E - Ufrgs

ecoqua.ecologia.ufrgs.br

E - Ufrgs

Functional and phylogenetic analysis

of vegetation change

Valério De Patta Pillar

Department of Ecology

Universidade Federal do Rio Grande do Sul, Brazil

vpillar@ufrgs.br http://ecoqua.ecologia.ufrgs.br

IAVS Symposium, Mokpo, Korea, 2012

Thursday, 26 July, 12


Outline

• Antecedents of the functional approach

• What functional and phylogenetic analyses may reveal?

– Drivers, assembly rules and patterns

– Ecosystem effects

• What analytical frameworks?

– within community (alpha) patterns

– between communities (beta) patterns

– phylogenetic signal

– community change

– functional redundancy and community resilience

– do we need types?

• Conclusion

Thursday, 26 July, 12


Antecedents

Warming (1884) pioneered a classification of plants based on traits,

which was grounded on adaptations developed through plant evolution:

• plant longevity,

• power of vegetative propagation,

• duration of tillers,

• hypogeous or epigeous type of shoots,

• mode of hibernation, and

• degree and mode of branching of rhizomes.

Warming, 1841-­‐1924

Warming, E. 1884. Om Skudbygning, Overvintring og Foryngelse [On shoot

architecture, perennation and rejuvenation]. Naturhistorisk Forenings Festskrift 1-105.

Warming, E. 1909. Oecology of Plants: An introduction to the study of plantcommunities.

Clarendon Press, Oxford.

Review in Pillar, V.D. & Orlóci, L. 1993. Character-Based Community Analysis; the

Theory and an Application Program. SPB Academic Publishing, The Hague.

PDF available at http://ecoqua.ecologia.ufrgs.br

Thursday, 26 July, 12


Antecedents

Warming (1884) pioneered a classification of plants based on traits,

which was grounded on adaptations developed through plant evolution:

• plant longevity,

• power of vegetative propagation,

• duration of tillers,

• hypogeous or epigeous type of shoots,

• mode of hibernation, and

• degree and mode of branching of rhizomes.

Warming, 1841-­‐1924

Similar functional approach was followed in plant classifications by Drude

(1887, 1896), Warming (1895, 1909), Raunkiaer (1904, 1907) ...

Grime (1979): strategies

Noble & Slatyer (1980): vital attributes

Pillar & Orlóci (1993): character set types

Steffen et al. (1992): plant functional types

Wilson (1999): character guilds

Warming, E. 1884. Om Skudbygning, Overvintring og Foryngelse [On shoot

architecture, perennation and rejuvenation]. Naturhistorisk Forenings Festskrift 1-105.

Warming, E. 1909. Oecology of Plants: An introduction to the study of plantcommunities.

Clarendon Press, Oxford.

Review in Pillar, V.D. & Orlóci, L. 1993. Character-Based Community Analysis; the

Theory and an Application Program. SPB Academic Publishing, The Hague.

PDF available at http://ecoqua.ecologia.ufrgs.br

Thursday, 26 July, 12


Antecedents

Warming (1884) pioneered a classification of plants based on traits,

which was grounded on adaptations developed through plant evolution:

• plant longevity,

• power of vegetative propagation,

• duration of tillers,

• hypogeous or epigeous type of shoots,

• mode of hibernation, and

• degree and mode of branching of rhizomes.

Warming, 1841-­‐1924

Similar functional approach was followed in plant classifications by Drude

(1887, 1896), Warming (1895, 1909), Raunkiaer (1904, 1907) ...

Grime (1979): strategies

Noble & Slatyer (1980): vital attributes

Pillar & Orlóci (1993): character set types

Steffen et al. (1992): plant functional types

Wilson (1999): character guilds

Do we really need types?

Warming, E. 1884. Om Skudbygning, Overvintring og Foryngelse [On shoot

architecture, perennation and rejuvenation]. Naturhistorisk Forenings Festskrift 1-105.

Warming, E. 1909. Oecology of Plants: An introduction to the study of plantcommunities.

Clarendon Press, Oxford.

Review in Pillar, V.D. & Orlóci, L. 1993. Character-Based Community Analysis; the

Theory and an Application Program. SPB Academic Publishing, The Hague.

PDF available at http://ecoqua.ecologia.ufrgs.br

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

Communities

Species

W

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

aiming at identifying community change in space

or time,

hoping to interpret and predict community

responses to environmental factors.

But analysis based only on species composition

is limited (biogeographically, and species

similarities ignored).

Environmental

factors

Community sites

Ef

Communities

Species

W

Community change

in 8me or space

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

aiming at identifying community change in space

or time,

hoping to interpret and predict community

responses to environmental factors.

But analysis based only on species composition

is limited (biogeographically, and species

similarities ignored).

• We describe the species (or populations,

individuals) by plant traits (morphological,

physiological, molecular)

Traits

Environmental

factors

Community sites

Ef

Communities

Species

B

Species

W

Community change

in 8me or space

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

aiming at identifying community change in space

or time,

hoping to interpret and predict community

responses to environmental factors.

But analysis based only on species composition

is limited (biogeographically, and species

similarities ignored).

• We describe the species (or populations,

individuals) by plant traits (morphological,

physiological, molecular)

Traits

Environmental

factors

Community sites

Ef

Communities

i. to analyze community responses to

environmental factors, to make more

general predictions and infer assembly

mechanisms.

Species

B

Species

W

Community change

in 8me or space

Thursday, 26 July, 12


Trait-convergence in community assembly

Ecological filter and gradient

Community A

Species pool

Community B

Community C

Metacommunity

Keddy, P.A. 1992. Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3: 157-164.

Grime, J.P. 2006. Trait convergence and trait divergence in herbaceous plant communities: Mechanisms and consequences. Journal of

Vegetation Science 17: 255-260.

