The Lichenologist 46(3): 285–294 (2014) 6British Lichen Society, 2014
Brianaria (Psoraceae), a new genus to accommodate the
Micarea sylvicola group
Stefan EKMAN and Ma
Abstract: The new genus Brianaria S. Ekman & M. Svensson is introduced for the Micarea sylvicola
group, with the new combinations Brianaria bauschiana (Ko¨rb.) S. Ekman & M. Svensson, B.lutulata
(Nyl.) S. Ekman & M. Svensson, B. sylvicola (Flot. ex Ko¨rb.) S. Ekman & M. Svensson and B.tuber-
culata (Sommerf.) S. Ekman & M. Svensson. The new genus is characterized by a chlorococcoid,
non-micareoid photobiont, small, convex apothecia without an excipulum, an ascus of the ‘Psora-
type’, 0–1-septate ascospores, dimorphic paraphyses, and immersed pycnidia containing bacilliform
conidia. Brianaria is shown to form a monophyletic group in the Psoraceae, where it is probably the
sister group to Psora and Protoblastenia.
Key words: lichens, micareoid, Pilocarpaceae,Protoblastenia,Psora
Accepted for publication 22 May 2013
The genus Micarea, described by Fries (1825)
and conserved with M. prasina as the type
species (Coppins 1989, ICBN appendix
III), was resurrected from oblivion at the
end of the 19th century ( Hedlund 1892). In
his precocious revision of small crustose li-
chens, Hedlund (1892) emended Micarea to
include 20 species. As circumscribed by him,
the genus included species with unicellular
or transversely septate ascospores, branched
and anastomosing, apically unthickened par-
aphyses, an excipulum composed of para-
physis-like hyphae, a small-celled photobiont
[4–8(–9) mm, later known as ‘micareoid’]
and immarginate, often tuberculate apothecia.
Several decades later, Zahlbruckner (1921–
40) introduced an artiﬁcial but highly inﬂu-
ential taxonomy in his Catalogum Lichenum
Universalis, in which lecideoid lichens were
sorted according to spore septation. Conse-
quently, the genus Micarea was split and its
species again transferred to other genera,
the Zahlbrucknerian ice sheet slowly melted
in the 1960s and 70s, the pursuit for a more
natural classiﬁcation was revitalized and fol-
lowed by the revival of previously described
genera, as well as the description of numer-
ous new ones.
One of the ﬁrst to yet again re-establish
Micarea was Anderson (1974), who trans-
ferred Lecidea tuberculata Sommerf. to this
genus. The inclusion of a species with a non-
micareoid photobiont was in effect an emen-
dation of Hedlund’s generic delimitation,
although Anderson did not discuss this. Sub-
ˇzda & Wirth (1976) published a
new key to the genus, accepting Hedlund’s
work but also adding several species, such as
Micarea bauschiana (Ko¨rb.) Ve
ˇzda & Wirth,
M.lutulata (Nyl.) Coppins (as M.umbrosa
ˇzda & Wirth) and M.sylvicola (Flot.)
ˇzda & Wirth. They noted that M.bauschi-
ana and M.lutulata were probably closely re-
lated, but did not comment on the similari-
ties between these species and M.sylvicola
In his breakthrough revision of the Euro-
pean species of Micarea, Coppins (1983)
noted that the genus included several small
S. Ekman: Museum of Evolution, Uppsala University,
Norbyva¨gen 16, SE-75236 Uppsala, Sweden.
M. Svensson: Department of Ecology, Swedish Univer-
sity of Agricultural Sciences, P. O. Box 7044, SE-75007
groups of apparently closely related species.
He recognized 11 infrageneric groups in
Micarea, one of which (group ‘I’, hereafter
known as the Micarea sylvicola group) con-
sisted of M.bauschiana, M.lutulata,M.sylvi-
cola, and M.tuberculata. Coppins concluded
that this group was ‘almost worthy of sub-
With the advent of molecular methods in
taxonomy came the opportunity to test this
hypothesis. Andersen & Ekman (2005), in a
phylogeny based on mitochondrial ribosomal
DNA, showed that Micarea was paraphyletic,
and that several of the infrageneric groups
identiﬁed by Coppins probably deserved
generic recognition. In this phylogeny, the
Micarea sylvicola group was represented by
M.bauschiana and M.sylvicola, and formed
a highly supported group together with Psora
decipiens (Hedw.) Hoffm. in the Psoraceae.
