2013
Journal article
Restricted
The iMarine data bonanza: improving data discovery and management through a hybrid data infrastructure
Castelli D, Pagano P, Candela L, Coro G
This paper briefly introduces the iMarine data infrastructure and the services it offers for data discovery and management.Source: BOLLETTINO DI GEOFISICA TEORICA E APPLICATA, vol. 54, pp. 105-107
Project(s): IMARINE
Castelli D, Pagano P, Candela L, Coro G
This paper briefly introduces the iMarine data infrastructure and the services it offers for data discovery and management.Source: BOLLETTINO DI GEOFISICA TEORICA E APPLICATA, vol. 54, pp. 105-107
Project(s): IMARINE
2013
Journal article
Restricted
A service for statistical analysis of marine data in a distributed e-infrastructure
Coro G, Gioia A, Pagano P, Candela L
This paper introduces a specific workbench software, named Statistical Manager (SM), that aids in the application of statistical computing and data mining to a variety of biological and marine related problems.Source: BOLLETTINO DI GEOFISICA TEORICA E APPLICATA, vol. 54, pp. 68-70
Project(s): IMARINE
Coro G, Gioia A, Pagano P, Candela L
This paper introduces a specific workbench software, named Statistical Manager (SM), that aids in the application of statistical computing and data mining to a variety of biological and marine related problems.Source: BOLLETTINO DI GEOFISICA TEORICA E APPLICATA, vol. 54, pp. 68-70
Project(s): IMARINE
See at:
CNR IRIS 
| CNR IRIS | www2.ogs.trieste.it
2013
Other
Restricted
IMarine - Management report, November 2013
Candela L, Castelli D, Coro G, Ellenbroek A, Fabriani P, Garavelli S, Gerbesiotis J, Kakaletris G, Manzi A, Michel Assoumou J, Pagano P
This deliverables is a project management report describing the developments, achievement and identified risks of the iMarine project across all activities performed in the period July '13 (M21)-November '13 (M24).Project(s): IMARINE
Candela L, Castelli D, Coro G, Ellenbroek A, Fabriani P, Garavelli S, Gerbesiotis J, Kakaletris G, Manzi A, Michel Assoumou J, Pagano P
This deliverables is a project management report describing the developments, achievement and identified risks of the iMarine project across all activities performed in the period July '13 (M21)-November '13 (M24).Project(s): IMARINE
2013
Other
Open Access
A Lightweight Guide on Gibbs Sampling and JAGS
Coro G
In this document we give some insight about how Gibbs Sampling works and how the JAGS modelling framework implements it. The hope is that, once the reader will have understood these concepts, building a model to perform Bayesian Inference with JAGS should be much easier or, at least, the reader should be more aware of what happens behind the scenes. We assume the reader to have basic knowledge about probability, sucient to understand the dierence between a probability and a probability density.
Coro G
In this document we give some insight about how Gibbs Sampling works and how the JAGS modelling framework implements it. The hope is that, once the reader will have understood these concepts, building a model to perform Bayesian Inference with JAGS should be much easier or, at least, the reader should be more aware of what happens behind the scenes. We assume the reader to have basic knowledge about probability, sucient to understand the dierence between a probability and a probability density.
