A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis

Radiation is one of the primary influences on vegetation composition and spatial pattern. Topographic orientation is often used as a proxy for relative radiation load due to its effects on evaporative demand and local temperature. Common methods for incorporating this information (i.e., site measures of slope and aspect) fail to include daily or annual changes in solar orientation and shading effects from local topography. As a result, these static measures do not incorporate the level of spatial and temporal heterogeneity required to examine vegetation patterns at the landscape level. We developed a widely applicable method for estimating potential relative radiation (PRR) using digital elevation data and a widely used geographic information system (Arc/Info). We found significant differences among four increasingly comprehensive radiation proxies. Our GIS-based proxy compared well with estimates from more data-intensive and computationally rigorous radiation models. We note that several recent studies have not found strong correlations between vegetation pattern and landscape-scale differences in radiation. We suggest that these findings may be due to the use of proxies that were not accurately capturing variability in radiation, and we recommend PRR or similar measures for use in future vegetation analyses.