2017
Other
Open Access
Gibbs sampling with JAGS: behind the scenes
Coro G
Gibbs sampling is a Bayesian inference technique that is used in various scientific domains to generate samples from a certain posterior probability density function, given experimental data. Several software implementations of Gibbs sampling exist, which generally adopt very different approaches, because it is not easy to make a Gibbs sampling implementation exactly correspond to the theoretical approach. In particular, these implementations may use different approximation algorithms to and solutions to sub-steps of the Gibbs sampling process. Scientists working in different domains often use Gibbs sampling software without knowing the details of the implementation. Nevertheless, it is our experience that understanding the implementation can be crucial to enhance the performance of a model, because a software configuration conceived to help the underlying implementation may end in better approximation of the estimated probabilities functions. JAGS (Just Another Gibbs Sampler) is a widely used open-source implementation of Gibbs sampling. Its installation and user's guide are accurate, but do not indicate how the software really implements Gibbs sampling and it is not easy to infer this information from the source code. The aim of this paper is to give a high-level overview of the JAGS algorithms and its extensions that implement Gibbs sampling. Our target reader is a scientist who may want to understand the basic concepts underlying Bayesian inference and Gibbs sampling and who want to be aware of what happens behind the scenes when building a model.
Coro G
Gibbs sampling is a Bayesian inference technique that is used in various scientific domains to generate samples from a certain posterior probability density function, given experimental data. Several software implementations of Gibbs sampling exist, which generally adopt very different approaches, because it is not easy to make a Gibbs sampling implementation exactly correspond to the theoretical approach. In particular, these implementations may use different approximation algorithms to and solutions to sub-steps of the Gibbs sampling process. Scientists working in different domains often use Gibbs sampling software without knowing the details of the implementation. Nevertheless, it is our experience that understanding the implementation can be crucial to enhance the performance of a model, because a software configuration conceived to help the underlying implementation may end in better approximation of the estimated probabilities functions. JAGS (Just Another Gibbs Sampler) is a widely used open-source implementation of Gibbs sampling. Its installation and user's guide are accurate, but do not indicate how the software really implements Gibbs sampling and it is not easy to infer this information from the source code. The aim of this paper is to give a high-level overview of the JAGS algorithms and its extensions that implement Gibbs sampling. Our target reader is a scientist who may want to understand the basic concepts underlying Bayesian inference and Gibbs sampling and who want to be aware of what happens behind the scenes when building a model.
See at:
CNR IRIS 
| ISTI Repository | CNR IRIS 
2012
Software
Metadata Only Access
Statistical manager service
Gioia A, Coro G
The Statistical Manager (SM) is a cross usage service aiming at providing users and services with tools for performing data mining operations. The goal of this service is to offer a unique access for performing data mining or statistical operations on heterogeneous data. These can reside on client side in the form of csv files or they can be remotely hosted, possibly in a database. The Service is able to take inputs and execute the operation requested by a client invoking the suited computational infrastructure from a set of available possibilities. Executions can run on multi-core machines or on different computational infrastructures, like the D4Science, Windows Azure and other different private and commercial Cloud providers.Project(s): IMARINE
Gioia A, Coro G
The Statistical Manager (SM) is a cross usage service aiming at providing users and services with tools for performing data mining operations. The goal of this service is to offer a unique access for performing data mining or statistical operations on heterogeneous data. These can reside on client side in the form of csv files or they can be remotely hosted, possibly in a database. The Service is able to take inputs and execute the operation requested by a client invoking the suited computational infrastructure from a set of available possibilities. Executions can run on multi-core machines or on different computational infrastructures, like the D4Science, Windows Azure and other different private and commercial Cloud providers.Project(s): IMARINE
See at:
CNR IRIS | www.gcube-system.org
2012
Software
Metadata Only Access
Generic worker executor library.
