Global change may induce shifts in plant species distributions at multiple spatial scales. At the ecosystem scale, such shifts may result in movement of vegetation boundaries or ecotones. Most indicators for ecosystem change require timeseries data, but here a new method is proposed enabling inference of vegetation boundary dynamics from a single ‘snapshot’ (e.g. an aerial photograph or satellite image) in time. The method is developed and tested with mathematical models for plant competition. Subsequently, the method is tested with snapshot data from three case studies: a northern hardwood-boreal forest mountain ecotone in Vermont, a forest-mire ecotone in New Zealand and an alpine treeline-tundra ecotone in Montana.
Our model simulations show that the proposed method can predict vegetation boundary dynamics, even when plant dispersal or competition characteristics are not known. This is an important result, because plant dispersal and competition characteristics are often difficult to quantify. The method also accurately predicts vegetation boundary dynamics in the three case studies. Predictions that the northern hardwood-boreal forest mountain ecotone in Vermont is moving upward are consistent with recent field observations. Also, the forest-mire ecotone in New Zealand is predicted to be stable, which is in compliance with previous studies. Finally, predictions that the trees are expanding into tundra at the alpine treeline-tundra ecotone in Montana also agree with recent field observations. However, we also discuss potential caveats of the method and the requirements for the snapshot data that are used in the analyses. With availability of snapshot data rapidly increasing, the new method proposed here may provide an easy tool to assess vegetation boundary dynamics and hence ecosystem responses to changing environmental conditions.