Hairiness: the missing link between pollinators and pollination

Background. Functional traits are the primary biotic component driving organism influence on ecosystem functions; in consequence, traits are widely used in ecological research. However, most animal trait-based studies use easy-to-measure characteristics of species that are at best only weakly associ...

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Autores: Stavert, Jamie R., Liñán-Cembrano, Gustavo, Beggs, Jacqueline R., Howlett, Brad G., Pattemore, David E., Bartomeus, Ignasi
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/142456
Acesso em linha:http://hdl.handle.net/10261/142456
Access Level:acceso abierto
Palavra-chave:Pollination
Pilosity
Entropy
Functional trait
Pollen deposition
Ecosystems function
Image analysis
Pollen load
SVD
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spelling Hairiness: the missing link between pollinators and pollinationStavert, Jamie R.Liñán-Cembrano, GustavoBeggs, Jacqueline R.Howlett, Brad G.Pattemore, David E.Bartomeus, IgnasiPollinationPilosityEntropyFunctional traitPollen depositionEcosystems functionImage analysisPollen loadSVDBackground. Functional traits are the primary biotic component driving organism influence on ecosystem functions; in consequence, traits are widely used in ecological research. However, most animal trait-based studies use easy-to-measure characteristics of species that are at best only weakly associated with functions. Animal-mediated pollination is a key ecosystem function and is likely to be influenced by pollinator traits, but to date no one has identified functional traits that are simple to measure and have good predictive power. Methods. Here, we show that a simple, easy to measure trait (hairiness) can predict pollinator effectiveness with high accuracy. We used a novel image analysis method to calculate entropy values for insect body surfaces as a measure of hairiness. We evaluated the power of our method for predicting pollinator effectiveness by regressing pollinator hairiness (entropy) against single visit pollen deposition (SVD) and pollen loads on insects. We used linear models and AICC model selection to determine which body regions were the best predictors of SVD and pollen load. Results. We found that hairiness can be used as a robust proxy of SVD. The best models for predicting SVD for the flower species Brassica rapa and Actinidia deliciosa were hairiness on the face and thorax as predictors (R2 D0:98 and 0.91 respectively). The best model for predicting pollen load for B. rapa was hairiness on the face (R2 D0:81). Discussion. We suggest that the match between pollinator body region hairiness and plant reproductive structure morphology is a powerful predictor of pollinator effectiveness. We show that pollinator hairiness is strongly linked to pollination an important ecosystem function, and provide a rigorous and time-efficient method for measuring hairiness. Identifying and accurately measuring key traits that drive ecosystem processes is critical as global change increasingly alters ecological communities, and subsequently, ecosystem functions worldwide.Peer reviewedPeerJConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201720172016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/142456reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.7717/peerj.2779Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1424562026-05-22T06:33:51Z
dc.title.none.fl_str_mv Hairiness: the missing link between pollinators and pollination
title Hairiness: the missing link between pollinators and pollination
spellingShingle Hairiness: the missing link between pollinators and pollination
Stavert, Jamie R.
Pollination
Pilosity
Entropy
Functional trait
Pollen deposition
Ecosystems function
Image analysis
Pollen load
SVD
title_short Hairiness: the missing link between pollinators and pollination
title_full Hairiness: the missing link between pollinators and pollination
title_fullStr Hairiness: the missing link between pollinators and pollination
title_full_unstemmed Hairiness: the missing link between pollinators and pollination
title_sort Hairiness: the missing link between pollinators and pollination
dc.creator.none.fl_str_mv Stavert, Jamie R.
Liñán-Cembrano, Gustavo
Beggs, Jacqueline R.
Howlett, Brad G.
Pattemore, David E.
Bartomeus, Ignasi
author Stavert, Jamie R.
author_facet Stavert, Jamie R.
Liñán-Cembrano, Gustavo
Beggs, Jacqueline R.
Howlett, Brad G.
Pattemore, David E.
Bartomeus, Ignasi
author_role author
author2 Liñán-Cembrano, Gustavo
Beggs, Jacqueline R.
Howlett, Brad G.
Pattemore, David E.
Bartomeus, Ignasi
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Pollination
Pilosity
Entropy
Functional trait
Pollen deposition
Ecosystems function
Image analysis
Pollen load
SVD
topic Pollination
Pilosity
Entropy
Functional trait
Pollen deposition
Ecosystems function
Image analysis
Pollen load
SVD
description Background. Functional traits are the primary biotic component driving organism influence on ecosystem functions; in consequence, traits are widely used in ecological research. However, most animal trait-based studies use easy-to-measure characteristics of species that are at best only weakly associated with functions. Animal-mediated pollination is a key ecosystem function and is likely to be influenced by pollinator traits, but to date no one has identified functional traits that are simple to measure and have good predictive power. Methods. Here, we show that a simple, easy to measure trait (hairiness) can predict pollinator effectiveness with high accuracy. We used a novel image analysis method to calculate entropy values for insect body surfaces as a measure of hairiness. We evaluated the power of our method for predicting pollinator effectiveness by regressing pollinator hairiness (entropy) against single visit pollen deposition (SVD) and pollen loads on insects. We used linear models and AICC model selection to determine which body regions were the best predictors of SVD and pollen load. Results. We found that hairiness can be used as a robust proxy of SVD. The best models for predicting SVD for the flower species Brassica rapa and Actinidia deliciosa were hairiness on the face and thorax as predictors (R2 D0:98 and 0.91 respectively). The best model for predicting pollen load for B. rapa was hairiness on the face (R2 D0:81). Discussion. We suggest that the match between pollinator body region hairiness and plant reproductive structure morphology is a powerful predictor of pollinator effectiveness. We show that pollinator hairiness is strongly linked to pollination an important ecosystem function, and provide a rigorous and time-efficient method for measuring hairiness. Identifying and accurately measuring key traits that drive ecosystem processes is critical as global change increasingly alters ecological communities, and subsequently, ecosystem functions worldwide.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/142456
url http://hdl.handle.net/10261/142456
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.7717/peerj.2779
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv PeerJ
publisher.none.fl_str_mv PeerJ
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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