See at:
CNR IRIS 
| ISTI Repository | CNR IRIS
2013
Other
Open Access
ENVRI - Integration, Harmonisation and Publication software components - version 1
Candela L, Coro G, Pagano P, Sinibaldi F
The ENVRI Integration, Harmonisation and Publication software comprises a number of components, services and subsystems offering facilities enabling the integration and harmonization of data. The actual deliverable is thus the software realising such facilities. This document briefly describes the software components realising version 1 of these facilities and offers a series of links to the software itself and its accompanying documentationProject(s): ENVRI
Candela L, Coro G, Pagano P, Sinibaldi F
The ENVRI Integration, Harmonisation and Publication software comprises a number of components, services and subsystems offering facilities enabling the integration and harmonization of data. The actual deliverable is thus the software realising such facilities. This document briefly describes the software components realising version 1 of these facilities and offers a series of links to the software itself and its accompanying documentationProject(s): ENVRI
See at:
envri.eu | CNR IRIS | ISTI Repository | CNR IRIS
2013
Conference article
Restricted
Cloud Computing for Ecological Modeling in the D4Science Infrastructure
Pagano P, Coro G, Castelli D, Candela L, Sinibaldi F
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. In order to further promote the diffusion of such an approach it is fundamental to develop a flexible, comprehensive, and robust environment enabling practitioners 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. We describe an approach to species distribution modeling based on the D4Science Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud), by discussing the services needed to support the phases of such a complex process.Project(s): D4SCIENCE
Pagano P, Coro G, Castelli D, Candela L, Sinibaldi F
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. In order to further promote the diffusion of such an approach it is fundamental to develop a flexible, comprehensive, and robust environment enabling practitioners 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. We describe an approach to species distribution modeling based on the D4Science Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud), by discussing the services needed to support the phases of such a complex process.Project(s): D4SCIENCE
2013
Other
Open Access
IMARINE - Cloud Computing for Ecological Modeling in the D4Science Infrastructure
Pagano P, Coro G, Castelli D, Candela L, Sinibaldi F
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. In order to further promote the diffusion of such an approach it is fundamental to develop a flexible, comprehensive, and robust environment enabling practitioners 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. We describe an approach to species distribution modeling based on the D4Science Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud), by discussing the services needed to support the phases of such a complex process.Project(s): IMARINE
Pagano P, Coro G, Castelli D, Candela L, Sinibaldi F
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. In order to further promote the diffusion of such an approach it is fundamental to develop a flexible, comprehensive, and robust environment enabling practitioners 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. We describe an approach to species distribution modeling based on the D4Science Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud), by discussing the services needed to support the phases of such a complex process.Project(s): IMARINE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2013
Other
Open Access
D4Science facilities for managing biodiversity databases
Candela L, Castelli D, Coro G, De Faveri F, Italiano A, Lelii L, Mangiacrapa F, Marioli V, Pagano P
During the last years, considerable progresses have been made in developing on-line species occurrence databases. These are crucial in scientific activities on biodiversity, including the generation of species distribution models, which play an important role in conservation efforts. Unfortunately, their exploitation is still difficult and time consuming for many scientists. No database currently exists that can claim to host, and make available in a seamless way, all the species occurrence data needed by the ecology scientific community. Occurrence data are scattered among several databases and information systems. It is not easy to retrieve records from them, because of differences in the adopted protocols, formats and granularity. Once collected, datasets have to be selected, homogenized and pre-processed before being ready-to-use in scientific analysis and modeling. This paper introduces a set of facilities offered by the D4Science Data Infrastructure to support these phases of the scientific process. It also exemplifies how they contribute to reduce the time spent in data quality assessment and curation thus improving the overall performance of the scientific investigation.Project(s): IMARINE
Candela L, Castelli D, Coro G, De Faveri F, Italiano A, Lelii L, Mangiacrapa F, Marioli V, Pagano P
