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© 2020 The Linnean Society of London, Botanical Journal of the Linnean Society, 2021, 196, 81–99 81
Botanical Journal of the Linnean Society, 2021, 196, 81–99. With 9 figures.
*Corresponding author. E-mail: m.vorontsova@kew.org
Geographical structure of genetic diversity in Loudetia
simplex (Poaceae) in Madagascar and South Africa
PETER ANTON HAGL1, ROBERTA GARGIULO2,, MICHAEL F. FAY2,3,,
CÉDRIQUE SOLOFONDRANOHATRA4,5, JORDI SALMONA6, UXUE SUESCUN6,
NANTENAINA RAKOTOMALALA4,5, CAROLINE E. R. LEHMANN7,8,9,
GUILLAUME BESNARD6,, ALEXANDER S. T. PAPADOPULOS2,10 and
MARIA S. VORONTSOVA1,*,
1Comparative Plant and Fungal Biology, Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AB, UK
2Conservation Science, Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AB, UK
3School of Plant Biology, University of Western Australia, Crawley, WA 6009, Australia
4Laboratoire de Botanique, Département de Biologie et Ecologie Végétales, Faculté des Sciences,
Université d’Antananarivo, Antananarivo 101, Madagascar
5Kew Madagascar Conservation Centre, Antananarivo, Antananarivo 101, Madagascar
6CNRS, Université de Toulouse, IRD, UMR5174, EDB (Laboratoire Évolution & Diversité Biologique),
118 Route de Narbonne, 31062 Toulouse, France
7School of GeoSciences, The University of Edinburgh, Edinburgh EH8 9XP, UK
8Centre for African Ecology, School of Animal and Plant Sciences, University of Witwatersrand, South Africa
9Royal Botanic Garden Edinburgh, Edinburgh EH3 5NZ, UK
10Molecular Ecology and Fisheries Genetics Laboratory, Environment Centre Wales, School of Natural
Sciences, Bangor University, Bangor LL57 2DG, UK
Received 10 June 2020; revised 6 October 2020; accepted for publication 27 October 2020
Ecologically dominant species are primary determinants of ecosystem function, especially in grassy ecosystems,
but the history and biology of grassy ecosystems in Madagascar are poorly understood compared to those of Africa.
Loudetia simplex is a C4 perennial grass that is adapted to fire and common to dominant across Africa. It is also
widespread across central Madagascar in what are often thought to be human-derived grasslands, leading us to
question how recently L. simplex arrived and how it spread across Madagascar. To address this, we collected population
genetic data for 11 nuclear and 11 plastid microsatellite loci, newly developed for this study, for > 200 accessions from
78 populations of L. simplex, primarily from Madagascar and South Africa. Malagasy and African populations are
genetically differentiated and harbour distinct plastid lineages. We demonstrate distinct geographically clustered
diploid, tetraploid and hexaploid groups. The Malagasy hexaploid populations cluster into northern and southern
types. In South Africa, diploid populations in the Drakensberg are distinct from tetraploid populations in north-eastern
South Africa. Different genetic clusters are associated with significantly different precipitation and temperature. We
conclude that L. simplex is native to both Madagascar and South Africa, probably with a single colonization event from
Africa to Madagascar followed by pre-human diversification of L. simplex populations in Madagascar.
ADDITIONAL KEYWORDS: grassland – grassy biomes – microsatellites – Panicoideae – polyploidy – population
genetics – savanna – Tristachya – Tristachyideae.
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82 P. A. HAGL ET AL.
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INTRODUCTION
Ecologically dominant grasses are ecosystem
architects that drive the distribution and function
of grassy biomes (Linder et al., 2018). Open-canopy
biomes dominated by C3 grasses have been present on
all continents since 5–40 Mya, before C4 grasslands
came to prominence during the Miocene grassland
expansion 3–8 Mya (Strömberg, 2011). Tropical open-
canopy ecosystems are dynamic: their distribution is
determined by interactions among climate, soil fertility,
vegetation traits and disturbance regimes of fire and
mammalian herbivory (Lehmann et al., 2011, 2014;
Hempson et al., 2019). The diversity of tropical grassy
biome landscapes, ranging from treeless grasslands to
miombo woodlands, has in a number of cases led to
a lack of recognition of these systems being ancient,
with an associated unique biodiversity (Parr et al.,
2014; Bond, 2016; Lehmann & Parr, 2016). Particularly
striking is the historically contrasting interpretation
of tropical grassy biomes in Africa versus Madagascar.
African savannas have long been respected as ancient
iconic ecosystems (e.g. Dunphy & Leonard, 1999). In
contrast, Malagasy grassy biomes have been classified
as being of anthropogenic origin, appearing in the
last 7000 years (Koechlin, Guillaumet & Morat, 1974;
Koechlin, 1993; Lowry, Schatz & Phillipson, 1997;
Moat & Smith, 2007; Gautier et al., 2018). However,
there is a growing body of research into the nature
of grasses and grassy ecosystems in Madagascar and
the role of human activities in shaping Malagasy
biodiversity (Bond et al., 2008; Vorontsova et al., 2016;
Solofondranohatra et al., 2018, 2020; Helmstetter
et al., 2020), and such new research is fundamental to
developing informed land management policy.
Grasses are challenging subjects for population
genetic studies due to their frequently complex
histories of introgression and hybridization, polyploidy
and diverse reproductive systems, often including
apomixis (de Wet, 1986; Gibson, 2009). Nevertheless, it
is unwise to assume that such widespread species act
as uniform entities. Population genetic data are crucial
to understanding the diversity and history of grassy
ecosystems. After decades of research restricted to crops
and sometimes forages, common wild grasses within
easy reach of laboratories are beginning to receive
attention; e.g. (1) in Europe, genetic signatures of
populations of Festuca rubra L. reveal their post-glacial
history, with higher ploidies occurring more often in
genetically poor northern populations (von Cräutlein
et al., 2019); (2) genetic diversity of Andropogon gerardi
Vitman in North America (McAllister et al., 2015;
McAllister & Miller, 2016) demonstrates a long complex
history with numerous locally adapted populations
and (3) several species of Triodia R.Br. in Australia
show infraspecific cytotype diversity reflecting range
expansion with aridification (Anderson et al., 2017,
2019). However, few modern studies have focused on
African grasses beyond crop relatives, invasive species
and commercially significant lawn and forage grasses
such as Cynodon Rich. (Wu et al., 2004) and Urochloa
P.Beauv. (e.g. Jungman et al., 2010). Investigations
into the cytotype diversity and geographical structure
of common African grasses of no commercial relevance
have not been attempted to date.
