Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability
- Autores
- Rolhauser, Andrés Guillermo; Nordenstahl, Marisa; Aguiar, Martin Roberto; Pucheta, Eduardo Raúl
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Research linking functional traits to competitive ability of coexisting species has largely relied on rectilinear correlations, yielding inconsistent results. Based on concepts borrowed from natural selection theory, we propose that trait?competition relationships can generally correspond to three univariate selection modes: directional (a rectilinear relationship), stabilising (an n-shaped relationship), and disruptive (a u-shaped relationship). Moreover, correlational selection occurs when two traits interact in determining competitive ability and lead to an optimum trait combination (i.e., a bivariate nonlinear selection mode). We tested our ideas using two independent datasets, each one characterising a group of species according to (a) their competitive effect on a target species in a pot experiment and (b) species-level values of well-known functional traits extracted from existing databases. The first dataset comprised 10 annual plant species frequent in a summer-rainfall desert in Argentina, while the second consisted of 37 herbaceous species from cool temperate vegetation types in Canada. Both experiments had a replacement design where the identity of neighbours was manipulated holding total plant density in pots constant. We modelled the competitive ability of neighbours (i.e., the log inverse of target plant biomass) as a function of traits using normal multiple regression. Leaf dry matter content (LDMC) was consistently subjected to negative directional selection in both experiments as well as to stabilising selection among temperate species. Leaf size was subjected to stabilising selection among desert species while among temperate species, leaf size underwent correlational selection in combination with specific leaf area (SLA): selection on SLA was negative directional for large-leaved species, while it was slightly positive for small-leaved species. Synthesis. Multiple quadratic regression adds functional flexibility to trait-based community ecology while providing a standardised basis for comparison among traits and environments. Our analyses of two datasets from contrasting environmental conditions indicate (a) that leaf dry matter content can capture an important component of plant competitive ability not accounted for by widely used competitive traits, such as specific leaf area, leaf size, and plant height and (b) that optimum relationships (either univariate or bivariate) between competitive ability and plant traits may be more common than previously realised.
Fil: Rolhauser, Andrés Guillermo. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Nordenstahl, Marisa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Aguiar, Martin Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Pucheta, Eduardo Raúl. Universidad Nacional de San Juan; Argentina - Materia
-
COMMUNITY ASSEMBLY
COMPETITION EXPERIMENT
CORRELATIONAL SELECTION
LEAF DRY MATTER CONTENT
LEAF SIZE
PHENOTYPIC SELECTION
PLANT–PLANT INTERACTIONS
QUADRATIC REGRESSION
SPECIFIC LEAF AREA
STABILISING SELECTION - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/148563
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oai:ri.conicet.gov.ar:11336/148563 |
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CONICET Digital (CONICET) |
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Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive abilityRolhauser, Andrés GuillermoNordenstahl, MarisaAguiar, Martin RobertoPucheta, Eduardo RaúlCOMMUNITY ASSEMBLYCOMPETITION EXPERIMENTCORRELATIONAL SELECTIONLEAF DRY MATTER CONTENTLEAF SIZEPHENOTYPIC SELECTIONPLANT–PLANT INTERACTIONSQUADRATIC REGRESSIONSPECIFIC LEAF AREASTABILISING SELECTIONhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Research linking functional traits to competitive ability of coexisting species has largely relied on rectilinear correlations, yielding inconsistent results. Based on concepts borrowed from natural selection theory, we propose that trait?competition relationships can generally correspond to three univariate selection modes: directional (a rectilinear relationship), stabilising (an n-shaped relationship), and disruptive (a u-shaped relationship). Moreover, correlational selection occurs when two traits interact in determining competitive ability and lead to an optimum trait combination (i.e., a bivariate nonlinear selection mode). We tested our ideas using two independent datasets, each one characterising a group of species according to (a) their competitive effect on a target species in a pot experiment and (b) species-level values of well-known functional traits extracted from existing databases. The first dataset comprised 10 annual plant species frequent in a summer-rainfall desert in Argentina, while the second consisted of 37 herbaceous species from cool temperate vegetation types in Canada. Both experiments had a replacement design where the identity of neighbours was manipulated holding total plant density in