Insects rarely encounter ecological information as isolated, continuously reliable signals. Visual structure, odor, airflow, heat, acoustic information and learned spatial cues vary over different distances and timescales, while the usefulness of each signal depends on the current movement task and the animal’s physiological condition. Movement models often simplify this complexity by treating cues as independent inputs or by assuming fixed cue weights. Such representations are useful for restricted tasks but become problematic when information is intermittent, modalities alter one another, or an insect switches between search, approach, avoidance and reassessment. This article develops a proposed Dynamic Multisensory Landscape Model in which movement emerges from the interaction among spatial cue availability, temporal reliability, cross-modal transformation and internal-state-dependent valuation. The model separates signal presence from effective information, behavioural synergy from demonstrated neural integration, and laboratory evidence from field-scale inference. The framework predicts that cue weights should change across movement phases, that conflict should trigger context-dependent reweighting or priority switching rather than uniform averaging, and that model validation requires observations spanning controlled sensory manipulations and naturalistic movement. The framework is an organizing synthesis, not an empirically validated universal mechanism.
