1
Electronic supplementary information (ESM) for “Repeated evolution of salt-tolerance in
grasses” T. H. Bennett, T. J. Flowers, L. Bromham
Contents:
1. Defining and recording salt tolerance in grasses
Table S1: list of halophytes included in species-level phylogeny and references
Table S2: list of genera containing halophytes and euhalophytes and references
2. Phylogenetic and taxonomic distribution of halophytes
3. Reconstructing patterns of gain and loss
3.1: Likelihood ASR
3.2: Parsimony ASR
Figure S1: Genus level phylogeny, including euhalophytes
Figure S2: Cumulative numbers of species and halophytes in genera
Figure S3: Examples of sub-family patterns in Andropogoneae and Pooideae
4. Sensitivity analysis
Figure S4: Weighted parsimony analysis
Figure S5: Likelihood sensitivity analysis
Figure S6: Likelihood sensitivity analysis
5. Rate variation between lineages
Table S3: Simulations of character evolution in subfamilies
ESM References
2
1. Defining and recording salt tolerance in grasses
We defined halophytes as species that can complete their life-cycle in soil with an electrical
conductivity equivalent to ~80 mM NaCl at saturation [1]. Under this definition, halophytes are
more salt-tolerant than an estimated ~98% of angiosperms [2]. This definition, encompassing
‘salt-tolerant glycophytes’, facultative halophytes and obligate halophytes was used because our
study focussed on the ability to grow in saline environments, rather than to grow exclusively or
preferentially in these. We treated salt-tolerance as a binary character (halophytic / nonhalophytic) because continuous measurements of tolerance are not available for most species.
Evidence of salt-tolerance mostly comes from observations of the natural growing conditions of a
species, and in some cases from growth experiments. The validity of observational data is
contingent on careful measurement over the life-cycle, and a number of variables that confound a
single definition of salinity being used for all plants, such as: variable soil moisture with rainfall or
tidal inundation events, or across seasons; the balance of ions contributing to the level of electrical
conductivity, which vary in their toxicities; and variation in salinity across the soil profile, and
between the bulk soil and the root zone.
There is generally little data available as to the stringency with which halophytes have been
identified in individual studies. Experimental approaches allow controlled testing of growth under
different salt concentrations, which can validate field observations. Patchy salinity and fluctuating
conditions make such experiments notoriously difficult ‘in the field,’ and consequently most are
conducted in laboratories [3]. However, estimates of tolerance in laboratory studies may have
little relevance to naturally occurring saline habitats - a number of crop varieties and transgenics
tolerate high salinity under laboratory conditions, but not in the field [4].
Because of these difficulties, it is possible that we have failed to identify all salt-tolerant grasses in
the phylogeny. Failure to include all halophytes is likely to be conservative in that it will generally
result in fewer reconstructed origins of salt tolerance. One exception would be failure to identify
halophytes that are nested within salt tolerant clades, which could lead us to incorrectly infer
multiple origins of salt tolerance within a mixed clade. However, there are relatively few such
clades in our reconstruction. Another exception would be if a miscoded species is closely related to
other halophytes, where this could result in either more or less origins. We find that halophytes
are so scattered across the phylogeny that the patterns we infer would not be qualitatively affected
unless a large proportion of species reported as halophytes were in fact glycophytes. Given that
most of our halophytes are identified as naturally occurring in salty habitats, miscoding
glycophytes as halophytes is less likely to be a major problem for our dataset.
Sparse taxon-sampling in the species level phylogeny is likely to result in a conservative estimate
of number of origins. The species level phylogeny used in our study sampled ~20% of known
grass species, so many salt tolerant taxa are not included. It seems likely that increased taxon
sampling would result in a greater number of gains and losses of salt-tolerance being inferred.
To allow for the limitations in defining salt tolerance based on the criteria discussed above, we
repeated some analyses using a stricter definition. We defined “euhalophyte” species as those with
a high level of salt-tolerance, able to complete their life cycle in conditions equivalent to >200 mM
NaCl (approximately half the saltiness of seawater) [5].
3
Table S1. List of halophytes in the species-level phylogeny. We collated data from the published literature,
starting with a worldwide database of halophytes compiled by Aronson [1] and updated by Menzel &
Leith [6, 7]. Further literature search focused on recent studies of halophyte diversity, in geographic
regions poorly covered in this global database. The ‘Grass Genera of the World’ [8] also lists genera
containing salt-tolerant taxa, and was used to guide further literature search on genera indicated to include
halophytes, which had not been covered by other data sources. This list represents all halophytes identified
in the species-level phylogeny, rather than all halophytes in the grass family.
