Detecting Rare Species With Passive Sampling Tools: Optimizing the Duration and Frequency of Sampling for Benthic Taxa
Bibliographic record
Abstract
The choice of the duration and frequency of sampling to detect relevant patterns in field experiments or for environmental monitoring is always challenging since time and material resources are limited. In practice, duration and frequency of sampling are often chosen based on logistical constraints, experience, or practices described in published works but are rarely justified and almost never optimized before initiating sampling. Settlement plates are commonly used as a passive sampling tool to study recruitment patterns of fouling organisms (including non-indigenous species) and their deployment is amenable to experimentation with respect to manipulating duration and frequency of sampling. This study aimed to determine the optimal sampling strategy to detect rare species (e.g., a non-indigenous species early in the invasion process when its population size is still small). To do so, we deployed a series of settlement plates of various durations (1–32 days) and sampling frequencies (daily to biweekly) during the seasonal onset of recruitment, when larval supply was low, a situation that mimics the low propagule pressure of the early stages of the invasion process. We demonstrated that a combination of longer sampling duration and higher sampling frequency was the best strategy to maximize taxonomic richness. However, we found that an intermediate sampling duration of 1–2 weeks was optimal for detecting most species. These results can guide species-specific and assemblage-level sampling strategies using settlement plates. Additionally, this study can serve as a practical template for optimizing sampling of other taxonomic groups that were not examined in the present study as well as for the use of other methods.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".