During the last years, considerable progresses have been made in developing on-line species occurrence databases. These are crucial in scientific activities on biodiversity, including the generation of species distribution models, which play an important role in conservation efforts. Unfortunately, their exploitation is still difficult and time consuming for many scientists. No database currently exists that can claim to host, and make available in a seamless way, all the species occurrence data needed by the ecology scientific community. Occurrence data are scattered among several databases and information systems. It is not easy to retrieve records from them, because of differences in the adopted protocols, formats and granularity. Once collected, datasets have to be selected, homogenized and pre-processed before being ready-to-use in scientific analysis and modeling. This paper introduces a set of facilities offered by the D4Science Data Infrastructure to support these phases of the scientific process. It also exemplifies how they contribute to reduce the time spent in data quality assessment and curation thus improving the overall performance of the scientific investigation.Project(s): IMARINE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2013
Other
Open Access
Deriving Fishing Monthly Effort and Caught Species from Vessel Trajectories
Coro G, Fortunati L, Pagano P
Vessel Monitoring Systems (VMSs) are mainly meant to monitor and control fishing activity in ocean areas. VMSs have been realized and used in many ways, their principal aim being the monitoring, controlling and surveillance of fishing activity. The term VMS may refer to electronic or software systems aiming at those scopes. VMSs often rely on information transmitted by vessels during navigation, which are used both by security systems in ports and by scientists who want to extract information about oceans fishing exploitation. In this paper we present a semi-automatic method, to be used in a VMS, to calculate fishing monthly effort in ocean areas during a certain period. The method is based on a collection of vessels trajectories referring to that specific period. The monthly effort is calculated at 0.5 degrees resolution and is displayed on an interactive GIS map. Furthermore, the method produces a list of species that are likely to live in the most exploited areas. Thus, our method can be integrated by a VMS to understand the degree of exploitation of ocean areas and to estimate the species that are involved in a catch, which can help in limiting destructive fishing practices.Project(s): IMARINE
Coro G, Fortunati L, Pagano P
Vessel Monitoring Systems (VMSs) are mainly meant to monitor and control fishing activity in ocean areas. VMSs have been realized and used in many ways, their principal aim being the monitoring, controlling and surveillance of fishing activity. The term VMS may refer to electronic or software systems aiming at those scopes. VMSs often rely on information transmitted by vessels during navigation, which are used both by security systems in ports and by scientists who want to extract information about oceans fishing exploitation. In this paper we present a semi-automatic method, to be used in a VMS, to calculate fishing monthly effort in ocean areas during a certain period. The method is based on a collection of vessels trajectories referring to that specific period. The monthly effort is calculated at 0.5 degrees resolution and is displayed on an interactive GIS map. Furthermore, the method produces a list of species that are likely to live in the most exploited areas. Thus, our method can be integrated by a VMS to understand the degree of exploitation of ocean areas and to estimate the species that are involved in a catch, which can help in limiting destructive fishing practices.Project(s): IMARINE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2013
Journal article
Open Access
Species distribution modeling in the cloud
Candela L, Castelli D, Coro G, Pagano P, Sinibaldi F
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, assessing the impact of accelerated land use. In order 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. Copyright c 0000 John Wiley & Sons, Ltd.Source: CONCURRENCY AND COMPUTATION (ONLINE)
DOI: 10.1002/cpe.3030
Project(s): ENVRI, IMARINE , EUBRAZILOPENBIO
Metrics:
Candela L, Castelli D, Coro G, Pagano P, Sinibaldi F
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, assessing the impact of accelerated land use. In order 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. Copyright c 0000 John Wiley & Sons, Ltd.Source: CONCURRENCY AND COMPUTATION (ONLINE)
DOI: 10.1002/cpe.3030
Project(s): ENVRI
, IMARINE , EUBRAZILOPENBIO Metrics:
See at:
CNR IRIS | onlinelibrary.wiley.com | ISTI Repository | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | CNR IRIS | CNR IRIS
2013
Contribution to book
Restricted
Supporting tabular data characterization in a large scale data infrastructure by lexical matching techniques
Candela L, Coro G, Pagano P
Digital Libraries continue to evolve towards research environments supporting access and management of multiform Information Objects spread across multiple data sources and organizational domains. This evolution has introduced the need to deal with Information Objects having traits different from those characterizing Digital Libraries at their early stages and to revise the services supporting their management. Tabular data represent a class of Information Objects that require to be efficiently managed because of their core role in many eScience scenarios. This paper discusses the tabular data characterization problem, i.e., the problem of identifying the reference dataset of any column of the dataset. In particular, the paper presents an approach based on lexical matching techniques to support users during the data curation phase by providing them with a ranked list of reference datasets suitable for a dataset column.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 21-32
