Evaluating Sampling Designs to Survey Fish Diversity in Lakes From Northern Temperate Zones
Bibliographic record
Abstract
ABSTRACT Long‐term biological monitoring and management depend on efficient protocols and methodology to characterize and precisely describe species distributions and diversity. In recent years, environmental DNA has progressively become a tool of choice in survey programs. However, the effect of variables such as sampling effort and sampling design still requires consideration. Simple random, grid, and transect‐based sampling methods are widely used in ecological surveys to obtain an unbiased estimation of species richness and community structure. However, under certain conditions where spatial information is available, sampling design and sequencing depth can be optimized to reduce effort and cost. Here, we evaluate different subsampling approaches to identify sampling strategies that are both easily implemented in the field and provide optimal recovery of species diversity for a given sampling effort. With a homogeneous grid‐based sampling (25–50 samples by lake) of 12 freshwater lakes in southeastern Québec, and using the 12S MiFish metabarcoding primer set, we demonstrate that random and stratified designs perform similarly to detect 90% and 95% of species. However, we found that, under certain circumstances, stratified sampling outperformed random sampling, requiring lower numbers of samples to detect the same species diversity. We also demonstrate that for the minimum sequence threshold and sample replication used in our study, a sequencing depth of 50K reads per sample is adequate to obtain a reliable portrayal of species richness. In this study, we contribute to the effort of eDNA sampling standardization by providing data for selecting the best sampling design, sequence depth, and sample size to detect 90%–95% of fish species found in temperate lakes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".