A call for increased integration of experimental approaches in movement ecology
Bibliographic record
Abstract
Rapid developments in animal-tracking technology have enabled major advances in the field of movement ecology, which seeks to understand the drivers and consequences of movement across scales, taxa, and ecosystems. The field has made ground-breaking discoveries, yet the majority of studies in movement ecology remain reliant on observational approaches. While important, observational studies are limited compared to experimental methods that can reveal causal relationships and underlying mechanisms. As such, we advocate for a renewed focus on experimental approaches in animal movement ecology. We illustrate a way forward in experimental movement ecology across two fundamental levels of biological organisation: individuals and social groups. We then explore the application of experiments in movement ecology to study anthropogenic influences on wildlife movement, and enhance our mechanistic understanding of conservation interventions. In each of these examples, we draw upon previous research that has effectively employed experimental approaches, while highlighting outstanding questions that could be answered by further experimentation. We conclude by highlighting the ways experimental manipulations in both laboratory and natural settings provide a promising way forward to generate mechanistic understandings of the drivers, consequences, and conservation of animal movement.
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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 | 0.204 | 0.196 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.007 | 0.026 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".