Thursday, 26 July, 12


Trait-convergence in community assembly

Species pool

Ecological filter and gradient

Community A

Community B

Community C

Metacommunity

Pillar et al. 2009. Discriminating trait-convergence and trait-divergence

assembly patterns in ecological community gradients. JVS 20: 334-348.

Keddy, P.A. 1992. Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3: 157-164.

Grime, J.P. 2006. Trait convergence and trait divergence in herbaceous plant communities: Mechanisms and consequences. Journal of

Vegetation Science 17: 255-260.

Thursday, 26 July, 12


Trait-convergence and trait-divergence in community assembly

Species pool

Ecological filter and gradient

Community A

Community B

Community C

Metacommunity

Pillar et al. 2009. Discriminating trait-convergence and trait-divergence

assembly patterns in ecological community gradients. JVS 20: 334-348.

Weiher, E. & Keddy, P.A. 1999. Ecological Assembly Rules: Perspectives, advances, retreats. Cambridge University Press, Cambridge, UK.

Wilson, J.B. 2007. Trait-divergence assembly rules have been demonstrated: Limiting similarity lives! A reply to Grime. Journal of Vegetation

Science 18: 451-452.

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

which may limit interpretation and prediction

about community responses to environmental

factors.

• Species (or populations, individuals) are described

by plant traits (morphological, physiological,

molecular)

Environmental

factors

Community sites

Ef

i. to analyze community responses to

environmental factors, to make more

general predictions and infer assembly

mechanisms.

Species

Traits

B

Species

Communities

W

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

which may limit interpretation and prediction

about community responses to environmental

factors.

• Species (or populations, individuals) are described

by plant traits (morphological, physiological,

molecular)

i. to analyze community responses to

environmental factors, to make more

general predictions and infer assembly

mechanisms.

ii. to analyze community effects on ecosystem

processes and services, which may involve

variables measured at different trophic

levels.

Species

Traits

B

Species

Communities

W

Ecosystem

effects

Community sites

Ee

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

which may limit interpretation and prediction

about community responses to environmental

factors.

• Species (or populations, individuals) are described

by plant traits (morphological, physiological,

molecular)

Environmental

factors

Community sites

Ef

i. to analyze community responses to

environmental factors, to make more

general predictions and infer assembly

mechanisms.

Traits

Communities

ii. to analyze community effects on ecosystem

processes and services, which may involve

variables measured at different trophic

levels.

Species

B

Species

W

• The traits are functional if the community patterns

are linked to the responses and/or to the effects

being considered. Functionality is context

dependent!

Ecosystem

effects

Community sites

Ee

Thursday, 26 July, 12


What traits and phylogenetic data may reveal?

• Vegetation ecologists describe communities by

species composition,

which may limit interpretation and prediction

about community responses to environmental

factors.

• Species (or populations, individuals) are described

by plant traits (morphological, physiological,

molecular)

i. to analyze community responses to

environmental factors, to make more

general predictions and infer assembly

mechanisms.

ii. to analyze community effects on ecosystem

processes and services, which may involve

variables measured at different trophic

levels.

• The traits are functional if the community patterns

are linked to the responses and/or to the effects

being considered. Functionality is context

dependent!

• Traits may be phylogenetically conserved and thus

evolutionary processes may have restricted

functional patterns.

Phylogeny

Traits

Species

B

Environmental

factors

Species

Ecosystem

effects

Community sites

Ef

Communities

W

Community sites

Ee

Thursday, 26 July, 12


Phylogenetic signal

Cavender-Bares et al. (2009)

Ecol. Lett. 12, 693–715

Warming (1909:3):

Plants carry hereditary restrictions that “render it possible for different species, in

their evolution under the influence of identical factors, to achieve the same

object by the most diverse methods.”

Warming, E. 1909. Oecology of Plants: An introduction to the study of plant-communities. Clarendon Press, Oxford.

Thursday, 26 July, 12


Phylogenetic signal at the species pool level

Phylogeny

Species pool

The trait is phylogenetically

conserved.

Thursday, 26 July, 12

Pillar, V.D. & Duarte, L.d.S. 2010. A framework for metacommunity

analysis of phylogenetic structure. Ecology Letters 13: 587–596.


Phylogenetic signal at the

metacommunity level

Ecological filter and gradient

Community A

Phylogeny

Community B

Community C

Thursday, 26 July, 12


Phylogenetic signal at the

metacommunity level

Ecological filter and gradient

Community A

Phylogeny

Community B

Community C

There is phylogenetic signal at the metacommunity

level for the trait “size”, but not for the trait “color”

Thursday, 26 July, 12


Thursday, 26 July, 12

What analytical frameworks?


α and β diversity

• Several indices are available for measuring

funcional and phylogenetic diversity within a

given community (alpha diversity).

Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. 2005. Functional

richness, functional evenness and functional divergence: the primary

components of functional diversity. Oikos 111: 112-118.

Mouchet, M.A., Villéger, S., Mason, N.W.H. & Mouillot, D. 2010.

Functional diversity measures: an overview of their redundancy and

their ability to discriminate community assembly rules. Functional

Ecology 24: 867-876.

Webb, C.O. 2000. Exploring the phylogenetic structure of ecological

communities: An example for rain forest trees. American Naturalist

156: 145-155.

Joner et al. in prep.

Thursday, 26 July, 12


α and β diversity

• Several indices are available for measuring

funcional and phylogenetic diversity within a

given community (alpha diversity).

Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. 2005. Functional

richness, functional evenness and functional divergence: the primary

components of functional diversity. Oikos 111: 112-118.

Mouchet, M.A., Villéger, S., Mason, N.W.H. & Mouillot, D. 2010.

Functional diversity measures: an overview of their redundancy and

their ability to discriminate community assembly rules. Functional

Ecology 24: 867-876.

Webb, C.O. 2000. Exploring the phylogenetic structure of ecological

communities: An example for rain forest trees. American Naturalist

156: 145-155.

α diversity

Joner et al. in prep.

