funspace: An R package to build, analyse and plot functional trait spaces
Aim Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes‐troublesome steps needed to build and analyse functional trait spaces. Innovation To fill this...
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Veröffentlicht in: | Diversity & distributions 2024-04, Vol.30 (4), p.1-14 |
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creator | Carmona, Carlos P. Pavanetto, Nicola Puglielli, Giacomo |
description | Aim
Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes‐troublesome steps needed to build and analyse functional trait spaces.
Innovation
To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate functional trait space analyses. The six functions that constitute the package can be grouped in three modules: ‘Building and exploring’, ‘Mapping’ and ‘Plotting’. The building and exploring module defines the main features of a functional trait space (e.g. functional diversity metrics) by leveraging kernel density‐based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and publication‐ready figures representing the outputs obtained from previous modules. We provide a worked example to demonstrate a complete funspace workflow.
Main Conclusions
funspace will provide researchers working with functional traits across the tree of life with a new tool to easily explore: (i) the main features of any functional trait space, (ii) the relationship between a functional trait space and any other biological or non‐biological factor that might contribute to shaping species' functional diversity. |
doi_str_mv | 10.1111/ddi.13820 |
format | Article |
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Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes‐troublesome steps needed to build and analyse functional trait spaces.
Innovation
To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate functional trait space analyses. The six functions that constitute the package can be grouped in three modules: ‘Building and exploring’, ‘Mapping’ and ‘Plotting’. The building and exploring module defines the main features of a functional trait space (e.g. functional diversity metrics) by leveraging kernel density‐based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and publication‐ready figures representing the outputs obtained from previous modules. We provide a worked example to demonstrate a complete funspace workflow.
Main Conclusions
funspace will provide researchers working with functional traits across the tree of life with a new tool to easily explore: (i) the main features of any functional trait space, (ii) the relationship between a functional trait space and any other biological or non‐biological factor that might contribute to shaping species' functional diversity.</description><identifier>ISSN: 1366-9516</identifier><identifier>EISSN: 1472-4642</identifier><identifier>DOI: 10.1111/ddi.13820</identifier><language>eng</language><publisher>Oxford: Wiley</publisher><subject>Bivariate analysis ; data imputation ; Datasets ; Eigenvalues ; Flowers & plants ; functional diversity ; functional traits ; Functionals ; general additive models ; kernel density ; Mapping ; METHOD ; Modules ; Plotting ; principal component analysis ; Species diversity ; trait space ; Variables ; Workflow</subject><ispartof>Diversity & distributions, 2024-04, Vol.30 (4), p.1-14</ispartof><rights>2024 The Authors</rights><rights>2024 The Authors. published by John Wiley & Sons Ltd.</rights><rights>2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3540-ac6ae96ffed4f266647fe9528c72aa81aba326611ba463600578e6627c25a9ce3</citedby><cites>FETCH-LOGICAL-c3540-ac6ae96ffed4f266647fe9528c72aa81aba326611ba463600578e6627c25a9ce3</cites><orcidid>0000-0002-9441-863X ; 0000-0001-6935-4913 ; 0000-0003-0085-4535</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/48764475$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/48764475$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,860,1411,11541,25332,27901,27902,45550,45551,46027,46451,54499,54505</link.rule.ids><linktorsrc>$$Uhttps://www.jstor.org/stable/48764475$$EView_record_in_JSTOR$$FView_record_in_$$GJSTOR</linktorsrc></links><search><creatorcontrib>Carmona, Carlos P.</creatorcontrib><creatorcontrib>Pavanetto, Nicola</creatorcontrib><creatorcontrib>Puglielli, Giacomo</creatorcontrib><title>funspace: An R package to build, analyse and plot functional trait spaces</title><title>Diversity & distributions</title><description>Aim
Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes‐troublesome steps needed to build and analyse functional trait spaces.
Innovation
To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate functional trait space analyses. The six functions that constitute the package can be grouped in three modules: ‘Building and exploring’, ‘Mapping’ and ‘Plotting’. The building and exploring module defines the main features of a functional trait space (e.g. functional diversity metrics) by leveraging kernel density‐based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and publication‐ready figures representing the outputs obtained from previous modules. We provide a worked example to demonstrate a complete funspace workflow.