Subsequent studies based on three or more
loci and better taxon sampling conﬁrmed
that the family Psoraceae consists of Protoblas-
tenia,Psora and the Micarea sylvicola group
(Ekman et al. 2008; Ekman & Blaalid 2011;
Schmull et al. 2011). In these analyses, the
M.sylvicola group has been represented by
M. sylvicola only (Ekman et al. 2008; Schmull
et al. 2011) or M. bauschiana and M. sylvicola
(Ekman & Blaalid 2011).
As no name at genus level appears to be
available for Micarea sylvicola and its close
relatives, a new genus is described here to ac-
commodate it. Furthermore, although there
is ample evidence to show that the M.sylvi-
cola group belongs in the Psoraceae, previous
phylogenetic analyses did not include M. lu-
tulata and M. tuberculata. In order to demon-
strate that these species are unequivocally
close relatives of M. sylvicola and M. bauschi-
ana, we present a new phylogeny that in-
cludes all four species in the same analysis.
Materials and Methods
We selected 27 species of the Pilocarpaceae in the sense
of Andersen & Ekman (2005) and Ekman et al. (2008),
and Psoraceae in the sense of Andersen & Ekman (2005)
and Ekman & Blaalid (2011) (i.e. including Psora,Proto-
blastenia, and the ‘Micarea sylvicola group’ described
here as Brianaria, but excluding Eremastrella,Glyphopeltis,
and Psorula). We did not include the genus Protomicarea
in the Psoraceae, as this genus was shown by Schmull
et al. (2011) not to belong in this family. Protomicarea
was tentatively referred to the Psoraceae by Hafellner &
¨rk (2001) and Lumbsch & Huhndorf (2010) but was
not included in the phylogenetic analysis by Ekman &
Blaalid (2011). Two species (Brianaria sylvicola and B.
tuberculata) were represented by two terminal units, re-
sulting in an ingroup with 29 members. We used Sphaer-
ophorus globosus as outgroup, the selection of which was
based on the phylogeny by Mia˛ dlikowska et al. (2006),
in which the Sphaerophoraceae is sister to the Psoraceae
Marker selection and sequence acquisition
For 26 of the 30 included terminals, we downloaded
sequence data from GenBank (http://www.ncbi.nlm.
nih.gov/genbank/) representing three different genes,
viz. the largest subunit of the RNA polymerase II gene
(RPB1), the internal transcribed spacer ( ITS) region
(including ITS1, 5.8S, and ITS2) of the nuclear riboso-
mal RNA gene, and the small subunit of the mitochon-
drial ribosomal RNA gene (referred to here as mrSSU).
For the remaining four terminals, we produced new
sequence data as described below. Most terminals were
composed of sequence data from a single herbarium
specimen. To avoid excessive amounts of missing data
in the resulting matrix, we accepted two cases with
terminals composed of sequence data from more than
one herbarium specimen, viz.Psora decipiens and P.
rubiformis. The sequence data used for this study is sum-
marized in Table 1.
PCR ampliﬁcation and DNA sequencing
New ITS and mrSSU sequences were generated from
four specimens representing three species of Brianaria,
namely B. lutulata,B. sylvicola, and B. tuberculata. In ad-
dition, several unsuccessful attempts were made to obtain
RPB1 sequences. Laboratory methods generally con-
formed to Ekman & Blaalid (2011), except that we used
the Hot StarTaq Mastermix Kit (Qiagen) and QIAquick
PCR Puriﬁcation Kit (Qiagen). In a few cases, we per-
formed direct PCR from apothecial sections without
preceding DNA extraction (Wolinski et al. 1999).