Thursday, 26 July, 12


α and β diversity

• Several indices are available for measuring

funcional and phylogenetic diversity within a

given community (alpha diversity).

Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. 2005. Functional

richness, functional evenness and functional divergence: the primary

components of functional diversity. Oikos 111: 112-118.

Mouchet, M.A., Villéger, S., Mason, N.W.H. & Mouillot, D. 2010.

Functional diversity measures: an overview of their redundancy and

their ability to discriminate community assembly rules. Functional

Ecology 24: 867-876.

Webb, C.O. 2000. Exploring the phylogenetic structure of ecological

communities: An example for rain forest trees. American Naturalist

156: 145-155.

α diversity

β diversity

Joner et al. in prep.

Thursday, 26 July, 12


α and β diversity

• Several indices are available for measuring

funcional and phylogenetic diversity within a

given community (alpha diversity).

Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. 2005. Functional

richness, functional evenness and functional divergence: the primary

components of functional diversity. Oikos 111: 112-118.

Mouchet, M.A., Villéger, S., Mason, N.W.H. & Mouillot, D. 2010.

Functional diversity measures: an overview of their redundancy and

their ability to discriminate community assembly rules. Functional

Ecology 24: 867-876.

Webb, C.O. 2000. Exploring the phylogenetic structure of ecological

communities: An example for rain forest trees. American Naturalist

156: 145-155.

• Low diversity within communities (trait

convergence or trait underdispersion) may

indicate environmental filtering.

α diversity

β diversity

Joner et al. in prep.

Thursday, 26 July, 12


α and β diversity

• Several indices are available for measuring

funcional and phylogenetic diversity within a

given community (alpha diversity).

Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. 2005. Functional

richness, functional evenness and functional divergence: the primary

components of functional diversity. Oikos 111: 112-118.

Mouchet, M.A., Villéger, S., Mason, N.W.H. & Mouillot, D. 2010.

Functional diversity measures: an overview of their redundancy and

their ability to discriminate community assembly rules. Functional

Ecology 24: 867-876.

Webb, C.O. 2000. Exploring the phylogenetic structure of ecological

communities: An example for rain forest trees. American Naturalist

156: 145-155.

• Low diversity within communities (trait

convergence or trait underdispersion) may

indicate environmental filtering.

• But what is the filter?

α diversity

β diversity

Joner et al. in prep.

Thursday, 26 July, 12


α and β diversity

• Several indices are available for measuring

funcional and phylogenetic diversity within a

given community (alpha diversity).

Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. 2005. Functional

richness, functional evenness and functional divergence: the primary

components of functional diversity. Oikos 111: 112-118.

Mouchet, M.A., Villéger, S., Mason, N.W.H. & Mouillot, D. 2010.

Functional diversity measures: an overview of their redundancy and

their ability to discriminate community assembly rules. Functional

Ecology 24: 867-876.

Webb, C.O. 2000. Exploring the phylogenetic structure of ecological

communities: An example for rain forest trees. American Naturalist

156: 145-155.

• Low diversity within communities (trait

convergence or trait underdispersion) may

indicate environmental filtering.

• But what is the filter?

• Environmental filters may be revealed by the

analysis of beta diversity, which refers to trait

variation between communities.

α diversity

β diversity

Joner et al. in prep.

Thursday, 26 July, 12


Journal of Vegetation Science 20: 334–348, 2009

& 2009 International Association for Vegetation Science

Discriminating trait-convergence and trait-divergence assembly

patterns in ecological community gradients

Pillar, Vale´ rio D. 1 ; Duarte, Leandro da S. 1,2 ; Sosinski, Enio E. 1,3 & Joner, Fernando 1,4

1 Departamento de Ecologia, Universidade Federal do Rio Grande do Sul, Brazil; 2 E-mail duarte.ldas@gmail.com;

3 E-mail esosinski@gmail.com; 4 E-mail fjoner@ufrgs.br;

Corresponding author; E-mail vpillar@ufrgs.br

Abstract

LETTER

Question: Whereas similar ecological requirements lead to

trait-convergence assembly patterns (TCAP) of species in

communities, the interactions controlling how species

associate produce trait-divergence assembly patterns

(TDAP). Yet, the linking of the latter S. toDuarte

community

processes has so far only been suggested. We offer a

method to elucidate TCAP and TDAP in ecological

community gradients that will help fill this gap.

Valério D. Pillar* and Leandro d.

Department of Ecology,

Laboratory of Quantitative

Ecology, Universidade Federal

do Rio Grande do Sul, Porto

Alegre, RS 91540-000, Brazil

*Correspondence: E-mail:

Method: We evaluated the correlation between trait-based

described communities and ecological gradients, vpillar@ufrgs.br and

using partial correlation, we separated the fractions reflecting

TCAP and TDAP. The required input data

matrices describe operational taxonomic units (OTUs) by

traits, communities by the quantities or presence-absence

of these OTUs, and community sites by ecological variables.

We defined plant functional types (PFTs) or species

as community components after fuzzy weighting by the

traits. The measured correlations for TCAP and TDAP

Thursday, 26 July, 12

Ecology Letters, (2010) 13: 587–596

A framework for metacommunity analysis of

Keywords: Assembly rules; Environmental filters; Limiting

similarity; phylogenetic Null model; Plant structure functional types; Species

coexistence.

doi: 10.1111/j.1461-0248.2010.01456.x

Abstract

Abbreviations:

It is well

OTU

known

5 Operational

that species evolutionary

Taxonomic

history

Unit;

plays a crucial role in community

PFT 5 Plant Functional Type; TCAP 5 Trait-convergence

assembly pattern; TDAP 5 Trait-divergence as-

assembly. Here, we offer a formal analytical framework to integrate in metacommunity

analysis the speciesÕ phylogeny with their functional traits and abundances. We define

sembly pattern.

phylogenetic structure of a community as phylogenetically weighted species composition.

