Assessing the spatial ecology and resource use of a mobile and endangered species in an urbanized landscape using satellite telemetry and DNA faecal metabarcoding
Bibliographic record
Abstract
The conservation of highly mobile species presents challenges to managers for assessment of threats to survival, given the difficulties in locating and observing such species. Here we evaluate satellite telemetry, DNA faecal metabarcoding and traditional field observations as three complementary techniques to acquire critical management information for an endangered species, Carnaby's Cockatoo Calyptorhynchus latirostris. Satellite telemetry of 23 birds resulted in 6026 location fixes accurate to within 500 m, and combined with extensive field observations and DNA faecal metabarcoding resulted in a more detailed understanding of how this species survives in an urbanized landscape. We identified 168 night roosts, 75% of which were previously unknown, which will contribute towards a more accurate population size estimate based on annual counts of roosting birds. We also determined the scale of daily movements (morning 5.4 ± 3.4 km from roost, afternoon 5.5 ± 3.3 km to roost; maximum distance between consecutive roosts 69.7 km) and the size of foraging areas around roosts (range 17–276 km2), and identified dependence on a variety of native and exotic food sources. Field observations identified 11 food‐plant families, but combined with DNA faecal metabarcoding this was extended to 21 food‐plant families. The three techniques were compared to assess their individual and collective values. By combining spatial ecology information from satellite telemetry with ecological knowledge from field observation and DNA faecal analysis, we gained deeper insights into the ecology of the species than would have been possible from any one technique alone. This information will lead to more strategic conservation planning to allow this species to persist within a rapidly expanding urban environment.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".