Detecting effects of spatial memory and dynamic information on animal movement decisions
Bibliographic record
Abstract
Summary Animals of many species demonstrate movement behaviour in which decisions are based on a variety of information. Effects of resources have been studied widely, often under the assumption that the environment is constant over the course of the study. Much less understood is the role of dynamic information about continuously changing resource availability and past experiences. Such information can be acquired during movement bouts and used for future decisions via memory. We present a new class of animal movement models, which incorporates a dynamic interplay of movement and information gain processes. Information is contained in a dynamic cognitive map. As an example, we consider time since last visit to locations and how this interacts with environmental information to shape movement patterns. Our models can be fitted to empirical movement trajectories and are therefore amenable to statistical inference (parameter estimation and model selection). We tested the functionality of our method using simulated data. Parameter estimates were in accordance with true values used in the simulations, and model selection via Bayesian information criterion (BIC) was able to identify true underlying mechanisms of simulated trajectories. Thus, if time since last visit to locations influences movement decisions, our method is able to detect this mechanism. The use of dynamic information such as the one demonstrated in our example models likely requires cognitive abilities such as spatial memory. Therefore, our method can be used to reveal evidence of spatial memory in empirical movement data. Understanding the components of individual movement decisions and their interactions ultimately helps us to predict how population distribution patterns respond to environmental changes, such as landscape fragmentation and changing climate.
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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 | 0.001 |
| 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".