This is used to reveal patterns of phylogenetic community variation and to measure and

test by specified null models the phylogenetic signal at the metacommunity level, which

Introduction we distinguish from phylogenetic signal at the species pool level. The former indicates

that communities more similar in their phylogenetic structure are also similar in their

Community average trait assembly values, which has puzzled may indicate ecologists speciesÕbe-

cause itWe apparently apply thisinvolves frameworktwo to an paradoxical example fromtrends.

grassland communities and find that traits

niche conservation for the given traits.

Species with in a significant community phylogenetic tend to besignal moreatsimilar the metacommunity in level exhibit ecological

their ecological filtering along requirements, the resource which gradient, may but since lead both to mechanisms act independently on

trait convergence traits, niche (underdispersion), conservatism not supported. but species coexistence

may be restricted by their trait similarity,

leading Keywords to trait divergence (overdispersion). Limiting

similarity Assembly (MacArthur rules, disturbance, & Levins ecological 1967) is agradient, form ofunctional traits, niche conservatism,


Journal of Vegetation Science 20: 334–348, 2009

& 2009 International Association for Vegetation Science

Discriminating trait-convergence and trait-divergence assembly

patterns in ecological community gradients

Pillar, Vale´ rio D. 1 ; Duarte, Leandro da S. 1,2 ; Sosinski, Enio E. 1,3 & Joner, Fernando 1,4

1 Departamento de Ecologia, Universidade Federal do Rio Grande do Sul, Brazil; 2 E-mail duarte.ldas@gmail.com;

3 E-mail esosinski@gmail.com; 4 E-mail fjoner@ufrgs.br;

Corresponding author; E-mail vpillar@ufrgs.br

Abstract

LETTER

Question: Whereas similar ecological requirements lead to

trait-convergence assembly patterns (TCAP) of species in

communities, the interactions controlling how species

associate produce trait-divergence assembly patterns

(TDAP). Yet, the linking of the latter S. toDuarte

community

processes has so far only been suggested. We offer a

method to elucidate TCAP and TDAP in ecological

community gradients that will help fill this gap.

pyedited by: SK MANUSCRIPT CATEGORY: APPLICATIONS NOTE

Valério D. Pillar* and Leandro d.

Department of Ecology,

Laboratory of Quantitative

Ecology, Universidade Federal

do Rio Grande do Sul, Porto

Alegre, RS 91540-000, Brazil

*Correspondence: E-mail:

BIOINFORMATICS APPLICATIONS NOTE

Method: We evaluated the correlation between trait-based

described communities and ecological gradients, vpillar@ufrgs.br and

using partial correlation, we separated the fractions reflecting

TCAP and TDAP. The required input data

matrices Vanderlei describe J. Debastiani operational ∗ andtaxonomic Valério D. units Pillar (OTUs) by

traits, communities by the quantities or presence-absence

of RSthese 91501-970, OTUs, Brazil and community sites by ecological variables.

We defined plant functional types (PFTs) or species

Associate Editor: David Posada

as community components after fuzzy weighting by the

ABSTRACT

traits. The measured correlations for TCAP and TDAP

Ecology Letters, (2010) 13: 587–596

A framework for metacommunity analysis of

Keywords: Assembly rules; Environmental filters; Limiting

similarity; phylogenetic Null model; Plant structure functional types; Species

coexistence.

Phylogenetics Advance Access publication June 4, 2012

SYNCSA—R tool for analysis of metacommunities based on

functional traits and phylogeny of the community components

Programa de Pós-Graduação em Ecologia, Universidade Federal do Rio Grande do Sul, Porto Alegre,

Thursday, 26 July, 12

Summary: SYNCSA is an R package for the analysis of

doi: 10.1111/j.1461-0248.2010.01456.x

Abstract

Abbreviations:

It is well

OTU

known

5 Operational

that species evolutionary

Taxonomic

history

Unit;

plays a crucial role in community

PFT 5 Plant Functional Type; TCAP 5 Trait-convergence

assembly pattern; TDAP 5 Trait-divergence as-

assembly. Here, we offer a formal analytical framework to integrate in metacommunity

analysis the speciesÕ phylogeny with their functional traits and abundances. We define

sembly pattern.

phylogenetic structure of a community as phylogenetically weighted species composition.

Vol. 28 no. 15 2012, pages 2067–2068

This doi:10.1093/bioinformatics/bts325

is used to reveal patterns These of phylogenetic methods community are implemented variation and to measure in and

test by specified null modelsSYNCSA, the phylogenetic freely signal available at the metacommunity for Mac level, and which

Introduction we distinguish from phylogenetic signal at the species pool level. The former indicates

Windows or as and R package.

that communities more similar in their phylogenetic structure are also similar in their

Community average trait assembly values, which has puzzled may See indicate http://ecoqua.ecologia.ufrgs.br/

ecologists speciesÕbe-

cause itWe apparently apply thisinvolves frameworktwo to an paradoxical example fromtrends.

grassland communities and find that traits

niche conservation for the given traits.

Species with in a significant community phylogenetic tend to besignal moreatsimilar the metacommunity in level exhibit ecological

their ecological filtering along requirements, the resource which gradient, may but since lead both to mechanisms act independently on

trait convergence traits, niche (underdispersion), conservatism not supported. but species coexistence

may be restricted by their trait similarity,

leading Keywords to trait divergence (overdispersion). Limiting

similarity Assembly (MacArthur rules, disturbance, & Levins ecological 1967) is agradient, form ofunctional traits, niche conservatism,

components, the detection of phylogenetic signal at the species

pool and metacommunity levels and the testing of phylogenetic

Downloaded


Phylogeny

Traits

Communities

Species

B

Species

W

Input data

Communities

Species

S F

Ecological

variables

E

Species

Thursday, 26 July, 12


Phylogeny

Traits

Communities

Species

B

Species

W

Input data

Communities

Species

S F

Ecological

variables

E

Species

The analysis requires scaling-up of species traits and

phylogeny to the community level.

