Efficiency of electrofishing in turbid lowland rivers: implications for measuring temporal change in fish populations
Bibliographic record
Abstract
To quantify how electrofishing capture probability varies over time and across physiochemical and disturbance gradients in a turbid lowland river, we tagged between 68 and 95 fish·year−1 with radio transmitters and up to 424 fish·year−1 with external and passive integrated transponder (PIT) tags. We surveyed the site noninvasively using radiotelemetry to determine which of the radio-tagged fish were present (effectively closing the radio-tagged population to emigration) and then electrofished to estimate the proportion of available fish that were captured based on both this and standard mark–recapture methods. We replicated the electrofishing surveys three times over a minimum of 12 days each year, for 7 years. Electrofishing capture probability varied between 0.020 and 0.310 over the 7 years and between four different large-bodied species (Murray cod (Maccullochella peelii), trout cod (Maccullochella macquariensis), golden perch (Macquaria ambigua ambigua), and silver perch (Bidyanus bidyanus)). River turbidity associated with increased river discharge negatively influenced capture probability. Increasing fish length increased detection of fish up to 500 mm for Murray cod, after which capture probability decreased. Variation in capture probability in large lowland rivers results in additional uncertainty when estimating population size or relative abundance. Research and monitoring programs using fish as an indicator should incorporate strategies to lessen potential error that might result from changes in capture probabilities.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".