Finding a clear signal: a systematic review of desert radio telemetry research
Bibliographic record
Abstract
Radio telemetry is a common tool to monitor animals in many ecosystems. Radio telemetry, or radio tracking, typically uses a tag or collar with a radio transmitter attached to an animal that is monitored by researchers with a receiver. This technique is used for research in many disciplines such as wildlife ecology or conservation biology. Within desert ecosystems, this approach has been used since the 1960s in many different research capacities. Many desert species exist at low density and can range widely within a region due to scarce resources, which can make radio telemetry a useful method to use in these environments. Here, we examined the peer-reviewed literature to assess how radio telemetry is used in deserts. Using the Web of Science with additional search validation on Google Scholar to formally summarize this research, we found 97 studies that fit our criteria. Most primary studies used radio telemetry to examine individual behavior and/or habitat use. The majority of published studies were done in the United States. The most common classes of animal studied were mammals (29.9 % large mammals and 25.8 % small mammals). Most species studied were classified as ‘least concern’ for risk status. Vhf radio telemetry devices predominated the technology selected (80.4 %) whilst GPS devices were used in 19.6 % of studies. Radio telemetry devices are an effective tool to survey individual animals and animal populations in harsh desert environments. However, future research can be improved using these tools to improve reproducibility encourage data reuse and comparison between studies. We encourage authors using radio telemetry to publish their data and include details of their study area and tracking methods to accomplish these goals.
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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 | 0.016 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".