Thursday, 26 July, 12

Pillar et al. (2009), Pillar & Duarte (2010)


Phylogeny

Traits

Communities

OTUs

B

OTUs

W

Input data

Communities

OTUs

S F

Ecological

variables

E

OTUs

Instead of species, any other defined unit of description of the

community components may be used: Operational Taxonomic

Units (OTUs).

Thursday, 26 July, 12


Phylogeny

Traits

Communities

OTUs

B

OTUs

W

Input data

Communities

OTUs

S F

Ecological

variables

E

OTUs

Instead of species, any other defined unit of description of the

community components may be used: Operational Taxonomic

Units (OTUs).

Thursday, 26 July, 12

Pillar et al. (2009), Pillar & Duarte (2010)


Scaling-up from traits to

communities

Traits

Species

B

Thursday, 26 July, 12


Scaling-up from traits to

communities

Traits

Species

B

Traits

B’

Species

Thursday, 26 July, 12


Scaling-up from traits to

communities

Traits

Species

B

Communities

Traits

B’

X

Species

W

Species

The traits in B must be

quantitative or binary.

Thursday, 26 July, 12


Trait-convergence Assembly

Patterns (TCAP)

Scaling-up from traits to

communities

Species

Traits

B

If matrix W is standardized to unit community

total, matrix T will contain community weighted

mean (CWM) trait values.

Communities

Traits

B’

Species

Species

W

X =

Traits

Communities

T

The traits in B must be

quantitative or binary.

Feoli & Scimone (1984), Díaz et al. 1992, Díaz & Cabido (1997), Garnier et al. 2004

Pillar et al. (2009)

Thursday, 26 July, 12


Trait-convergence Assembly

Patterns (TCAP)

Scaling-up from traits to

communities

Species

Traits

B

If matrix W is standardized to unit community

total, matrix T will contain community weighted

mean (CWM) trait values.

Communities

Traits

B’

Species

Species

W

X =

Traits

Communities

T

The traits in B must be

quantitative or binary.

Feoli & Scimone (1984), Díaz et al. 1992, Díaz & Cabido (1997), Garnier et al. 2004

Pillar et al. (2009)

Thursday, 26 July, 12


Trait-convergence Assembly

Patterns (TCAP)

Scaling-up from traits to

communities

Species

Traits

B

If matrix W is standardized to unit community

total, matrix T will contain community weighted

mean (CWM) trait values.

Communities

Traits

B’

Species

Species

W

X =

Traits

Communities

T

The traits in B must be

quantitative or binary.

Ecological

variables

Community sites

E

Feoli & Scimone (1984), Díaz et al. 1992, Díaz & Cabido (1997), Garnier et al. 2004

Pillar et al. (2009)

Thursday, 26 July, 12


Trait-convergence Assembly

Patterns (TCAP)

Scaling-up from traits to

communities

Traits

Species

B

Communities

Traits

B’

Species

X

Species

W

=

Traits

Communities

T

The traits in B must be

quantitative or binary.

Ecological

variables

Community sites

E

Feoli & Scimone (1984), Díaz et al. 1992, Díaz & Cabido (1997)

Pillar et al. (2009)

Thursday, 26 July, 12


Trait-convergence Assembly

Patterns (TCAP)

Scaling-up from traits to

communities

Species

Traits

B

ρ(TE) measures the correlation between

variation in the community weighted mean

(CWM) trait values in T and variation in E.

Indicates habitat filtering (if E contains factors)

Communities

Traits

B’

Species

X

Species

W

=

Traits

Communities

T

D T

The traits in B must be

quantitative or binary.

Ecological

variables

Community sites

E

D E

ρ(TE)

Feoli & Scimone (1984), Díaz et al. 1992, Díaz & Cabido (1997)

Pillar et al. (2009)

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

Species

S B

Species

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

u ig : fuzzy degree of belonging, in the interval [0, 1], of

species i to species g, based on their trait similarities in S B

standardized to unit column total in U’.

Species

S B

Species

Species

Species

U’

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

u ig : fuzzy degree of belonging, in the interval [0, 1], of

species i to species g, based on their trait similarities in S B

standardized to unit column total in U’.

Species

S B

Species

Species

Communities

Communities

Species

U’

Species

W

X =

Species

X

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

u ig : fuzzy degree of belonging, in the interval [0, 1], of

species i to species g, based on their trait similarities in S B

standardized to unit column total in U’.

Species

S B

Species

If X is standardized to unit community total, each element in X

is the probability of finding a given species from the pool in

the community considering the similarities of this species to

the ones that occur in the community.

Species

Communities

Communities

Species

U’

Species

W

X =

Species

X

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

u ig : fuzzy degree of belonging, in the interval [0, 1], of

species i to species g, based on their trait similarities in S B

standardized to unit column total in U’.

Species

S B

Species

If X is standardized to unit community total, each element in X

is the probability of finding a given species from the pool in

the community considering the similarities of this species to

the ones that occur in the community.

Species

Communities

Communities

Species

U’

Species

W

X =

Species

X

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

!

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

u ig : fuzzy degree of belonging, in the interval [0, 1], of

species i to species g, based on their trait similarities in S B

standardized to unit column total in U’.

Species

S B

If X is standardized to unit community total, each element in X

is the probability of finding a given species from the pool in

the community considering the similarities of this species to

the ones that occur in the community.

Species

Ecological

variables

E

Species

Communities

Communities

Species

U’

Species

W

X =

Species

X

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

!

Thursday, 26 July, 12


Traits

Scaling-up of traits to communities:

Species

B

Preserving all trait information

Species

S B

Species

Ecological

variables

E

D E

Species

Communities

Communities

Species

U’

Species

W

X =

Species

X

D X

ρ(XE)

Pillar & Orlóci (1993), Pillar (1999), Pillar & Sosinski (2003), Pillar et al. (2009)

Thursday, 26 July, 12


Scaling-up of species trait similarities to the

community level.