Coro G
The Generic Worker Executor is a Java library used in the gCube framework. It can transform a gCube based e-Infrastructure into a distributed computational infrastructure. The GWEL can be installed on a gCube node and is able to execute scripts on the local machine and read commands from an Active Message Queue instance. The GWEL was developed by extending the interface of the gCube Executor component.Project(s): IMARINE
Coro G
The Generic Worker Executor is a Java library used in the gCube framework. It can transform a gCube based e-Infrastructure into a distributed computational infrastructure. The GWEL can be installed on a gCube node and is able to execute scripts on the local machine and read commands from an Active Message Queue instance. The GWEL was developed by extending the interface of the gCube Executor component.Project(s): IMARINE
See at:
CNR IRIS | www.gcube-system.org
2012
Software
Metadata Only Access
Ecological Engine Library
Coro G
Ecological Engine is a Java library which allows to develop and run algorithms for performing Ecological Modeling experiments. The term Ecological Modeling indicates a set of functionalities for managing complex phenomena in order, for example, to predict the impact of climate changes on biodiversity, prevent the spread of invasive species or disease, help in ecosystems conservation planning. The Ecological Engine allows to dynamically add new algorithms as plug-ins. The algorithms can be executed as sequential or multi-thread computations.Project(s): IMARINE
Coro G
Ecological Engine is a Java library which allows to develop and run algorithms for performing Ecological Modeling experiments. The term Ecological Modeling indicates a set of functionalities for managing complex phenomena in order, for example, to predict the impact of climate changes on biodiversity, prevent the spread of invasive species or disease, help in ecosystems conservation planning. The Ecological Engine allows to dynamically add new algorithms as plug-ins. The algorithms can be executed as sequential or multi-thread computations.Project(s): IMARINE
See at:
CNR IRIS | www.gcube-system.org
2012
Software
Metadata Only Access
Environment Explorer Library
Coro G
The Environment Explorer Library is a Java library for exploring environmental G.I.S. layers. It addresses geographic layers containing physical or chemical characteristics of the world, which can be stored on a Geo Server or on a Thredds Service. The library is able to retrieve the values of NetCDF files residing on Thredds associated to some coordinates, or to get the values of an environmental layer residing on a Geo Server. Furthermore it manages the indexing procedure of those elements on a Geo Network Server, from which it can retrieve information about files or layers locations. The library is part of the gCube framework.Project(s): IMARINE
Coro G
The Environment Explorer Library is a Java library for exploring environmental G.I.S. layers. It addresses geographic layers containing physical or chemical characteristics of the world, which can be stored on a Geo Server or on a Thredds Service. The library is able to retrieve the values of NetCDF files residing on Thredds associated to some coordinates, or to get the values of an environmental layer residing on a Geo Server. Furthermore it manages the indexing procedure of those elements on a Geo Network Server, from which it can retrieve information about files or layers locations. The library is part of the gCube framework.Project(s): IMARINE
See at:
CNR IRIS | www.gcube-system.org
2012
Software
Metadata Only Access
Times Series Geo-Tools and Vessel Information analysis library
Coro G
Time Series Geo Tools is a java library for transforming a Time Series of observations about fishery catch statistics into a geographical map to be published by a GeoServer software. Furthermore it is able to manage and perform classification on Vessels Activity in the sea and to produce maps for displaying their trajectories and for calculating the fishing monthly effort at 0.5 degree resolutionProject(s): D4SCIENCE-II
Coro G
Time Series Geo Tools is a java library for transforming a Time Series of observations about fishery catch statistics into a geographical map to be published by a GeoServer software. Furthermore it is able to manage and perform classification on Vessels Activity in the sea and to produce maps for displaying their trajectories and for calculating the fishing monthly effort at 0.5 degree resolutionProject(s): D4SCIENCE-II
See at:
CNR IRIS | www.gcube-system.org
2012
Other
Open Access
Imarine D9.1 - Imarine Data management software
Coro G, De Faveri F, Lelii L
This deliverable describes the novelties within the iMarine Data Management Software up to M6 (Apr. '12), which include: Data Access, Data Transfer, Assessment,Harmonization and Certification facilities.Project(s): IMARINE
Coro G, De Faveri F, Lelii L
This deliverable describes the novelties within the iMarine Data Management Software up to M6 (Apr. '12), which include: Data Access, Data Transfer, Assessment,Harmonization and Certification facilities.Project(s): IMARINE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
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
2012
Other
Open Access
iMarine D10.4 - iMarine Data Consumption Software (1.0)
Antoniadis A, Brito F, Coro G, Gerbesiotis J, Marketakis Y
This document describes the novelties within the iMarine Data Consumption Software which were achieved from the 7th to the 12th month of the project and provide pointers to the documentation and artifacts of the related components.Project(s): IMARINE