Main Conclusions
funspace will provide researchers working with functional traits across the tree of life with a new tool to easily explore: (i) the main features of any functional trait space, (ii) the relationship between a functional trait space and any other biological or non‐biological factor that might contribute to shaping species' functional diversity.</description><subject>Bivariate analysis</subject><subject>data imputation</subject><subject>Datasets</subject><subject>Eigenvalues</subject><subject>Flowers & plants</subject><subject>functional diversity</subject><subject>functional traits</subject><subject>Functionals</subject><subject>general additive models</subject><subject>kernel density</subject><subject>Mapping</subject><subject>METHOD</subject><subject>Modules</subject><subject>Plotting</subject><subject>principal component analysis</subject><subject>Species diversity</subject><subject>trait space</subject><subject>Variables</subject><subject>Workflow</subject><issn>1366-9516</issn><issn>1472-4642</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1j01LAzEQhoMotFYP_gBPnjxsm89JcpTWj0LBi57DNJvALrW7Jl2k_97oqjfnMsPwvDM8hFwxOmelFnXdzJkwnJ6QKZOaVxIkPy2zAKisYjAh5zm3lFIhFJ-SSRz2uUcfLshZxF0Olz99Rl4f7l-WT9Xm-XG9vNtUXihJK_SAwUKMoZaRA4DUMVjFjdcc0TDcoihrxrYoQQClSpsAwLXnCq0PYkZuxrt96t6HkA-u7Ya0Ly8dt2ApM1zoQt2OlE9dzilE16fmDdPRMeq-RF0Rdd-ihV2M7EezC8f_QbdarX8T12OizYcu_SWk0SClVuITa_FasA</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Carmona, Carlos P.</creator><creator>Pavanetto, Nicola</creator><creator>Puglielli, Giacomo</creator><general>Wiley</general><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SN</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2O</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-9441-863X</orcidid><orcidid>https://orcid.org/0000-0001-6935-4913</orcidid><orcidid>https://orcid.org/0000-0003-0085-4535</orcidid></search><sort><creationdate>20240401</creationdate><title>funspace</title><author>Carmona, Carlos P. ; Pavanetto, Nicola ; Puglielli, Giacomo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3540-ac6ae96ffed4f266647fe9528c72aa81aba326611ba463600578e6627c25a9ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Bivariate analysis</topic><topic>data imputation</topic><topic>Datasets</topic><topic>Eigenvalues</topic><topic>Flowers & plants</topic><topic>functional diversity</topic><topic>functional traits</topic><topic>Functionals</topic><topic>general additive models</topic><topic>kernel density</topic><topic>Mapping</topic><topic>METHOD</topic><topic>Modules</topic><topic>Plotting</topic><topic>principal component analysis</topic><topic>Species diversity</topic><topic>trait space</topic><topic>Variables</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Carmona, Carlos P.</creatorcontrib><creatorcontrib>Pavanetto, Nicola</creatorcontrib><creatorcontrib>Puglielli, Giacomo</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Research Library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Diversity & distributions</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Carmona, Carlos P.</au><au>Pavanetto, Nicola</au><au>Puglielli, Giacomo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>funspace: An R package to build, analyse and plot functional trait spaces</atitle><jtitle>Diversity & distributions</jtitle><date>2024-04-01</date><risdate>2024</risdate><volume>30</volume><issue>4</issue><spage>1</spage><epage>14</epage><pages>1-14</pages><issn>1366-9516</issn><eissn>1472-4642</eissn><abstract>Aim
Functional trait space analyses are pivotal to describe and compare organisms' functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes‐troublesome steps needed to build and analyse functional trait spaces.
Innovation
To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate functional trait space analyses. The six functions that constitute the package can be grouped in three modules: ‘Building and exploring’, ‘Mapping’ and ‘Plotting’. The building and exploring module defines the main features of a functional trait space (e.g. functional diversity metrics) by leveraging kernel density‐based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and publication‐ready figures representing the outputs obtained from previous modules. We provide a worked example to demonstrate a complete funspace workflow.
Main Conclusions
funspace will provide researchers working with functional traits across the tree of life with a new tool to easily explore: (i) the main features of any functional trait space, (ii) the relationship between a functional trait space and any other biological or non‐biological factor that might contribute to shaping species' functional diversity.</abstract><cop>Oxford</cop><pub>Wiley</pub><doi>10.1111/ddi.13820</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-9441-863X</orcidid><orcidid>https://orcid.org/0000-0001-6935-4913</orcidid><orcidid>https://orcid.org/0000-0003-0085-4535</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Bivariate analysis data imputation Datasets Eigenvalues Flowers & plants functional diversity functional traits Functionals general additive models kernel density Mapping METHOD Modules Plotting principal component analysis Species diversity trait space Variables Workflow |
title | funspace: An R package to build, analyse and plot functional trait spaces |
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