DOI: 10.1007/978-3-642-35834-0_5
Project(s): ENVRI, IMARINE , EUBRAZILOPENBIO
Metrics:
Candela L, Coro G, Pagano P
Digital Libraries continue to evolve towards research environments supporting access and management of multiform Information Objects spread across multiple data sources and organizational domains. This evolution has introduced the need to deal with Information Objects having traits different from those characterizing Digital Libraries at their early stages and to revise the services supporting their management. Tabular data represent a class of Information Objects that require to be efficiently managed because of their core role in many eScience scenarios. This paper discusses the tabular data characterization problem, i.e., the problem of identifying the reference dataset of any column of the dataset. In particular, the paper presents an approach based on lexical matching techniques to support users during the data curation phase by providing them with a ranked list of reference datasets suitable for a dataset column.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 21-32
DOI: 10.1007/978-3-642-35834-0_5
Project(s): ENVRI
, IMARINE , EUBRAZILOPENBIO Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
2013
Other
Open Access
BiOnym - a flexible workflow approach to taxon name matching
Vanden Berghe E, Bailly N, Aldemita C, Fiorellato F, Coro G, Ellenbroek A, Pagano P
Several taxon name matching services are available on line, and many more are no doubt living on computers of individual scientists. While these systems may work very well, most suffer from the fact that the list of names used as a reference, and the criteria to decide on a match, are hard-coded in the engine that performs the name matching. One of the objectives of the EU FP7 project 'iMarine' (http://www.i-marine.eu) is to create a taxonomic name matching system, BiOnym, that would separate these elements. The user will be offered a choice of several taxonomic reference lists, including the option to upload his/her own list to the iMarine infrastructure. Where possible, internationally recognized references are dynamically linked to the iMarine infrastructure; this avoids issues with intellectual property rights, and eliminates the inconvenience of keeping the reference lists up to date. The following lists are available in the infrastructure: the Catalogue of Life, World Register of Marine Species, Interim Register of Marine and Non-marine Genera, National Center for Biotechnology Information, and the Integrated Taxonomic Information System.Project(s): IMARINE
Vanden Berghe E, Bailly N, Aldemita C, Fiorellato F, Coro G, Ellenbroek A, Pagano P
Several taxon name matching services are available on line, and many more are no doubt living on computers of individual scientists. While these systems may work very well, most suffer from the fact that the list of names used as a reference, and the criteria to decide on a match, are hard-coded in the engine that performs the name matching. One of the objectives of the EU FP7 project 'iMarine' (http://www.i-marine.eu) is to create a taxonomic name matching system, BiOnym, that would separate these elements. The user will be offered a choice of several taxonomic reference lists, including the option to upload his/her own list to the iMarine infrastructure. Where possible, internationally recognized references are dynamically linked to the iMarine infrastructure; this avoids issues with intellectual property rights, and eliminates the inconvenience of keeping the reference lists up to date. The following lists are available in the infrastructure: the Catalogue of Life, World Register of Marine Species, Interim Register of Marine and Non-marine Genera, National Center for Biotechnology Information, and the Integrated Taxonomic Information System.Project(s): IMARINE
See at:
ISTI Repository | CNR IRIS | CNR IRIS
2013
Other
Restricted
Providing Statistical Algorithms as-a-Service
Coro G, Pagano P, Candela L
In computational statistics, algorithms often have specialized implementations that address very specific problems. Every so often, these algorithms are applicable also to other problems than the original ones. Today, interest is growing towards modular and pluggable solutions that enable the repetition and validation of the experiments made by other scientists and allow the exploitation of those algorithms in other contexts. Furthermore, such procedures are requested to be remotely hosted and to "hide" the complexity of the calculations, managed by remote computational infrastructures behind the scenes. For such reasons, the usual solution of supplying modular software libraries containing implementations of algorithms is leaving the place to Web Services accessible through standard protocols and hosting such implementations. The protocols describing the computational capabilities of these Services are more and more elaborate, so that modular workflows can rely on them. We propose a Web Service, named Statistical Manager (SM) that hosts both general and special purpose algorithms implementations for statistical computing and data mining, which can be applied to a variety of biological and marine related problems.Project(s): IMARINE
Coro G, Pagano P, Candela L
