A multilayer perspective for inferring spatial and social functioning in animal movement networks
Bibliographic record
Abstract
ABSTRACT Animal movement patterns are increasingly analysed as spatial networks. Currently, structures of complex movements are typically represented as a single-layer (or monoplex) network. However, aggregating individual movements, to generate population-level inferences, considerably reduces information on how individual or species variability influences spatial connectivity and thus identifying the mechanisms driving network structure remains difficult. Here, we propose incorporating the recent conceptual advances in multilayer network analyses with the existing movement network approach to improve our understanding of the complex interaction between spatial and/or social drivers of animal movement patterns. Specifically, we explore the application and interpretation of this framework using an empirical example of shark movement data gathered using passive remote sensors in a coral reef ecosystem. We first show how aggregating individual movement networks can lead to the loss of information, potentially misleading our interpretation of movement patterns. We then apply multilayer network analyses linking individual movement networks (i.e. layers) to the probabilities of social contact between individuals (i.e. interlayer edges) in order to explore the functional significance of different locations to an animal’s ecology. This approach provides a novel and holistic framework incorporating individual variability in behaviour and inter-individual interactions. We discuss how this approach can be used in applied ecology and conservation to better assess the ecological significance of variable space use by mobile animals within a population. Further, we argue that the uptake of multilayer networks will significantly broaden our understanding of long-term ecological and evolutionary processes, particularly in the context of information or disease transfer between individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".