Gradient Analysis: A new paradigm in vegetation modeling

Vegetation remote sensing has commonly depended on a-priory definitions of vegetation communities that do not necessarily account for scale or specific applications. These classification schemes convolve species relationships and provide discrete representation of data that is inherently continuous in nature. We introduce an analytical hierarchy, where a continuous gradient of vegetation occurrence, structure, or suitability is the foundation, and all subsequent levels in the hierarchy are derived from this gradient. By starting with a continuous measurement of vegetation a coherent down-scaling strategy can be developed, thus avoiding many statistical and aggregation issues. This analytical framework allows for integration of ecological theory including niche, adaptation, and meta-populations. We use a random forest niche model and Lidar derived structural variables to demonstrate vegetation gradients. We then introduce a few simple landscape metrics for analyzing gradients. Finally, we demonstrate how this data can be integrated into analysis addressing climate change and habitat relationships.