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Contents lists available at ScienceDirect
Flora
j ournal homepage: www.elsevier.com/locate/flora
Species distribution models backing taxa delimitation: the case
of the lichen Squamarina cartilaginea in Italy
a,∗ b c b
Stefano Martellos , Fabio Attorre , Alessio Farcomeni , Fabio Francesconi ,
a a
Elena Pittao , Mauro Tretiach
a
Dipartimento di Scienze della Vita, Università degli Studi di Trieste, Giorgieri 10, 34123 Trieste, Italy
b
Dipartimento di Biologia Ambientale, Università degli Studi “La Sapienza”, Roma, Italy
c
Dipartimento di Sanità Pubblica e Malattie Infettive, Università degli Studi “La Sapienza”, Roma, Italy
a r t i c l e i n f o a b s t r a c t
Article history: Species distribution models (SDMs) have been extensively used for a variety of purposes, including inves-
Received 21 January 2014
tigation of taxonomic problems, together with molecular, chemical and morphological analysis. The two
Received in revised form 11 August 2014
varieties of the lichen Squamarina cartilaginea known to occur in Italy (var. cartilaginea and var. pseu-
Accepted 12 August 2014
docrassa), which are morphologically indistinguishable, can be identified only by a medullar chemical
Edited by Brigitta Erschbamer
spot test. In this paper, SDMs are used to support the separation of these two varieties, to determine
Available online xxx
whether they are also characterized by a differential spatial distribution. Occurrence data were obtained
by geo-referencing a posteriori 114 herbarium specimens identified to variety level by a medullar spot
Keywords:
test. The spatial distribution was modeled by using Random Forest (RF) and Generalized Linear Mod-
Ecological niche
GLM els. Suitability areas were obtained by applying the 0% omission error criterion in the probability map
Phytogeography produced by RF, which proved to be the more statistically reliable of the two methods. Kendall’s tau
Pseudocrassa statistic test applied to RF suitability maps indicates that the two varieties tend to segregate ecologically
Random Forest in the Italian peninsula. Var. pseudocrassa appears to be more widespread in the Mediterranean region,
as well as in coastal and hilly areas, while var. cartilaginea is more abundant in the temperate region and
mountainous areas. For both varieties the spatial distribution is determined by similar climatic variables
(mean yearly temperature, mean temperature of the coldest month and summer precipitation). These
findings lead to a new hypothesis on the role of these environmental factors on the evolutionary history
and geographical distribution of the two varieties. This study also corroborates the usefulness of SDMs
in delimiting taxonomical entities.
© 2014 Elsevier GmbH. All rights reserved.
Introduction for evaluating Network Natura 2000, protected areas for the con-
servation of lichens in Spain (Martínez et al., 2006; Rubio-Salcedo
Species distribution models (SDMs) have been extensively used et al., 2013).
for a variety of ecological and biogeographical studies as well as Recently, SDMs have also been used together with morpho-
in the applied field of conservation. Examples of the use of SDMs logical, molecular and chemical data to investigate taxonomic
include the improvement of survey design of rare epiphytic macro- uncertainties, or evolutionary processes (Ross et al., 2010; Tomovic´
lichens (Edwards et al., 2005), the assessment of climatic drivers et al., 2010). SDMs appear to be particularly suitable for this kind of
and the effect of forest type on the spatial distribution of epiphytic analysis, as their output comprises of statistically robust suitabil-
lichens (Bollinger et al., 2007) and the evaluation of the impact of ity maps, that allow for an estimation of species ecological niches,
climate change on the distribution of epiphytic lichens (Ellis et al., even when there are few or unevenly distributed occurrence data
2007a,b). Examples of the use of SDMs in conservation include: (Attorre et al., 2013; Jiménez-Alfaro et al., 2012). This issue is par-
assessing the conservation status and the effectiveness of conser- ticularly relevant when using herbarium data, as in the present
vation strategies for the endangered lichen Erioderma pedicellatum study. Herbaria host a large amount of useful distribution infor-
on the island of Newfoundland (Wiersma and Skinner, 2011); and mation (primary biodiversity data) in the form of specimens, i.e.,
falsifiable occurrence records. However, particular care should be
taken when using this type of information (Newbold, 2010) due to
∗ the difficulty in geo-referencing specimens a posteriori (Guo et al.,
Corresponding author. Tel.: +39 040 558 3884, +39 328 7692650 (mobile).