Loudetia simplex (Nees) C.E.Hubb. (Poaceae,
Panicoideae, Tristachyideae) constitutes the primary
ground cover in the hills surrounding Antananarivo,
Madagascar (Solofondranohatra et al., 2020; Fig. 1A).
According to the standard reference on the grasslands
of Madagascar (Koechlin, 1993), it dominates a ‘vast and
nearly flat plateau’ in the central highlands and occupies
thin ferritic soils in the western savannas. In tropical
Africa, L. simplex is also one of the most common grasses
(White, 1983), but it achieves dominance in only some
of its ecosystems such as grassland in the uKahlamba
Drakensberg Park (Fig. 1B), rangelands in Mufulira in
Zambia (Mukutu, 2019) and the Cuito catchment area in
Angola (Goyder et al., 2018). Loudetia simplex is a tufted,
perennial C4 bunchgrass 30–150 cm tall (Clayton, 1974;
Clayton et al., 2006). The dense bases of its leaf sheaths
protect the tussock from fire, enabling it to re-sprout from
buds close to ground level. The morphology of L. simplex
is variable, and the species encompasses significant
morphological diversity as circumscribed by Clayton
(1974). Loudetia is a morphologically fairly uniform
pantropical genus of 26 species centred in Africa (Clayton
& Renvoize, 1986), but it is probably polyphyletic and
remains difficult to delimit phylogenetically in relation
to the other members of Tristachyideae: Tristachya Nees,
Trichopteryx Nees and Loudetiopsis Conert (Phipps, 1967;
Clayton, 1972; Hackel et al., 2018). The base chromosome
number for Tristachyideae is 10–12 (Kellogg, 2015), and
chromosome counts of 20, 40 and 60 have been recorded
for L. simplex, representing diploids, tetraploids and
hexaploids, respectively (Moffet & Hurcombe, 1949; Li,
Lubke & Phipps, 1966; Dujardin & Beyne, 1975); isolated
counts of 24 have also been recorded (Rice et al., 2015).
Is L. simplex naive to Madagascar or was it brought
there by people since their first arrival on the island c.
10 kya (Hansford et al., 2018)? Bosser (1969) cited all
the components of the modern L. simplex as endemic
and therefore native to Madagascar, and L. simplex is
not included in published lists of the introduced plants
and weeds of Madagascar (Kull et al., 2012; Le Bourgeois
et al., 2019). Nevertheless, it lacks a recognized native
status. A possible non-native origin was implied by
Koechlin (1993), who stated that the grasslands of
Madagascar are ‘almost entirely made up of secondary
communities with grasses dominating’, and discussion
by Lowry et al. (1997) of ‘secondary grasslands with
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 83
© 2020 The Linnean Society of London, Botanical Journal of the Linnean Society, 2021, 196, 81–99
extreme floristic impoverishment, the most dominant of
which are widespread, e.g. Loudetia spp.’ To answer the
question of whether L. simplex is native to Madagascar,
we aim to explore the genetic structure of its populations
in Africa and Madagascar, to understand whether spatial
genetic diversity is random or geographically structured.
If populations in Madagascar derive from a single
introduction event, we would expect them to be more
Figure 1. Savanna ecosystems with Loudetia simplex in A, Isalo National Park, Madagascar and B, uKahlamba Drakensberg
Park World Heritage Site Game Reserve, South Africa. Photographs by Maria S. Vorontsova.
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84 P. A. HAGL ET AL.
© 2020 The Linnean Society of London, Botanical Journal of the Linnean Society, 2021, 196, 81–99
genetically uniform than populations in other regions.
If they originate from multiple introductions, we would
expect polyphyly of the Malagasy lineages. We use a
population genetic approach to compare L. simplex from
mainland Africa and Madagascar, analysing nuclear and
plastid microsatellite loci, and briefly compare climate
envelopes of the population clusters.
MATERIAL AND METHODS
Sampling
Two hundred and thirty-six accessions from 78
populations of L. simplex from Madagascar and Africa
(Supporting Information, Table S1, Fig. 2) were analysed
with L. arundinacea (Hochst. ex A.Rich.) Hochst. ex
Steud., L. filifolia Schweick., L. flavida (Stapf) C.E.Hubb.,
L. lanata (Stent & J.M.Rattray) C.E.Hubb. and Tristachya
nodiglumis K.Schum. (which has been shown to be
nested in Loudetia Hochst. ex Steud.; Hackel et al., 2018;
Supporting Information, Table S1). We included seven
DNA extracts from previous work (Hackel, 2017; Hackel
et al., 2018). Twenty-five sites dominated by L. simplex
(22 from Madagascar and three from South Africa)
were sampled at the population level during the field
seasons 2016–2018: at each site, five to 11 leaf samples
were preserved in silica gel from individuals separated
by c. 200 m; one herbarium voucher was collected from
each site. Single samples were also collected, and 28 leaf
samples were removed from herbarium specimens in
Madagascar (TAN herbarium, three samples, herbarium
acronyms fide Thiers, 2019) and South Africa (PRE
herbarium, 25 samples).
genome Size eStimation
Seeds of L. simplex from Ibity in Madagascar (accession
number 226251) and Burkina Faso in mainland
Africa (accession number 184113) were provided by
the Millennium Seed Bank Partnership (MSB, Royal
Botanic Gardens, Kew) and germinated. Nuclear
DNA content was estimated for one individual of each
accession following Doležel, Greilhuber & Suda (2007),
using a Partec Cyflow SL3 flow cytometer (Partec
GmbH, Münster, Germany). Three replicates from each
individual were measured separately and Petroselinum
crispum (Mill.) Fuss (2C = 4.5 pg) was used as the
calibration standard. The mean value of the genome
size (2C) of the three replicates was calculated.
microSatellite analySiS
Nuclear microsatellites
Total genomic DNA was extracted using 15–20 mg of
dry plant material and 20 mg of fresh leaf material
according to the CTAB method of Doyle & Doyle
(1987), followed by a purification step using the
QIAquick PCR Purification Kit (Qiagen). DNA from
two individuals of L. simplex from Madagascar
(IB-09-A and AN-06-B) was sequenced on an
Illumina MiSeq after double-digest restriction site-
associated library preparation following Peterson
et al. (2012). Restriction enzymes used were EcoRI
and MspI, and size selection was performed on a
Pippin prep (468–546 bp). Restriction-associated
DNA libraries were used for microsatellite discovery.
