PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Coro G., Magliozzi C., Ellenbroek A., Kaschner K., Pagano P. Automatic classification of climate change effects on marine species distributions in 2050 using the AquaMaps model. In: Environmental and Ecological Statistics, vol. 23 (1) pp. 155 - 180. Springer [8 August 2015], 2016.
 
 
Abstract
(English)
Habitat modifications driven by human impact and climate change may influence species distribution, particularly in aquatic environments. Niche-based models are commonly used to evaluate the availability and suitability of habitat and assess the consequences of future climate scenarios on a species range and shifting edges of its distribution. Together with knowledge on biology and ecology, niche models also allow evaluating the potential of species to react to expected changes. The availability of projections of future climate scenarios allows comparing current and future niche distributions, assessing a species' habitat suitability modification and shift, and consequently estimating potential species' reaction. In this study, differences between the distribution maps of 406 marine species, which were produced by the AquaMaps niche models on current and future (year 2050) scenarios, were estimated and evaluated. Discrepancy measurements were used to identify a discrete number of categories, which represent different responses to climate change. Clustering analysis was then used to automatically detect these categories, demonstrating their reliability compared to human supervised classification. Finally, the distribution of characteristics like extinction risk (based on IUCN categories), taxonomic groups, population trends and habitat suitability change over the clustering categories was evaluated. In this assessment, direct human impact was neglected, in order to focus only on the consequences of environmental changes. Furthermore, in the comparison between two climate snapshots, the intermediate phases were assumed to be implicitly included into the model of the 2050 climate scenario.
URL: http://link.springer.com/article/10.1007/s10651-015-0333-8/fulltext.html
DOI: 10.1007/s10651-015-0333-8
Subject Big Data
Ecological Modelling
Ecological Niche Modelling
Statistics
Cloud computing
GIS
H.3.7 INFORMATION STORAGE AND RETRIEVAL. Digital Libraries
H.2.8 DATABASE MANAGEMENT. Database Applications


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