E-mail address: [email protected] (S. Martellos). 2008; Wieczorek et al., 2004), the absence of systematic sampling
http://dx.doi.org/10.1016/j.flora.2014.08.008
0367-2530/© 2014 Elsevier GmbH. All rights reserved.
Please cite this article in press as: Martellos, S., et al., Species distribution models backing taxa delimitation: the case of the lichen
Squamarina cartilaginea in Italy. Flora (2014), http://dx.doi.org/10.1016/j.flora.2014.08.008
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procedures which leads to an unevenness in spatial coverage In order to identify the two varieties a medullar spot test with
(Hortal et al., 2008; Kadmon et al., 2004; Reddy and Deddyos, 2003) an alcohol solution of para-phenylendiammine (Pd) was carried out
and temporal bias, due to the idiosyncratic time frames over which for all the specimens (Torrey, 1935): race I (var. pseudocrassa) reacts
different experts have collected specimens (Soberón et al., 2000). Pd−, while race II (var. cartilaginea) reacts Pd+ yellow (Nimis and
In this paper, SDMs were used to investigate the ecological Martellos, 2004).
differentiation of the two varieties of the lichen Squamarina car- Since no specimen reported geographic coordinates, it was nec-
tilaginea which are known to occur in Italy: var. cartilaginea and essary to geo-reference them a posteriori, on the basis of the locality
var. pseudocrassa, which are morphologically indistinguishable and altitudinal information recorded by collectors. Geo-referenced
but chemically different (Feige et al., 1997). Specifically, we test data were then assigned to a 1 km × 1 km UTM square defined
whether the two varieties can be characterized by distinct spa- by the environmental data layers selected for the SDMs, which
tial distributions as quantified by SDMs. This was calculated using included climate, geology and land cover. Climate data were inter-
occurrence data obtained from herbarium specimens, which were polated from a network of meteorological stations using universal
geo-referenced a posteriori and specimens identified to variety level kriging, with a trend function defined on the basis of a set of
by a medullar spot test. covariates (Attorre et al., 2007). These include: mean annual tem-
perature (MeanT), mean of minimum temperature of the coldest
month (MinT), mean of maximum temperature of the warmest
Material and methods
month (MaxT), sum of mean monthly precipitation over summer
Analyzed taxa (Psum), autumn (Paut), winter (Pwin) and spring (Pspr) months
and total annual precipitation (Ptot). A simplified geological map
included five substrata: volcanic, arenaceous, carbonatic, clayey
The genus Squamarina (Lecanoraceae; Poelt, 1958) consists of
and sandy and conglomeratic. Land cover data, expressing a gradi-
mainly saxicolous or terricolous squamulose lichens, occurring
ent of naturalness (2: forest; 1: shrubland; 0: pasture, agricultural
on base-rich substrata all over the world, with the exception of
land and urban areas), were obtained reclassifying the categories
Antarctica. Formerly included in the collective genus Lecanora, the
of the Corine Land Cover map for 2006 (Büttner and Kosztra, 2007).
European taxa were segregated by Poelt (1958), and divided into
two sections: sect. Petroplaca, which includes saxicolous taxa, with
Data analysis
small thalli, occurring mostly in northern and central Europe, and
sect. Squamarina, which includes saxicolous and terricolous taxa,
The spatial distribution of the two varieties was analyzed with
with larger thalli, occurring all over Europe, but most frequently
two well established SDM techniques: Random Forest (RF; Attorre
in the Mediterranean area (Poelt, 1958; Poelt and Krüger, 1970).
et al., 2013; Barbet-Massin et al., 2012; Cutler et al., 2007) and Gen-
The most common species of the genus in Italy is Squamarina car-
eralized Linear Models (GLM; Elith et al., 2006; Guisan et al., 2006;
tilaginea (With.) P. James. It has been recorded by several authors
Serra-Diaz et al., 2012; Thuiller et al., 2009; Williams et al., 2009).
(Nimis, 1993; Nimis and Martellos, 2003; Nimis and Tretiach, 1999;
For the GLM we used a classic logistic regression model, where a
Puntillo, 1996; Piervittori and Isocrono, 1999; Valcuvia Passadore
log-odds link for the probability of occurrence was coupled with the
and Vittadini Zorzoli, 1982), especially from the Mediterranean to
assumption that the outcome is distributed as a binomial random
the mountain belt, though only rarely in the highlands (Martellos,
variable with one trial. In order to choose the predictive model,
2012; Nimis and Martellos, 2008).
