Document of bibliographic reference 232524

BibliographicReference record

Type
Bibliographic resource
Type of document
Journal article
BibLvlCode
AS
Title
Species distribution modeling in the cloud
Abstract
Species distribution modeling is a process aiming at computationally predicting the distribution of species in geographic areas on the basis of environmental parameters including climate data. Such a quantitative approach has a lot of potentialities in many areas that include setting up conservation priorities, testing biogeographic hypotheses, and assessing the impact of accelerated land use. To further promote the diffusion of such an approach, it is fundamental to develop a flexible, comprehensive, and robust environment capable of enabling practitioners and communities of practice to produce species distribution models more efficiently. A promising way to build such an environment is offered by modern infrastructures promoting the sharing of resources, including hardware, software, data, and services. This paper describes an approach to species distribution modeling based on a Hybrid Data Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud). It discusses the whole set of services needed to support the phases of such a complex process including access to occurrence records and environmental parameters and the processing of such information to predict the probability of a species’ occurrence in given areas.
WebOfScience code
https://www.webofscience.com/wos/woscc/full-record/WOS:000370646300007
Bibliographic citation
Candela, L.; Castelli, D.; Coro, G.; Pagano, P.; Sinibaldi, F. (2016). Species distribution modeling in the cloud. Concurr. Comput. Practice Experience 28(4): 1056-1079. https://dx.doi.org/10.1002/cpe.3030
Is peer reviewed
true

Authors

author
Name
Leonardo Candela
author
Name
Donatella Castelli
author
Name
Gianpaolo Coro
author
Name
Pasquale Pagano
author
Name
Fabio Sinibaldi

Links

referenced creativework
type
DOI
accessURL
https://dx.doi.org/10.1002/cpe.3030

Document metadata

date created
2014-01-07
date modified
2018-02-15