Species
Aeluropus littoralis
Agropyron cristatum
Agrostis stolonifera
Alopecurus myosuroides
Ammophila arenaria subsp arundinacea
Apluda mutica
Aristida adscensionis
Aristida funiculata
Aristida mendocina
Aristida setacea
Arundinella nepalensis
Arundo donax
Astrebla lappacea
Avena sterilis
Beckmannia syzigachne
Bothriochloa bladhii
Bothriochloa ischaemum
Bothriochloa pertusa
Bromus arvensis
Bromus inermis
Bromus japonicus
Bromus rubens
Bromus scoparius
Bromus tectorum
Buchloe dactyloides
Calamagrostis epigeios
Calamagrostis pseudophragmites
Calamovilfa gigantea
Capillipedium parviflorum
Catabrosa aquatica
Cenchrus calyculatus
Cenchrus ciliaris
Cenchrus setiger
Chloris barbata
Chloris gayana
Chloris truncata
Chloris virgata
Chrysopogon fulvus
Chrysopogon serrulatus
Coix lacryma-jobi
Cottea pappophoroides
Crypsis schoenoides
Crypsis vaginiflora
Cutandia memphitica
Cymbopogon citratus
Cymbopogon flexuosus
Cymbopogon martinii
Cynodon arcuatus
Cynodon dactylon
Dactyloctenium aegyptium
Desmazeria rigida
Dichanthium annulatum
Dichanthium aristatum
Digitaria ciliaris
Digitaria sanguinalis
Dinebra retroflexa
Reference:
[1, 2]
[1, 2]
[3]
[4]
[4]
[5, 6]
[7]
[5, 6]
[1, 2]
[5, 6]
[5, 6]
[3]
[1, 2]
[4]
[3]
[5, 6]
[5, 6]
[5, 6]
[4]
[4]
[4]
[4]
[4]
[4]
[1, 2]
[1, 2]
[4]
[1, 2]
[5, 6]
[4]
[8]
[3]
[5, 6]
[5, 6]
[1, 2]
[9]
[1, 2]
[5, 6]
[5, 6]
[5, 6]
[1, 2]
[4]
[3]
[10]
[5, 6]
[5, 6]
[5, 6]
[8]
[3]
[3]
[11]
[1, 2]
[5, 6]
[5, 6]
[1, 2]
[5, 6]
4
Distichlis distichophylla
Distichlis humilis
Distichlis palmeri
Distichlis scoparia
Distichlis spicata
Echinochloa colona
Echinochloa crus galli
Echinochloa frumentacea
Echinochloa stagnina
Eleusine indica
Elymus farctus
Elymus repens
Enteropogon dolichostachyus
Enteropogon macrostachyus
Eragrostis ciliaris
Eragrostis curvula
Eragrostis dielsii
Eragrostis obtusiflora
Eragrostis pilosa
Eragrostis superba
Eragrostis tenella
Eragrostis unioloides
Festuca arundinacea subsp arundinacea
Festuca pseudovina
Festuca rubra
Holcus lanatus
Hordeum bogdanii
Hordeum brachyantherum subsp brachyantherum
Hordeum euclaston
Hordeum flexuosum
Hordeum jubatum
Hordeum marinum
Hordeum marinum subsp gussoneanum
Hordeum marinum subsp marinum
Hordeum murinum
Hordeum murinum subsp murinum
Hordeum roshevitzii
Hordeum secalinum
Hordeum stenostachys
Hordeum vulgare
Hordeum vulgare subsp spontaneum
Hordeum vulgare subsp vulgare
Imperata brasiliensis
Imperata cylindrica
Ischaemum afrum
Jouvea pilosa
Lagurus ovatus
Leptochloa fusca
Leptochloa fusca subsp uninervia
Lepturus repens
Leymus angustus
Leymus arenarius
Leymus chinensis
Leymus cinereus
Leymus mollis
Leymus multicaulis
Leymus paboanus
Leymus racemosus
Leymus secalinus
Lolium multiflorum
Lolium rigidum
Lolium subulatum
Lophopyrum elongatum
Lygeum spartum
Melinis repens
Micropyrum tenellum
Miscanthus sinensis
Molinia caerulea
[3]
[1, 2]
[3]
[3]
[3]
[7]
[7]
[5, 6]
[5, 6]
[3]
[3]
[1, 2]
[5, 6]
[1, 2]
[5, 6]
[1, 2]
[3]
[3]
[5, 6]
[1, 2]
[5, 6]
[5, 6]
[4]
[1, 2]
[3]
[4]
[7]
[3]
[1, 2]
[1, 2]
[3]
[3]
[1, 2]
[3]
[4]
[4]
[8]
[1, 2]
[3]
[1, 2]
[1, 2]
[1, 2]
[1, 2]
[4]
[5, 6]
[3]
[4]
[3]
[3]
[8]
[8]
[3]
[8]
[12]
[8]
[8]
[8]
[3]
[7]
[1, 2]
[4]
[4]
[3]
[3]
[5, 6]
[3]
[1, 2]
[4]
5
Monanthochloe littoralis
Monerma cylindrica
Oryza coarctata
Oryza meyeriana
Panicum antidotale
Panicum repens
Panicum schinzii