a forward stepwise selection minimizing the Akaike Information
Squamarina cartilaginea is chemically heterogeneous, with two
Criterion (Akaike, 1974) was performed. In contrast, a well-known
chemical races taxonomically recognized at variety or form level
issue with RF is that even if it gives good predictions and has a
(Feige et al., 1997): race I, with an usnic acid chemosyndrome (var.
strong resistance to overfitting, it is difficult to identify the con-
pseudocrassa (Mattik) D. Hawksw.); and race II, with usnic and
tribution and direction of effect of each predictor. In this paper
psoromic acids chemosyndromes (var. cartilaginea). Another chem-
we evaluate variable importance (without attempting to identify
ical race [S. cartilaginea f. iberica (Mattick) Clauzade and Cl. Roux]
the effect direction) through the mean decrease in the Gini coeffi-
is reported in Clauzade and Roux (1985), with no specification of
cient as computed by the software used. The Gini coefficient is the
the chemosyndrome, and it is not listed in the later checklist of the
average decrease in node impurity when the predictor is used. The
lichens of the Iberian Peninsula (Llimona and Hladun, 2001). The
larger the importance index, the stronger the ability of the predic-
first two varieties are widespread, with their distribution includ-
tor to separate presence from absence. R software was used in all
ing Italy, whereas f. iberica is only known from Spain (Clauzade and
statistical analyses, based on the package Random Forest (Liaw and
Roux, 1985) and Israel (Kondratyuk and Zelenko, 2002). Leuckert
Wiener, 2002).
and Poelt (1978) report that the distribution of var. cartilaginea is
Since the use of presence-only data can bias the analysis and
mainly localized in the northern part of the distributional range of
lead to optimistic predictions of the potential distribution, both RF
the species, while var. pseudocrassa is more frequent in the south-
and GLM use “pseudo-absences”, generated randomly and equal in
ern part. Feige et al. (1997) found that var. cartilaginea is the most
number to present data, without replacement along a case-control
common of the three in Europe, with the majority of the specimens
scheme (Rothman, 1986). A simple random sampling was chosen
from Germany (96.4%), France (79.5%), Spain and Italy (79%) belong-
to allow for pseudo-absences to be regarded as a random sample
ing to this variety. Timdal (1983) reports that in Scandinavia, var.
from the background population. The probabilistic properties of a
pseudocrassa is present only in Gotland, in the south-eastern area
random sample obtained in this way are known (see, for instance,
of the country; while Smith et al. (2009) reports that in the British
Di Lorenzo et al., 2011; Ward et al., 2009). We report the results of
Isles var. cartilaginea is mainly confined to coastal areas, with var.
a single random sample of pseudo-absences. Additional re-runs of
pseudocrassa occurring inland.
analyses with 20 other samples, in order to check the effect of the
choice of pseudo-absences, lead to the same conclusions.
Data set
The applicability of the SDMs was evaluated through an ‘out-of-
bag’ prediction error for each taxon. We split data into a training set
One hundred and fourteen specimens of S. cartilaginea from based on 80% of the sample, chosen uniformly at random. RF and
seven Italian herbaria [CLU (9), GE (11), MESN (7), MOD (18), RO GLM were estimated based on the training set. The remaining 20%
(9), SIENA (20), TSB (53)] were examined. (that is, six pseudocrassa and 17 cartilaginea specimens) was used
Please cite this article in press as: Martellos, S., et al., Species distribution models backing taxa delimitation: the case of the lichen
Squamarina cartilaginea in Italy. Flora (2014), http://dx.doi.org/10.1016/j.flora.2014.08.008
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Table 1 Table 3
Distribution of the specimens in the administrative regions of Italy. The number of Variable importance predicted by RF according to the mean decrease of the Gini
specimens per region are reported in absolute numbers, and in percentages among coefficient.
brackets. Data for the whole country, as well as for its Northern, Eastern and Western
Var. cartilaginea Var. pseudocrassa
parts are reported in italics.
