Developing Standardized Methods for Sampling Freshwater Fishes with Multiple Gears: Effects of Sampling Order versus Sampling Method
Bibliographic record
Abstract
Abstract Developing robust methodologies for sampling freshwater fishes is important for the assessment and recovery of aquatic biodiversity. Commonly, sampling protocols recommend the use of both electrofishing and seining to reduce bias when sampling freshwater fishes. The objective of this study was to determine whether sampling method, sampling order, or the combination of these influenced the assessment of diversity and abundance of common or rare freshwater fishes at both the species and assemblage level. Fifty sites were sampled in the Sydenham River, Ontario, Canada, where paired sites were sampled using both electrofishing and seine netting in close proximity but also reversing the initial sampling order. Differences in the abundance and richness of common and rare freshwater fishes were tested with two‐way ANOVA and multivariate ANOVA. Multivariate differences in the representation of species assemblages were compared using Procrustes analysis across ordinations built using differences in sampling method and sampling order. Electrofishing outperformed seine netting for maximizing the abundance of both all species caught ( P = 0.004) and only those species considered rare ( P = 0.049). While capture efficiencies generally decreased with subsequent sampling, the interaction between sampling method and sampling order for maximizing richness estimates of rare species was significant ( P = 0.049). There were important species and assemblage differences when sampling order was reversed. The representation of either sunfishes (family Centrarchidae ) or catfishes (family Ictaluridae ) differed in multivariate space, given different sampling order. In addition, the capture of Blackstripe Topminnow Fundulus notatus was enhanced when seine netting was used after electrofishing. This study highlights the often neglected aspect of choosing an appropriate sampling order when developing protocols for sampling freshwater fishes with multiple methods. Overall, the combination of electrofishing, followed by seine netting, appears to maximize capture efficiencies of both common and rare fishes; however, researchers should be aware that sampling order can alter the representation of species and assemblages in multivariate assessments.
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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 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".