The influence of climate on the distribution of lichens:a case study in a borderline area (Liguria, NW Italy)

Paolo Giordani Æ Guido Incerti

Received: 7 June 2006 / Accepted: 24 May 2007 / Published online: 22 June 2007

� Springer Science+Business Media B.V. 2007

Abstract The purposes of this article are to quantify

the relationship between epiphytic lichen distribution

and macroclimatic variables in the study area and to

provide a case study for evaluating the predictive role

of epiphytic lichens as bioclimatic indicators. The

study was carried out in the Liguria region (NW-

Italy), a small (5432 km2) borderline area, where

phytoclimatic features range from the dry Mediterra-

nean to the Alpine in a few kilometers. Epiphytic

lichen diversity was sampled using a standardized

protocol [Asta et al (2002) In: Nimis et al (eds)

Monitoring with lichens: monitoring lichens. Kluwer,

Dordrecht]. Abundance of the species in the sampling

sites was related to macroclimatic parameters (yearly

average temperature and rainfall) and non-parametric

multivariate models were calculated to find significa-

tive relationships among predictive and response

variables. A total of 59 species showed highly

significant relation with macroclimatic parameters.

Four groups were selected, by means of a cluster

analysis, related to four climatic niches (warm-humid,

cold-humid, mesothermic-humid, warm-dry). Distri-

butional pattern of the groups in the survey area

showed a good correspondence with the bioclimatic

units of Liguria region described by Nimis [(2003)

Checklist of the Lichens of Italy 3.0. University of

T r i e s t e , D e p t o f B i o l o g y . h t t p : / /

www.dbiodbs.univ.trieste.it. Cited 1 Jun 2006]. A

significant subset of epiphytic lichen species in the

study area have been proved to be efficient bioclimatic

indicator and it is supposed to give good results to

monitor climatic changes, in a long-term perspective.

Keywords Distributional pattern � Epiphytic

lichen � NPMR � Bioclimatic indicators �Poikilohydric organisms

Introduction

Lichens physiology is strongly related to both micro-

and macro-climatic factors, since it is well known

that relative air humidity and rainfall are the main

sources of water supply for these organisms (e.g.,

Barkman 1958). The relationship between atmo-

spheric water supply and lichen physiology has been

investigated by several authors, showing that thallus

hydric saturation is an important parameter in regu-

lating metabolic processes such as gas exchanges,

nitrogen fixation and photosynthesis (Rundel 1988;

Nash 1996).

In last years several studies have pointed out the

role of climatic factors in the relationship between

P. Giordani (&)

Dipartimento per lo Studio del Territorio e delle sue

Risorse, Laboratorio di Biologia e Diversita dei Vegetali,

Corso Dogali 1M, 16136 Genova, Italy

e-mail: giordani@dipteris.unige.it

G. Incerti

Dipartimento di Biologia, Universita di Trieste, Via L.

Giorgieri 10, 34127 Trieste, Italy

123

Plant Ecol (2008) 195:257–272

DOI 10.1007/s11258-007-9324-7

lichen physiology and distribution (Kappen 1988;

McCune et al. 1997; Van Herk et al. 2002; Brunialti

and Giordani 2003). According to Nimis and Losi

(1984), lichen distribution could be an useful tool to

assess bioclimatic features of a territory, since these

symbiotic organisms, with respect to higher plants

and animals, are more selectively distributed along

ecological gradients where the local climate plays a

major role.

A number of epiphytic lichen species have been

proved to be distributed along ecological ranges

defined by the variation of main climatic factors

(Sancho et al. 1997; Fos et al. 1999; Shirazi et al.

1996). To better define bioclimatic variability at

regional scale, the relationship between species

distribution and climatic variability should be eval-

uated. It deeply depends from the scale of observa-

tion: at a strictly local level, lichens distribution is

scarcely affected by the macroclimate variability, and

the ecological response is rather related to microcli-

matic factors (for a case study see Coxson and Coyle

2003), so that narrow-ranging species should not be

considered as bioclimatic indicators. Also broad-

ranging species appear inadequate, since they are too

little sensible to the minimal mesoclimatic differ-

ences observable at regional level. Some species are

distributed according to extremely specific mesocli-

matic conditions, and they are susceptible to give

detailed information (Brunialti and Giordani 2003).

Unfortunately most of them are extremely rare and,

since their spot-like distribution, it is rather compli-

cated to use them to mark borders of bioclimatic

units.

