A call for using rangeland-based livestock operations as model systems for studying the movement ecology of terrestrial animals
Bibliographic record
Abstract
The popularity of the field of movement ecology has increased in recent decades in part due to advances in tracking and computing technology. However, the field still contains many knowledge gaps that will be filled not by improvements in technology, but by employing novel experimental approaches. Most animal movement studies are based on wildlife populations, where complete system knowledge and experimental control are typically minimal. Here we propose the use of rangeland-based livestock operations, where livestock range freely in large, heterogeneous pastures, as model systems for addressing outstanding questions related to the movement ecology of large mammalian herbivores. This is a particularly timely topic due to recent advances in precision ranching technology, which enable high-resolution remote monitoring (and, in some cases, manipulation) of livestock and their surrounding resources. We walk through four examples of open questions in animal movement ecology that can be addressed with rangeland-based livestock operations as model systems: (1) How does animal nutritional state affect movement patterns? (2) What are the roles of genetics vs. social learning in determining movement traits? (3) How do movement traits affect life history syndromes? and (4) How does population density affect movement traits and patterns? Rangeland-based livestock systems contain robust, readily accessible, individual-level genealogical and life history information; complete, herd-level coverage of individuals with spatial tracking and physiological monitoring devices; and opportunities for straightforward and safe experimental manipulation of population and environmental characteristics to an extent that is infeasible in wild populations. We argue that by leveraging this wealth of information, researchers can make great strides toward advancing the field of animal movement ecology.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".