Assessing the effect of tagging and the vulnerability to predation in tigerfish (Hydrocynus vittatus, Castelnau 1861) in a water-stressed system using telemetry methods
Bibliographic record
Abstract
Increase in anthropogenic pressures on freshwater ecosystems, globally and locally in South Africa, has made it difficult to meet environmental flow requirements and maintain these systems. The Letaba-Olifants catchment is one such example, where the upstream water demands place pressure on the river downstream as it flows through the Kruger National Park. We used the activity rates of Hydrocynus vittatus as a line of evidence to assess (1) the effects of telemeter tagging on their activity rates in three potential post-tagging recovery periods and (2) their vulnerability to predation and the causality related to the environmental stressors placed on the river catchment in the Olifants River Gorge, Kruger National Park, South Africa. We determined H. vittatus activity rates as locomotive movement using radio telemetry methods linked remotely to an online data management system. We telemeter tagged nine individuals from 08 May to 28 June 2018. However, only seven fish were successfully tracked for the duration of our study, and two individuals moved out of range of the remote network shortly after release and could not be located. The tagged H. vittatus individuals were found to have reduced activity at least within the first 7 days after tagging compared with the time after that. The results showed that three individuals were preyed on by predators after the tagging procedure recovery period. This coincided with abnormal low flow conditions, where the Letaba River ran dry. African fish eagles Haliaeestus vocifer were the only confirmed predator, whilst predation by other species was also evident. Conservation implications: Monitoring of H. vittatus using telemetry is a viable tool to use when assessing environmental stressors in remote locations. The abnormally low water levels in the Olifants and Letaba Rivers (cessation of flow in the Letaba River) during our study may have compromised the predator avoidance strategies of tagged H. vittatus and may further be affecting the viability of the H. vittatus populations in Kruger National Park. The synergistic effects of natural and anthropogenic stressors impact negatively H. vittatus populations and potentially other aquatic biotas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".