In computational statistics, algorithms often have specialized implementations that address very specific problems. Every so often, these algorithms are applicable also to other problems than the original ones. Today, interest is growing towards modular and pluggable solutions that enable the repetition and validation of the experiments made by other scientists and allow the exploitation of those algorithms in other contexts. Furthermore, such procedures are requested to be remotely hosted and to "hide" the complexity of the calculations, managed by remote computational infrastructures behind the scenes. For such reasons, the usual solution of supplying modular software libraries containing implementations of algorithms is leaving the place to Web Services accessible through standard protocols and hosting such implementations. The protocols describing the computational capabilities of these Services are more and more elaborate, so that modular workflows can rely on them. We propose a Web Service, named Statistical Manager (SM) that hosts both general and special purpose algorithms implementations for statistical computing and data mining, which can be applied to a variety of biological and marine related problems.Project(s): IMARINE
2013
Conference article
Open Access
Automatic Procedures to Assist in Manual Review of Marine Species Distribution Maps
Coro G, Pagano P, Ellenbroek A
Ecological Niche Modeling (ENM) is a branch of biology that uses algorithms to predict the distribution of species in a geographic area on the basis of a numerical representation of their preferred habitat and environment. Algorithmic maps can be produced for suitable or native habitats and require a review by human experts. During the review operation biologists use their knowledge about a species to modify the maps. They usually take algorithmic maps as starting point in the review. In this paper we provide a methodology for biologists to use the automatic maps as references also during and after the review process. Our approach is based on a comparison between the reviewed map and two systems: an expert system and a Feed Forward Neural Network. Furthermore we suggest an evaluation procedure of the quality of the environmental features used as training set, for assessing the models reliability.DOI: 10.1007/978-3-642-37213-1_36
Project(s): D4SCIENCE-II
Metrics:
Coro G, Pagano P, Ellenbroek A
Ecological Niche Modeling (ENM) is a branch of biology that uses algorithms to predict the distribution of species in a geographic area on the basis of a numerical representation of their preferred habitat and environment. Algorithmic maps can be produced for suitable or native habitats and require a review by human experts. During the review operation biologists use their knowledge about a species to modify the maps. They usually take algorithmic maps as starting point in the review. In this paper we provide a methodology for biologists to use the automatic maps as references also during and after the review process. Our approach is based on a comparison between the reviewed map and two systems: an expert system and a Feed Forward Neural Network. Furthermore we suggest an evaluation procedure of the quality of the environmental features used as training set, for assessing the models reliability.DOI: 10.1007/978-3-642-37213-1_36
Project(s): D4SCIENCE-II
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2013
Other
Open Access
Variazioni climatiche ed effetto sulle specie marine = Climate changes and effect on marine species
Castelli D, Pagano P, Coro G
Many scientific and industrial environments show lively interest in the capability of analyzing climate changes and how these changes affect marine species. In the context of the European VENUS-C project, the Networked Multimedia Information Systems Laboratory of the Institute of information Science and Technology "Alessandro Faedo" (ISTI) has developed a service that allows biologists to identify and process a set of parameters which affect the presence of marine species in the oceans (e.g., salinity, ice concentration, temperature, etc.). By exploiting this service scientists can specify for each parameter the actual measures and which are the assumed ones for a certain year (e.g., 2050), interpolate such values and generate intermediate climate scenarios. Further, this tool allows to train a probabilistic model on the basis of such parameters. The trained model can then be used to measure the variation of the occurrence of certain species in certain ocean zone. The huge datasets involved for supporting this service are efficiently processed by exploiting parallel and distributed computing solutions.Project(s): VENUS-C
Castelli D, Pagano P, Coro G
Many scientific and industrial environments show lively interest in the capability of analyzing climate changes and how these changes affect marine species. In the context of the European VENUS-C project, the Networked Multimedia Information Systems Laboratory of the Institute of information Science and Technology "Alessandro Faedo" (ISTI) has developed a service that allows biologists to identify and process a set of parameters which affect the presence of marine species in the oceans (e.g., salinity, ice concentration, temperature, etc.). By exploiting this service scientists can specify for each parameter the actual measures and which are the assumed ones for a certain year (e.g., 2050), interpolate such values and generate intermediate climate scenarios. Further, this tool allows to train a probabilistic model on the basis of such parameters. The trained model can then be used to measure the variation of the occurrence of certain species in certain ocean zone. The huge datasets involved for supporting this service are efficiently processed by exploiting parallel and distributed computing solutions.Project(s): VENUS-C