Primers were designed using the msatcommander-
1.0.8-beta software (Faircloth, 2008). Microsatellite
sequences, which had between six and 19 motif
repeats with each motif consisting of two to four
nucleotides, were selected. Only microsatellite
sequences nested in a sequence-fragment for which
a BLAST-search (Altschul et al., 1990) led to hits
representing the nuclear DNA of plants (in particular
of grasses) were considered; plastid sequences were
excluded. Corresponding primers that were likely
to form secondary structures or that had multiple
annealing sites were also excluded. The final primer
pairs (Table 1) were chosen based on successful
amplification of DNA-fragments of the expected size
in test-PCRs. Forward primers were ordered with
FAM- or JOE-fluorophore labels (Eurofins Genomics).
Amplification of the microsatellite regions
was carried out in a volume of 10 μL with 10 ng
genomic DNA, 6 μL 2× DreamTaq PCR Mastermix
(ThermoFisher Scientific), 0.5 μL 0.4 % bovine serum
albumin (w/v), 2.5 pmol reverse primer, 2.5 pmol FAM-
or JOE-labelled forward primer and deionized water,
in a GeneAmp PCR System 9700 (Applied Biosystems).
PCR-conditions were: initial denaturation at 94 °C for
3 min followed by 30 (25 for primers LS6) cycles of
denaturation at 94 °C for 30 s, annealing at primer
specific temperatures (Table 1) for 30 s and extension
at 72 °C for 45 s and one final extension step at 72 °C
for 10 min. The microsatellite specific PCR products
were quantified on a 1 % agarose gel, and in the case
of high product yield, diluted with deionized water to
adjust them to the same level of PCR products with
moderate yield.
Allele size was estimated on an ABI3730 DNA
Analyzer (Applied Biosystems). An aliquot (1 μL) of
the PCR product (diluted if necessary) was added to
a mix of 10 μL HiDi formamide (Applied Biosystems)
and 0.15 μL GeneScan 500 ROX Size Standard
(Applied Biosystems) and denatured at 94 °C for
3 min. GeneMapper Software 5 (Applied Biosystems)
was used for allele calling, and results were visually
inspected. Peaks differing by one nucleotide were
rounded to the closest allele size to avoid the over-
estimation of genetic variation due to stuttering.
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 85
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The ploidy of populations was also estimated based
on the maximum number of alleles (MNA) at a locus.
The power of this method, however, depends on the
number and frequency of alleles revealed at each
locus (Besnard & Baali-Cherif, 2009). Considering
the Drakensberg samples diploid (see results), we
estimated allele frequencies for the five loci (LS2, LS4,
LS5, LS7 and LS10) for which at least three alleles
were revealed in the uKahlamba Drakensberg Park
population (31 individuals). Based on these allele
frequencies, we estimated the probability (P3x; Besnard
& Baali-Cherif, 2009) that a non-diploid individual (i.e.
a triploid) present in this population is revealed with
these five loci (i.e. at least one locus showing three
alleles in its genotype). This method was not applied
for higher ploidies since we can precisely estimate
allele frequencies only for diploids, without assuming
random mating or a fixed rate of selfing (De Silva
et al., 2005; Meirmans, Liu & van Tienderen, 2018).
Plastid microsatellites
We analysed polymorphisms from the maternally
inherited plastid genome. Because low polymorphism
was revealed in plastid DNA barcodes (rbcL and matK;
data not shown), we decided to analyse 12 microsatellite
loci (plastid SSR) that are more variable (Table 2). Based
on a complete plastid genome of L. simplex (MF563366;
Piot et al., 2018), we defined primers in regions
flanking a mononucleotide stretch with a minimum of
ten repeats. Four loci were simultaneously amplified
following the PCR protocol described in Besnard et al.
(2011) and using the universal M13 primer labelled
with the YAK, 6-FAM or AT550 fluorochrome (Table 2).
PCR products were then multiplexed together with
GenScan-600 Liz (Applied Biosystems) and separated
on an ABI Prism 3730 DNA Analyzer (Applied
Biosystems). Allele size was determined with Geneious
v.9.0.5 (Kearse et al., 2012). Multistate plastid SSRs
were coded by the number of repeated motifs for each
allele (e.g. number of T or A), as described by Besnard
et al. (2011). The combination of polymorphisms at all
plastid SSRs allowed us to define a plastid haplotype
for each genotyped individual. We thus analysed 173
and 54 individuals of L. simplex from Madagascar and
South Africa, respectively (Supporting Information,
Table S1). Other taxa were also analysed (including the
three L. simplex accessions from Burundi and Burkina
Faso; Supporting Information, Table S1).
genetic diverSity and population
differentiation
To understand genetic differentiation among
populations, Bayesian clustering of individuals was
performed using the software STRUCTURE v.2.3.4
Table 1. Primers for nuclear microsatellite loci used to characterize populations of Loudetia simplex. F = forward,
R = reverse, Ta = annealing temperature, FAM/JOE = fluorophore labels
Locus Label Primers Ta (°C) Allele size range (bp)
LS1 FAM F: CCTCTTACCATTCTCCCAAAGC 53.3 179–197
R: TTCAACATGCCCGAATCGTG
LS2 FAM F: CAAGATCAACCACAGCAGGC 56.0 97–139
R: TGCTTATGAGGCGGGAGTAG
LS3 FAM F: TCCTATCAGCCCGCGAAATG 55.4 119–155
R: ACCTGGCTCCGTTGTACTAC
LS4 FAM F: TCCAACCAACAGTCTGCATG 53.3 85–287
R: TGGTGTGGAGTGTCTAGCTG
LS5 FAM F: ATGGGACTCTTCAGCCACTG 55.4 155–223
R: TCAAAGCTTGGAAAGGGCAG
LS6 FAM F: CGGGAACAACAATCAGGGTG 55.4 197–209
R: CGGGTGGATCGAATTGACAG
LS7 JOE F: CGTTAGGAAAGGGACATGTGTG 55.4 174–261
R: ACGAGGGCTGTAATGGTGAC
LS8 JOE F: CGTAGCTTGCCTGTGATGTG 55.4 149–164
R: TCAGTTCTCACCCGTCGAAG
LS9 JOE F: AATTCAGCAGTCCATGTCC 53.3 119–217
R: GAGGGATCTCGTCGTCTC
LS10 JOE F: GTTCTGTGATGTGCTACCGC 60.0 140–156
R: GAAGCACCGATTCGCCTTAC
LS11 JOE F: GACTCGCTAACAATTTCAAGGG 54.2 183–189
R: GGCGTGAGTGTGCTATCTTG
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86 P. A. HAGL ET AL.
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(Pritchard, Stephens & Donnelly, 2000; Falush,
Stephens & Pritchard, 2003; Hubisz et al., 2009), which
has been shown to be the least biased clustering method
in mixed-ploidy populations (Stift, Kolář & Meirmans,
2019). We used a combination of the RECESSIVE-
ALLELE model and the ADMIXTURE model; the
RECESSIVE-ALLELE model is appropriate when the
information about allele dosage is incomplete (i.e. the
number of copies of each allele is not known for all
the genotypes; Falush, Stephens & Pritchard, 2007).
