A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca
Historical data sets from vast and relatively inaccessible areas are sources of potentially unique information still valuable for biodiversity studies today. In many research fields, ranging from climate change to projection of species loss, great efforts have been made to integrate historical data...
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Online Access: | http://hdl.handle.net/11567/897085 https://doi.org/10.1002/env.2462 |
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ftunivgenova:oai:iris.unige.it:11567/897085 2024-04-14T08:18:47+00:00 A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca C. Carota C. R. Nava C. Ghiglione S. Schiaparelli Carota, C. Nava, C. R. Ghiglione, C. Schiaparelli, S. 2017 ELETTRONICO http://hdl.handle.net/11567/897085 https://doi.org/10.1002/env.2462 eng eng Wiley info:eu-repo/semantics/altIdentifier/pmid/WOS:000417157600001 info:eu-repo/semantics/altIdentifier/wos/WOS:000417157600001 volume:28 (8) firstpage:e2462-1 lastpage:e2462-12 numberofpages:12 journal:ENVIRONMETRICS http://hdl.handle.net/11567/897085 doi:10.1002/env.2462 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85028778607 info:eu-repo/semantics/closedAccess Bayesian hierarchical GLMM Dirichlet process random effects opportunistic sampling schemes presence-only data Ross Sea Mollusca species richness info:eu-repo/semantics/article 2017 ftunivgenova https://doi.org/10.1002/env.2462 2024-03-21T02:27:43Z Historical data sets from vast and relatively inaccessible areas are sources of potentially unique information still valuable for biodiversity studies today. In many research fields, ranging from climate change to projection of species loss, great efforts have been made to integrate historical data sets with recent data to create databases that are as complete as possible. Unlocking the information contained in presence-only data, largely prevalent in such databases, presents a challenge for statistical modeling because of insidious observational errors due to the opportunistic nature of the data-gathering process. In this article, we propose an appropriate statistical method for the joint analysis of historical and newly collected presence-only data, that is, a Bayesian semiparametric generalized linear mixed model with Dirichlet process random effects. The potential of the method is illustrated by considering the Ross Sea section of the SOMBASE, an international compilation of Southern OceanMollusc distributional records, from 1899 to 2004 and beyond. Despite the presence of sampling bias and non detection errors, the proposedmodel draws latent information from the data, such that the resulting estimates of the parameters of interest not only are coherent with those obtained in indirectly related studies based on well-structured data but also suggest interesting ideas for further research. Article in Journal/Newspaper Ross Sea Università degli Studi di Genova: CINECA IRIS Ross Sea Environmetrics 28 8 e2462 |
institution |
Open Polar |
collection |
Università degli Studi di Genova: CINECA IRIS |
op_collection_id |
ftunivgenova |
language |
English |
topic |
Bayesian hierarchical GLMM Dirichlet process random effects opportunistic sampling schemes presence-only data Ross Sea Mollusca species richness |
spellingShingle |
Bayesian hierarchical GLMM Dirichlet process random effects opportunistic sampling schemes presence-only data Ross Sea Mollusca species richness C. Carota C. R. Nava C. Ghiglione S. Schiaparelli A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca |
topic_facet |
Bayesian hierarchical GLMM Dirichlet process random effects opportunistic sampling schemes presence-only data Ross Sea Mollusca species richness |
description |
Historical data sets from vast and relatively inaccessible areas are sources of potentially unique information still valuable for biodiversity studies today. In many research fields, ranging from climate change to projection of species loss, great efforts have been made to integrate historical data sets with recent data to create databases that are as complete as possible. Unlocking the information contained in presence-only data, largely prevalent in such databases, presents a challenge for statistical modeling because of insidious observational errors due to the opportunistic nature of the data-gathering process. In this article, we propose an appropriate statistical method for the joint analysis of historical and newly collected presence-only data, that is, a Bayesian semiparametric generalized linear mixed model with Dirichlet process random effects. The potential of the method is illustrated by considering the Ross Sea section of the SOMBASE, an international compilation of Southern OceanMollusc distributional records, from 1899 to 2004 and beyond. Despite the presence of sampling bias and non detection errors, the proposedmodel draws latent information from the data, such that the resulting estimates of the parameters of interest not only are coherent with those obtained in indirectly related studies based on well-structured data but also suggest interesting ideas for further research. |
author2 |
Carota, C. Nava, C. R. Ghiglione, C. Schiaparelli, S. |
format |
Article in Journal/Newspaper |
author |
C. Carota C. R. Nava C. Ghiglione S. Schiaparelli |
author_facet |
C. Carota C. R. Nava C. Ghiglione S. Schiaparelli |
author_sort |
C. Carota |
title |
A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca |
title_short |
A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca |
title_full |
A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca |
title_fullStr |
A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca |
title_full_unstemmed |
A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca |
title_sort |
bayesian semiparametric glmm for historical and newly collected presence-only data: an application to species richness of ross sea mollusca |
publisher |
Wiley |
publishDate |
2017 |
url |
http://hdl.handle.net/11567/897085 https://doi.org/10.1002/env.2462 |
geographic |
Ross Sea |
geographic_facet |
Ross Sea |
genre |
Ross Sea |
genre_facet |
Ross Sea |
op_relation |
info:eu-repo/semantics/altIdentifier/pmid/WOS:000417157600001 info:eu-repo/semantics/altIdentifier/wos/WOS:000417157600001 volume:28 (8) firstpage:e2462-1 lastpage:e2462-12 numberofpages:12 journal:ENVIRONMETRICS http://hdl.handle.net/11567/897085 doi:10.1002/env.2462 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85028778607 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1002/env.2462 |
container_title |
Environmetrics |
container_volume |
28 |
container_issue |
8 |
container_start_page |
e2462 |
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1796318334912823296 |