Psum 4.00 4.88
var. pseudocrassa var. cartilaginea Total
Paut 2.33 2.45
Friuli-Venezia Giulia 3 (25%) 9 (75%) 12 Pwint 2.77 2.17
Lombardia 1 (100%) 0 1 Pspr 3.00 3.55
Piemonte 0 11 (100%) 11 Ptot 2.97 2.98
Trentino-Alto Adige 2 (100%) 0 2 Tmean 4.89 4.45
Veneto 0 2 (100%) 2 Tmin 4.36 4.17
Northern regions 6 (21.4%) 22 (78.6%) 28 Tmax 3.17 2.93
Abruzzo 1 (25%) 3 (75%) 4 Volcanic 0.38 0.38
Emilia-Romagna 1 (5.6%) 17 (94.4%) 18 Arenaceous 0.37 0.31
Marche 0 4 (100%) 4 Carbonatic 0.36 0.39
Molise 0 3 (100%) 3 Sand 0.48 0.44
Puglia 0 3 (100%) 3 Clayey 0.47 0.44
Eastern regions 2 (6.7%) 30 (93.3%) 32 Land cover 0.22 0.21
Calabria 3 (37.5%) 5 (62.5%) 8
Abbreviations: Psum, Paut, Pwint, Pspr, Ptot = Summer, Autumn, Winter, Spring and
Campania 1 (12.5%) 7 (87.5%) 8
total precipitation; Tmean = mean annual temperature, Tmin = minimum tempera-
Lazio 0 1 (100%) 1
ture of the coldest month, Tmax = mean of maximum temperature of the warmest
Liguria 5 (62.5%) 3 (37.5%) 8
month.
Sardegna 7 (77.8%) 2 (22.2%) 9
Sicilia 2 (66.7%) 1 (33.3%) 3
Toscana 4 (23.5%) 13 (76.5%) 17
was present, with six specimens, in the altitudinal range
Western regions 22 (40.7%) 32 (59.3%) 54
1401–1800 m a.s.l., while both varieties were recorded sporadically
Italy 30 (26.3%) 84 (73.7%) 114
at higher altitudes (>1800 m a.s.l.).
Because of the occurrence of more than one specimen within the
2
as a test set, in order to estimate the prediction error. We report same grid cell of 1 km , eight and 26 specimens of var. pseudocrassa
the average of such prediction errors obtained for 1000 repeated and var. cartilaginea, respectively, were omitted from analyses. RF
calculations. The modeling technique (RF or GLM) with the lowest had lower average prediction errors than GLM for both varieties
prediction error was chosen to predict the distribution of study (Table 2). Furthermore, applying the threshold of 0% omission error
taxa. areas predicted to be suitable were smaller for RF (Fig. 1). Suitabil-
Probability distribution maps of the best model were then trans- ity maps derived from RF differed significantly between the races
formed into suitability maps by applying a threshold to each taxon (Kendall’s tau: p < 0.0001) indicating an allopatric distribution. Var.
to obtain a 0% omission error, which ensures that all the occur- cartilaginea and var. pseudocrassa were potentially distributed over
2 2
rences are correctly predicted (Engler et al., 2004). Many different areas of 200,000 km and 70,000 km , respectively, with an over-
2
approaches have been employed for setting thresholds (Liu et al., lap of only 7000 km . Moreover, var. pseudocrassa tended to occupy
2005), however we decided to apply this criterion, which has been the coastal Mediterranean and dry inner areas, while var. carti-
used in the analysis of the spatial distribution of rare species based laginea was more abundant in the temperate hilly and mountain
on occurrence-only data (Attorre et al., 2013; Jiménez-Alfaro et al., areas (Fig. 1).
2012; Sérgio et al., 2007) and avoid other more liberal but less As expressed by the mean decrease of the Gini coefficient, the
comparable criteria. potential distribution of both varieties is determined by similar
The Kendall’s tau statistic test was applied to the suitability environmental variables, particularly the mean yearly temperature,
maps in order to test whether the two taxa have a sympatric or the mean temperature of the coldest month and the summer pre-
allopatric distribution. cipitation (Table 3). The contribution of geological and land cover
variables appeared to be minimal. Results
Discussion
The 114 specimens used in this study were collected from 18
out of the 20 Italian regions (Table 1), most from the western part In this study, we aimed to increase the knowledge of the ecol-
of the Italian peninsula (54 specimens), followed by the eastern ogy and distribution of the two varieties of the lichen Squamarina
and northern parts. Of these specimens, 30 (26.3%) were iden- cartilaginea, as well as their taxonomic delimitation by applying
−
tified as var. pseudocrassa, reacting Pd , and 84 (73.7%) as var. SDMs. Our results confirm that Squamarina cartilaginea is poten-
cartilaginea, reacting Pd+ yellow. Var. cartilaginea was absent only tially widespread in Italy (Feige et al., 1997), although we only
from Lombardia and Trentino-Alto Adige, while var. pseudocrassa analyzed specimens from 18 out of the 20 Italian regions (Table 1),
is practically absent in the whole of the eastern peninsula, except the species been reported in the remaining two, Basilicata (Nimis
Emilia-Romagna and Abruzzo. and Tretiach, 1999) and Valle d’Aosta (Piervittori and Isocrono,
The two varieties were collected mainly at low altitudes (59 1999). Var. cartilaginea is the most common (73.7% of the spec-
specimens out of 109 from 0 to 400 m a.s.l.). Only var. cartilaginea imens analyzed in this paper), and this result is similar to what
Table 2
Random Forest and Generalized Linear Model mean prediction error (MPE), mean and 95% confidence interval (CI) and probability threshold (PT) to obtain a 0% omission
error, that is all the occurrences are correctly predicted.