pots constant. We modelled the competitive ability of neighbours (i.e., the log inverse of target plant biomass) as a function of traits using normal multiple regression. Leaf dry matter content (LDMC) was consistently subjected to negative directional selection in both experiments as well as to stabilising selection among temperate species. Leaf size was subjected to stabilising selection among desert species while among temperate species, leaf size underwent correlational selection in combination with specific leaf area (SLA): selection on SLA was negative directional for large-leaved species, while it was slightly positive for small-leaved species. Synthesis. Multiple quadratic regression adds functional flexibility to trait-based community ecology while providing a standardised basis for comparison among traits and environments. Our analyses of two datasets from contrasting environmental conditions indicate (a) that leaf dry matter content can capture an important component of plant competitive ability not accounted for by widely used competitive traits, such as specific leaf area, leaf size, and plant height and (b) that optimum relationships (either univariate or bivariate) between competitive ability and plant traits may be more common than previously realised.Fil: Rolhauser, Andrés Guillermo. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Nordenstahl, Marisa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aguiar, Martin Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Pucheta, Eduardo Raúl. Universidad Nacional de San Juan; ArgentinaWiley Blackwell Publishing, Inc2018-11-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/148563Rolhauser, Andrés Guillermo; Nordenstahl, Marisa; Aguiar, Martin Roberto; Pucheta, Eduardo Raúl; Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability; Wiley Blackwell Publishing, Inc; Journal of Ecology; 107; 3; 5-11-2018; 1457-14680022-0477CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/1365-2745.13094info:eu-repo/semantics/altIdentifier/url/https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.13094info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:17:31Zoai:ri.conicet.gov.ar:11336/148563instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:17:32.114CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
title |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
spellingShingle |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability Rolhauser, Andrés Guillermo COMMUNITY ASSEMBLY COMPETITION EXPERIMENT CORRELATIONAL SELECTION LEAF DRY MATTER CONTENT LEAF SIZE PHENOTYPIC SELECTION PLANT–PLANT INTERACTIONS QUADRATIC REGRESSION SPECIFIC LEAF AREA STABILISING SELECTION |
title_short |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
title_full |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
title_fullStr |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
title_full_unstemmed |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
title_sort |
Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability |
dc.creator.none.fl_str_mv |
Rolhauser, Andrés Guillermo Nordenstahl, Marisa Aguiar, Martin Roberto Pucheta, Eduardo Raúl |
author |
Rolhauser, Andrés Guillermo |
author_facet |
Rolhauser, Andrés Guillermo Nordenstahl, Marisa Aguiar, Martin Roberto Pucheta, Eduardo Raúl |
author_role |
author |
author2 |
Nordenstahl, Marisa Aguiar, Martin Roberto Pucheta, Eduardo Raúl |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
COMMUNITY ASSEMBLY COMPETITION EXPERIMENT CORRELATIONAL SELECTION LEAF DRY MATTER CONTENT LEAF SIZE PHENOTYPIC SELECTION PLANT–PLANT INTERACTIONS QUADRATIC REGRESSION SPECIFIC LEAF AREA STABILISING SELECTION |
topic |
COMMUNITY ASSEMBLY COMPETITION EXPERIMENT CORRELATIONAL SELECTION LEAF DRY MATTER CONTENT LEAF SIZE PHENOTYPIC SELECTION PLANT–PLANT INTERACTIONS QUADRATIC REGRESSION SPECIFIC LEAF AREA STABILISING SELECTION |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Research linking functional traits to competitive ability of coexisting species has largely relied on rectilinear correlations, yielding inconsistent results. Based on concepts borrowed from natural selection theory, we propose that trait?competition relationships can generally correspond to three univariate selection modes: directional (a rectilinear relationship), stabilising (an n-shaped relationship), and disruptive (a u-shaped relationship). Moreover, correlational selection occurs when two traits interact in determining competitive ability and lead to an optimum trait combination (i.e., a bivariate nonlinear selection mode). We tested our ideas using two independent datasets, each one characterising a group of species according to (a) their competitive effect on a target species in a pot experiment and (b) species-level values of well-known functional traits extracted from existing databases. The first dataset comprised 10 annual plant species frequent in a summer-rainfall desert in Argentina, while the second consisted of 37 herbaceous species from cool temperate vegetation types in Canada. Both experiments had a replacement design where the identity of neighbours was manipulated holding total plant density