Sequences were aligned using MAFFT version 6.935
(Katoh & Toh 2008a). The three ITS components, ITS1,
5.8S and ITS2, were aligned separately using the X-INS-i
algorithm with MXSCARNA pairwise structural align-
ments and Contrafold base-pairing probabilities (Katoh
& Toh 2008b). A structural euascomycete mrSSU refer-
ence alignment was downloaded from the Comparative
RNA Web Site (http://rna.ccbb.utexas.edu; Cannone et
al. 2002). This alignment was used as a proﬁle, to which
our mrSSU sequences were added using the L-INS-i
algorithm. This choice was motivated by the fact that
the reference alignment included complete or near-
THE LICHENOLOGIST286 Vol. 46
complete mrSSU sequences that were much longer than
our sequences. Subsequently, the reference alignment
was removed and gap-only sites stripped. RPB1 sequences
were aligned using the G-INS-i algorithm at the amino
acid level and subsequently back-translated into DNA
sequences. The choice of algorithm was motivated by
the very few expected gaps in the RPB1 sequences once
introns had been removed.
Ambiguous alignments were ﬁltered out using Ali-
score version 2.0 (Misof & Misof 2009). All pairs of
taxa were used to calculate the consensus proﬁle. Gaps
were treated as ambiguities and window size was set to
4. These are the most conservative options available in
Maximum likelihood (ML) estimation of phylogeny
was performed using GARLI version 2.0 (Zwickl 2006)
under a single GTR model with rate heterogeneity across
sites, modelled as a discretized gamma distribution with
six categories and a proportion of invariable sites. Phy-
logeny estimates were produced for 1) each of the three
genes for the purpose of assessing potential gene tree
conﬂicts, and 2) the complete concatenated but unparti-
tioned data, primarily for the purpose of generating an
empirical branch-length prior for downstream Bayesian
inferences. For the concatenated data, we performed
1000 optimizations from starting trees generated by
stepwise random addition of taxa, every possible attach-
ment point being evaluated. For all data sets, branch
support was assessed using 1000 non-parametric boot-
strap replicates. Majority-rule consensus trees from the
single genes were subsequently input into Compat.py
(Kauff & Lutzoni 2002) for conﬂict identiﬁcation above
70% bootstrap support.
The concatenated data were divided into seven poten-
tial character subsets, ITS1, 5.8S, ITS2, mrSSU, as well
as RPB1 ﬁrst, second and third codon positions. These
subsets were subsequently input into PartitionFinder
version 1.0.1 (Lanfear et al. 2012) for a simultaneous ex-
haustive search for thebest-ﬁtting partitioning scheme and
the best-ﬁtting model of each partition under the con-
straint that only models with one, two, or six substitution
Table 1. GenBank accession numbers for DNA sequences included in this study. Newly obtained sequences are in bold.
Dashes represent missing data
ITS mrSSU RPB1
Bapalmuia palmularis AY756457 AY567781 —
Brianaria bauschiana — AY567770 —
B. lutulata JX983582 JX983586 —
B. sylvicola I JX983583 JX983587 —
B. sylvicola II — AY567769 AY756392
B. tuberculata I JX983584 JX983588 —
B. tuberculata II JX983585 JX983589 —
Byssolecania variabilis AY756458 AY567780 —
Byssoloma leucoblepharum AY756459 AY567778 AY756380
Micarea adnata AY756468 AY567751 AY756388
M. alabastrites AY756469 AY567764 AY756389
M. assimilata AY756470 AY567739 —
M. byssacea AY756485 AY567749 —
M. erratica AY756475 AY567737 AY756390
M. lignaria var. lignaria AY756481 AY567748 —
M. lithinella AY756482 AY567734 —
M. melaena AY756483 AY567743 —
M. misella AY756486 AY567752 —
Protoblastenia calva EF524319 DQ986904 EF524338
P. rupestris EF524318 — EF524329
Psora californica EF524322 EF524292 EF524334
P. cerebriformis EF524325 EF524293 EF524335
P. decipiens EF524326 AY567772 EF524337
P. globifera EF524323 EF524294 EF524331
P. nipponica EF524312 — EF524336
P. paciﬁca EF524314 EF524297 EF524332
P. rubiformis HQ650620 AY756374 DQ986831
P. tuckermanii EF524317 — —
P. vallesiaca EF524324 EF524291 —