Antoniadis A, Brito F, Coro G, Gerbesiotis J, Marketakis Y
This document describes the novelties within the iMarine Data Consumption Software which were achieved from the 7th to the 12th month of the project and provide pointers to the documentation and artifacts of the related components.Project(s): IMARINE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2014
Other
Open Access
iMarine - iMarine Data Consumption Software
Antoniadis A, Brito F, Coro G, Gerbesiotis J, Laskaris N, Marketakis Y
The iMarine Data Management Software comprises a number of components and subsystems offering facilities for accessing, transferring and harmonising a rich array of data typologies. This document describes the novelties within the iMarine Data Management Software from M12 (Oct.'12) to M27 (Jan.'14). It complements D9.2 [10] which describes iMarine Data Management Software up to M11 (Sept.'12). This deliverable is intended for documentation purposes only. The actual deliverable is represented by the software artifacts and their accompanying documentation.Project(s): IMARINE
Antoniadis A, Brito F, Coro G, Gerbesiotis J, Laskaris N, Marketakis Y
The iMarine Data Management Software comprises a number of components and subsystems offering facilities for accessing, transferring and harmonising a rich array of data typologies. This document describes the novelties within the iMarine Data Management Software from M12 (Oct.'12) to M27 (Jan.'14). It complements D9.2 [10] which describes iMarine Data Management Software up to M11 (Sept.'12). This deliverable is intended for documentation purposes only. The actual deliverable is represented by the software artifacts and their accompanying documentation.Project(s): IMARINE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2011
Software
Metadata Only Access
Ecological Modelling Library for gCube VRE
Coro G
The Ecological Modelling library supplies the gCube platform with algorithms for calculating the probability distributions of living species across the world. Data mining and machine learning algorithms as well as expert systems are included for performing implicit or explicit modelling.Project(s): D4SCIENCE-II
Coro G
The Ecological Modelling library supplies the gCube platform with algorithms for calculating the probability distributions of living species across the world. Data mining and machine learning algorithms as well as expert systems are included for performing implicit or explicit modelling.Project(s): D4SCIENCE-II
See at:
CNR IRIS | www.gcube-system.org
2011
Software
Metadata Only Access
Statistical Tools for GCube Platform
Coro G
The Statistical Library is a support library for the gCube platform, which supplies Data Mining functionalities for performing lexical comparisons among user's data as well as data analysis and prevision. Machine Learning algorithms are included with parallel processing procedures, in order to give support for statistical data management and classification.Project(s): D4SCIENCE-II
Coro G
The Statistical Library is a support library for the gCube platform, which supplies Data Mining functionalities for performing lexical comparisons among user's data as well as data analysis and prevision. Machine Learning algorithms are included with parallel processing procedures, in order to give support for statistical data management and classification.Project(s): D4SCIENCE-II
See at:
CNR IRIS
2014
Journal article
Restricted
Revisiting safe biological limits in fisheries
Froese R, Coro G, Kleisner K, Demirel N
Fisheries management reference points used for stocks in the Northeast Atlantic were investigated as to the appropriateness of their current levels based on three practical limits of exploitation in fisheries management: (i) the smallest sustainable size of the fished stock (SSBpa), (ii) the maximum sustainable rate of exploitation (Fmsy), and (iii) the age at maturity, i.e., the smallest body size of captured fish that still allows for individual reproduction. SSBpa is a widely used reference point for low population size. In 46% of the examined stocks, the official value for this reference point was found to be below the consensus estimates determined from three different methods. Additionally, the natural rate of mortality M is widely regarded as an upper limit for sustainable fishing pressure (Fmsy) that can produce the maximum sustainable yield (MSY). However, the official estimates of Fmsy exceeded the rate of natural mortality in 76% of the stocks. Finally, there is wide agreement that age at maturity is a lower limit for age at first capture. However, age at first capture was below maturity in 74% of the stocks. No official estimates of the stock size (SSBmsy) that can produce MSY are available for the Northeast Atlantic. However, an analysis of stocks from other areas confirmed that twice SSBpa provides a reasonable preliminary estimate. Using this proxy with Northeast Atlantic stock sizes in 2013 showed that 88% were below the level that can produce MSY. Also, 54% of the stocks were outside of safe biological limits and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 72% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the Northeast Atlantic in order to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture.Source: FISH AND FISHERIES (OXF., ONLINE)
Project(s): IMARINE
Froese R, Coro G, Kleisner K, Demirel N