The admixture model assumes the genome of each
individual to consist of fractions of every genetic cluster,
which was the most appropriate ancestry model for
our dataset available in STRUCTURE. Analyses were
run with a burn-in period of 5 × 105 and subsequent
5 × 105 Markov chain Monte Carlo replicates, testing
1 ≤ K ≤ 5 using GNU parallel (Tange, 2011).
The number of genetic clusters (K value) best fitting
the data was selected according to the method of
Evanno, Regnaut & Goudet (2005) by using the log-
likelihood values and the second-order rate of change of
the likelihood distribution of the K values (ΔK) inferred
with the online tool STRUCTURE HARVESTER (Earl
& von Holdt, 2012). The STRUCTURE analysis was
performed on the total dataset and on the dataset
containing only individuals from Madagascar, using
the same parameters.
The assignment of individuals to genetic clusters and
their membership coefficients were computed using
the program CLUMPP v.1.1.2 (Jakobsson & Rosenberg,
2007), applying the Greedy algorithm. Therefore, the
assignment of samples to populations was not taken
into consideration. The software DISTRUCT v.1.1
(Rosenberg, 2004) was used to display the clustering
results and the individual membership coefficients
graphically.
GenoDive v.2.Ob27 (Meirmans & Van Tienderen, 2004)
was used to account for the different ploidies in our dataset.
We calculated genetic diversity indices (Nei, 1987) for the
individuals occurring in mainland Africa and Madagascar,
by applying the maximum likelihood method to correct
for the unknown dosage of alleles (Meirmans & Van
Tienderen, 2004). The following indices were calculated:
number of observed alleles (NA), effective number of alleles
(Eff-NA), expected frequency of heterozygotes (HS) and
total heterozygosity (HT). Results with a P value < 0.05
were considered to be statistically significant. Tests for
deviations from the Hardy–Weinberg proportions were
Table 2. Primers for the 12 plastid microsatellite loci used to characterize populations of Loudetia simplex, including the
fluorophore used (label), microsatellite motif (from the reference plastid genome; MF563366), allele size range (in bp) and
the number of alleles (Na; given for Malagasy and South African populations)
Locus Label Primers Plastid SSR motifaAllele size rangeaNa
LScp-1 6-FAM F. AAGGACTCCCAAGCACACGTA A10 122 1
R. *GGGCTCGTTTGGTTGACATTG
LScp-2 6-FAM F. ATAGGATCTTAGATACGATCGA T10CTTTT 194–202 5
R. *TTTACCCCTAGTGAAATTTAACC
LScp-3 YAK F. GCATTATTCCATGRTTCCTATTTC A12 216–220 5
R. *ATAGGAATAAGAAGAAATCGCAAC
LScp-4 YAK F. GGATCAGTTGGATCTTTGATTG T15 178–183 6
R. *TCAAATCCTACAGAGCGTGAT
LScp-5 6-FAM F. CTTCGAATTTGTTTTGTCCAAGTG A7(G)A10 - -
R. *CTATAACAAGGTTTGAGACCTTGT
LScp-6 YAK F. CCTGTGAAATAATTGGTTAGATAC A10 133–135 3
R. *ATAGGCTACGAGCATAAATGCA
LScp-7 6-FAM F. GTGGTAACTTCCAAATTCAGAG T6(G)A13 147–155 9
R. *CCATTGAGTCTCTGCACCT
LScp-8 AT550 F. TTACTTATTATAGAGATGGTGCGA T12 132–134 3
R. *GAGGATACACGACAGAAGGA
LScp-9 AT550 F. ATGTCATAATAGACCCGAACAC T11 209–215 6
R. *TCCGGACAAGACATACAAAGA
LScp-10 AT550 F. GAACAAATTGGAACCATTAACTAG T12 96–101 6
R. *GAGAATACCGATTTAAGAGTCG
LScp-11 YAK F. TTTTCCTCTCCATGGGATTACA A12 100–104 5
R. *GCAGTAGCAATAAATGCGAGA
LScp-12 AT550 F. TCTAGTTGTATGTGAAAGACATCT A10 152–163 6
R. *CTTGTCTTATCCACATTAGACAA +(TTTCAGTAT)2
*with a M13 tail (TGTAAAACGACGGCCAGT) added at the 3′ extremity (Schuelke, 2000).
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 87
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only attempted for the diploid uKahlamba Drakensberg
Park population, as information about allelic dosage
was incomplete (Meirmans, Liu & van Tienderen, 2018;
Gargiulo et al., 2019); tests for both heterozygosity excess
and deficiency were performed in GENEPOP (Raymond
& Rousset, 1995; Rousset, 2008). Population structure was
further evaluated in GenoDive with analysis of molecular
variance (AMOVA; Excoffier, Smouse & Quattro, 1992;
Michalakis & Excoffier, 1996), by employing the ‘ploidy
independent infinite allele model’ that computes the
FST-analogous ϱ (Rho) (Ronfort et al., 1998; Meirmans &
Liu, 2018), with 999 permutations. We also performed
a pairwise population differentiation analysis within
groups with the same ploidy with 999 permutations. We
used the R package POLYSAT v.1.7-3 (Clark & Jasieniuk,
2011) to calculate a pairwise matrix of genetic distances
(using the Bruvo distance; Bruvo et al., 2004) and to carry
out principal component analyses (PCA) on the matrix.
Finally, plastid DNA data were analysed separately.
The probability that two L. simplex individuals
taken at random from Malagasy or South African
populations display a different plastid haplotype was
computed as D = 1 − Σ pi2, where pi is the frequency of
the plastid haplotype (Nei & Tajima, 1981; Nei, 1987).
We then investigated plastid haplotype relationships
using minimum spanning networks based on Bruvo’s
genetic distance (Bruvo et al., 2004). The networks
were reconstructed with the R package poppr (Kamvar,
Tabima & Grünwald, 2014).
environmental envelope analySiS
To look for potential relationships between genetic
diversity and ecological niche, annual mean
temperature (Bio_1) and annual precipitation (Bio_12)
were downloaded at 30s spatial resolution from
Worldclim Global Climate Data v.2 (Fick and Hijmans,
2017) for each sample location. We compared these
climate variables for the four largest nuclear clusters
with an analysis of variance while accounting for
spatial autocorrelation.
