Statistical models for species richness in the Ross Sea
In recent years, a large international effort has been placed in compiling a complete list of Antarctic mollusc distributional records based both on historical occurrences, dating back to 1899, and on newly collected data. Such dataset is highly asymmetrical in the quality of contained information,...
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Alessandro Fassò and Alessio Pollice
2015
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ftunivtorino:oai:iris.unito.it:2318/1521392 2023-10-25T01:30:38+02:00 Statistical models for species richness in the Ross Sea CAROTA, Cinzia NAVA, CONSUELO RUBINA Soldani, I. Schiaparelli, S. C. Ghiglione A. Fassò and A. Pollice Carota, C. Nava, C. R. Soldani, I. Schiaparelli, S. C. Ghiglione 2015 http://hdl.handle.net/2318/1521392 http://www.graspa.org/wp-content/uploads/2015/06/Graspa2015_Proceedings.pdf eng eng Alessandro Fassò and Alessio Pollice country:ITA place:Bari ispartofbook:Proceedings of the GRASPA2015 Conference GRASPA firstpage:49 lastpage:52 numberofpages:4 serie:GRASPA WORKING PAPERS http://hdl.handle.net/2318/1521392 http://www.graspa.org/wp-content/uploads/2015/06/Graspa2015_Proceedings.pdf info:eu-repo/semantics/openAccess Bayesian hierarchical model Dirichlet Proce GAM GLMM Ross Sea info:eu-repo/semantics/conferenceObject 2015 ftunivtorino 2023-09-26T22:26:07Z In recent years, a large international effort has been placed in compiling a complete list of Antarctic mollusc distributional records based both on historical occurrences, dating back to 1899, and on newly collected data. Such dataset is highly asymmetrical in the quality of contained information, due to the variety of sampling gears used and the amount of information recorded at each sampling station (e.g. sampling gear used, sieve mesh size used, etc.). This dataset stimulates to deploy all statistical potential in terms of data representation, estimation, clusterization and prediction. In this paper we aim at selecting an appropriate statistical model for this dataset in order to explain species richness (i.e. the number of observed species) as a function of several covariates, such as gear used, latitude, etc. Given the nature of data, we preliminary implement a Poisson regression model and we extend it with a Negative Binomial regression to manage over-dispersion. Generalized linear mixed models (GLMM) and generalized additive models (GAM) are also explored to capture a possible extra explicative power of the covariates. However, preliminary results under them suggest that more sophisticated models are needed. Therefore, we introduce a hierarchical Bayesian model, involving a nonparametric approach through the assumption of random effects with a Dirichlet Process prior. Conference Object Antarc* Antarctic Ross Sea Università degli studi di Torino: AperTo (Archivio Istituzionale ad Accesso Aperto) Antarctic Ross Sea Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
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Open Polar |
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Università degli studi di Torino: AperTo (Archivio Istituzionale ad Accesso Aperto) |
op_collection_id |
ftunivtorino |
language |
English |
topic |
Bayesian hierarchical model Dirichlet Proce GAM GLMM Ross Sea |
spellingShingle |
Bayesian hierarchical model Dirichlet Proce GAM GLMM Ross Sea CAROTA, Cinzia NAVA, CONSUELO RUBINA Soldani, I. Schiaparelli, S. C. Ghiglione Statistical models for species richness in the Ross Sea |
topic_facet |
Bayesian hierarchical model Dirichlet Proce GAM GLMM Ross Sea |
description |
In recent years, a large international effort has been placed in compiling a complete list of Antarctic mollusc distributional records based both on historical occurrences, dating back to 1899, and on newly collected data. Such dataset is highly asymmetrical in the quality of contained information, due to the variety of sampling gears used and the amount of information recorded at each sampling station (e.g. sampling gear used, sieve mesh size used, etc.). This dataset stimulates to deploy all statistical potential in terms of data representation, estimation, clusterization and prediction. In this paper we aim at selecting an appropriate statistical model for this dataset in order to explain species richness (i.e. the number of observed species) as a function of several covariates, such as gear used, latitude, etc. Given the nature of data, we preliminary implement a Poisson regression model and we extend it with a Negative Binomial regression to manage over-dispersion. Generalized linear mixed models (GLMM) and generalized additive models (GAM) are also explored to capture a possible extra explicative power of the covariates. However, preliminary results under them suggest that more sophisticated models are needed. Therefore, we introduce a hierarchical Bayesian model, involving a nonparametric approach through the assumption of random effects with a Dirichlet Process prior. |
author2 |
A. Fassò and A. Pollice Carota, C. Nava, C. R. Soldani, I. Schiaparelli, S. C. Ghiglione |
format |
Conference Object |
author |
CAROTA, Cinzia NAVA, CONSUELO RUBINA Soldani, I. Schiaparelli, S. C. Ghiglione |
author_facet |
CAROTA, Cinzia NAVA, CONSUELO RUBINA Soldani, I. Schiaparelli, S. C. Ghiglione |
author_sort |
CAROTA, Cinzia |
title |
Statistical models for species richness in the Ross Sea |
title_short |
Statistical models for species richness in the Ross Sea |
title_full |
Statistical models for species richness in the Ross Sea |
title_fullStr |
Statistical models for species richness in the Ross Sea |
title_full_unstemmed |
Statistical models for species richness in the Ross Sea |
title_sort |
statistical models for species richness in the ross sea |
publisher |
Alessandro Fassò and Alessio Pollice |
publishDate |
2015 |
url |
http://hdl.handle.net/2318/1521392 http://www.graspa.org/wp-content/uploads/2015/06/Graspa2015_Proceedings.pdf |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Antarctic Ross Sea Gam |
geographic_facet |
Antarctic Ross Sea Gam |
genre |
Antarc* Antarctic Ross Sea |
genre_facet |
Antarc* Antarctic Ross Sea |
op_relation |
ispartofbook:Proceedings of the GRASPA2015 Conference GRASPA firstpage:49 lastpage:52 numberofpages:4 serie:GRASPA WORKING PAPERS http://hdl.handle.net/2318/1521392 http://www.graspa.org/wp-content/uploads/2015/06/Graspa2015_Proceedings.pdf |
op_rights |
info:eu-repo/semantics/openAccess |
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1780741138373672960 |