Panicum virgatum var cubense
Parapholis incurva
Pascopyrum smithii
Paspalidium geminatum
Paspalum conjugatum
Paspalum notatum
Paspalum vaginatum
Pennisetum alopecuroides
Pennisetum glaucum
Phalaris arundinacea
Phalaris canariensis
Phragmites australis
Poa annua
Poa bulbosa
Poa bulbosa subsp vivipara
Poa pratensis
Poa secunda
Poa trivialis
Polypogon maritimus
Polypogon monspeliensis
Psathyrostachys juncea
Puccinellia distans
Puccinellia glaucescens
Puccinellia interior
Puccinellia stricta
Reederochloa eludens
Rhynchelytrum repens
Rostraria cristata
Saccharum arundinaceum
Saccharum ravennae
Saccharum robustum
Saccharum spontaneum
Setaria sphacelata
Setaria verticillata
Setaria viridis
Spartina alterniflora
Spartina alterniflora var glabra
Spartina anglica
Spartina densiflora
Spartina densiflora x Spartina foliosa
Spartina foliosa
Spartina gracilis
Spartina maritima
Spartina patens
Spartina pectinata
Spartina spartinae
Sphenopus divaricatus
Spinifex littoreus
Sporobolus consimilis
Sporobolus fimbriatus
Sporobolus pyramidatus
Sporobolus wrightii
Stenotaphrum dimidiatum
Stenotaphrum secundatum
Stipagrostis namaquensis
Stipagrostis pennata
Stipagrostis plumosa
Tetrachne dregei
Thinopyrum intermedium
Thinopyrum ponticum
Thinopyrum scirpeum
[3]
[3]
[3]
[1, 2]
[7]
[8]
[5, 6]
[1, 2]
[3]
[13]
[1, 2]
[5, 6]
[5, 6]
[5, 6]
[1, 2]
[5, 6]
[1, 2]
[4]
[3]
[4]
[4]
[4]
[1, 2]
[3]
[4]
[3]
[3]
[13]
[3]
[3]
[3]
[3]
[3]
[5, 6]
[4]
[3]
[14]
[3]
[3]
[5, 6]
[5, 6]
[1, 2]
[3]
[3]
[8]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[3]
[1, 2]
[3]
[3]
[3]
[3]
[5, 6]
[3]
[3]
[1, 2]
[1, 2]
[1, 2]
[4]
[3]
[3]
6
Trichloris crinita
Uniola paniculata
Urochloa mutica
Urochloa ramosa
Vetiveria zizanioides
Zizania aquatica
Zoysia japonica
Zoysia matrella
[3]
[3]
[5, 6]
[5, 6]
[5, 6]
[3]
[8]
[8]
References for Table S1:
1.
Menzel U., Lieth H. 1999 Annex 4: Halophyte Database Version 2. Halophyte uses in
different climates 1, 159-258.
2.
Menzel U., Lieth H. 2003 Halophyte database version 2.0. In Cash crop halophytes: recent
studies, Kluwer Academic Publishers, Dordrecht, pp.221-250.
3.
Aronson J.A. 1989 HALOPH: a data base of salt tolerant plants of the world, Office of Arid
Lands Studies, Arizona.
4.
Guvensen A., Gork G., Ozturk M. 2006 An overview of the halophytes in Turkey. In
Sabkha Ecosystems Volume II: West and Central Asia (eds. Lieth H., Kratochwil A.) pp9-30, Springer
Netherlands.
5.
Dagar J.C. 2003 Biodiversity of Indian Saline Habitats and Management & Utilization of
High Salinity Tolerant Plants with Industrial Application for Rehabilitation of Saline Areas. In
Desertification in the third millennium, p151, Swets and Zeitlinger, The Netherlands.
6.
Dagar J.C., Gurbachan S. 2007 Biodiversity of Saline and Waterlogged Environments:
Documentation, Utilization and Management. NBA Scientific Bulletin Number 9, National
Biodiversity Authority, Chennai, India, p78.
7.
Khan M.A., Qaiser M. 2006 Halophytes of Pakistan: characteristics, distribution and
potential economic usages. In Sabkha Ecosystems Volume II: West and Central Asia (eds. Lieth H.,
Kratochwil A.), pp.129-153. Springer Netherlands
8.
Zhao K., Song J., Feng G., Zhao M., Liu J. 2011 Species, types, distribution, and economic
potential of halophytes in China. Plant and Soil 342(1-2), 495-509.