See at:
CNR IRIS | www.edizioni.cnr.it | CNR IRIS
2013
Other
Open Access
Modellazione della nicchia ecologica di specie marine = Marine species ecological niche modelling
Castelli D, Candela L, Coro G, Pagano P, Sinibaldi F
A certain species' niche, i.e. those environmental conditions and resources which permit that species can survive and reproduce, is a basic piece of knowledge for monitoring ocean's populousness from both commercial point of view (to steer fishery activities to defined areas) and from the biological one (to monitor dying species). In the context of the European iMarine project (www.i-marine.eu), the Networked Multimedia Information Systems Laboratory (NeMIS) of the CNR Institute of Information Science and Technology (ISTI) has developed a tool for generating probabilistic species distribution maps that are proved to closely involve the interest of both industrial and scientific communities. This tool exploits Cloud Computing techniques to compare systems able to calculate the probability that a certain species can thrive in a given ocean's area, i.e., the probability that such a zone corresponds to its ecological niche.Project(s): IMARINE
Castelli D, Candela L, Coro G, Pagano P, Sinibaldi F
A certain species' niche, i.e. those environmental conditions and resources which permit that species can survive and reproduce, is a basic piece of knowledge for monitoring ocean's populousness from both commercial point of view (to steer fishery activities to defined areas) and from the biological one (to monitor dying species). In the context of the European iMarine project (www.i-marine.eu), the Networked Multimedia Information Systems Laboratory (NeMIS) of the CNR Institute of Information Science and Technology (ISTI) has developed a tool for generating probabilistic species distribution maps that are proved to closely involve the interest of both industrial and scientific communities. This tool exploits Cloud Computing techniques to compare systems able to calculate the probability that a certain species can thrive in a given ocean's area, i.e., the probability that such a zone corresponds to its ecological niche.Project(s): IMARINE
See at:
CNR IRIS | www.edizioni.cnr.it | CNR IRIS
2013
Other
Open Access
Elaborazione di dati trasmessi da pescherecci = Processing of fishing vessel transmitted information
Castelli D, Candela L, Coro G, Pagano P
The over-exploitation of marine resources could lead to the extinction of some species and to the reduction of the food availability for several countries. In order to avoid such situation, a supervision of the fishing activity is required. In order to support this monitoring feature the Networked Multimedia Information Systems Laboratory (NeMIS) of the CNR Institute of Information Science and Technology (ISTI), in the context of the iMarine European project (www.i-marine.eu), has built an infrastructural service which is able to analyze the trajectories of a group of vessels, collected during a certain temporal range, and to classify activities point-by-point. In addition, This service supports a monitoring system with different resolutions for monthly fishing activity in the different areas of the oceans. It also allows to visualize on geographical maps the fishing activity effort and, consequently, the exploitation of some ocean zones.Project(s): IMARINE
Castelli D, Candela L, Coro G, Pagano P
The over-exploitation of marine resources could lead to the extinction of some species and to the reduction of the food availability for several countries. In order to avoid such situation, a supervision of the fishing activity is required. In order to support this monitoring feature the Networked Multimedia Information Systems Laboratory (NeMIS) of the CNR Institute of Information Science and Technology (ISTI), in the context of the iMarine European project (www.i-marine.eu), has built an infrastructural service which is able to analyze the trajectories of a group of vessels, collected during a certain temporal range, and to classify activities point-by-point. In addition, This service supports a monitoring system with different resolutions for monthly fishing activity in the different areas of the oceans. It also allows to visualize on geographical maps the fishing activity effort and, consequently, the exploitation of some ocean zones.Project(s): IMARINE
See at:
CNR IRIS | www.edizioni.cnr.it | CNR IRIS
2013
Conference article
Restricted
Deriving fishing monthly effort and caught species from vessel trajectories
Coro G, Fortunati L, Pagano P
Vessel Monitoring Systems (VMSs) are mainly meant to monitor and control fishing activity in ocean areas. VMSs have been realized and used in many ways, their principal aim being the monitoring, controlling and surveillance of fishing activity. The term VMS may refer to electronic or software systems aiming at those scopes. VMSs often rely on information transmitted by vessels during navigation, which are used both by security systems in ports and by scientists who want to extract information about oceans fishing exploitation. In this paper we present a semi-automatic method, to be used in a VMS, to calculate fishing monthly effort in ocean areas during a certain period. The method is based on a collection of vessels trajectories referring to that specific period. The monthly effort is calculated at 0.5 degrees resolution and is displayed on an interactive GIS map. Furthermore, the method produces a list of species that are likely to live in the most exploited areas. Thus, our method can be integrated by a VMS to understand the degree of exploitation of ocean areas and to estimate the species that are involved in a catch, which can help in limiting destructive fishing practices.DOI: 10.1109/oceans-bergen.2013.6607976