According to Will-Wolf et al. (2002), the inter-

pretation of lichen distribution can be improved by

considering groups of species as bioclimatic indica-

tors, once the ecological features of all the species in

a group are homogeneous and well-defined. Upon

these basis, one could mark bioclimatic borders by

means of an a priori defined group of species with

well-known climatic requirements, such as lichens

with oceanic affinities, as described in Degelius

(1935), Rose (1976) and McCune (1984). Neverthe-

less this approach highly depends on the definition of

species affinities, which are often reported at global

scale, whilst the needed ecological information

should be referred to a regional level.

Furthermore, climatic affinities of species are often

expressed in a qualitative, or at least in a semi-

quantitative way, when some ecological scores along

climatic gradients are attributed to the species and

expressed on ordinal scales (Wirth 1991; Nimis

2003). This approach has the advantage of giving a

synthetic ecological characterization of almost every

taxa in national floras, in spite of a main shortcoming

consisting in the difficult transfer of the information,

which is associated to the taxa at a large geographic

scale, when one study is carried out at local level.

The ecological characterization of epiphytic

lichens, based on a quantitative approach and on

detailed field data collecting, is far from being

completed. Thus, the identification of groups of

species ecologically homogeneous, whose distribu-

tion could be used to mark bioclimatic borders, is a

difficult task to perform in a quantitative way on the

basis of the current knowledge of epiphytic lichens

ecology.

A first proposal to achieve this goal is presented as

a case-study, in which epiphytic lichen distribution

was estimated, through the application of a rigorous

sampling design and a standardized field strategy, in a

climatically heterogeneous area (Liguria, NW-Italy),

and distributional data were related to main climatic

parameters (namely rainfall and temperature) by

means of non-parametric multivariate models. In this

study a quantitative methodology is introduced to

identify groups of species with significantly similar

response to climatic variability. The study has three

main goals: (a) To quantify the relationship between

epiphytic lichens distribution and macroclimatic gra-

dients in the study area; (b) to provide a case study for

evaluating the possible use of epiphytic lichens as

bioclimatic indicators; (c) to compare the actual

bioclimatic subdivision of the study area (Nimis and

Martellos 2002), based on a synthetic and semi-

quantitative approach, with the one resulting from the

application of the proposed methodology.

Methods

Study area

The Liguria region, in north-western Italy, has a

surface area of 5,314 km2. To the south, it borders on

the Ligurian Sea. A continuous mountainous ridge

(Ligurian Alps and Ligurian Apennines) separates the

Tyhrrenian slopes from the Po Valley basin. Liguria

258 Plant Ecol (2008) 195:257–272

123

was selected as the study area because of the

occurrence of significant environmental gradients,

for both natural and anthropogenic variables. Accord-

ing to Nimis (2003), the survey area can be subdi-

vided in four main bioclimatic units (Fig. 1A–D): (A)

Montane, including some Alpine and Apennine areas

of the hinterland above 1000 m, dominated by beech

forests; (B) Humid Sub-Mediterranean, occurring

both in the Tyhrrenian and Po valley hinterland

along the coast to the east of Genoa and to the

extreme west of Genoa near the French border,

ranging from 400 m to 1000 m and characterized by

deciduous Quercus-Carpinus forests; (C) Mediterra-

nean, limited to coastal districts (less than 10 km

from the sea) ranging from 0 m to 800 m and (D) Dry

Sub-Mediterranean, occurring mainly in the valleys

of the River Po in the western part of the region.

The population of about 1.5 million inhabitants is

concentrated in the regional capital, Genoa, and in

coastal areas (ca. 2000 persons/km2), where high

levels of atmospheric pollution occur, due mainly to

traffic and industry. On the other hand, many

mountainous areas are scarcely populated (ca. 10 per-

sons/km2) and have no local sources of air pollution.

Data collection

A total of 165 sampling sites were selected by means

of a stratified random sampling design, based on

habitat type and altitude, proportionally to the surface

occupied by each stratum within the survey area. At

each site, consisting of a 30-m radius plot, all suitable

trees (circumference > 60 cm, inclination of the

bole < 108, absence of damage and decorticated areas

on the trunk) were sampled. On each tree, the

frequencies of all epiphytic lichen species in a

sampling grid consisting of four 10 · 50 cm ladders,

each divided into five 10 · 10 cm squares were

considered. The grid was placed systematically on the

cardinal exposures (N, E, S, W) of the bole, at a

height of 1 m above ground, following the standards

suggested by Asta et al. (2002). Nomenclature

follows Nimis (2003).