9.
Bann G. 2006 The use of native (endemic) grass and tree species for dryland salinity
mitigation, remediation and agronomy activities in south-east Australia. In: Proceedings of the 13th
Australian Society of Agronomy Conference, Perth, Western Australia, 10-14 September 2006.
10.
Abbas J.A., Khan M.A., Böer B., Kust G.S., Barth H.-J. 2006 Economic halophytes of
Bahrain. In Sabkha Ecosystems Volume II: West and Central Asia (eds. Lieth H., Kratochwil A.), pp.
113-120, Springer Netherlands.
11.
Hafsi C., Atia A., Lakhdar A., Debez A., Abdelly C. 2011 Differential Responses in
Potassium Absorption and Use Efficiencies in the Halophytes Catapodium rigidum and Hordeum
maritimum to Various Potassium Concentrations in the Medium. Plant Production Science 14(2),
135-140.
12.
Gorham J., McDonnell E., Jones R.G.W. 1984 Salt tolerance in the Triticeae: Leymus
sabulosus. Journal of Experimental Botany 35(8), 1200-1209.
13.
Rogers M.E., Noble C.L., Pederick R.J. 1996 Identifying suitable grass species for saline
areas. Australian Journal of Experimental Agriculture 36(2), 197.
14.
Shehu J., Mullaj A., Ibraliu A. 2010 Salt Marshes Plant Diversity of Coastal Zone in
Albania. In Proceedings in the Fourth International Scientific Conference BALWOI 2010 on water
observation and information systems for decision support, Republic of Macedonia, 25–29 May 2010.
7
Table S2. List of genera containing salt-tolerant species under a less restrictive definition
(halophytes) and a more restrictive definition (euhalophytes). As most studies identify species that
meet the less restrictive definition of a halophyte, but provide no information of whether they
meet a higher level of tolerance, the list of genera coded as containing a 200 mM halophyte is
likely to be a conservative minimum.
Genus
Reference
Contains Euhalophyte Euhalophyte Reference
Achnatherum
Aeluropus
Agropyron
Agropyropsis
Agrostis
Aira
Alopecurus
Ammophila
Apluda
Aristida
Arundinella
Arundo
Astrebla
Avena
Beckmannia
Bothriochloa
Brachiaria
Bromus
Buchloe
Calamagrostis
Calamovilfa
Capillipedium
Catabrosa
Catapodium
Cenchrus
Chloris
Chrysopogon
Coelachyrum
Coix
Cottea
Crypsis
Cymbopogon
Cynodon
Dactyloctenium
Desmostachya
Dichanthium
Digitaria
Dinebra
Distichlis
Echinochloa
Ehrharta
Eleusine
Elymus
Elytrigia
Enteropogon
Eragrostis
Eremochloa
Festuca
Hainardia
Halopyrum
Holcus
Hordeum
Imperata
Ischaemum
Jouvea
Lagurus
Lasiurus
Leptochloa
Leptothrium
[1, 2]
[3]
[4]
[5]
[4]
[5]
[5]
[5]
[6, 7]
[1, 2]
[6, 7]
[4]
[3]
[5]
[4]
[6, 7]
[6, 7]
[5]
[3]
[3]
[3]
[6, 7]
[5]
[8]
[1, 2]
[6, 7]
[6, 7]
[4]
[6, 7]
[3]
[5]
[6, 7]
[5]
[4]
[4]
[3]
[3]
[6, 7]
[4]
[1, 2]
[4]
[4]
[6, 7]
[9]
[6, 7]
[6, 7]
[3]
[5]
[4]
[4]
[5]
[1, 2]
[3]
[6, 7]
[4]
[5]
[3]
[6, 7]
[6, 7]
Yes
Yes
Yes
[1, 2]
[6, 7]
[6, 7]
Yes
[6, 7]
Yes
Yes
[6, 7]
[6, 7]
Yes
Yes
[1, 2]
[1, 2]
Yes
Yes
[1, 2]
[1, 2]
Yes
[1, 2]
Yes
Yes
[1, 2]
[6, 7]
Yes
Yes
[6, 7]
[6, 7]
Yes
[6, 7]
Yes
[6, 7]
Yes
[6, 7]
Yes
[1, 2]
Yes
[1, 2]
Yes
Yes
[6, 7]
[6, 7]
8
Lepturidium
Lepturus
Leymus
Lolium
Lopholepis
Lygeum
Melanocenchris
Melinis
Micropyrum
Miscanthus
Molinia
Monanthochloe
Muhlenbergia
Myriostachya
Ochthochloa
Odyssea
Orinus
Orthochloa
Oryza
Panicum
Pappophorum
Parapholis
Paspalidium
Paspalum
Pennisetum
Phacelurus
Phalaris
