Territoriality and home‐range dynamics in meerkats, <i><scp>S</scp>uricata suricatta</i>: a mechanistic modelling approach
Bibliographic record
Abstract
Multiple approaches exist to model patterns of space use across species, among them resource selection analysis, statistical home-range modelling and mechanistic movement modelling. Mechanistic home-range models combine the benefits of these approaches, describing emergent territorial patterns based on fine-scale individual- or group-movement rules and incorporating interactions with neighbours and the environment. These models have not, to date, been extended to dynamic contexts. Using mechanistic home-range models, we explore meerkat (Suricata suricatta) territorial patterns, considering scent marking, direct group interactions and habitat selection. We also extend the models to accommodate dynamic aspects of meerkat territoriality (territory development and territory shift). We fit models, representing multiple working hypotheses, to data from a long-term meerkat study in South Africa, and we compare models using Akaike's and Bayesian Information Criteria. Our results identify important features of meerkat territorial patterns. Notably, larger groups do not seem to control larger territories, and groups apparently prefer dune edges along a dry river bed. Our model extensions capture instances in which 1) a newly formed group interacts more strongly with its parent groups over time and 2) a group moves its territory core out of aversive habitat. This extends our mechanistic modelling framework in previously unexplored directions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".