To explore the relationship between lichen distri-

bution and macroclimatic factors, data were related to

average yearly rainfall (values ranging between

950 mm and 1800 mm) and average yearly temper-

ature (between 108C and 148C), provided by the

Ligurian Regional Council (1999) and calculated on

the basis of local models (Buzzi et al. 1994). These

two simple climatic parameters were selected on the

basis of previous studies (Giordani 2006, 2007) since

they have been proved to be related to epiphytic

lichen diversity in the survey area.

Data analysis

Species abundance was calculated for each plot as the

sum of frequencies of each species within the

sampling grids on a tree, averaged for all trees within

a plot. Quantitative data were subsequently converted

into binary (presence–absence) with respect to the

median of the distribution of the abundance of each

species in the whole dataset.

A

C D

B

Fig. 1 Bioclimatic units of Liguria region, according to Nimis (2003): Montane (A), Humid sub-Mediterranean (B), Mediterranean

(C), Dry sub-Mediterranean (D)

Plant Ecol (2008) 195:257–272 259

123

Two matrices were considered: (1) sample

units · species presence, and (2) sample units · cli-

matic variables. Quantitative variables of matrix 2

were log-transformed (McCune et al. 2002), in order

to reduce the effect of the high variability and of the

different order of magnitude within variables on the

final model. To detect and exclude from analysis

possible outliers, an explorative multivariate analysis

was carried out using PC-ORD method (McCune and

Mefford 1999). To describe the relationship between

species distribution and macroclimatic variables, the

transformed matrix of the whole dataset was analysed

by Non Parametric Multiplicative Regression—

NPMR (Bowman and Azzalini 1997; McCune et al.

2003), using the software Hyperniche 1.0 (McCune

and Mefford 2004) and the Species Occurrence

(SpOcc) NPMR model (McCune et al. 2003).

The model estimates the probability of occurrence

of a given species in a target site, by applying data

from sites that lie close (environmental neighbour-

hood) to the target one in the bi-dimensional space

defined by values of the two macroclimatic variables

(neighbourhood tolerance window).

For each species the best SpOcc-NPMR model

was considered, to describe the contribution of

rainfall and temperature in predicting its distribution.

Following McCune and Mefford (2004), the quality

of a SpOcc-NPMR model (M2) was estimated as its

improvement over an a priori model (M1), given by

the average frequency of the species in the whole

dataset. First, an equal probability was assumed for

both models, so that Pr(M1) = Pr(M2) = ½. Then,

Bayes factors (B12) were calculated as the ratio of the

likelihood of the observed values under the posterior

model M1 to the likelihood of the result under the

a priori model M2. As suggested by Kass and Raftery

(1995), the results were interpreted as log10(B12),

where values > 2 indicate a decisive relationship

between the response variable (species occurrence)

and the predictive ones (temperature and rainfall)

included in each model.

Furthermore, a model evaluation was carried out

with a Monte Carlo permutation test, by comparing

the estimation of the response variable given by the

selected models to the average estimation calculated

by 1,000 random permutations among the dataset.

Finally a matrix of sample units · species presence

was submitted to cluster analysis (Euclidean Distance

and Minimum Variance clustering algorithm), includ-

ing only the species significantly related to climatic

variables, to test if their similar response to macro-

climatic factors do correspond to their local distribu-

tion (i.e., abundance in the sampling sites).

Results

SpOcc-NPMR models

A total of 190 species were recorded in the 165

sampling sites. The abundance of 70 species, out of

the 190, resulted significantly related to yearly

average temperature and rainfall, when evaluated

with Monte–carlo test (P < 0.05). In particular (see

Table 1), 59 species (e.g., Buellia griseovirens)

showed significantly high log B (> 2), indicating a

strong relationship with the predictive variables

included in the model. A statistically significant

model was also found for other 11 species (e.g.,

Amandinea punctata), but, in these cases, a low log B

value (< 2) indicates a weaker relation with climatic

variables included in the model. Finally, 120 species

(e.g., Physcia aipolia) showed no relation with

climatic variables (P > 0.05; log B < 2).

Cluster analysis

Three groups were identified (Fig. 2):

• Cluster A includes 23 species, mainly related to

cold-humid climate (e.g., Parmelina pastillifera,

Hypogymnia physodes, Buellia griseovirens).

