Predicting species distributions across the Amazonian and Andean regions using remote sensing data Export

@article{citeulike:2461011, abstract = {Abstract Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1-km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian-Andean elevational gradients. Within remote sensing subsets, microwave-based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large-scale land-use change.}, author = {Buermann, Wolfgang and Saatchi, Sassan and Smith, Thomas B. and Zutta, Brian R. and Chaves, Jaime A. and Mila, Borja and Graham, Catherine H.}, citeulike-article-id = {2461011}, citeulike-linkout-0 = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-2699.2007.01858.x}, citeulike-linkout-1 = {http://dx.doi.org/10.1111/j.1365-2699.2007.01858.x}, citeulike-linkout-2 = {http://www3.interscience.wiley.com/cgi-bin/abstract/120775192/ABSTRACT}, doi = {10.1111/j.1365-2699.2007.01858.x}, journal = {Journal of Biogeography}, keywords = {amazon, andes, distribution\_models, ecuador, maxent, satellite}, number = {0}, pages = {???}, posted-at = {2008-03-03 15:16:43}, priority = {2}, title = {Predicting species distributions across the Amazonian and Andean regions using remote sensing data}, url = {http://dx.doi.org/10.1111/j.1365-2699.2007.01858.x}, volume = {0}, year = {0} }

TY - JOUR ID - citeulike:2461011 L3 - citeulike-article-id:2461011 N2 - Abstract Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1-km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian-Andean elevational gradients. Within remote sensing subsets, microwave-based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large-scale land-use change. IS - 0 JF - Journal of Biogeography EP - ??? TI - Predicting species distributions across the Amazonian and Andean regions using remote sensing data VL - 0 SP - ??? KW - amazon KW - andes KW - distribution_models KW - ecuador KW - maxent KW - satellite AU - Buermann, Wolfgang AU - Saatchi, Sassan AU - Smith, Thomas AU - Zutta, Brian AU - Chaves, Jaime AU - Mila, Borja AU - Graham, Catherine PY - 2000/// UR - http://dx.doi.org/10.1111/j.1365-2699.2007.01858.x ER -