Open AccessSpecies distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions
Author(s) -
Shirley S. M.,
Yang Z.,
Hutchinson R. A.,
Alexander J. D.,
McGarigal K.,
Betts M. G.
Publication year - 2013
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12093
Subject(s) - remote sensing , environmental science , land cover , reflectivity , range (aeronautics) , species distribution , normalized difference vegetation index , scale (ratio) , vegetation (pathology) , physical geography , predictive modelling , ecology , geography , cartography , land use , statistics , climate change , habitat , mathematics , biology , medicine , physics , materials science , pathology , optics , composite material
Aim Assessing the influence of land cover in species distribution modelling is limited by the availability of fine‐resolution land‐cover data appropriate for most species responses. Remote‐sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote‐sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western O regon. Location Central Coast Range, C ascade and K lamath Mountains O regon, USA . Methods Information on bird observations was collected from 4598 fixed‐radius point counts. Reflectance data were obtained using 30‐m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree ( BRT ) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve ( AUC ) values. Results Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD ). Further, model performance using individual reflectance bands exceeded those that used only N ormalized D ifference V egetation I ndex ( NDVI ). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest. Main Conclusions We demonstrate that unclassified remote‐sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high‐resolution predictions are required, including habitat change detection studies, identification of fine‐scale biodiversity hotspots and reserve design.