Movement in <scp>3D</scp> : Novel Opportunities for Understanding Animal Behaviour and Space Use
Bibliographic record
Abstract
ABSTRACT Animals move in three spatial dimensions, but many animal movement tools have only focused on the use of 2D coordinates for modelling space use, habitat selection, behavioural classification, social interactions and movement. Here, we submit that many common movement ecology analyses can and should be extended to consider all three spatial dimensions to make more robust inferences about ecological processes. We provide an overview of how home range analysis, network analysis and social network analysis, hidden Markov models, resource selection and step selection functions and hierarchical linear and additive models are used for studying animal movement in two dimensions. Then, we explain how the third dimension, z, can be used within these existing frameworks to consider how depth and altitude affect key ecological inferences drawn from animal tracking data. Our position builds on empirical and theoretical work about how three‐dimensional methods can contribute to stronger inferences in movement ecology. Key limitations to operationalisation of this framework include calibration of uncertainty in pressure sensors used to measure depth and altitude, visualisation and rendering of three‐dimensional data to make them interpretable and understandable to end‐users and generally more conventional and accepted methods for using three dimensions when conducting standard animal movement analyses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".