RESULTS
genome Size eStimation
A comparable content of nuclear DNA was measured
for L. simplex from Burkina Faso in Africa (2C = 2.23
pg) and Ibity in Madagascar (2C = 2.52 pg), tentatively
suggesting the same ploidy for both samples.
nuclear microSatellite analySeS
Allele size ranges for the nuclear microsatellite
markers are reported in Table 1. All loci were
polymorphic across the individuals analysed. Allele
calling in GeneMapper suggested different ploidies
among African samples (Tables 3, S1 in the Supporting
Information), with plants sharing the same apparent
number of alleles occurring closer together. Dosage
information was recorded when possible. However,
uncertainties in allelic dosage were common, as
expected with high ploidies (Dufresne et al., 2014;
Meirmans et al., 2018). Plants from the Drakensberg
Region consistently showed a maximum of two different
alleles at a locus, and therefore were assumed to be
diploid. No significant excess of homozygosity in the
uKahlamba Drakensberg Park population (HO = 0.39
vs. HE = 0.44; computed on loci LS2, LS4, LS5, LS7 and
LS10) is also consistent with a diploid state and a low
frequency of null alleles on these loci. The probability
that a triploid individual is present in this population
remains relatively low (P3x = 51.18%), suggesting
that the maximum number of alleles revealed at a
locus (MNA) for a given individual is indicative of
its minimum ploidy. Based on this assumption, the
one sample from Burkina Faso appears to be at least
hexaploid, whereas the two plants from Burundi and
the plants from north-eastern South Africa are at least
tetraploid (Table 3). All L. simplex from Madagascar
are polyploid, as samples had up to six different peaks
indicative of hexaploidy (or higher ploidy) (Table 3).
Analysis of the genetic structure according to the
Evanno method showed that the most appropriate K
value for all L. simplex from Africa and Madagascar
was 2, corresponding to two genetic clusters. The
analysis with GenoDive led to the same result.
The samples were assigned to two genetic clusters,
reflecting the populations of Africa and Madagascar
(Fig. 3A). When considering only the individuals
Table 3. Estimated minimum ploidy of Loudetia simplex
individuals as inferred by the maximum number of peaks
in electropherograms (for a locus). The number (and per-
centage) of accessions with each minimum ploidy are sum-
marized from Table S1
Region Minimum ploidy
2x4x6x
Madagascar (total) - 32 (20.9) 121 (79.1)
Madagascar South - 11 (45.8) 13 (54.2)
Madagascar North - 21 (16.3) 108 (83.7)
South Africa (total) 43 (78.2) 12 (21.8) -
Drakensberg 33 (100) - -
North-East South
Africa
10 (45.5) 12 (54.5) -
Burkina Faso - - 1 (100)
Burundi - 2 (100) -
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88 P. A. HAGL ET AL.
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from Madagascar, the most likely K value associated
with ΔK was 3, whereas the K value estimated by
GenoDive was 2. The barplot for K = 3 showed high
levels of admixture, with some samples collected
from the same location belonging to two different
clusters (Fig. 3C). The barplot for K = 2 showed two
clusters that matched their geographical distribution
almost perfectly, samples being grouped into clusters
from northern and southern Madagascar with only
slightly mixed membership (Fig. 3B). K = 2 was
therefore considered to be more appropriate.
Genetic diversity indices averaged over the
microsatellite marker loci are shown in Table 4. On
average, 7.60 and 12.64 alleles per locus were observed
in African and Malagasy populations, respectively.
The effective number of alleles per locus was more
comparable with 2.41 alleles in Africa and 3.47 alleles
in Madagascar. The total heterozygosity matched
the expected frequency of heterozygosity, which was
slightly lower in the African populations (HT: 0.46)
than in Madagascar (HT: 0.54). In the Drakensberg
population, tests for deviations from the Hardy–
Weinberg proportions conducted on polymorphic loci
(LS2, LS3, LS4, LS5, LS7, LS9, LS10) revealed no
significant heterozygote excess. A heterozygote deficit
was detected at LS9 (P ≤ 0.001).
Pairwise differentiation analysis within groups with
the same ploidy showed that Africa and Madagascar are
significantly different from each other (P ≤ 0.001) with
a moderate level of differentiation and an FST value of
0.141 (Table 5). South African populations group into
two significantly different genetic clusters (P ≤ 0.001)
with a moderate level of differentiation (FST = 0.107).
The populations from southern Madagascar are also
significantly different from the populations of the
northern part (P ≤ 0.001); differentiation between
samples from northern and southern Madagascar is
low with an FST value of 0.01. The AMOVA analysis
implemented in GenoDive resulted in a value of
ϱ = 0.517, indicating strong population structure.
The PCA scatter-plot of all L. simplex populations
(Fig. 4A) showed that the populations from Madagascar
were more densely clustered than the populations
from Africa. The South African samples were divided
into two sub-clusters, reflecting their different ploidies
and their spatial grouping into samples from the
North-East and from the Drakensberg Region. The
samples from Madagascar were roughly divided into
sub-clusters that matched their spatial occurrence
in northern and southern Malagasy highlands. The
samples from Burundi and Burkina Faso were not
part of any sub-clusters, being placed between South
Africa and Madagascar. The relationship between the
genetic clustering, ploidy grouping and geographical
location is illustrated in Figures 2 and 7. Other species
of Loudetia and Tristachya nodiglumis were not close
to the African or Malagasy populations (Fig. 4B).
plaStid microSatellite analySeS
Of the 12 plastid SSR loci tested, 11 were successfully
amplified for all L. simplex samples from Madagascar
and South Africa. Ten of these loci revealed length
polymorphisms with three to nine alleles (Table 2).
Plastid microsatellite genotypes are available
in the Supporting Information (Table S1). Most
polymorphisms were probably due to single step
mutations (1-bp polymorphisms), but loci LScp-2
and LScp-12 showed longest indels (of 5 and 9 bp,
respectively; Table 2) that were coded separately. When
analysing all Loudetia taxa, long indels (> 10 bp) and/
or null alleles were also observed for loci LScp-2 and
LScp-8, limiting their use for assessing relationships
above the species level. Among the 173 Malagasy
individuals of L. simplex, 50 plastid haplotypes
were identified, with 40 observed in just one or two
accessions. Similarly, 28 plastid haplotypes were
identified among the 54 South African individuals, of
which 24 were observed in just one or two accessions.
Overall, the plastid haplotype diversity was higher in
South Africa than in Madagascar (D = 0.902 vs. 0.773,
respectively). This lower diversity in Madagascar
could, however, be the result of repetitive sampling
on the northern High Plateau where a single plastid
haplotype was observed in 80 individuals.