Project(s): D4SCIENCE, IMARINE
Metrics:
Coro G, Fortunati L, Pagano P
Vessel Monitoring Systems (VMSs) are mainly meant to monitor and control fishing activity in ocean areas. VMSs have been realized and used in many ways, their principal aim being the monitoring, controlling and surveillance of fishing activity. The term VMS may refer to electronic or software systems aiming at those scopes. VMSs often rely on information transmitted by vessels during navigation, which are used both by security systems in ports and by scientists who want to extract information about oceans fishing exploitation. In this paper we present a semi-automatic method, to be used in a VMS, to calculate fishing monthly effort in ocean areas during a certain period. The method is based on a collection of vessels trajectories referring to that specific period. The monthly effort is calculated at 0.5 degrees resolution and is displayed on an interactive GIS map. Furthermore, the method produces a list of species that are likely to live in the most exploited areas. Thus, our method can be integrated by a VMS to understand the degree of exploitation of ocean areas and to estimate the species that are involved in a catch, which can help in limiting destructive fishing practices.DOI: 10.1109/oceans-bergen.2013.6607976
Project(s): D4SCIENCE
, IMARINE Metrics:
2013
Journal article
Restricted
Combining simulated expert knowledge with Neural Networks to produce Ecological Niche Models for Latimeria chalumnae
Coro G, Pagano P, Ellenbroek A
The order Coelacanthiformes, once thought extinct, is much studied mainly because it contains species that share characteristics with lungfishes and tetrapods. Only a few years ago living specimens were discovered to science, and observations are so rare that the species are considered to be critically endangered. Observations include Latimeria chalumnae in deep waters of the coast of south eastern Africa while Latimeria menadoensis is known from similar habitats in Indonesian waters. Because of the interest around these enigmatic species, Ecological Niche Modelling techniques have been applied to estimate their distribution. The underlying assumption is that the environmental characteristics of the observation points are representative for the species. In this article we evaluate the difference in the output between the niche distributions produced by two expert systems and by two models based on Artificial Neural Networks. We evaluate the predictive behaviour of such models by focusing on L. chalumnae, as more observations are available for this species with respect to L. menadoensis. Finally, we assess the reliability of the maps by numerically evaluating the representativeness of the environmental characteristics in the observation locations, with respect to an area where the models show significant differences. This approach is different from previous ones because one of the expert systems is used to infer pseudo-absence points, that are successively employed to feed a Neural Network. One of the models based on this Neural Network is used to estimate the potential distribution and to produce a more extended map. The method promises to be applicable to other species with few observations, and allows to exploit the power of presence\absence based techniques.Source: ECOLOGICAL MODELLING, vol. 268, pp. 55-63
DOI: 10.1016/j.ecolmodel.2013.08.005
Project(s): IMARINE
Metrics:
Coro G, Pagano P, Ellenbroek A
The order Coelacanthiformes, once thought extinct, is much studied mainly because it contains species that share characteristics with lungfishes and tetrapods. Only a few years ago living specimens were discovered to science, and observations are so rare that the species are considered to be critically endangered. Observations include Latimeria chalumnae in deep waters of the coast of south eastern Africa while Latimeria menadoensis is known from similar habitats in Indonesian waters. Because of the interest around these enigmatic species, Ecological Niche Modelling techniques have been applied to estimate their distribution. The underlying assumption is that the environmental characteristics of the observation points are representative for the species. In this article we evaluate the difference in the output between the niche distributions produced by two expert systems and by two models based on Artificial Neural Networks. We evaluate the predictive behaviour of such models by focusing on L. chalumnae, as more observations are available for this species with respect to L. menadoensis. Finally, we assess the reliability of the maps by numerically evaluating the representativeness of the environmental characteristics in the observation locations, with respect to an area where the models show significant differences. This approach is different from previous ones because one of the expert systems is used to infer pseudo-absence points, that are successively employed to feed a Neural Network. One of the models based on this Neural Network is used to estimate the potential distribution and to produce a more extended map. The method promises to be applicable to other species with few observations, and allows to exploit the power of presence\absence based techniques.Source: ECOLOGICAL MODELLING, vol. 268, pp. 55-63
DOI: 10.1016/j.ecolmodel.2013.08.005
Project(s): IMARINE
Metrics:
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