Phleum
Phragmites
Poa
Polypogon
Psathyrostachys
Pseudoraphis
Pseudosclerochloa
Psilolemma
Puccinellia
Reederochloa
Rostraria
Rytidosperma
Saccharum
Sacciolepis
Sclrerochloa
Schoenefeldia
Sehima
Setaria
Spartina
Sphenopus
Spinifex
Sporobolus
Stenotaphrum
Stipa
Stipagrostis
Tetrachne
Thuarea
Trachys
Tragus
Trichloris
Trikeraia
Uniola
Urochloa
Urochondra
Vetiveria
Zizania
Zizaniopsis
Zoysia
[10]
[1, 2]
[1, 2]
[3]
[6, 7]
[4]
[6, 7]
[6, 7]
[4]
[3]
[5]
[4]
[3]
[6, 7]
[4]
[4]
[3]
[1, 2]
[4]
[1, 2]
[3]
[4]
[3]
[6, 7]
[3]
[3]
[3]
[5]
[4]
[5]
[4]
[11]
[6, 7]
[1, 2]
[4]
[5]
[4]
[5]
[3]
[4]
[6, 7]
[1, 2]
[6, 7]
[6, 7]
[6, 7]
[4]
[4]
[4]
[4]
[6, 7]
[5]
[4]
[3]
[3]
[6, 7]
[6, 7]
[4]
[3]
[4]
[4]
[1, 2]
[6, 7]
[4]
[4]
[6, 7]
Yes
Yes
[1, 2]
[1, 2]
Yes
[6, 7]
Yes
[6, 7]
Yes
[1, 2]
Yes
[6, 7]
Yes
[1, 2]
Yes
[6, 7]
Yes
[6, 7]
Yes
[1, 2]
Yes
[6, 7]
Yes
Yes
[6, 7]
Yes
[1, 2]
Yes
Yes
[1, 2]
[6, 7]
Yes
[6, 7]
Yes
[6, 7]
Yes
Yes
[6, 7]
Yes
[6, 7]
9
References for Table S2:
1.
Zhao K., Hai F., Ungar I.A. 2002 Survey of halophyte species in China. Plant Science
163(3), 491-498.
2.
Zhao K., Song J., Feng G., Zhao M., Liu J. 2011 Species, types, distribution, and economic
potential of halophytes in China. Plant and Soil 342, 495-509.
3.
Menzel U., Lieth H. 2003 Halophyte database version 2.0. Cash crop halophytes: recent studies
Kluwer Academic Publishers, Dordrecht, 221-250.
4.
Aronson J.A. 1989 HALOPH: a data base of salt tolerant plants of the world, Office of Arid
Lands Studies, Arizona.
5.
Shay G. (ed.) 1990 Saline agriculture: salt-tolerant plants for developing countries, Report of a
Panel of the Board on Science and Technology for International Development Office of
International Affairs, National Academies Press.
6.
Dagar J.C., Gurbachan S. 2007 Biodiversity of Saline and Waterlogged Environments:
Documentation, Utilization and Management. NBA Scientific Bulletin Number 9, National
Biodiversity Authority, Chennai, India, p78.
7.
Dagar J.C. 2003 Biodiversity of Indian Saline Habitats and Management & Utilization of
High Salinity Tolerant Plants with Industrial Application for Rehabilitation of Saline Areas. In
Desertification in the third millennium, p151, Swets and Zeitlinger, The Netherlands.
8.
Barhoumi Z., Atia A., Rabhi M., Djebali W., Abdelly C., Smaoui A. 2010 Nitrogen and
NaCl salinity effects on the growth and nutrient acquisition of the grasses Aeluropus littoralis,
Catapodium rigidum, and Brachypodium distachyum. Journal of Plant Nutrition and Soil Science
173(1), 149-157.
9.
Colmer T.D., Flowers T.J., Munns R. 2006 Use of wild relatives to improve salt tolerance
in wheat. Journal of Experimental Botany 57(5), 1059.
10.
Maunder M., Leiva A., Santiago-Valentin E., Stevenson D.W., Acevedo-Rodruiguez P.,
Meerow A.W., Mejia M., Clubbe C., Francisco-Ortega J. 2008 Plant conservation in the Caribbean
Island biodiversity hotspot. The Botanical Review 74(1), 197-207.
11.
Rogers M.E., Noble C.L., Pederick R.J. 1996 Identifying suitable grass species for saline
areas. Australian Journal of Experimental Agriculture 36(2), 197.
10
2. Phylogenetic and taxonomic distribution of halophytes
We used Kew’s grass synonymy database [9] to match halophytes from our database to those
included in the phylogenies.