Sphaerophorus globosus AY256769 AY256751 AY756424
2014 Brianaria—Ekman & Svensson 287
rate categories could be selected. We used the Bayesian
Information Criterion to select among models and parti-
We performed Bayesian phylogenetic inference using
Markov chain Monte Carlo (MCMC) as implemented
in PHYCAS version 1.2.0 (Lewis et al. 2010). Five dif-
ferent analyses were performed for the purpose of quan-
tifying the support for the Psoraceae and the new genus
Brianaria under a variety of model assumptions and
The ﬁrst analysis assumed independent best-ﬁtting
models for each of the partitions inferred by Partition-
Finder. Rate heterogeneity was, when applicable, mod-
elled as a discretized gamma distribution with six catego-
ries. We used ﬂat Dirichlet priors on state frequencies, as
well as the substitution rate matrix for six-rate models, a
beta prime (1, 1) distribution on the transition and trans-
version rates for two-rate models, a uniform (0001,
200) on the gamma distribution shape parameter, a uni-
form (0, 1) on the proportion of invariable sites, and a
ﬂat relative rate distribution, a transformed Dirichlet
distribution described by Fan et al. (2011), on the subset
rate multipliers. The prior on branch lengths was set to
an exponential with rate parameter 25. This rate param-
eter was estimated by ﬁtting an exponential distribution
to the branch lengths obtained from the ML analysis.
Curve-ﬁtting was performed with EasyFit Professional
version 5.5 (MathWave Technologies).
The second analysis was identical to the ﬁrst, except
that the prior on branch lengths was set to an exponen-
tial seeded by an exponential hyperprior with rate 10.
This is a hierarchical model on branch lengths, in which
the mean of the prior distribution is not ﬁxed but treated
as a parameter to be estimated. As a hierarchical model
on branch lengths does not impose a speciﬁc exponen-
tial distribution, it should have less inﬂuence on pos-
terior parameter distributions (Ekman & Blaalid 2011;
Rannala et al. 2012).
The third analysis was identical to the ﬁrst except we
allowed trees with polytomies to be sampled according
to the model of Lewis et al. (2005). The purpose of this
analysis was to investigate whether the forced sampling
of fully resolved trees in other analyses could cause ex-
cessive branch support (Lewis et al. 2005). We chose to
set the polytomy prior (C) to 1. Thereby every tree to-
pology was treated as a priori equally probable, regard-
less of the number of internal nodes. As there are c.1000
times more unique topologies with at least one polytomy
than there are fully resolved 30-taxon trees (Felsenstein
2004), our prior amounts to treating the class of polyto-
mous trees as a priori far more likely than the fully re-
The fourth analysis was identical to the ﬁrst, except
that we used a ﬁxed branch-length prior drawn from a
gamma distribution with shape 0647 and scale 0 062.
The parameterization of the gamma distribution was
taken from the above-mentioned EasyFit curve-ﬁtting
procedure. The rationale behind this analysis was that a
branch-length prior violating the true distribution of
branch lengths may bias posterior probabilities (Kolacz-
kowski & Thornton 2007).
The ﬁfth analysis was identical to the ﬁrst, except that
independent GTR+I+Gmodels were used for the sub-
sets. This analysis was carried out to safeguard against
potential overestimates of branch support, in case of hid-
den inadequacies in the best-ﬁtting models (Huelsenbeck
& Rannala 2004). It should be noted, however, that the
potential adequacy of the GTR+I+Gmodel is restricted
to temporally reversible and homogeneous processes.
Each analysis included three runs, each with one cold
and three heated chains, the hottest with power 05. We
ran each analysis for 200 000 generations, sampling every
100th generation. The reason for the smaller number of
generations compared to the commonly used MrBayes
(Ronquist & Huelsenbeck 2003) is that a generation is
deﬁned differently in PHYCAS, one generation in this
software corresponding to c.100generationsinMrBayes.