Analysis of the plastid haplotype relationships
demonstrated that, similarly to nuclear data, L. simplex
accessions from Madagascar and South Africa form two
large highly diversified clusters (Fig. 6). Other Loudetia
accessions, including L. simplex from Burundi and
Burkina Faso, are only distantly related to South African
and Malagasy L. simplex matrilineages. Matrilineage
Table 4. Genetic diversity indices in Loudetia simplex as implemented in GenoDive, averaged over the microsatellite loci
for populations from Africa and Madagascar. N = number of individuals analysed, NA = number of alleles observed, Eff-
NA = effective number of alleles, HS = expected frequency of heterozygotes, HT = total heterozygosity
Geographical region N NA (SE) Eff-NA (SE) HS (SE) HT (SE)
Africa 58 07.60 (1.87) 2.41 (0.42) 0.46 (0.10) 0.46 (0.10)
Madagascar 153 12.64 (3.08) 3.47 (0.78) 0.54 (0.09) 0.54 (0.09)
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 89
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relationships among such distantly related taxa should
be interpreted with care as these are probably subject
to a high rate of homoplasy and could be biased by a
complex history of insertions and deletions.
The plastid haplotype network of Malagasy L. simplex
accessions highlights two major clusters formed by most
northern High Plateau accessions and the south-western
accessions, respectively (Fig. 5A), in relative concordance
with the clusters defined with nuclear microsatellites
(Figs 2, 7). In contrast, the plastid haplotypes from the
Central High Plateau and a few plastid haplotypes from
the northern High Plateau are scattered through the
network without particular clustering pattern (Fig. 5A)
suggesting that complex processes of seed mediated
gene flow have shaped the plastid genetic makeup
of Malagasy populations. Furthermore, the network
indicates that two plastid haplotypes are relatively
frequent on the northern High Plateau (Fig. 5A). The
most frequent plastid haplotype (80 accessions) is
shared by most populations from the northern High
Plateau, and six rare, closely related plastid haplotypes
were also observed in a star-like pattern (Fig. 5A).
Phylogenetic relationships among the South African
plastid haplotypes of L. simplex were similarly
analysed. South-eastern diploid accessions cluster in
the centre of the plastid haplotype network (Fig. 5B;
uKahlamba Drakensberg Park, Loteni NR and
Underberg localities), whereas tetraploid accessions
are scattered without a strong clustering pattern
(Fig. 8 ). The same clusters are presented geographically
in Figure 2B.
Figure 2. Study populations of Loudetia simplex coloured according to genetic clusters. A, Nuclear genome diversity
identified by STRUCTURE and the principal component analysis, same clusters as shown in Figures 3B and 4, minimum
ploidy summarized from the Supporting Information (Table S1). B, Plastid haplotypes, same clusters as shown in Figure 5
defined by geographical proximity. Countries of L. simplex native occurrence are shown in green, data from WCSP (wcsp.
science.kew.org). Map by Sarah Z. Ficinski.
Table 5. FST values for three pairs of genetic clusters in
Loudetia simplex. Level of significance tested with 999
permutations, with P < 0.001 for each FST value
Pairwise comparison FST value
South Africa vs. Madagascar 0.141
Drakensberg vs. North-East South Africa 0.107
Northern vs. southern Madagascar 0.010
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90 P. A. HAGL ET AL.
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environmental envelope analySiS
The comparisons between the annual precipitation and
the annual mean temperature for the nuclear clusters
are presented in Figure 9. For precipitation there are
significant differences between all pairs of clusters (all
P < 0.001). For temperature there are significant differences
between: Drakensberg and Madagascar North clusters,
Drakensberg and Madagascar South, Drakensberg
and North-East South Africa, and Madagascar South
and North-East South Africa (all P < 0.001); and also
Madagascar North and Madagascar South (P = 0.027).
For temperature there is also a marginally significant
difference between Madagascar North and North-East
South Africa (P = 0.057).
DISCUSSION
This study presents a population genetic analysis
of the commonly dominant grass L. simplex in
Madagascar and South Africa. The data from 11
nuclear polymorphic microsatellite loci and ten
polymorphic plastid SSRs analysed from 211 and 230
accessions, respectively, are consistent: populations in
Madagascar and South Africa are genetically distinct
and variable in both locations, with strong geographical
structuring of the genetic diversity. Genetic diversity
in Madagascar is high. Plastid data indicate that a
single maternal lineage has colonized Madagascar
from Africa. Climate envelopes of the major clusters
show differences suggesting that L. simplex occupies a
range of ecological niches across space.
evolution and biogeographical hiStory
The flora of Madagascar is allied primarily with
Africa (Buerki et al., 2013) and its grasses are allied
with the African grass flora more than any other
Figure 3. Clustering of populations of Loudetia simplex
using STRUCTURE analyses based on alleles of 11
nuclear microsatellite loci. A, Clustering of all African
and Malagasy populations for K = 2. B. Clustering of
the Malagasy populations for K = 2. C, Clustering of the
Malagasy populations for K = 3.
Figure 4. Principal component analysis (PCA) based on
alleles of 11 nuclear microsatellite loci of the African and
Malagasy populations. A, Loudetia simplex; B, Loudetia
simplex with other species of Loudetia and Tristachya
nodiglumis.
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 91
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(Vorontsova et al., 2016; Hackel et al., 2018). The median
stem ages for clades of Poaceae endemic to Madagascar
are between 1 and 5 Myr (Hackel et al., 2018). Estimates
available for the arrival of non-endemic C4 lineages in
Madagascar are similar: c. 1.4 Mya for Themeda triandra
Forssk. (Dunning et al., 2017) and c. 1 Mya for Alloteropsis
semialata (R.Br.) Hitchc. (Olofsson et al., 2016). The best
current estimate for the split of L. simplex from African
Loudetia is the Late Miocene or Early Pleistocene (3–8
Mya; Hackel et al., 2018), from an analysis of plastid
DNA sequences including just two samples of L. simplex
from southern and south-central Madagascar. These
broadly consistent results suggest that palaeotropical
perennial C4 grasses colonized Madagascar c. 1–8
Mya, at the same time as endemic species originated
in the Malagasy savannas (e.g. Salmona et al., 2020),
significantly before human arrival. Our plastid SSR
analysis shows that Malagasy and South African
populations of L. simplex harbour clearly distinct
plastid lineages and a remarkable diversity of plastid
haplotypes. This not only suggests that more extensive
sampling of other regions may unravel greater diversity,
but also indicates that the processes of diversification
have taken place over long periods of time in both regions.
Loudetia simplex is thus native to both Madagascar and
South Africa. Although we do not date the crown age
of South African and Malagasy lineages or the time of
their divergence (i.e. the dispersal event out of Africa),
the high diversity of the plastid genome confirms the
antiquity of the Malagasy lineage diversification, which
could have co-occurred with one of the worldwide waves
of the savanna biome expansion (Edwards et al., 2010;
Salmona et al., 2020). These findings are in agreement
with the recent work on Malagasy open habitat biota
demonstrating that Malagasy grasses are highly
diverse and endemic (Solofondranohatra et al., 2020).