Fisheries management reference points used for stocks in the Northeast Atlantic were investigated as to the appropriateness of their current levels based on three practical limits of exploitation in fisheries management: (i) the smallest sustainable size of the fished stock (SSBpa), (ii) the maximum sustainable rate of exploitation (Fmsy), and (iii) the age at maturity, i.e., the smallest body size of captured fish that still allows for individual reproduction. SSBpa is a widely used reference point for low population size. In 46% of the examined stocks, the official value for this reference point was found to be below the consensus estimates determined from three different methods. Additionally, the natural rate of mortality M is widely regarded as an upper limit for sustainable fishing pressure (Fmsy) that can produce the maximum sustainable yield (MSY). However, the official estimates of Fmsy exceeded the rate of natural mortality in 76% of the stocks. Finally, there is wide agreement that age at maturity is a lower limit for age at first capture. However, age at first capture was below maturity in 74% of the stocks. No official estimates of the stock size (SSBmsy) that can produce MSY are available for the Northeast Atlantic. However, an analysis of stocks from other areas confirmed that twice SSBpa provides a reasonable preliminary estimate. Using this proxy with Northeast Atlantic stock sizes in 2013 showed that 88% were below the level that can produce MSY. Also, 54% of the stocks were outside of safe biological limits and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 72% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the Northeast Atlantic in order to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture.Source: FISH AND FISHERIES (OXF., ONLINE)
Project(s): IMARINE
2017
Journal article
Open Access
Estimating fisheries reference points from catch and resilience
Froese R, Demirel N, Coro G, Kleisner K, Winker H
This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data-limited stocks. It also presents a Bayesian state-space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch-per-unit-of-effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data-limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock-recruitment model, accounting for reduced recruitment at severely depleted stock sizes.Source: FISH AND FISHERIES (OXF., ONLINE), vol. 18 (issue 3), pp. 506-526
Project(s): BlueBRIDGE
Froese R, Demirel N, Coro G, Kleisner K, Winker H
This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data-limited stocks. It also presents a Bayesian state-space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch-per-unit-of-effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data-limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock-recruitment model, accounting for reduced recruitment at severely depleted stock sizes.Source: FISH AND FISHERIES (OXF., ONLINE), vol. 18 (issue 3), pp. 506-526
Project(s): BlueBRIDGE
See at:
CNR IRIS | onlinelibrary.wiley.com | ISTI Repository | CNR IRIS | CNR IRIS
2016
Other
Open Access
BlueBRIDGE - Blue skills activity: interim report
Barde J, Coro G, Davies A, Eleni P
The goal of Blue Skills is to develop and deploy Virtual Research Environments (VREs) essential for boosting education and knowledge bridging between research and innovation in the area of protection and management of marine resources. In order to achieve this goal, plans have been laid for real courses, both with physical attendance and online courses, which have been, and continue to be implemented throughout the project span. The course programme is expected to evolve during the project lifetime, e.g. new courses to be supported can be constantly identified. The initial list has been produced in October 2015 (MS4 Blue Skills VRE Plan) and whenever a course to be supported is identified it is recorded through dedicated tickets. The up to date list of target courses is always available by an automatic report available at https://support.d4science.org/issues/684. Depending on the characteristics of each course either a new Virtual Laboratory can be created or one or more existing ones can be re-used to support the activity of course participants. In the reporting period, 15 courses took place with the support of 7 dedicated VLabs. These courses were attended by more than 335 participants in total.Project(s): BlueBRIDGE
Barde J, Coro G, Davies A, Eleni P
The goal of Blue Skills is to develop and deploy Virtual Research Environments (VREs) essential for boosting education and knowledge bridging between research and innovation in the area of protection and management of marine resources. In order to achieve this goal, plans have been laid for real courses, both with physical attendance and online courses, which have been, and continue to be implemented throughout the project span. The course programme is expected to evolve during the project lifetime, e.g. new courses to be supported can be constantly identified. The initial list has been produced in October 2015 (MS4 Blue Skills VRE Plan) and whenever a course to be supported is identified it is recorded through dedicated tickets. The up to date list of target courses is always available by an automatic report available at https://support.d4science.org/issues/684. Depending on the characteristics of each course either a new Virtual Laboratory can be created or one or more existing ones can be re-used to support the activity of course participants. In the reporting period, 15 courses took place with the support of 7 dedicated VLabs. These courses were attended by more than 335 participants in total.Project(s): BlueBRIDGE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2019