Average standard deviations of splits (with frequency
b01) between runs, identical to the default measure
used to diagnose MrBayes runs, were calculated from
summaries provided by the ‘showsplits’ command in
AWTY online (Wilgenbush et al. 2004) after having re-
moved the ﬁrst half of each tree sample as burn-in. Mar-
ginal likelihoods of the data were calculated with Tracer
version 1.5 (Rambaut & Drummond 2009) using the
importance sampling estimator originally suggested by
Newton & Raftery (1994) and modiﬁed by Suchard et al.
(2003). Importance sampling, as well as the widely used
harmonic mean, have, however, been shown to be unreli-
able when comparing models with high dimensionality
(Lartillot & Philippe 2006). Therefore, we also calculated
marginal likelihoods using the stepping-stone procedure
described by Fan et al. (2011) and implemented in PHY-
CAS. This implementation requires a ﬁxed tree topology,
for the purpose of which we used the majority-rule con-
sensus trees with all compatible groups. We took 1000
samples from each of 21 stepping stones, with the excep-
tion that 2000 samples were taken from the posterior.
The ﬁxed-topology requirement rendered stepping-stone
estimation impossible under the polytomy model.
The resulting ﬁltered alignment consisted of
1731 sites, 352 of which belonged to the
ITS, 807 to the mrSSU, and 642 to the
RPB1. The number of variable alignment
positions (assuming that gaps are treated as
missing data) was 166, 300, and 289, respec-
tively in the ITS, mrSSU, and RPB1. The
total amount of missing data, including
gaps, was 28%, RPB1 being clearly over-
represented due to technical difﬁculties am-
plifying this gene successfully.
We did not record any conﬂicts between
the three genes. The best-ﬁtting partitioning
scheme, given the seven potential partitions,
THE LICHENOLOGIST288 Vol. 46
included ﬁve partitions: ITS1 + ITS2, 5.8S,
mrSSU, RPB1 ﬁrst and second codon posi-
tions, and RPB1 third codon positions. The
best-ﬁtting models for each of these partitions
was found to be SYM+G, K80+I, GTR+I+G,
K80+I+G, and K80+G, respectively. The
total number of free parameters in this model,
including the subset rate multipliers, was 26,
compared to the 54 free parameters in the
analysis with ﬁve independent GTR+I+G
models. Kolmogorov-Smirnov tests of good-
ness-of-ﬁt of preliminary maximum-likeli-
hood branch lengths did not reject an expo-
nential distribution (D ¼013, P¼030),
although a gamma distribution had better ﬁt
(D ¼009, P¼075). Average standard de-
viations of split frequencies between MCMC
runs ranged from 0007 to 0 009 depending
on the analysis, which is low enough to con-
clude that MCMC analyses had converged
and that our tree samples represent valid
samples from the posterior distributions.
The ﬁve Bayesian analyses are summar-
ized in Table 2. Depending on model and
branch-length prior, the posterior probability
of the branch uniting the Psoraceae ranges
from 098 to 100, whereas the posterior
probability of the branch uniting the genus
Brianaria ranges from 095 to 0 98. By com-
parison, ML bootstrap proportions were
073 for the Psoraceae and 089 for Brianaria.
Stepping-stone estimation of marginal likeli-
hoods seems to provide better resolution to
discriminate between models and priors than
importance sampling. A majority-rule con-
sensus tree with all compatible groups from
the ﬁrst of the Bayesian inferences, the one
with independent best-ﬁtting model for each
partition and an exponential (25) branch-
length prior, is shown in Fig. 1.
The phylogenetic analysis indicates that Bria-
naria, including B. bauschiana,B. lutulata, B.
sylvicola and B. tuberculata, forms a monophy-
letic group within the Psoraceae, which is in
agreement with the ﬁndings of Andersen &
Ekman (2005), Ekman & Blaalid (2011) and
Schmull et al. (2011). Evidence also suggests
that Brianaria is the sister group to the rest of
the currently known members of the Psora-
ceae,viz. Psora and Protoblastenia.Conversely,
there is no indication that Brianaria belongs
in Micarea or the Pilocarpaceae, where its spe-
cies have previously been included.