Variety No. occurrences RF GLM
MPE CI PT MPE CI PT
var. cartilaginea 22 0.31 0.30–0.36 0.60 0.66 0.56–0.69 0.45
var. pseudocrassa 58 0.27 0.22–0.39 0.55 0.37 0.32–0.41 0.30
Please cite this article in press as: Martellos, S., et al., Species distribution models backing taxa delimitation: the case of the lichen
Squamarina cartilaginea in Italy. Flora (2014), http://dx.doi.org/10.1016/j.flora.2014.08.008
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Fig. 1. Potential distribution of Squamarina cartilaginea var. pseudocrassa (a–b) and Squamarina cartilaginea var. cartilaginea (c–d) in Italy as predicted by Random Forest (left)
and Generalized Linear Model (right), respectively. Black triangles indicate occurrences proved by herbarium specimens. Dark grey indicates suitable areas.
was reported by Feige et al. (1997), with 79% of their specimens. several authors (Feige et al., 1997; Timdal, 1983), var. pseudocrassa
In the northern regions of the country, our results match those is less frequent in cold, continental climates, with the exception
of Feige et al. (1997). In the eastern regions var. pseudocrassa was of Smith et al. (2009). While the latter claimed that in the British
represented by only two specimens (6.7%), while it is more com- Isles var. cartilaginea is apparently confined to the coastal area, with
mon in the western part of the peninsula (40.7%). As reported by var. pseudocrassa also occurring inland, they do not provide further
Please cite this article in press as: Martellos, S., et al., Species distribution models backing taxa delimitation: the case of the lichen
Squamarina cartilaginea in Italy. Flora (2014), http://dx.doi.org/10.1016/j.flora.2014.08.008
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details (literature and/or analysed specimens). The western part same environmental factors can have an influence on the evolution-
of the Italian peninsula is influenced by mild, humid air masses ary history, morphological differentiation, intraspecific taxonomy
originating from the Thyrrenian Sea, while eastern Italy is affected, and geographical distribution of close taxonomic groups (Tomovic´
especially during the winter, by cold air masses from the Balkans et al., 2010). For this purpose, approaches integrating SDMs with
(Nimis and Schiavon, 1986; Nimis and Tretiach, 2004). Hence, the molecular, chemical and morphological analyses appear promis-
distribution of the two varieties seems to follow this pattern, with ing. In this regard, the authors are aware of the importance of
var. pseudocrassa being less frequent in the east (cold, continen- transplant experiments for testing hypotheses on the ecological
tal climate). These results are also confirmed by the Kendall’s tau segregation of taxa. While transplant experiments were outside the
statistic test on the RF suitability maps providing evidence that scope of the present study, one of the co-authors is carrying out a
the two varieties tend to segregate ecologically, with var. pseu- transplant experiment with two varieties of the lichen Pseudever-
docrassa more widespread in the Mediterranean region and in the nia furfuracea, which also differ for the presence/absence of some
coastal and hilly areas characterized by a drier and hotter climate, lichen substances. Preliminary results however, suggest that the
and var. cartilaginea abundant in the temperate hilly and mountain chemosyndrome does not change while moving a variety to a dif-
areas (Fig. 1). This difference is also supported if we consider pat- ferent environment.
terns along an altitudinal gradient: var. pseudocrassa is rare above
800 m, and practically absent above 1400 m, while var. cartilaginea
Acknowledgments
is present at all altitudes, although it is less abundant above 1200 m
a.s.l.
The authors are grateful to all the people who sent the speci-
According to our results, and acknowledging the limitations of
mens used in this survey. A special thanks to Dr. Rachel Atkinson
our approach, we support the recognition of two taxa within S. car-
for greatly improving the English language of the paper.
tilaginea, but we also open up a question on their status. In the past,
the two chemical races were taxonomically recognized at variety
or form level (Feige et al., 1997). In our opinion, they could deserve References
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