The spread of L. simplex across Madagascar through
multiple climate change episodes is likely to have been
complex and locally variable (Burney, 1987a, b; Burney
et al., 2004).
Not all the genetic diversity documented here is
necessarily pre-human: the star-like shape of the network
around the most frequent plastid haplotype (Fig. 5A)
can be interpreted as a signal of a recent expansion of
L. simplex in the northern High Plateau (Fig. 2B). Native
Malagasy populations of L. simplex are likely to have
expanded since the broad establishment of fire-driven
agriculture in Madagascar c. 1000 years ago (Crowley,
2010) and could be a part of an anthropogenically driven
landscape transition and further expansion of L. simplex
out of its ancient habitat range. Further sampling across
Madagascar would be necessary to understand the
ancient range of L. simplex.
polyploidy and climate
Multiple chromosome counts have previously been
documented for L. simplex: 2n = 60 (Moffett &
Figure 5. Plastid haplotype networks of Loudetia simplex in A, Madagascar and B, South Africa. Line length is proportional
to Bruvo’s genetic distance between plastid haplotypes. Node size is proportional to the number of plastid haplotype
observations. All edges of equal weight are represented. Numbers in nodes indicate the number of accessions sharing the
plastid haplotype. Haplotypes are represented using a colour code based on their geographical origin shown in Figure 2B.
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Hurcombe, 1949; Davidse, Hoshino & Simon, 1986);
2n = 24 (de Wet, 1958); 2n = 24, 40 and 60 (compiled
by Oyen, 2012); and 2n = 20 and 40 (Kammacher et al.,
1973). The most likely chromosome base number based
on all the literature is x = 10 and sometimes x = 12
(in line with a base chromosome number of 10–12 for
Tristachyideae, fide Kellogg, 2015); diploid, tetraploid
and hexaploid populations are known in the wild.
Most samples from Madagascar have five or six alleles
for at least one microsatellite locus. From the same
populations, a few individuals had four alleles (full
counts in the Supporting Information, Table S1). The
total number of alleles scored clearly indicates that
the Malagasy populations are hexaploid. Genome size
measurements for the two living accessions provided
an additional source of evidence: the Malagasy living
sample analysed had a maximum of only four different
alleles per locus, whereas the sample from Burkina
Faso had a maximum of six. However, similar amounts
of DNA in both samples indicate the same ploidy.
South African samples of L. simplex analysed
consistently show a lower number of alleles, suggesting
that there are diploid and tetraploid populations (Table 3,
Fig. 2A, Supporting Information, Table S1), although a
lower number of alleles could also be a consequence of
null alleles or higher homozygosity in southern Africa.
The sole exception is represented by the single sample
from Burkina Faso, which shows a hexaploid pattern,
although hexaploids have also been reported from South
Africa and Zimbabwe (Moffet & Hurcombe, 1949; Davidse
Figure 6. Plastid haplotype network of Loudetia simplex from all geographical locations, L. arundinacea, L. filifolia,
L. lanata and Tristachya nodiglumis. Line length is proportional to Bruvo’s genetic distance between plastid haplotypes.
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 93
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et al., 1986). Other chromosone counts indicate further
complexity across Africa: diploids have been reported
from Cameroon (Dujardin & Beyne, 1975). Extensive
additional sampling is necessary to understand the
situation across Africa.
The analyses of genetic differentiation we have
conducted (Figs 3, 4; Table 5) show a consistent
difference in ploidy between South Africa and
Madagascar (diploid/tetraploid vs. hexploid).
Alternative hypotheses to account for this difference
could include a hybridization or introgression event
after L. simplex colonized Madagascar leading
to hexaploidy without remarkable morphological
changes (Keeler, 1998; Kolář et al., 2017), an
autopolyploidization event (Ramsey & Schemske,
1998) or the occurrence of cryptic taxa in Madagascar
and Africa. These hypotheses could also account for the
higher heterozygosity in the Malagasy populations.
A broad population genomic analysis of L. simplex
populations from Africa and related species from
Madagascar will be necessary to clarify the origin of
different ploidies in different geographical regions and
their relationship with the history of the colonization
of Madagascar. Plastid haplotypes of South African
polyploids are not closely related but instead scattered
throughout the network (Fig. 8). This pattern suggests
that the polyploids had a polytopic origin rather than
a single origin, probably due to recurrent genetic
exchanges between diploid and polyploid populations.
No consistent and globally applicable relationships
have so far been observed between climate, elevation,
plant genome size and polyploidy, except for the fact
that larger genomes require more nutrients (e.g.
Pellicer et al., 2018). The only conclusion we are able
to make for L. simplex is that its geographical ploidy
structure does not seem directly climate driven or
habitat responsive but is more a consequence of
historic constraints in the evolution of this species.
Genetic clusters occur in significantly different
climates suggesting a long history of local adaptation
similar to that shown for Andropogon gerardi
(McAllister et al., 2015; McAllister & Miller, 2016) and
Triodia (Anderson et al., 2017, 2019). In the Australian
populations of Themeda triandra, genomic data
indicated that polyploid populations are associated
with warmer climates (Ahrens et al., 2020), and field
experiments found a higher polyploid fitness under
heat and drought stress (Godfree et al., 2017). Apart
from clarifying the origin of polyploidy in L. simplex,
expanding population sampling would also shed
light on the factors associated with the persistence of
polyploid populations.
Figure 7. Plastid haplotype network of Loudetia simplex
in Madagascar (copy of Figure 5A) compared with nuclear
clusters (indicated in colours). The nuclear Madagascar
South (cluster 1) and Madagascar North (cluster
2) correspond to these shown in Figures 2A, 3B and 4. Line
length is proportional to Bruvo’s genetic distance between
plastid haplotypes. Node size is proportional to the number
of plastid haplotype observations. All edges of equal weight
are represented.
Figure 8. Plastid haplotype networks for Loudetia simplex
in South Africa (copy of Figure 5A) mapped against ploidy
(indicated in colours). Line length is proportional to
Bruvo’s genetic distance between plastid haplotypes. Node
size is proportional to the number of plastid haplotype
observations. All edges of equal weight are represented.
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morphology, taxonomy and genetic cluStering
Loudetia simplex is a polymorphic species with
a complex taxonomic history and 11 heterotypic
synonyms (Clayton, 1974). The grasses of Madagascar
were mostly described in France by Camus from
specimens sent to Paris (Leandri, 1966), largely
independently of the African grass taxonomy carried
out in the UK, Belgium and Germany. These species
names based on a few collections each were revised by
Bosser, the only resident agrostologist in Madagascar
with extensive field experience. Bosser’s (1966) revision
of Loudetia in Madagascar stated that the variation
seen across Africa is so polymorphic that Hubbard’s
(1936) infrageneric classification of Loudetia is almost
arbitrary. Bosser (1966) recognized three endemic taxa
in Madagascar now included in the modern concept
of L. simplex: L. simplex subsp. stipoides (Hack.)