Journal article
Open Access
A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning
Magliozzi C, Coro G, Grabowski R, Packman Ai, Krause S
The hyporheic zone (HZ) is an area of interaction between surface and ground waters present in and around river beds. Bidirectional mixing within the HZ, termed hyporheic exchange flow (HEF), plays significant roles in nutrient transport, organic matter and biogeochemical processing in rivers. The functional importance of the HZ in river ecology and hydrology suggests that river managers should consider the HZ in their planning to help compromised systems recover. However, current river restoration planning tools do not take into account the HZ. This paper describes a novel multiscale, transferable method that combines existing environmental information at different spatial scales to identify areas with potentially significant HEF for use in restoration prioritization and planning. It uses a deductive approach that is suited for data-poor case studies, which is common for most rivers, given the very limited data on the spatial occurrence of areas of hyporheic exchange. Results on nine contrasting European rivers, demonstrate its potential to inform river management.Source: ENVIRONMENTAL MODELLING & SOFTWARE, vol. 111, pp. 311-323
Project(s): BlueBRIDGE
Magliozzi C, Coro G, Grabowski R, Packman Ai, Krause S
The hyporheic zone (HZ) is an area of interaction between surface and ground waters present in and around river beds. Bidirectional mixing within the HZ, termed hyporheic exchange flow (HEF), plays significant roles in nutrient transport, organic matter and biogeochemical processing in rivers. The functional importance of the HZ in river ecology and hydrology suggests that river managers should consider the HZ in their planning to help compromised systems recover. However, current river restoration planning tools do not take into account the HZ. This paper describes a novel multiscale, transferable method that combines existing environmental information at different spatial scales to identify areas with potentially significant HEF for use in restoration prioritization and planning. It uses a deductive approach that is suited for data-poor case studies, which is common for most rivers, given the very limited data on the spatial occurrence of areas of hyporheic exchange. Results on nine contrasting European rivers, demonstrate its potential to inform river management.Source: ENVIRONMENTAL MODELLING & SOFTWARE, vol. 111, pp. 311-323
Project(s): BlueBRIDGE
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | CNR IRIS | CNR IRIS
2019
Conference article
Open Access
Distinguishing Violinists and Pianists Based on Their Brain Signals
Coro G, Masetti G, Bonhoeffer P, Betcher M
Many studies in neuropsychology have highlighted that expert musicians, who started learning music in childhood, present structural differences in their brains with respect to non-musicians. This indicates that early music learning affects the development of the brain. Also, musicians' neuronal activity is different depending on the played instrument and on the expertise. This difference can be analysed by processing electroencephalographic (EEG) signals through Artificial Intelligence models. This paper explores the feasibility to build an automatic model that distinguishes violinists from pianists based only on their brain signals. To this aim, EEG signals of violinists and pianists are recorded while they play classical music pieces and an Artificial Neural Network is trained through a cloud computing platform to build a binary classifier of segments of these signals. Our model has the best classification performance on 20 seconds EEG segments, but this performance depends on the involved musicians' expertise. Also, the brain signals of a cellist are demonstrated to be more similar to violinists' signals than to pianists' signals. In summary, this paper demonstrates that distinctive information is present in the two types of musicians' brain signals, and that this information can be detected even by an automatic model working with a basic EEG equipment.
Coro G, Masetti G, Bonhoeffer P, Betcher M
Many studies in neuropsychology have highlighted that expert musicians, who started learning music in childhood, present structural differences in their brains with respect to non-musicians. This indicates that early music learning affects the development of the brain. Also, musicians' neuronal activity is different depending on the played instrument and on the expertise. This difference can be analysed by processing electroencephalographic (EEG) signals through Artificial Intelligence models. This paper explores the feasibility to build an automatic model that distinguishes violinists from pianists based only on their brain signals. To this aim, EEG signals of violinists and pianists are recorded while they play classical music pieces and an Artificial Neural Network is trained through a cloud computing platform to build a binary classifier of segments of these signals. Our model has the best classification performance on 20 seconds EEG segments, but this performance depends on the involved musicians' expertise. Also, the brain signals of a cellist are demonstrated to be more similar to violinists' signals than to pianists' signals. In summary, this paper demonstrates that distinctive information is present in the two types of musicians' brain signals, and that this information can be detected even by an automatic model working with a basic EEG equipment.
