Beyond Presence and Absence: Using <scp>eDNA</scp> and Microsatellite Genotyping to Estimate Densities of Microscopic Life Forms in Wild Populations
Bibliographic record
Abstract
ABSTRACT Many challenges arise when monitoring organisms with cryptic life‐histories. For example, some cryptic life‐stages are hard to identify or sample due to their microscopic nature, which creates unknowns surrounding an organism's population dynamics. Environmental DNA (eDNA) is a non‐invasive sampling technique used to monitor cryptic species when traditional survey methods are challenging. Generally, eDNA has been used to quantify the presence/absence of species in various habitats. However, recent advances in high‐throughput amplicon sequencing techniques have enabled researchers to detect intraspecific genetic diversity with eDNA. In this study, we present two complementary R packages that can be used to estimate the number of individuals in an eDNA sample. The first package (Amplicomsat) cleans high‐throughput amplicon microsatellite sequences and counts the observed alleles identified in eDNA. Our second package (GenotypeQuant) then uses a numerical maximum likelihood estimator (NMLE) to estimate the number of contributors most likely to have produced the sequenced panel of microsatellite alleles amplified from eDNA. We first present simulations to characterise the accuracy and precision of the method. We then estimated densities of Nereocystis luetkeana (bull kelp) microscopic gametophytes from eDNA collected from an experiment with a manipulated number of gametophytes. Finally, we analysed benthic eDNA from kelp forest habitats. We found that gametophyte estimates produced by the NMLE varied within +3/−2 individuals when processing eDNA from rocks with 8 seeded gametophytes. We estimated 500 to 800 gametophytes·m −2 densities in July, five or more months since spore germination and before the current year's spore release. Gametophyte abundance scaled with the sampling area and numbers were higher than total sporophyte densities.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".