Successful carnivore identification with faecal DNA across a fragmented Amazonian landscape
Bibliographic record
Abstract
The use of scat surveys to obtain DNA has been well documented in temperate areas, where DNA preservation may be more effective than in tropical forests. Samples obtained in the tropics are often exposed to high humidity, warm temperatures, frequent rain and intense sunlight, all of which can rapidly degrade DNA. Despite these potential problems, we demonstrate successful mtDNA amplification and sequencing for faeces of carnivores collected in tropical conditions and quantify how sample condition and environmental variables influence the success of PCR amplification and species identification. Additionally, the feasibility of genotyping nuclear microsatellites from jaguar (Panthera onca) faeces was investigated. From October 2007 to December 2008, 93 faecal samples were collected in the southern Brazilian Amazon. A total of eight carnivore species was successfully identified from 71% of all samples obtained. Information theoretic analysis revealed that the number of PCR attempts before a successful sequence was an important negative predictor across all three responses (success of species identification, success of species identification from the first sequence and PCR amplification success), whereas the relative importance of the other three predictors (sample condition, season and distance from forest edge) varied between the three responses. Nuclear microsatellite amplification from jaguar faeces had lower success rates (15-44%) compared with those of the mtDNA marker. Our results show that DNA obtained from faecal samples works efficiently for carnivore species identification in the Amazon forest and also shows potential for nuclear DNA analysis, thus providing a valuable tool for genetic, ecological and conservation studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".