Differences in tree lengths and support for
the Psoraceae and Brianaria between analyses
based on best-ﬁtting models for each parti-
tion are minuscule, whether or not allowing
for polytomies and irrespective of the partic-
ular branch-length prior. Marginal likelihood
differences are modest as estimated by im-
portance sampling, whereas the more reliable
stepping-stone estimation indicates reason-
ably strong support for a gamma distributed
branch-length prior. This is not surprising,
as the initial ﬁtting of ML branch lengths
provided better ﬁt to a gamma distribution
than to an exponential distribution. The
analysis based on independent GTR+I+G
models, on the other hand, resulted in a
much worse marginal likelihood than other
analyses, whether estimated by importance
Table 2. Overview of Bayesian phylogenetic analyses performed with PHYCAS. The ‘‘best’’ model refers to the combination
of partition models selected by PartitionFinder. Polytomies were modelled according to Lewis et al. (2005). Stepping-stone
estimation of the marginal likelihood was not possible because of a ﬁxed-topology requirement in the implementation. Support
for the Psoraceae refers to the posterior probability of the node uniting Brianaria, Protoblastenia, and Psora
Model Branch-length prior
Best Exponential (25) None -- 10960308 -- 11837 908 2517 0 98 100
Best Exponential Exponential (10) -- 10960513 -- 11833 221 2554 0 98 099
Best+polytomies Exponential (25) None -- 10967942 — 2519 0 95 098
Best Gamma (0647, 0 062) None --10959854 -- 11824 099 2558 0 97 099
5(GTR+I+G) Exponential (25) None -- 11149 117 -- 12088786 2 524 097 1 00
2014 Brianaria—Ekman & Svensson 289
sampling or stepping stones, suggesting that
it suffers from severe overﬁtting. However,
whereas underﬁtting is known to cause severe
topological bias, overﬁtting seems to be much
less of a problem ( Huelsenbeck & Rannala
2004; Lemmon & Moriarty 2004). Conse-
quently, the overﬁtted analysis provides some
indication that support for the Psoraceae and
Brianaria is not overestimated. Ekman &
Blaalid (2011), based on more characters
but fewer taxa in Brianaria, found support
for a Psoraceae including Brianaria to be
096 and 095 for independent best-ﬁtting
and GTR+I+Gpartition models, respec-
tively, when integrating over a wide interval
of exponential branch-length priors. They
did not, however, investigate the effect of
allowing for polytomies or other than expo-
nential branch-length priors. Nominal ML
bootstrap support values for the Psoraceae
and Brianaria nodes (073 and 089, respec-
tively) seem to be in line with the posterior
probabilities, given previous suggestions that
bootstrap support at 070 corresponds to a
095 probability of a clade being real (Hillis
& Bull 1993). This approximation assumes
that rates of change are moderate and more
or less equal across the tree, which may hold
true in our case. Altogether, support for the
monophyly of Psoraceae and Brianaria appears
to be high and does not seem to be affected by
any of the known causes of inﬂated posterior
probabilities in Bayesian inference of phylog-
eny, viz. model underﬁtting, branch-length
prior misspeciﬁcation, or the forcing of fully
Fig. 1. Majority-rule consensus tree with all compatible groups, average branch lengths, and posterior probabilities
of nodes resulting from Bayesian MCMC using PHYCAS under independent best-ﬁtting models for ﬁve partitions
and an exponential branch-length prior with rate parameter 25. Familial afﬁliations are indicated.
THE LICHENOLOGIST290 Vol. 46
Brianaria S. Ekman & M. Svensson gen.
MycoBank No.: MB803358
Distinguished from Micarea s. str. by having a non-
micareoid photobiont, dimorphic paraphyses, and a
wider tube structure in the tholus, from Psora by the
crustose thallus, lack of oxalate and anthraquinone
crystals in the apothecia, and from Protoblastenia by the
absence of anthraquinones in the apothecia and the lack
of an excipulum.
Type species: Brianaria sylvicola (Flot. ex Ko¨rb.) S.
Ekman & M. Svensson.
Thallus scurfy-granular-verruculose areo-
late, grey-greyish green. Photobiont of two
types, 1) chlorococcoid, 5–12(–15) mmor2)
irregularly ellipsoid and up to 15 10 mm.