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2019
Journal article
Open Access
Estimating stock status from relative abundance and resilience
Froese R, Winker H, Coro G, Demirel N, Tsikliras Ac, Dimarchopoulou D, Scarcella G, Deng Palomares Ml, Dureuil M, Pauly D
The Law of the Sea and regional and national laws and agreements require exploited populations or stocks to be managed so that they can produce maximum sustainable yields. However, exploitation level and stock status are unknown for most stocks because the data required for full stock assessments are missing. This study presents a new method [abundance maximum sustainable yields (AMSY)] that estimates relative population size when no catch data are available using time series of catch-per-unit-effort or other relative abundance indices as the main input. AMSY predictions for relative stock size were not significantly different from the "true" values when compared with simulated data. Also, they were not significantly different from relative stock size estimated by data-rich models in 88% of the comparisons within 140 real stocks. Application of AMSY to 38 data-poor stocks showed the suitability of the method and led to the first assessments for 23 species. Given the lack of catch data as input, AMSY estimates of exploitation come with wide margins of uncertainty, which may not be suitable for management. However, AMSY seems to be well suited for estimating productivity as well as relative stock size and may, therefore, aid in the management of data-poor stocks.Source: ICES JOURNAL OF MARINE SCIENCE, vol. 77 (issue 2), pp. 527-538
Froese R, Winker H, Coro G, Demirel N, Tsikliras Ac, Dimarchopoulou D, Scarcella G, Deng Palomares Ml, Dureuil M, Pauly D
The Law of the Sea and regional and national laws and agreements require exploited populations or stocks to be managed so that they can produce maximum sustainable yields. However, exploitation level and stock status are unknown for most stocks because the data required for full stock assessments are missing. This study presents a new method [abundance maximum sustainable yields (AMSY)] that estimates relative population size when no catch data are available using time series of catch-per-unit-effort or other relative abundance indices as the main input. AMSY predictions for relative stock size were not significantly different from the "true" values when compared with simulated data. Also, they were not significantly different from relative stock size estimated by data-rich models in 88% of the comparisons within 140 real stocks. Application of AMSY to 38 data-poor stocks showed the suitability of the method and led to the first assessments for 23 species. Given the lack of catch data as input, AMSY estimates of exploitation come with wide margins of uncertainty, which may not be suitable for management. However, AMSY seems to be well suited for estimating productivity as well as relative stock size and may, therefore, aid in the management of data-poor stocks.Source: ICES JOURNAL OF MARINE SCIENCE, vol. 77 (issue 2), pp. 527-538
See at:
academic.oup.com | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2019
Journal article
Open Access
On the pile-up effect and priors for Linf and M/K: response to a comment by Hordyk et al. on "A new approach for estimating stock status from length frequency data"
Froese R, Winker H, Coro G, Demirel N, Tsikliras Ac, Dimarchopoulou D, Scarcella G, Probst Wn, Dureuil M, Pauly D
There is a recognized need for new methods with modest data requirements to provide preliminary estimates of stock status for data-limited stocks (e.g. Rudd and Thorson, 2018). Froese et al. (2018)provide such a method, which derives estimates of relative stock size from length frequency (LF) data of exploited stocks. They show that their length-based Bayesian biomass estimation method (LBB) can reproduce the "true" parameters used in simulated data and can approximate the relative stock size as estimated independently by more data-demanding methods in 34 real stocks. However, in a comment on LBB, Hordyk et al. (2019) claim (i) that the master equation of LBB is incomplete because it does not correct for the pile-up effect caused by aggregating length measurements into length classes or "bins", (ii) that LBB is highly sensitive to equilibrium assumptions and wrongly uses maximum observed length (Lmax) for guidance in setting a prior for the estimation of asymptotic length (Linf), and (iii) that the default prior used by LBB for the ratio between natural mortality and somatic growth rate (M/K) of 1.5 (SD = 0.15) is inadequate for many exploited species.Source: ICES JOURNAL OF MARINE SCIENCE, vol. 76 (issue 2), pp. 461-465
Froese R, Winker H, Coro G, Demirel N, Tsikliras Ac, Dimarchopoulou D, Scarcella G, Probst Wn, Dureuil M, Pauly D
