Incidental invertebrate‐derived <scp>DNA</scp> detection of invasive and threatened species in temperate dry Southeast Australian forest
Bibliographic record
Abstract
Abstract Well‐informed biodiversity conservation practice can often be precluded by poor species detectability. For example, populations being missed during surveys can lead to them being omitted from species lists or area management plans. iDNA (invertebrate‐derived DNA) is a recently developed set of techniques for improving the detectability of elusive vertebrates by exploiting their associated invertebrates. Parasitic and scavenging invertebrates can be readily collected, and their gut contents DNA barcoded to detect local vertebrate diversity. However, most iDNA surveys have targeted mammals and have been carried out in tropical areas and/or rainforests. We carried out iDNA surveys targeting frogs in temperate dry sclerophyll forests in south‐eastern Australia. We set mosquito traps broadcasting recorded frog calls with the aim of collecting frog‐biting flies, which are attracted to frog calls. We collected 156 fly specimens, although none were of frog‐biting species, and no frogs were detected via iDNA, despite many being observed in the field. However, two mammal and one reptile species were detected via iDNA: the feral cat (Felis catus: Felidae), domestic dog or dingo (Canis lupus: Canidae) and the threatened Rosenberg's monitor (Varanus rosenbergi: Varanidae). Vertebrate‐sampling flies are likely highly abundant in the area since they were collected apparently incidentally in traps lacking appropriate attractants; a promising result for further surveys is different attractants are employed. This study is one of the few in which an invasive species has been detected through iDNA, and highlights its potential for improved detectability of threatened species outside of the tropics and early detection of invasive species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".