds using silver carp as a case study. We conducted eDNA surveys at 12 sites in two neighbouring watersheds, and determined that the species has expanded its range within the Olifants River and to the south in the Sabie River. Expansion in the former is consistent with the presence of suitable spawning conditions. We discuss the implications of this survey for biodiversity monitoring in similar aquatic systems in the tropics and advocate an integrative approach to biomonitoring in these ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".