To test if our results were robust to the phylogeny used, we conducted analyses on two different
phylogenies: a species-level phylogeny containing 2684 taxa, or ~20% of described grass species
[10], and a genus-level phylogeny containing representatives of all ~800 grass genera [11]. The
genus-level phylogeny was also used to investigate the distribution of “euhalophyte” species with
a high level of salt-tolerance equivalent to >200 mM NaCl (approximately half seawater) [5].
Minimal length branches in the species-level phylogeny were collapsed to polytomies for all
analyses, and unless otherwise specified, results of our analyses refer to this species-level
phylogeny.
We estimated the phylogenetic signal of salt-tolerance using two measures. The D statistic [12]
measures phylogenetic signal in a binary character by calculating the observed sum of sister-clade
differences and comparing it to that expected under a random and a Brownian evolutionary model.
This has the advantage of providing an indication of the strength of phylogenetic signal, and is
roughly comparable across a range of trees and state frequencies. The Maddison-Slatkin
randomization test uses a parametric bootstrapping approach to compare the distribution of the
trait on the tips of the phylogeny [13].
The Maddison-Slatkin test indicates that the phylogenetic distribution of salt tolerance is
significantly non-random (P=0.0001), indicating that salt tolerance is more likely to be found in
some clades than others. The D-statistic also indicates that the trait is phylogenetically nonrandom (D = 0.67, P(random) <0.001), but that it shows less phylogenetic signal than expected if
salt-tolerance is evolving by Brownian motion (P(Brownian) <0.001) on a tree with equal branchlengths. This suggests that although salt tolerance shows some clustering on the phylogeny, it is
an evolutionary labile trait. Results were similar under all scalings of branch-lengths (molecular,
equal, ultrametric).
11
3. Reconstructing patterns of gain and loss
We implemented maximum likelihood (ML) and unweighted maximum parsimony methods of
ancestral state reconstruction with the ‘trace character history’ function of Mesquite 2.73 [14].
Importantly, unless a node on the phylogeny was ‘unambiguously’ reconstructed as salt-sensitive
(i.e. either a salt-sensitive state was the single most parsimonious reconstruction (MPR) at the
node with parsimony, or 2 Log-likelihood units better than the salt-tolerant state for likelihood,
discussed further below), it was assigned to be salt-tolerant. To estimate the pattern of gain and
loss we then inferred the most parsimonious set of state-changes to explain the estimated
character states. A change from a parent node ‘unambiguously’ estimated to be non-halophytic to
a salt-tolerant terminal or node represents an origin.
By coding ‘ambiguous’ nodes as salt-tolerant, we conservatively biased our reconstructions
towards estimating fewer origins, deeper in the phylogeny, and this is one reason to think that our
estimate of the number of origins may be conservative. Another reason to think that our ML
estimate of the number of origins is conservative is that we did not account for rate variation
across the phylogeny (table S3), which is likely to have led to underestimation of rates in clades
with many halophytes.
3.1: Likelihood ASR
Using ML reconstruction with the best fitting model, we estimate that salt-tolerance has evolved
76 times across the grass family, with origins reconstructed in a wide range of grass lineages
(figure 1). This best-fitting model had a rate of loss of salt-tolerance ~10 times greater than the
rate of gain (Mk2), with branch-lengths scaled to be equal as this resulted in significantly better fit
with the Mk2 model than using branch-length estimates from molecular data (P<0.001) [15].
Details of the analysis are provided below. As well as evolving frequently, salt-tolerance appears
to show a ‘tippy’ pattern of evolution, with most origins occurring close to the tips of the
phylogeny and giving rise to few salt-tolerant species (figure 1). Using the best fitting ML model
we estimate 76 origins for 200 halophyte, or ~2.6 halophyte per origin.
For likelihood reconstructions, we compared the fit of a Markov k-state model with a single
parameter for the instantaneous transition rate of gain and loss (Mk1) to one where these rates are
separate (Mk2). The significance of the improvement in model fit when adding additional model
parameters (e.g. a second rate parameter) was tested using a likelihood-ratio (LR) test. For this
test, the more complex mode is preferred at a given significance level over the simpler model if the
improvement in the log-likelihood under that model is half the critical value for a chi-squared
distribution with one degree of freedom (=1.92, or ~2) [16]. The two-rate Markov model fit had a
rate of loss ~10 times greater than that of gain, and fit the data significantly better than the equalrates model, as attested by the large likelihood difference between their fit under all branch-length
scalings (LR test, P<0.001). This improved fit is likely to have been affected by the skewed
proportions of the two character states in the tree, which allow better fit with a two-rate model,
but which can also be produced by rate variation across the tree, or differential diversification
between character states [17]. Regardless, reconstructions with the two-rate model produced
more conservative estimates of the number of origins than the best-fitting equal-rates model.
