Improving the Understanding of Detections From <scp>iDNA</scp> Surveys in Malaysian Borneo With Multiscale Occupancy Models: A Case‐Study Using Leech Blood Meals
Bibliographic record
Abstract
ABSTRACT Invertebrate‐derived DNA (iDNA) has been successfully utilized for surveying mammalian biodiversity in several ecosystems. Yet, as with all sampling methods, this approach suffers from potential biases, including those introduced by the choice of invertebrate sampler, as well as the stochasticity of DNA amplification during PCR. Occupancy modeling is a statistical framework that can help account for imperfect detections in sampling and can be used to improve iDNA surveys. Using a case study based on the DNA screened from the blood meals of leeches, we demonstrate how multiscale occupancy models can be applied to the molecular detection of vertebrates to reveal the nuances in iDNA detections. Leeches were collected across a habitat degradation gradient in Sabah, Malaysian Borneo, in 2015 and 2016. We estimated three probabilities describing the occupancy, availability, and detection of three abundant mammals (bearded pig, muntjac and sambar deer) and compared how these values were impacted by environmental and technical covariates. For 2015, we found that null models without covariates revealed no clear differences in each of the three probabilities across taxa. However, in 2016, although the taxa have comparable occupancy, deviations occurred in the other two probabilities, with the sambar deer showing the lowest availability and muntjac with the lowest detection probability. Univariate models constructed for each taxon and year revealed differential impacts of the covariates; for example, a strong positive effect of DNA concentration on the detection of sambar deer and bearded pig was seen in 2016 only. Finally, our estimation of the minimum numbers of biological and technical replicates highlights the important trade‐off between achieving high probabilities of availability and detection and realistic amounts of sampling. Our results showcase the use of occupancy models for leech‐iDNA biodiversity surveys but highlight the potential effects of sample type, methodological design, and sample size.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".