Evaluating sampling sufficiency in fish assemblage surveys: a similarity-based approach
Bibliographic record
Abstract
The number and identity of fish species occurring at a site at a particular time provide basic information for assessing biological integrity, inferring fish assemblage environment relationships, and determining biodiversity patterns. Conclusions are often dependent on how sufficiently species richness and composition of fish assemblages are characterized by sampling. The proportion of total species richness obtained in a sample is an explicit measure of sampling sufficiency. However, because total species richness (TSRtru) at a site is often unknown, sampling sufficiency cannot be determined directly. To overcome this difficulty, we developed a new approach, which is based on a relationship between the proportion of TSRtru or %TSRtru and the similarity among replicate samples (autosimilarity). With autosimilarity measured with the Jaccard coefficient (JC), a simple relationship was established: %TSRtru = 100JC. Fourteen sites where TSRtru was reached or approached during sampling were selected from four surveys to validate this relationship. We used the approach to estimate the sample sizes required for 90, 95, and 100% TSRtru, indicating that widely differing sampling efforts among sites are needed to obtain the same proportion of the local species pool. The results strongly support the use of the new approach in evaluating sampling sufficiency in stream and river fish surveys.
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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 | 0.066 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".