A comparison of remotely sensed environmental predictors for avian distributions

Abstract

With greater accessibility and processing power from online platforms, summaries of remotely sensed data are increasingly used in species distribution models (SDMs). Comparisons of the predictive power of these environmental variables could inform SDMs moving forward. We evaluated the performance of freely available Landsat data as predictor sets for SDMs. Our objectives were to (1) compare the performance of single season SDMs built on mean values of raw spectral bands, Tasseled Cap transformations, and eight different indices, including NDVI, (2) evaluate the performance gain with the addition of standard deviation, textural metrics, and additional seasons, and (3) compare the performance of SDMs built on these continuous spectral predictor sets to SDMs built on classified land cover data (e.g., percent forest cover). We used statewide point counts to build multi-scale SDMs for 13 avian species across Oregon, USA. We compared the performance of SDMs built on each predictor set based on our objectives. Of the Landsat-derived predictor sets, SDMs built on raw spectral bands had the highest overall performance with nearly equivalent performance in Tasseled-Cap models. While performance gains from standard deviations, textural metrics, and additional seasons were minimal in raw-band and Tasseled-Cap models, gains were appreciable in single-index models. Classified land cover models performed equivalently to raw band models. When predictive performance is paramount, means of raw Landsat bands are strong predictors for avian SDMs. When parsimonious variables are essential, SDMs of single indices (e.g., NDVI) greatly benefit from additional information, such as standard deviation.