Ascomata immarginate, convex-hemispher-
ical, often becoming tuberculate, 015–070
(–120) mm diam. Excipulum absent. Hy-
pothecium 80–200 mm tall, composed of
interwoven hyphae 1–3 mm thick. Hymenium
30–75 mm. Paraphyses dimorphic, either
evenly distributed, sparingly branched, often
anastomosing below, 08–15mm wide or
fewer in number, single; or in fascicles, sim-
ple or occasionally forked above, distinctly
septate, 15–30mm wide, up to 4 mm api-
cally. Ascospores non-septate (sometimes 1-
septate in B. tuberculata), 55–12015–
50mm. Asci 8-spored, cylindrical-clavate,
25–45 7–12 mm. Tholus with a wide, dark
tube structure that expands towards the top,
without a pale axial body (‘Psora-type’ sensu
Ekman et al. 2008).
Pycnidia immersed in thallus, 004–020
mm diam., black. Conidiogenous cells ecylin-
drical, 5–10 10–15mm. Conidia bacilli-
form to oblong to obovoid, 3–7 1–2 mm.
Chemistry. No lichen substances detected
by TLC. Three different pigments occur in
the apothecia of Brianaria,viz. blue-green,
K-- , N+ red (‘Pigment A’ sensu Coppins
1983, ‘Cinereorufa-green’ sensu Meyer &
Printzen 2000), brown, K-- ,N
-- or N+
orange-brown (‘Pigment F’ sensu Coppins
1983) and purple, K+ green, N+ red (‘Pig-
ment B’ sensu Coppins 1983, ‘Melaena-red’
sensu Meyer & Printzen 2000).
Etymology. The genus is named in honour
of Brian Coppins, in recognition of his out-
standing contribution to the taxonomy of
crustose lichens in general, and to the genus
Micarea in particular.
Ecology. All four species of Brianaria are
essentially saxicolous and prefer shaded acid
rock, often in rain-protected situations. Other
substrata (e.g., wood, rusted iron) are occa-
sionally inhabited, especially by B.sylvicola.
Notes. As delimited by Lumbsch & Huhn-
dorf (2010), the Psoraceae consists of the
Psora, Psorula and possibly also Protomicarea.
Of these, Eremastrella,Glyphopeltis, Psorula
and Protomicarea have subsequently been
shown not to belong in this family ( Ekman
& Blaalid 2011; Schmull et al. 2011). Psora,
the type genus of the family, differs from
Brianaria in having a well-developed squa-
mulose thallus, anthraquinones in the epi-
thecium and calcium oxalate in the hypo-
thecium (Timdal 1984, 2002). Protoblastenia
differs in having anthraquinones in the
apothecial tissues, in having an exciple com-
posed of parallel-radiate hyphae, and in hav-
ing a preference for calcareous substrata
(Kainz 2004; Kainz & Rambold 2004). Bria-
naria,Psora and Protoblastenia are similar in
having asci of the ‘Psora-type’ and immersed
pycnidia containing bacilliform conidia.
Although not closely related, Brianaria
is anatomically similar to Micarea, differing
from Micarea s. str. (i.e. M.prasina Fr. and
closely related species) primarily by having a
non-micareoid photobiont, dimorphic para-
physes, and a slightly different tholus. Al-
though similar, tube structures in members
of the Pilocarpaceae tend to be thin without
or with a slight tendency to expand near the
apex. This appearance contrasts with the thick
tube that expands near the apex found in the
Psoraceae, including Brianaria (see Fig. 1 in
Ekman et al. 2008).
In spite of the exclusion of Brianaria, the
genus Micarea still includes species with a
non-micareoid photobiont, such as M. lyn-
ceola (Th. Fr.) Palice and M.myriocarpa V.