We determined the scaling of branch-lengths used for our analyses by comparing the fit of models
when branch-lengths were taken from molecular data, scaled to be equal, or made ultrametric
using the ‘arbitrarily ultrametricise’ function of Mesquite, which produces ultrametric branchlengths similar to those suggested by Pagel [18]. Model fit was significantly improved when
branch-lengths were made equal (Mk1 -lnL = 729.4, Mk2 –lnL = 656.2), compared to using
molecular branch lengths (Mk1 -lnL = 823.5, Mk2 –lnL = 696.3) or ultrametric branch lengths
(Mk1 -lnL = 895.2, Mk2 –lnL = 765.2). While improved model fit with equal branch-lengths has
been taken as evidence for a ‘speciational’ model of character change [19], this was probably
12
favoured because of poor model fit with molecular and ultrametric branch lengths, which was
likely due to character changes being forced onto short branches of the phylogeny necessitating
very high rates of character change across the tree, a problem with likelihood methods described
by Pagel [20]. We estimated Pagel’s kappa parameter, which is used to scale branch-lengths
between those estimated from molecular data and equal branch lengths, in the R package ‘geiger’
[21]. The ML value of kappa under the two-rate model was zero, implying that equal branchlengths were the optimal scaling. Using equal branch-lengths also produced smaller estimates of
the number of origins and estimated rates of character change than when using alternative
branch-length scalings. Given that equal branch-lengths showed the best fit, and were the most
conservative scaling for estimating the amount of character change, they were used for analyses
unless otherwise stated (following [15, 22]).
We also tested different assumptions of the root-state under all models and branch-length
scalings, however, these had minimal effects on the pattern estimated. Alternative states were set
as equally likely at the root, to the equilibrium state frequencies (calculated from the proportion of
character states in extant taxa), and fixed to either state. We only report results when assuming
that the likelihood of either state at the root is at the equilibrium frequencies of extant character
states.
To estimate confidence of character states at internal nodes we compared the likelihoods of both
states, considering a state significantly supported if the probability of that state is >2 loglikelihood units better than the other, a rule-of-thumb suggested by Edwards [23]. These limits
are generally viewed as conservative, and would be analogous to 95% confidence intervals if
assuming that the maximum likelihood values were normally distributed [24]. Nodes that could
not be confidently assigned to a salt-sensitive state were assigned as salt-tolerant. As discussed
above, this biased our results towards estimating relatively few origins, early in the tree.
3.2 Parsimony ASR
Using unweighted parsimony reconstruction, we reconstructed over 100 origins of salt tolerance.
We also conducted a sensitivity analysis using weighted parsimony, which supported a high
number of origins (figure S4).
13
Figure S1. Distribution of genera which contain at least one halophyte (defined in [1] - red circles) and
one euhalophyte (defined in [5] - blue circles), on a completely sampled phylogeny of grass genera (the
strict consensus of the 100 most parsimonious trees from [11]). As halophytes were the minority of species
in most genera coded as salt-tolerant (figure S2), we used unweighted parsimony to estimate the number of
origins. We estimate that salt-tolerance has evolved 107 times. We also estimate that a higher level of salttolerance, equivalent to carrying out the life cycle in 200 mM salt, has evolved 43 times. In one way this
estimate is conservative, as most studies of halophyte diversity do not report which species meet this more
restrictive criteria.
Cumulative Frequency of Genera
14
Number of halophytes in genus
Figure S2: Cumulative frequency plot of the number and proportion of halophytes in those genera that
contain salt-tolerant species. Most of these genera contain a small number (median =1) and proportion
(median = 25%) of halophytes. The total number of species in each genus came from ‘Grass Genera of the
World’, available through Kew’s Grassbase [9], while the total number of halophytes came from our
database.
15
Figure S3: (a) Unweighted parsiomony reconstruction of the evolution of salt-tolerance in a major
subclade of the subfamily Pooideae b) Unweighted parsimony reconstruction in the subfamily
Andropogoneae. Halophytes are marked in red. While the exact interpretation of the evolutionary pattern
in these clades depends on the model used, halophytes are widely scattered across both groups, and are
rarely found in large clades, suggesting that salt-tolerance has evolved many times in each.
16
a) Subsection of Pooideae
17
(b)
Andropogoneae
18
4. Sensitivity analysis
We conducted weighted parsimony analysis in Mesquite by creating stepmatrices with a range of
costs assigned to gain and loss, from a slight bias towards parallel gains to a strong bias favouring
loss. Where multiple MPR’s were available, we selected the history that inferred the fewest
parallel gains of salt-tolerance, by treating ‘ambiguous’ nodes as halophytic, pushing origins
deeper in the tree. Using unweighted parsimony, 162 origins of salt-tolerance were estimated. The
number of origins inferred was sensitive to weightings of gain and loss, however a high number of
origins (i.e. >50) were supported in all models except those in which the inferred number of losses
was much higher than that of gains (Figure S4). Given that parsimony is conservative in
estimating the amount of character change, these results suggest a large number of independent
origins.