Example:

Species

B

Traits

1 0 0 0

1 1 0 0

0 1 1 0

0 0 1 1

Thursday, 26 July, 12


Scaling-up of species trait similarities to the

community level.

Example:

Species

B

Traits

1 0 0 0

1 1 0 0

0 1 1 0

0 0 1 1

S B

Species

Species

1.00 0.50 0.00 0.00

0.50 1.00 0.33 0.00

0.00 0.33 1.00 0.33

0.00 0.00 0.33 1.00

Thursday, 26 July, 12


Scaling-up of species trait similarities to the

community level.

Example:

Species

B

Traits

1 0 0 0

1 1 0 0

0 1 1 0

0 0 1 1

S B

U’

Species

Species

Species

1.00 0.50 0.00 0.00

0.50 1.00 0.33 0.00

0.00 0.33 1.00 0.33

0.00 0.00 0.33 1.00

Species

0.67 0.27 0.00 0.00

0.33 0.55 0.20 0.00

0.00 0.18 0.60 0.25

0.00 0.00 0.20 0.75

Thursday, 26 July, 12


Scaling-up of species trait similarities to the

community level.

Example:

Species

B

Traits

1 0 0 0

1 1 0 0

0 1 1 0

0 0 1 1

S B

U’

W

Species

Species

Communities

Species

1.00 0.50 0.00 0.00

0.50 1.00 0.33 0.00

0.00 0.33 1.00 0.33

0.00 0.00 0.33 1.00

Species

0.67 0.27 0.00 0.00

0.33 0.55 0.20 0.00

0.00 0.18 0.60 0.25

0.00 0.00 0.20 0.75

x

Species

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

Thursday, 26 July, 12


Scaling-up of species trait similarities to the

community level.

Example:

Species

B

Traits

1 0 0 0

1 1 0 0

0 1 1 0

0 0 1 1

S B

U’

W

X

Species

Species

Communities

Communities

Species

1.00 0.50 0.00 0.00

0.50 1.00 0.33 0.00

0.00 0.33 1.00 0.33

0.00 0.00 0.33 1.00

Species

0.67 0.27 0.00 0.00

0.33 0.55 0.20 0.00

0.00 0.18 0.60 0.25

0.00 0.00 0.20 0.75

x

Species

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

0.0 0.5 6.0 7.1 7.9

1.4 2.1 5.7 5.8 6.8

6.5 5.6 3.0 2.5 1.3

8.2 7.8 1.4 0.6 0.0

The result in X is a weighted species composition of communities based

on the species’ trait similarities in S B expressed as fuzzy-sets in U.

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

X

Communities

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

X

Communities

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

Species are

completely

disjunct for

the traits

Species

S B

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

X

Communities

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

Species are

completely

disjunct for

the traits

Species

S B

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

Species

U’

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

X

Communities

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

Species are

completely

disjunct for

the traits

Species

S B

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

Species

U’

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

Thursday, 26 July, 12


Extreme cases

Species are

completely

identical for

the traits

Species

S B

Species

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

Species

U’

Species

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

0.25 0.25 0.25 0.25

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

X

Communities

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

4 4 4 4 4

Species are

completely

disjunct for

the traits

Species

S B

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

Species

U’

Species

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

x

Species

W

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

=

Species

X

Communities

0 0 7 9 9

0 2 5 4 7

7 5 3 3 0

9 9 1 0 0

Thursday, 26 July, 12


Trait-divergence assembly patterns (TDAP)

Species

Communities

Communities

Traits

B’

X

Species

W

=

Traits

T

D T

Species

S B

Species

Species

Communities

Communities

Ecological

variables

E

Communities

D E

ρ(XE.T)

ρ(XE)

Species

U’

X

Species

W

=

Species

X

D X

Pillar et al. 2009. JVS 20: 334-348.

Thursday, 26 July, 12


Community-weighted means of leaf-area (LA) and

specific-leaf area (SLA) in tree saplings along a

gradient of canopy openness. Traits and

community description were by individuals (1,132

individuals) in 40 forest plots.

(ρ(TE))

(ρ(XE.T))

Carlucci, M.B., Streit, H., Duarte, L.D.S. & Pillar, V.D. 2012. Individualbased

trait analyses reveal assembly patterns in tree sapling

communities. Journal of Vegetation Science 176-186.

Thursday, 26 July, 12


Functional diversity (Rao quadratic entropy) in tree sapling communities along a gradient of canopy

openness. Traits and community description were by individuals (1,132 individuals) in 40 forest plots.

(ρ(TE))

(ρ(XE.T))

Functional diversity (Rao)

Carlucci, M.B., Streit, H., Duarte, L.D.S. & Pillar, V.D. 2012. Individualbased

trait analyses reveal assembly patterns in tree sapling

communities. Journal of Vegetation Science 176-186.

Thursday, 26 July, 12


Thursday, 26 July, 12

Joner et al. in prep.


Trait underdispersion,

but no TCAP related to E

Joner et al. in prep.

Thursday, 26 July, 12


Trait underdispersion,

but no TCAP related to E

No trait underdispersion,

no TCAP related to E

Joner et al. in prep.

Thursday, 26 July, 12


Trait underdispersion,

but no TCAP related to E

No trait underdispersion,

no TCAP related to E

Trait under- and maybe

overdispersion, TDAP

related to E

Joner et al. in prep.

Thursday, 26 July, 12


Trait underdispersion,

but no TCAP related to E

No trait underdispersion,

no TCAP related to E

Trait under- and maybe

overdispersion, TDAP

related to E

Trait underdispersion,

and TCAP related to E

Joner et al. in prep.