• Cluster B includes 18 species, related to humid

conditions, but with a broader ecological range

for temperature, occurring both in warm and in

fresh climate (e.g., Flavoparmelia caperata, Par-

motrema chinense, Normandina pulchella).

• Cluster C includes 18 species occurring in meso-

to-warm areas with humid to dry climate (e.g.,

Xanthoria parietina, Flavoparmelia soredians

Heterodermia obscurata, Parmotrema reticula-

tum).

Geographical distribution vs. ecological responses

of the species

The joint distribution of the species of each cluster is

reported at local scale in Fig. 3. The species of cluster

260 Plant Ecol (2008) 195:257–272

123

Table 1 List of the 190 species releved in the survey area

Taxon logB Cluster Code

Acrocordia gemmata (Ach.) A. Massal. < 2 001

Amandinea punctata (Hoffm.) Coppins & Scheid. < 2* 002

Anaptychia ciliaris (L.) Korb. < 2 003

Arthonia punctiformis Ach. < 2* 004

Arthonia radiata (Pers.) Ach. 3.8* A 005

Arthopyrenia cerasi (Schrad.) A. Massal. < 2 006

Arthrosporum populorum A. Massal. < 2 007

Bacidia arceutina (Ach.) Arnold < 2 008

Bacidia rosella (Pers.) De Not. < 2 009

Bacidia rubella (Hoffm.) A. Massal. < 2 010

Bactrospora patellarioides (Nyl.) Almq. var. patellarioides < 2 011

Bryoria fuscescens (Gyeln.) Brodo & D. Hawksw. < 2 012

Buellia disciformis (Fr.) Mudd < 2 013

Buellia griseovirens (Sm.) Almb. 9.1* A 014

Calicium abietinum Pers. < 2 015

Caloplaca cerina (Hedw.) Th. Fr. var. cerina < 2 016

Caloplaca ferruginea (Huds.) Th. Fr. 5.2* C 017

Caloplaca flavorubescens (Huds.) J. R. Laundon var. flavorubescens < 2 018

Caloplaca herbidella (Hue) H. Magn. < 2 019

Caloplaca luteoalba (Turner) Th. Fr. < 2 020

Caloplaca pollinii (A.Massal.) Jatta 7.7* C 021

Caloplaca pyracea (Ach.) Th. Fr. < 2* 022

Candelaria concolor (Dicks.) Stein < 2* 023

Candelariella reflexa (Nyl.) Lettau <2* 024

Candelariella xanthostigma (Ach.) Lettau < 2* 025

Canoparmelia crozalsiana (de Lesd.) Elix & Hale < 2 026

Catillaria nigroclavata (Nyl.) Schuler < 2 027

Catinaria atropurpurea (Schaer.) Vezda & Poelt < 2 028

Chrysothrix candelaris (L.) J. R. Laundon 2.9* C 029

Cladonia caespiticia (Pers.) Florke < 2 030

Cladonia coniocraea (Florke) Spreng. < 2 031

Cladonia fimbriata (L.) Fr. 2.5* C 032

Cladonia parasitica (Hoffm.) Hoffm. 2.2* B 033

Cladonia peziziformis (With.) J. R. Laundon < 2 034

Cladonia pyxidata (L.) Hoffm. < 2 035

Collema conglomeratum Hoffm. < 2 036

Collema flaccidum (Ach.) Ach. < 2 037

Collema fragrans (Sm.) Ach. < 2 038

Collema furfuraceum (Arnold) Du Rietz 4.2* C 039

Collema ligerinum (Hy) Harm. < 2 040

Collema multipunctatum Degel. < 2 041

Collema nigrescens (Huds.) DC. < 2 042

Collema subflaccidum Degel. < 2 043

Degelia atlantica (Degel.) M. Jørg. & P. James < 2 044

Degelia plumbea (Lightf.) M. Jørg. & P. James < 2 045

Plant Ecol (2008) 195:257–272 261

123

Table 1 continued

Taxon logB Cluster Code

Dendriscocaulon umhausense (Auersw.) Degel. < 2 046

Dimerella pineti (Ach.) Vezda < 2 047

Evernia prunastri (L.) Ach. 2.6* A 048