Figure S4: Weighted parsimony analysis, showing the minimum number of origins and ‘unambiguous’
losses of salt-tolerance on the species-level phylogeny.
We also conducted a likelihood sensitivity analysis to test whether the pattern of multiple origins
was sensitive to a range of biases in the transition rates [following methods of 21, 22, 23]. We
changed the ratio of the rate of loss relative to the rate of gain, and estimated the number of
origins and model likelihood under a range of ratios. Importantly, we allowed the transition rates
to take the values estimated to maximize the reconstruction likelihood under each model, to avoid
arbitrarily choosing rates that create a biased value [25].
As the estimated amount of change depends on the absolute rates under a given bias, we also
investigated the effect of slowing the transition rate under the optimal ratio of gain:loss. We found
the range of model parameters that did not produce a significantly poorer fit to the data by using a
LR test with one degree of freedom [following 23, 25, 26 p.19], and all of these supported a high
number of origins (Figure S5, Figure S6). Even when the transition rates were highly biased
towards the loss of salt-tolerance, a large number of origins were estimated (Figure S6). Models
with an even slightly stronger bias towards loss were significantly less likely than the maximum
likelihood estimates (Figure S6), as were those with slightly slower rates under the optimal bias of
rates of gain and loss (Figure S5).
19
Figure S5: Likelihood sensitivity analysis of model-fit under slower and faster rates of character change,
under the maximum-likelihood estimate of the bias between the forwards and backwards rates (0.082).
Models that implied significantly slower rates of change (the dashed line indicates the values threshold of
values significantly rejected with a likelihood difference <1.92) recovered the same number of origins of
salt-tolerance as with the ML rate estimates.
Figure S6: a) Number of origins under the two-rate model when altering the ratio of the rate parameters
for gain and loss, estimating the ML rates under the bias in each case; and b) Model fit at different rate
biases. The dashed line in b) indicates the values not significantly rejected (likelihood difference <1.92).
The pattern of many origins is robust to a strong bias in the rate of loss over gain, and the range of values
that were not significantly rejected are very similar to the ML values.
20
5. Variation between lineages:
The Markov model used in our ML analyses assumes a constant rate of gain and loss over the
whole phylogeny, which is likely to be violated, given that we see many halophytes in some clades
(e.g. subfamily Chloridoideae) and none in others (e.g. subfamily Danthonioideae / Bambuseae).
Accounting for variation in the rate of character change can improve model fit compared to using
a single model across the whole tree [26].We found that estimating the model independently in
clades where halophytes were common significantly improved model-fit, and implied that salttolerance evolved more frequently, and closer to the tips of the phylogeny, than when using the
transition rates estimated from the whole phylogeny. In this way, or reconstructions of the
number of origins on the whole phylogeny is likely to have been conservative. For example,
simulating character evolution in Mesquite using ‘stochastic character mapping’ based on the ML
model parameters estimated from the Andropogoneae gave significantly better model fit, and
estimated 35 origins of salt-tolerance in this group, compared to eight when using the model
parameters estimated over the whole tree (table S3). A similar pattern was seen in other groups
(table S3). Thus, it appears that to some extent the evolutionary lability of salt-tolerance may have
been underestimated using likelihood methods on the whole phylogeny, and that taking rate
variation into account, even on a very coarse scale can improve model fit and our inference of the
evolutionary pattern. While differential diversification has been frequently identified as an
important factor in misleading ancestral state reconstruction with likelihood methods [e.g. 27],
our results follow those of Skinner [26] in highlighting how violation of the assumption of equal
rates of character change across the phylogeny can also mislead commonly used likelihood
reconstruction methods.
Table S3. Simulations of character evolution with a two-rate Markov model in a several grass subfamilies,
treating branch-lengths as equal. The models used for simulation were either the best fitting model
estimated across the whole tree, or the best-fitting model estimated for the individual clade. In clades with
many halophytes, the model estimated in the clade fits significantly better, and estimates a greater number
of origins.
Subfamily
Simulation using rates estimated
from the whole phylogeny
Simulation using rates
estimated from the clade
Gains
Losses
-Log Likelihood
Gains
Losses
-Log Likelihood
Chloridoideae
12
11
136.5
29
45
122.3
Paniceae (x=9 group, with basic
chromosome number of 9)
Andropogoneae
7
3
71.05
16
6
68.42
8
8
75.68
35
34
69.36
21
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