Thursday, 26 July, 12


Trait underdispersion,

but no TCAP related to E

No trait underdispersion,

no TCAP related to E

Trait under- and maybe

overdispersion, TDAP

related to E

Trait underdispersion,

and TCAP related to E

Trait under- and maybe

overdispersion, and TCAP

and TDAP related to E

Joner et al. in prep.

Thursday, 26 July, 12


Trait underdispersion,

but no TCAP related to E

No trait underdispersion,

no TCAP related to E

Trait under- and maybe

overdispersion, TDAP

related to E

Trait underdispersion,

and TCAP related to E

No trait underdispersion,

but TCAP related to E

Trait under- and maybe

overdispersion, and TCAP

and TDAP related to E

Joner et al. in prep.

Thursday, 26 July, 12


ρ(TE1)= 0.09 P= 0.5289 ρ(XE1.T)= 0.65 P= 0.0325

ρ(TE2)= 0.66 P= 0.0036 ρ(XE2.T)= 0.34 P= 0.2642

Thursday, 26 July, 12


ρ(TE1)= 0.09 P= 0.5289 ρ(XE1.T)= 0.65 P= 0.0325

ρ(TE2)= 0.66 P= 0.0036 ρ(XE2.T)= 0.34 P= 0.2642

ρ(TE2)= 0.66 P= 0.0036 ρ(XE2.T)= 0.34 P= 0.2642

Joner et al. In prep.

Thursday, 26 July, 12


Phylogeny

Scaling-up of phylogeny to

communities

Species

S F

Species

Community sites

ρ(PE)

Ecological

variables

E

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Scaling-up of phylogeny to

communities

Species

Species

S F

Species

Species

Q’

ρ(PE)

q ig : fuzzy degree of belonging, in the interval

[0, 1], of species i to species g, based on their

phylogenetic similarities in S F standardized to

unit column total in Q’.

Ecological

variables

Community sites

E

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Scaling-up of phylogeny to

communities

Species

Communities

Species

S F

Species

Species

Q’

X

Species

W

ρ(PE)

q ig : fuzzy degree of belonging, in the interval

[0, 1], of species i to species g, based on their

phylogenetic similarities in S F standardized to

unit column total in Q’.

Ecological

variables

Community sites

E

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Scaling-up of phylogeny to

communities

Species

Communities

Communities

Species

S F

Species

Species

Q’

X

Species

W

=

Species

P

ρ(PE)

q ig : fuzzy degree of belonging, in the interval

[0, 1], of species i to species g, based on their

phylogenetic similarities in S F standardized to

unit column total in Q’.

Ecological

variables

Community sites

E

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Scaling-up of phylogeny to

communities

Species

Communities

Communities

Species

S F

Species

Species

Q’

X

Species

W

=

Species

P

ρ(PE)

q ig : fuzzy degree of belonging, in the interval

[0, 1], of species i to species g, based on their

phylogenetic similarities in S F standardized to

unit column total in Q’.

Ecological

variables

Community sites

E

The result in P is a phylogenetically-weighted species composition of

communities based on the species’ phylogenetic similarities in S F

expressed as fuzzy-sets in Q.

Thursday, 26 July, 12

Pillar & Duarte 2010. Ecology Letters 13: 587-596


Phylogenetic habitat filtering

Small Patches

•Basal plant clades

associated to large forest

patches

•Asterids associated to

small forest patches

Medium Patches

•Medium patches were

phylogenetically diverse.

Large Patches

Saplings of woody plants colonizing Araucaria forest patches of different sizes in grassland,

southern Brazil. Principal Coordinates Analysis applied on species composition weighted by

phylogenetic similarities. PCPS: Principal Component of Phylogenetic Structure.

Duarte (2011) Oikos 120: 208–215, 2011

Thursday, 26 July, 12


Phylogeny

Phylogenetic signal and traitconvergence

assembly patterns

Species

S F

Species

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Phylogenetic signal and traitconvergence

assembly patterns

Species

ρ(BF)

S F

Species

Species

Traits

B’

Species

S B

Species

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Phylogenetic signal and traitconvergence

assembly patterns

Species

S F

ρ(BF)

Species

Species

Communities

Communities

Species

S B

Traits

B’

X

Species

W

=

Traits

T

Species

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Phylogenetic signal and traitconvergence

assembly patterns

Species

Communities

Communities

Species

S F

Species

Q’

X

Specie

s

W

=

Species

P

ρ(BF)

Species

Species

Communities

Communities

Species

S B

Traits

B’

X

Species

W

=

Traits

T

Species

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Thursday, 26 July, 12


Phylogeny

Phylogenetic signal and traitconvergence

assembly patterns

Species

Communities

Communities

Species

S F

Species

Q’

X

Specie

s

W

=

Species

P

D P

ρ(BF)

Species

Species

S B

Traits

Species

B’

X

Species

Communities

W

=

Traits

Communities

T

D T

ρ(PT)

Species

Pillar & Duarte 2010. Ecology Letters 13: 587-596

Ecological

variables

Communities

E

D E

ρ(TE)

Thursday, 26 July, 12


Phylogene8c niche conserva8sm

Where habitat filtering leads to trait convergence along environmental gradients,

phylogene8cally conserved traits should be favored (Webb et al. 2002). In this case, the

correla8on between E and T is mediated by P. The causal rela8onship between E and T only

exists indirectly by the effect of E on P, indica8ng phylogene8c niche conserva8sm.

E P T

Pillar & Duarte 2010. Ecology Le-ers 13: 587-­‐596.

Thursday, 26 July, 12


Phylogene8c niche conserva8sm

Where habitat filtering leads to trait convergence along environmental gradients,

phylogene8cally conserved traits should be favored (Webb et al. 2002). In this case, the

correla8on between E and T is mediated by P. The causal rela8onship between E and T only

exists indirectly by the effect of E on P, indica8ng phylogene8c niche conserva8sm.