in pots constant. We modelled the competitive ability of neighbours (i.e., the log inverse of target plant biomass) as a function of traits using normal multiple regression. Leaf dry matter content (LDMC) was consistently subjected to negative directional selection in both experiments as well as to stabilising selection among temperate species. Leaf size was subjected to stabilising selection among desert species while among temperate species, leaf size underwent correlational selection in combination with specific leaf area (SLA): selection on SLA was negative directional for large-leaved species, while it was slightly positive for small-leaved species. Synthesis. Multiple quadratic regression adds functional flexibility to trait-based community ecology while providing a standardised basis for comparison among traits and environments. Our analyses of two datasets from contrasting environmental conditions indicate (a) that leaf dry matter content can capture an important component of plant competitive ability not accounted for by widely used competitive traits, such as specific leaf area, leaf size, and plant height and (b) that optimum relationships (either univariate or bivariate) between competitive ability and plant traits may be more common than previously realised. Fil: Rolhauser, Andrés Guillermo. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Nordenstahl, Marisa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Aguiar, Martin Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Pucheta, Eduardo Raúl. Universidad Nacional de San Juan; Argentina |
description |
Research linking functional traits to competitive ability of coexisting species has largely relied on rectilinear correlations, yielding inconsistent results. Based on concepts borrowed from natural selection theory, we propose that trait?competition relationships can generally correspond to three univariate selection modes: directional (a rectilinear relationship), stabilising (an n-shaped relationship), and disruptive (a u-shaped relationship). Moreover, correlational selection occurs when two traits interact in determining competitive ability and lead to an optimum trait combination (i.e., a bivariate nonlinear selection mode). We tested our ideas using two independent datasets, each one characterising a group of species according to (a) their competitive effect on a target species in a pot experiment and (b) species-level values of well-known functional traits extracted from existing databases. The first dataset comprised 10 annual plant species frequent in a summer-rainfall desert in Argentina, while the second consisted of 37 herbaceous species from cool temperate vegetation types in Canada. Both experiments had a replacement design where the identity of neighbours was manipulated holding total plant density in pots constant. We modelled the competitive ability of neighbours (i.e., the log inverse of target plant biomass) as a function of traits using normal multiple regression. Leaf dry matter content (LDMC) was consistently subjected to negative directional selection in both experiments as well as to stabilising selection among temperate species. Leaf size was subjected to stabilising selection among desert species while among temperate species, leaf size underwent correlational selection in combination with specific leaf area (SLA): selection on SLA was negative directional for large-leaved species, while it was slightly positive for small-leaved species. Synthesis. Multiple quadratic regression adds functional flexibility to trait-based community ecology while providing a standardised basis for comparison among traits and environments. Our analyses of two datasets from contrasting environmental conditions indicate (a) that leaf dry matter content can capture an important component of plant competitive ability not accounted for by widely used competitive traits, such as specific leaf area, leaf size, and plant height and (b) that optimum relationships (either univariate or bivariate) between competitive ability and plant traits may be more common than previously realised. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/148563 Rolhauser, Andrés Guillermo; Nordenstahl, Marisa; Aguiar, Martin Roberto; Pucheta, Eduardo Raúl; Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability; Wiley Blackwell Publishing, Inc; Journal of Ecology; 107; 3; 5-11-2018; 1457-1468 0022-0477 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/148563 |
identifier_str_mv |
Rolhauser, Andrés Guillermo; Nordenstahl, Marisa; Aguiar, Martin Roberto; Pucheta, Eduardo Raúl; Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability; Wiley Blackwell Publishing, Inc; Journal of Ecology; 107; 3; 5-11-2018; 1457-1468 0022-0477 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1111/1365-2745.13094 info:eu-repo/semantics/altIdentifier/url/https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.13094 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Wiley Blackwell Publishing, Inc |
publisher.none.fl_str_mv |
Wiley Blackwell Publishing, Inc |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614128987013120 |
score |
13.070432 |