Linking phylogenetic similarity and pollution sensitivity to develop ecological assessment methods: a test with river diatoms

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Linking phylogenetic similarity and pollution sensitivity to develop ecological assessment methods: a test with

river diatoms

François Keck, Agnès Bouchez, Alain Franc, Frédéric Rimet

To cite this version:

François Keck, Agnès Bouchez, Alain Franc, Frédéric Rimet. Linking phylogenetic similarity and pollution sensitivity to develop ecological assessment methods: a test with river diatoms. Journal of Applied Ecology, Wiley, 2016, 53 (3), pp.856-864. �10.1111/1365-2664.12624�. �hal-01426853�

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Linking phylogenetic similarity and pollution sensitivity to develop ecological assessment methods: a test with river diatoms

François Keck

^1,2,*

, Agnès Bouchez

^1,2

, Alain Franc

³

and Frédéric Rimet

^1,2

Version of Record online: 3 MAR 2016

DOI: 10.1111/1365-2664.12624

Journal of Applied Ecology

Special Feature: Quantifying resilience

Volume 53, Issue 3, pages 856–864, June 2016

Author Information

1 

UMR Carrtel, Institut National de la Recherche Agronomique (INRA), Thonon, France 2 

UMR Carrtel, Université de Savoie, Chambéry, France 3 

UMR BIOGECO, Institut National de la Recherche Agronomique (INRA), Cestas, France

*

Correspondence author. E-mail: francois.keck@gmail.com

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n

G= (V, E) V

E G

n×n ij = 1

i j _ij = 0

Aij =

!1 −_p^t ij ≥ ij;i̸=j 0

t p

ai

i v_i s_i

IP S =

"n

i=1a_i×v_i×s_i

"_n

i=1a_i×v_i

v_i s_i a_i

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P

2 0 2

AB CD EF GH IJ KL MN OP QR A S

0 20 40 60 80 100

01234

p

Phylogenetic distance

Trait distance

A B C

D E F

G H

I J K

L M

N O P

Q R S

A B C

t

Trait values

p t

γ

IP SP =

"

γa_γ×v_γ×s_γ

"

γa_γ×v_γ

a_γ =#

i∈γ

a_i, v_γ= 1 n_γ

#

i∈γ

v_i, s_γ= 1 n_γ

#

i∈γ

s_i, n_γ = {i:i∈γ}

s v

t

∆v,i =#

i∈γ

ai(vi−vγ), ∆s,i=#

i∈γ

ai(si−sγ)

a_i s_i v_i

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s_γ v_γ

|x_i−x_γ| x=s x=v

γ "

i∈γ(s_i−s_γ) ="

i∈γ(v_i−v_γ) = 0

s v

i j

ij =$

(s_i−s_j)²

t p

t p = {0.01,0.02,0.03, ...,0.99,1} t = {0.2,0.4,0.6,0.8} p = {0.05,0.1,0.15}

[t,p] t p

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t p

t = 0.2 p = 0.05 t= 0.8 p= 0.15

[0.2,0.1] [0.2,0.15]

[0.2,0.1]

≥ 2

≥2

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-2 -1 0 1

IPS sensi tivity

PSULMVAR MNUMAUSU AUAL AAMBAUIS AUVA AGCUAUDI MHELOAUR TGES TWEITFLU SKPCSKSS TMEDTTEN CBODTNOR CCOSCOCE CINVCTHO SNEOCDUB SHANSAGA STMISPAV DPSTSBIN TPSNDSTE CSCDCDTG CATOCSTR CCRYCCHO TMUSCMEN CBELCACY CHMUUERI SSMUPSBR SLMASELI SSVESCON DMONSPIN DVBRDITE DHIETFLO GRMAAFOR FFVIFDEL CTPUTHNI CGAIFFAM TTABTFAS SMIN UULN UUAN UUAC UACU FCA1 FRUM CEREFBID FAUTFCRO FCVAFPEM NACDNFON NOVANIFR FPCYNMIC NHANNZSU NINCDKUE CCLONAPI TAPINIVI NFILNSIG NTHMNCOM NACINIPU NDRANPAE NPADNCPL NILGNPAL PPANHAMP NDISNLIN NLOR NSIOBPAX HSPCHPDO HCRU GTHYPELO GYACGLIM NSLC HCAPNDIT NPNU NGRENVEN NCRYNPHY NCTONREI NROSNCTE NLUN NVRONCIN NDUR NSYM NLAN NCARNTPT NRADNCPR DGAL CPEDPLFR CPLE CPLALHUN DULV ASPHPELG

PCOSECAE EMNTESNC

ESLE CASPCAEX

CAFF CLANCBNA

CTUMCPRX RSIN GROSGBOB

GCAPGTRU GCLAGACU

GPROGANGGAFF GCLEGPAR

GLGNGPASGEXL EULAADMI

FVULBRUTALIB AMFOAPEDACOF

AMMOANORRGIB ETUREARG

ESORSSPL STCUCELLSUMI

SANGEORN EALA EPTU

EPAL FPEL FSAP STKR STACSGRL SPHO STAN CRCU ESBM CMLF CRAC DSBO

LGOE SCPE NEPR NBIS NEAF MAPE MAATMAFO MPMI CABUPNOD PACR PSGIPPVS PMI3PGIB PSELPSCAPOMUPIANPINTPMEUPBSIPBORPBMUPNGFPRUPCLAUCSILFFORFMOCSEBASELASPUPSSEMSEMNEMONEBILEPECEMINEIMPEFOREGLAPGSTRAMPCWAITPSD

s t= 0.6 p= 0.1

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t p

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p t

Nu mb er

of clusters

Samples correctly classiﬁed (%)

30 50 70

50 100 150

30 40 50 60 70 80

Number of clusters

Samples correctly classified (%)

A

B

0.2 0.4 0.6 0.8 1.0

0.2 0.4

0.6 0.8 50

100 150 200 250

t p

p

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0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

0 5 10 15 20 0

5 10 15 20

p = 0.1 p = 0.15

p = 0.05

t = 0.2t = 0.4t = 0.6t = 0.8

[t,p] t

p

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p t

t p

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p > 0.25

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