successioncombining

Successioncombining is a methodological framework used to integrate multiple sequential models or trajectories of change into a single composite succession path. It aims to produce a robust forecast or representation of how a system progresses through states over time by reconciling differences among source sequences.

The core idea is to align, weight, and merge successive states from diverse models or data sources. Common techniques include time-series alignment, dynamic time warping, ensemble averaging, and probabilistic fusion across state transitions. Some approaches treat successioncombining as a form of multi-model ensemble, where each model contributes a pathway and a confidence measure, and the resulting trajectory reflects a consensus or a weighted mixture of paths.

Applications span several fields. In ecology, it is used to forecast vegetation or ecosystem development after disturbances by combining different successional models. In urban planning and geography, it aids in predicting land-use trajectories when multiple growth scenarios exist. In systems biology or medical research, successioncombining can merge progression pathways from different datasets to model disease evolution or treatment response. In operations research and supply chain planning, it helps synthesize sequential decision processes under uncertainty.

Challenges include aligning disparate state definitions, managing differing time scales, and avoiding bias toward any single source. Computational complexity can be high when handling large ensembles. Data quality and model structural differences influence the reliability of the aggregated trajectory.

See also: ensemble modeling, time-series analysis, dynamic time warping, data fusion, ecological succession.