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Bayesian Methods to Analyze Historical Collections in Time and Space: A Case Study Using Cabo Verde Endemic Flora

2020Journal article Open access
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dc.contributor.authorRomeiras, Maria M.
dc.contributor.authorCarine, Mark
dc.contributor.authorDuarte, Maria Cristina
dc.contributor.authorCatarino, Silvia
dc.contributor.authorDias, Filipe S.
dc.contributor.authorBorda-de-Água, Luís
dc.date.accessioned2020-12-10T13:00:05Z
dc.date.available2020-12-10T13:00:05Z
dc.date.issued2020
dc.description.abstractBiological collections, including herbarium specimens, are unique sources of biodiversity data presenting a window on the history of the development and accumulation of knowledge of a specific geographical region. Understanding how the process of discovery impacts that knowledge is particularly important for oceanic islands which are often characterized by both high levels of endemic diversity and high proportions of threatened taxa. The archipelagos of the Macaronesian region (i.e. Azores, Canaries, Savages, Madeira, and Cabo Verde) have been the focus of attention for scientific expeditions since the end of the 17th century. However, there is no integrated study describing the historical process of collecting, discovery and description of its flora. Using as a case study the Cabo Verde endemic angiosperm flora, we review the history of collecting in the flora and apply a Bayesian approach to assess the accumulation of species discovery, through time and space across the nine islands of the archipelago. Our results highlight the central role not only of natural characteristics (e.g. area, age, maximum altitude and average value of the terrain ruggedness index) but also historical factors (i.e. the location of major harbors) for the development of knowledge of the flora. The main factors that have determined the process of species description in the archipelago and how this impact our understanding of diversity patterns across archipelagos are discussed.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRomeiras, M.M., Carine, M., Duarte, M.C., Catarino, S., Dias, F.S. & Borda-de-Água, L. (2020) Bayesian methods to analyze historical collections in time and space: a case study using Cabo Verde endemic flora. Frontiers in Plant Science, 11, 278. DOI:10.3389/fpls.2020.00278pt_PT
dc.identifier.doi10.3389/fpls.2020.00278pt_PT
dc.identifier.urihttp://hdl.handle.net/10451/45235
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherFrontierspt_PT
dc.relationFundação para a Ciência e Tecnologia (FCT) and Aga Khan Development Network (AKDN) under the project CVAgrobiodiversity/333111699pt_PT
dc.relationLBÁ was supported through Portuguese national funds through FCT, I.P., under the Norma Transitória L57/2016/CP1440/CT0022pt_PT
dc.relationFCT POCI-01-0145-FEDER-028729pt_PT
dc.relationConservation planning in tropical regions under climate change: a case-study of Angola flora.
dc.relationUID/BIA/00329/2019 (cE3c)pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.titleBayesian Methods to Analyze Historical Collections in Time and Space: A Case Study Using Cabo Verde Endemic Florapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleConservation planning in tropical regions under climate change: a case-study of Angola flora.
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F120054%2F2016/PT
oaire.citation.startPage278pt_PT
oaire.citation.titleFrontiers in Plant Sciencept_PT
oaire.citation.volume11pt_PT
person.familyNameDuarte
person.givenNameMaria Cristina
person.identifierL-7571-2013
person.identifier.ciencia-id351A-A358-7D49
person.identifier.orcid0000-0002-3823-4369
person.identifier.scopus-author-id15841302300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication51b41b16-07ee-4b65-a009-f30e17afed9c
relation.isAuthorOfPublication.latestForDiscovery51b41b16-07ee-4b65-a009-f30e17afed9c
relation.isProjectOfPublication2bde2522-772f-416a-a338-ed7add6d182c
relation.isProjectOfPublication.latestForDiscovery2bde2522-772f-416a-a338-ed7add6d182c

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