There is a recognized need for new methods with modest data requirements to provide preliminary estimates of stock status for data-limited stocks (e.g. Rudd and Thorson, 2018). Froese et al. (2018)provide such a method, which derives estimates of relative stock size from length frequency (LF) data of exploited stocks. They show that their length-based Bayesian biomass estimation method (LBB) can reproduce the "true" parameters used in simulated data and can approximate the relative stock size as estimated independently by more data-demanding methods in 34 real stocks. However, in a comment on LBB, Hordyk et al. (2019) claim (i) that the master equation of LBB is incomplete because it does not correct for the pile-up effect caused by aggregating length measurements into length classes or "bins", (ii) that LBB is highly sensitive to equilibrium assumptions and wrongly uses maximum observed length (Lmax) for guidance in setting a prior for the estimation of asymptotic length (Linf), and (iii) that the default prior used by LBB for the ratio between natural mortality and somatic growth rate (M/K) of 1.5 (SD = 0.15) is inadequate for many exploited species.Source: ICES JOURNAL OF MARINE SCIENCE, vol. 76 (issue 2), pp. 461-465
See at:
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2020
Journal article
Open Access
Predicting geographical suitability of geothermal power plants
Coro G, Trumpy E
A large and increasing number of countries use geothermal energy as power source for domestic and industrial applications. Geothermal power plants produce energy out of this natural and renewable source in a sustainable way and contribute to reduce global warming. However, power plants effectiveness depends on the suitability of an area to geothermal energy production, which is a complex and unknown combination of many environmental factors. Nowadays, geothermal suitability assessments require invasive inspections, high costs, and legal permissions. Thus, having a global suitability map of geothermal sites as reference would be useful prior knowledge during assessments, and would help saving time and money. In this paper, the first suitability map of potential geothermal sites at global scale is presented. The map is the result of the application of data collection and preparation processes, and a Maximum Entropy model, to geospatial data potentially correlated with geothermal site suitability and geothermal plants operation. The reliability of our map is assessed against currently active and planned geothermal power plants. Our approach follows the Open Science paradigm that guarantees results reproduction and transparency, and allows stakeholders to reuse the produced standardised data, services, and Web interfaces in other experiments or to generate new maps at regional scale. Overall, our results can help scientists, industry operators, and policy makers in geothermal sites assessments. Also, our approach supports communication with citizens whose territories are involved in probing and assessments, in order to transparently inform them about the reasons driving the selection of their territory and the potential future benefits.Source: JOURNAL OF CLEANER PRODUCTION
Coro G, Trumpy E
A large and increasing number of countries use geothermal energy as power source for domestic and industrial applications. Geothermal power plants produce energy out of this natural and renewable source in a sustainable way and contribute to reduce global warming. However, power plants effectiveness depends on the suitability of an area to geothermal energy production, which is a complex and unknown combination of many environmental factors. Nowadays, geothermal suitability assessments require invasive inspections, high costs, and legal permissions. Thus, having a global suitability map of geothermal sites as reference would be useful prior knowledge during assessments, and would help saving time and money. In this paper, the first suitability map of potential geothermal sites at global scale is presented. The map is the result of the application of data collection and preparation processes, and a Maximum Entropy model, to geospatial data potentially correlated with geothermal site suitability and geothermal plants operation. The reliability of our map is assessed against currently active and planned geothermal power plants. Our approach follows the Open Science paradigm that guarantees results reproduction and transparency, and allows stakeholders to reuse the produced standardised data, services, and Web interfaces in other experiments or to generate new maps at regional scale. Overall, our results can help scientists, industry operators, and policy makers in geothermal sites assessments. Also, our approach supports communication with citizens whose territories are involved in probing and assessments, in order to transparently inform them about the reasons driving the selection of their territory and the potential future benefits.Source: JOURNAL OF CLEANER PRODUCTION
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