Bosser dominating central highlands, the smaller
Figure 9. Environmental envelope analysis comparing populations in four main nuclear clusters in Loudetia simplex for
A, annual precipitation (Bio_12) and B, annual mean temperature (Bio_1). The North-East South Africa and Drakensberg
clusters correspond to those shown in Figures 2B and 4. The Madagascar North and Madagascar South clusters correspond
to those shown in Figures 2A, 3B and 4. For precipitation, there are significant differences between all pairs of clusters (all
P < 0.001). For temperature, there are significant differences between the Drakensberg and Madagascar North clusters;
Drakensberg and Madagascar South; Drakensberg and North-East South Africa, Madagascar South and North-East South
Africa (all P < 0.001); Madagascar North and Madagascar South (P = 0.027). For temperature, there is also a marginally
significant difference between Madagascar North and North-East South Africa (P = 0.057).
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GENETIC DIVERSITY IN LOUDETIA SIMPLEX 95
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L. madagascariensis (Baker) Bosser with filiform
leaves occurring at > 1000 m elevation and L. perrieri
A.Camus from a single collection with a variant lower
glume. The distinction between L. simplex subsp.
stipoides and L. madagascariensis was maintained by
Bosser (1969) until L. madagascariensis was subsumed
into a broader L. simplex by Clayton (1974), who
noted that ‘variation in East Africa readily engulfs’ all
segregate taxa of L. simplex.
Clusters identified in this study lend support to
some of the earlier taxonomic divisions between
African and Malagasy L. simplex, even though no
macromorphological distinction is apparent. Our
results are also in agreement with a genetic difference
between L. simplex, the related species L. filifolia,
L. flavida, L. lanata and Tristachya nodiglumis, lending
support to the current classification. The separation
of Malagasy populations into northern and southern
clusters does not, however, have any correlation with
Bosser’s (1966, 1969) recognition of a higher elevation
taxon L. madagascariensis as separate from L. simplex
subsp. stipoides. Since L. perrieri was defined on the
basis of a single specimen with no exact locality data,
assessing its relationship to the clusters identified by
this study is not possible. Analysis of leaf anatomy
(Lubke & Phipps, 1973) also found that the Malagasy
L. madagascariensis and L. simplex subsp. stipoides
did not consistently cluster with the African L. simplex
subsp. simplex.
Improving population sampling from lower
elevations in the western and southern parts of
Madagascar and a greater part of Africa (especially
western Africa) would lend greater power to this
analysis. Phylogenomics and population genomics
would allow a reconstruction of the spatiotemporal
history of this species. Integration of functional traits
into this analysis could also provide deeper insights
into the significance of morphological variability for
ecological dominance of L. simplex, in the context of
the evolutionary history of the group.
CONCLUSIONS
We show that L. simplex populations of Madagascar
are genetically different from those occurring in
continental Africa and occupy different environmental
niches. This genetic diversity pattern is a clear
indication that Malagasy populations colonized the
island long before human arrival, probably via a
single colonization event. We conclude that L. simplex
is a native Malagasy species. Malagasy ecosystems
dominated by L. simplex are in need of more in-depth
studies to ascertain the detailed history of this species
in Madagascar and the drivers of its dominance.
Widespread and common grasses have the potential
to serve as excellent models for the reconstruction of
biome history. Prerequisites for such a model species
should include easy access to collection localities, a
small genome of low complexity and ideally a diploid
genome. Genetic population work on such grasses
would allow reconstruction of past population size
fluctuations and population splits over hundreds to
millions of years in the past.
ACKNOWLEDGEMENTS
This work was made possible by the RBG Kew Pilot
Study Fund 2017 and the QMUL and RBG Kew
MSc programme in Plant and Fungal Taxonomy,
Diversity and Conservation on which the first author
was enrolled during 2017–2018. The Howard Lloyd
Davies Legacy Fund supported the RADseq work. We
thank the staff of the Kew Madagascar Conservation
Centre, Stuart Cable (RBG Kew), Vololoniaina
Jeannoda (University of Antananarivo) and Parc
de Tsimbazaza for their long-term collaboration
and support for Madagascar Poaceae research. The
Direction Générale des Forêts, Madagascar National
Parks, and Ezemvelo KZN Wildlife generously
granted our research permits. Field collections
in South Africa were made possible by a Newton
grant awarded to Caroline Lehmann and Gareth
Hempson. The microsatellite marker primers were
designed with the help of Méline Saubin (RBG Kew).
Jaume Pellicer Moscardó, Robyn Faye Powell and
María Conejero (RBG Kew) kindly performed the
estimation of nuclear DNA contents. The curators
of BM, K, PRE and TAN herbaria provided access
to their collections; curators of TAN and PRE gave
permission to sample herbarium specimens in their
collections. Sarah Z. Ficinski drew and edited the
figures. JS, US and GB are members of the EDB
lab that is supported by LABEX TULIP (ANR-10-
LABX-0041) and CEBA (ANR-10-LABX-25-01) and
LIA BEEG-B (Laboratoire International Associé –
Bioinformatics, Ecology, Evolution, Genomics and
Behaviour, CNRS). They were also funded by an ERA-
NET BiodivERsA project: INFRAGECO (Inference,
Fragmentation, Genomics, and Conservation,
ANR-16-EBI3-0014).
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article at the publisher’s web-site:
Table S1. Accessions analysed, nuclear and plastid microsatellite genotypes and genetic cluster assignments.
Abbreviations: No. = collector number; Coll. type refers to a single collection (s) or population sampling (p);
Herb. = Herbarium where the voucher specimen has been deposited, acronym fide Thiers, 2019; Lat. = Latitude
(Decimal degrees); Long. = Longitude (Decimal degrees); Elev. = Elevation (m); Source gives the origin of material
when not collected for this study; MNA summarizes the maximum number of alleles per microsatellite locus
amplified for each accession; Genome = genotyped with nuclear SSRs (Nu) and/or plastid SSRs. Data for 11
nuclear and 11 plastid loci are fragment lengths (bp). Full data for accessions from Madagascar and for South
African collections are available from gbif.org and http://newposa.sanbi.org/sanbi, respectively. Data for the two
seed accessions are from the Millennium Seed Bank Partnership Data Warehouse http://brahmsonline.kew.org/
mspb.
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