Automated VHF-Radio Telemetry Reveals Resource-Driven Site Utilisation Patterns in a Small-Bodied, Highly Mobile Bird (Erythrura Gouldiae)
Bibliographic record
Abstract
Abstract BackgroundInformation on site utilisation and movement is essential for managing species' resource requirements. Collecting these data requires frequent location sampling of multiple individuals, which can be challenging for small-bodied animals due to the often-large size of animal-borne satellite-based telemetry devices. We show how coded VHF-radio nano-transmitters and an array of passive receivers to collect location data at sufficient frequency and accuracy can be utilised to test whether the highly mobile and small-bodied Gouldian finch adheres to optimal foraging theory within a landscape of high spatiotemporal heterogeneity.MethodsCoded VHF-radio nano-transmitters (0.23 g wt.) were attached to 38 Gouldian finches (Erythrura gouldiae; < 12g bd. wt). Tagged birds were then detected by an array of 6 passive VHF-radio receivers (~ 530 m detection range) spread over 120 km2. We tracked the tagged birds during two sessions corresponding with high and low abundance of their primary food resource (Sorghum spp. seed). The mean and total duration that an individual spent within any receiver detection field (residence time) were used as dependants in a generalised linear mixed model approach within a Bayesian framework to assess the influence of tracking session, life stage, receiver site, and release site. Network analysis revealed the importance of specific areas for the finches within the receiver array using the proportional time a bird spent within any receiver's detection field and the movement between adjacent detection fields. ResultsThe daily mean and total duration that a tagged individual spent within a receiver detection field decreased by ~50% between periods of high and low seed availability, while the extent of movements across the landscape increased with a decline in seed resources. These data support the hypothesis that the Gouldian finch adheres to optimal foraging theory to survive periods of food scarcity. ConclusionsThe data collected via passive telemetry technology support the hypothesis that the Gouldian finch adheres to optimal foraging theory and uses alternative behavioural strategies to survive periods of food scarcity. Specifically, we found that Gouldian finches reduced the time spent foraging within a patch, increased the frequency of transit flights among patches, and expanded the size of their activity space as grass resources declined.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".