E P T

However, both E and P may be correlated to T, but independent from

each other, in which case, even with high phylogene8c signal at the

metacommunity level (ρ(PT), phylogene8c niche conserva8sm does not

hold.

E

P

T

Pillar & Duarte 2010. Ecology Le-ers 13: 587-­‐596.

Thursday, 26 July, 12


Phylogene8c niche conserva8sm

Where habitat filtering leads to trait convergence along environmental gradients,

phylogene8cally conserved traits should be favored (Webb et al. 2002). In this case, the

correla8on between E and T is mediated by P. The causal rela8onship between E and T only

exists indirectly by the effect of E on P, indica8ng phylogene8c niche conserva8sm.

E P T

ρ(TE.P)=0.631 P=0.0017

However, both E and P may be correlated to T, but independent from

each other, in which case, even with high phylogene8c signal at the

metacommunity level (ρ(PT), phylogene8c niche conserva8sm does not

hold.

E

T

Pillar & Duarte 2010. Ecology Le-ers 13: 587-­‐596.

P

ρ(PE)=0.226 P=0.2386

No evidence of phylogene8c niche conserva8sm

Thursday, 26 July, 12


Plant'func2onal'types'

22"

12.9%'

FT10$

24"

a'

FT2$

38"

62.4%'

53"

FT5$ FT3$

12"

FT7$

FT4$

FT1$

28"

FT6$

10.5"

H6' H5' H4' H3' H2' H1' 0' 1' 2' 3'

20"

FT8$

FT9$

18"

23"

21"

8"

8.1"

8.5"

27"

7"

7.5"

6.5"

4"

5"

Species'composi2on'

19.6%'

53"

28"

22"

38"

12"

10.5"

18"

8.1"

8.5"

8"

H6' H5' H4' H3' H2' H1' 0' 1' 2' 3'

Figure'2:'Trajectories'connec2ng'forest'sites'of'4'to'53'years'a8er'restora2on'described'

by'Plant'Func2onal'Types'(PFT)'(a)'and'species'composi2on'(b)'obtained'by'Principal'

Coordinates' Analysis.' Data' was' log' tranformed.' The' orange' markers' indicate' age' and'

the'green'ones'the'PFTs.'

23"

13.2%'

20"

21"

27"

24"

7"

6.5" 4" 7.5"

5"

b'

Description of woody natural regeneration

(saplings >50cm height and


Vegetation Change

Species composition

(Matrix W)

Community mean traits

(Matrix T)

Fuzzy-weighted composition

(Matrix X or P)

Species B

Trait y

Trait y

T2

T2

T2

T1

d 12

T1

d 12

T1

d 12

Species A

T1 T2 Δ12

A1 A2 A2–A1

B1 B2 B2–B1

Trait x

T1 T2 Δ12

x1 x2 x2–x1

y1 y2 y2–y1

Trait x

T1 T2 Δ12

x1 x2 x2–x1

y1 y2 y2–y1

Thursday, 26 July, 12


1.42#

1.41#

d

1.42#

1.41#

1.42#

1.41#

Community)stability)S12)

1.41#

1.41#

1.41#

1.41#

1.41#

1.41#

R²#=#0.13449#

Community)stability)S12)

1.41#

1.41#

1.41#

1.41#

1.41#

1.41#

R²#=#0.005#

Community)stability)S12)))

1.41#

1.41#

1.41#

1.41#

1.41#

1.41#

R²#=#0.26171#

1.41#

0# 0.2# 0.4# 0.6# 0.8# 1#

1.41#

0# 0.1# 0.2# 0.3# 0.4# 0.5#

1.41#

0# 0.2# 0.4# 0.6# 0.8#

Species)diversity)

Func3onal)diversity)

Func3onal)redundancy)

Species diversity, functional diversity, functional redundancy and community functional stability in grassland communities

in south Brazil.

Grassland quadrats were evaluated for species composition (78 spp) and traits, the area was grazed, and then after 47

community stability (S12) was measured as the chord distance between T1 and T2.

Gini-Simpson index is used for species diversity (D), Rao's quadratic entropy for functional diversity (Q), and functional

redundancy is their difference (FR=D – Q). Biomass height and leaf tensile strength defined Q and FR, which were traits

most likely functional for the community effect on grazing intensity by cattle.

(From Pillar et. al. JVS submitted)

Thursday, 26 July, 12


1.42#

1.41#

d

1.42#

1.41#

1.42#

1.41#

Community)stability)S12)

1.41#

1.41#

1.41#

1.41#

1.41#

1.41#

R²#=#0.13449#

Community)stability)S12)

1.41#

1.41#

1.41#

1.41#

1.41#

1.41#

R²#=#0.005#

Community)stability)S12)))

1.41#

1.41#

1.41#

1.41#

1.41#

1.41#

R²#=#0.26171#

1.41#

0# 0.2# 0.4# 0.6# 0.8# 1#

1.41#

0# 0.1# 0.2# 0.3# 0.4# 0.5#

1.41#

0# 0.2# 0.4# 0.6# 0.8#

Species)diversity)

Func3onal)diversity)

Func3onal)redundancy)

Species diversity, functional diversity, functional redundancy and community functional stability in grassland communities

in south Brazil.

Grassland quadrats were evaluated for species composition (78 spp) and traits, the area was grazed, and then after 47

community stability (S12) was measured as the chord distance between T1 and T2.

Gini-Simpson index is used for species diversity (D), Rao's quadratic entropy for functional diversity (Q), and functional

redundancy is their difference (FR=D – Q). Biomass height and leaf tensile strength defined Q and FR, which were traits

most likely functional for the community effect on grazing intensity by cattle.

(From Pillar et. al. JVS submitted)

Thursday, 26 July, 12


Prospects and

Challenges

Millennium Ecosystem Assessment 2005. Ecosystems

and Human Well-being: Biodiversity Synthesis. In:

World Resources Institute, Washington, DC.

Thursday, 26 July, 12

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