Wirth & Ve
ˇzda ex Coppins, as well as species
2014 Brianaria—Ekman & Svensson 291
with dimorphic paraphyses, such as M.
botryoides (Nyl.) Coppins and M. lithinella
(Nyl.) Hedl. (Andersen & Ekman 2005;
The photobiont of Brianaria remains un-
identiﬁed to genus. The photobiont in Psora
decipiens and P. globifera has been identiﬁed
as Myrmecia biatorellae (Geitler 1963; Galun et
al. 1971; Tschermak-Woess 1988), although
Schaper & Ott (2003) claimed to have found
a species of Asterochloris (Schaper & Ott 2003)
in Psora decipiens. Both Myrmecia and Astero-
chloris are members of the Trebouxiaceae in
the Trebouxiales (Guiry & Guiry 2012). The
primary ‘micareoid’ photobionts in Micarea
prasina,M. peliocarpa, and M. misella,on
the other hand, appear to be Elliptochloris
bilobata,E. reniformis, and E. subsphaerica
(Voytsekhovich et al. 2011). The genus Ellip-
tochloris has an unsettled position in the Pra-
siolales (Guiry & Guiry 2012).
Descriptions of the species of Brianaria, as
well as heterotypic synonyms, can be found
in Coppins (1983) and Czarnota (2007).
Brianaria bauschiana ( Ko
¨rb.) S. Ekman
& M. Svensson comb. nov.
MycoBank No.: MB803360
Biatora bauschiana Ko¨rb., Parerga lich.: 157 (1860).—
Lecidea bauschiana (Ko¨rb.) Lettau in Hedwigia 55: 28
(1914).—Micarea bauschiana (Ko¨ rb.) Ve
ˇzda & Wirth in
Folia Geobot. Phytotax.11: 95 (1976); type: Germany,
¨rttemberg, ‘‘auf Porphyr bei Baden,’’ Bausch,
distributed as Rabenhorst: Lich. Europ. 648 (M—lecto-
type, selected by Ve
ˇzda & Wirth 1976, not seen; UPS—
Brianaria lutulata (Nyl.) S. Ekman &
M. Svensson comb. nov.
MycoBank No.: MB803361
Lecidea lutulata Nyl. in Flora Jena 56: 297 (1853).—
Micarea lutulata (Nyl.) Coppins in D. Hawksw., P.
James & B. Coppins, Lichenologist 12: 107 (1980); type:
British Isles, ‘‘Jersey, Rozel meadow, bases of rocks,’’
1873, Larbalestier (H-NYL 10696 —lectotype, selected
by Coppins 1983, seen).
Brianaria sylvicola (Flot. ex Ko
S. Ekman & M. Svensson comb. nov.
MycoBank No.: MB803359
Lecidea sylvicola Flot., Lich. Schles.: 171 (1829), nom. in-
val. (Art. 32.1d, 34.1a).—Lecidea sylvicola Flot. ex Ko¨rb.,
Syst. Lich. German.: 254 (1855).— Micarea sylvicola
ˇzda & Wirth in Folia Geobot. Phytotax. 11: 99
(1976); type: Czech Republic/Germany/Poland, Lich.
Schles. 171 (UPS —lectotype, selected by Hertel 1975,
Nomenclatural note. Flotow (1829) intro-
duced the name Lecidea sylvicola, but did not
himself consider this name valid (‘Lecidea syl-
vicola ad int.’) and did not provide a diagnosis
or reference to a validly published diagnosis.
The taxon was validated by Ko¨rber (1855),
who provided a diagnosis and made explicit
reference to nr. 171 in Flotow’s exsiccate.
The earliest known synonyms of L.sylvicola
are L.aggerata Mudd and L.incincta Nyl.,
both of which were published in 1861 (Cop-
pins 1983; Czarnota 2007).
Brianaria tuberculata (Sommerf.)
S. Ekman & M. Svensson comb.nov.
MycoBank No.: MB803362
Lecidea tuberculata Sommerf., Suppl. Fl. Lapp.: 160
(1826).—Micarea tuberculata (Sommerf.) R. A. Ander-
son in Bryologist 77: 46 (1974); type: Norway, Nordland,
Saltdalen, Fiskevaagmo¨llen, March 1822, Sommerfelt
(O—lectotype, selected by Coppins 1983, not seen;
We thank Katja Fedrowitz, Mattias Lif and Veera Tuovi-
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