Community ‐ level natural selection modes a quadratic framework to link multiple functional traits with competitive ability

1. 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). 2. 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. 3. 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. 4. 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.

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Main Authors: Rolhauser, Andrés Guillermo, Nordenstahl, Marisa, Aguiar, Martín Roberto, Pucheta, Eduardo Raúl
Format: Texto biblioteca
Language:eng
Subjects:COMMUNITY ASSEMBLY, COMPETITION EXPERIMENT, CORRELATIONAL SELECTION, LEAF DRY MATTER CONTENT, LEAF SIZE, PHENOTYPIC SELECTION, PLANT–PLANT INTERACTIONS, QUADRATIC REGRESSION, SPECIFIC LEAF AREA, STABILISING SELECTION,
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id KOHA-OAI-AGRO:47910
record_format koha
institution UBA FA
collection Koha
country Argentina
countrycode AR
component Bibliográfico
access En linea
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databasecode cat-ceiba
tag biblioteca
region America del Sur
libraryname Biblioteca Central FAUBA
language eng
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
COMMUNITY ASSEMBLY
COMPETITION EXPERIMENT
CORRELATIONAL SELECTION
LEAF DRY MATTER CONTENT
LEAF SIZE
PHENOTYPIC SELECTION
PLANT–PLANT INTERACTIONS
QUADRATIC REGRESSION
SPECIFIC LEAF AREA
STABILISING SELECTION
spellingShingle COMMUNITY ASSEMBLY
COMPETITION EXPERIMENT
CORRELATIONAL SELECTION
LEAF DRY MATTER CONTENT
LEAF SIZE
PHENOTYPIC SELECTION
PLANT–PLANT INTERACTIONS
QUADRATIC REGRESSION
SPECIFIC LEAF AREA
STABILISING SELECTION
COMMUNITY ASSEMBLY
COMPETITION EXPERIMENT
CORRELATIONAL SELECTION
LEAF DRY MATTER CONTENT
LEAF SIZE
PHENOTYPIC SELECTION
PLANT–PLANT INTERACTIONS
QUADRATIC REGRESSION
SPECIFIC LEAF AREA
STABILISING SELECTION
Rolhauser, Andrés Guillermo
Nordenstahl, Marisa
Aguiar, Martín Roberto
Pucheta, Eduardo Raúl
Community ‐ level natural selection modes a quadratic framework to link multiple functional traits with competitive ability
description 1. 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). 2. 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. 3. 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. 4. 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.
format Texto
topic_facet COMMUNITY ASSEMBLY
COMPETITION EXPERIMENT
CORRELATIONAL SELECTION
LEAF DRY MATTER CONTENT
LEAF SIZE
PHENOTYPIC SELECTION
PLANT–PLANT INTERACTIONS
QUADRATIC REGRESSION
SPECIFIC LEAF AREA
STABILISING SELECTION
author Rolhauser, Andrés Guillermo
Nordenstahl, Marisa
Aguiar, Martín Roberto
Pucheta, Eduardo Raúl
author_facet Rolhauser, Andrés Guillermo
Nordenstahl, Marisa
Aguiar, Martín Roberto
Pucheta, Eduardo Raúl
author_sort Rolhauser, Andrés Guillermo
title Community ‐ level natural selection modes a quadratic framework to link multiple functional traits with competitive ability
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
url http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47910
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http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=
work_keys_str_mv AT rolhauserandresguillermo communitylevelnaturalselectionmodesaquadraticframeworktolinkmultiplefunctionaltraitswithcompetitiveability
AT nordenstahlmarisa communitylevelnaturalselectionmodesaquadraticframeworktolinkmultiplefunctionaltraitswithcompetitiveability
AT aguiarmartinroberto communitylevelnaturalselectionmodesaquadraticframeworktolinkmultiplefunctionaltraitswithcompetitiveability
AT puchetaeduardoraul communitylevelnaturalselectionmodesaquadraticframeworktolinkmultiplefunctionaltraitswithcompetitiveability
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spelling KOHA-OAI-AGRO:479102022-09-27T09:13:10Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47910http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=AAGCommunity ‐ level natural selection modes a quadratic framework to link multiple functional traits with competitive abilityRolhauser, Andrés GuillermoNordenstahl, MarisaAguiar, Martín RobertoPucheta, Eduardo Raúltextengapplication/pdf1. 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). 2. 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. 3. 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. 4. 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.1. 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). 2. 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. 3. 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. 4. 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.COMMUNITY ASSEMBLYCOMPETITION EXPERIMENTCORRELATIONAL SELECTIONLEAF DRY MATTER CONTENTLEAF SIZEPHENOTYPIC SELECTIONPLANT–PLANT INTERACTIONSQUADRATIC REGRESSIONSPECIFIC LEAF AREASTABILISING SELECTIONJournal of ecology