Flavoparmelia caperata (L.) Hale 2.0* B 049

Flavoparmelia soredians (Nyl.) Hale 3.7* C 050

Fuscidea stiriaca (A. Massal.) Hafellner < 2 051

Fuscopannaria mediterranea (Tav.) M. Jørg. < 2 052

Graphis scripta (L.) Ach. < 2 053

Gyalecta liguriensis (Vezda) Vezda < 2 054

Gyalecta truncigena (Ach.) Hepp < 2 055

Heterodermia obscurata (Nyl.) Trevis. 7.6* C 056

Hyperphyscia adglutinata (Florke) H. Mayrhofer & Poelt 6.1* B 057

Hypocenomyce scalaris (Ach.) M. Choisy < 2 058

Hypogymnia physodes (L.) Nyl. 3.0* A 059

Hypogymnia tubulosa (Schaer.) Hav. < 2 060

Hypotrachyna revoluta (Florke) Hale < 2 061

Japewiella carrollii (Coppins & P. James) Printzen < 2 062

Koerberia biformis A. Massal. < 2 063

Lecania cyrtella (Ach.) Th. Fr. < 2 064

Lecania fuscella (Schaer.) A. Massal. < 2 065

Lecania naegelii (Hepp) Diederich & Van den Boom < 2 066

Lecanographa amylacea (Pers.) Egea & Torrente < 2 067

Lecanora albella (Pers.) Ach. 6.2* A 068

Lecanora allophana Nyl. < 2 069

Lecanora argentata (Ach.) Malme 3.8* A 070

Lecanora carpinea (L.) Vain. 2.5* A 071

Lecanora chlarotera Nyl. < 2* 072

Lecanora cinereofusca H. Magn. < 2 073

Lecanora expallens Ach. < 2* 074

Lecanora glabrata (Ach.) Malme < 2 075

Lecanora hagenii (Ach.) Ach. var. hagenii < 2 076

Lecanora horiza (Ach.) Linds. < 2 077

Lecanora intumescens (Rebent.) Rabenh. 6.7* A 078

Lecanora leptyrodes (Nyl.) Degel. < 2 079

Lecanora meridionalis H. Magn. < 2 080

Lecanora pulicaris (Pers.) Ach. 4.6* A 081

Lecanora rubicunda Bagl. < 2 082

Lecanora saligna (Schrad.) Zahlbr. < 2 083

Lecanora strobilina (Spreng.) Kieff. 3.4* A 084

Lecanora subintricata (Nyl.) Th. Fr. < 2 085

Lecanora symmicta (Ach.) Ach. < 2 086

Lecidea turgidula Fr. < 2 087

Lecidella elaeochroma (Ach.) M. Choisy 2.1* B 088

Lepraria incana (L.) Ach. 3.7* B 089

262 Plant Ecol (2008) 195:257–272

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Table 1 continued

Taxon logB Cluster Code

Lepraria lobificans Nyl. < 2 090

Leprocaulon microscopicum (Vill.) Gams 9.1* B 091

Leptogium cyanescens (Rabenh.) Korb. < 2 092

Leptogium lichenoides (L.) Zahlbr. < 2 093

Leptogium tenuissimum (Dicks.) Korb. < 2 094

Leptogium teretiusculum (Wallr.) Arnold < 2 095

Leptorhaphis atomaria (Ach.) Szatala < 2 096

Lobaria amplissima (Scop.) Forssell < 2 097

Lobaria pulmonaria (L.) Hoffm. < 2 098

Melanelia elegantula (Zahlbr.) Essl. < 2 099

Melanelia exasperata (De Not.) Essl. < 2 100

Melanelia exasperatula (Nyl.) Essl. < 2 101

Melanelia fuliginosa (Duby) Essl. subsp. glabratula 2.8* B 102

Melanelia glabra (Schaer.) Essl. < 2 103

Melanelia laciniatula (H. Olivier) Essl. < 2 104

Melanelia subaurifera (Nyl.) Essl. 2.4* A 105

Melaspilea urceolata (Fr.) Almb. < 2 106

Micarea prasina Fr. < 2 107

Mycobilimbia pilularis (Korb.) Hafellner & Turk < 2 108

Mycomicrothelia confusa D. Hawksw. < 2 109

Naetrocymbe punctiformis (Pers.) R. C. Harris < 2 110

Nephroma laevigatum Ach. < 2 111

Nephroma parile (Ach.) Ach. < 2 112

Normandina pulchella (Borrer) Nyl. 4.9* B 113

Ochrolechia subviridis (Høeg) Erichsen < 2 114

Opegrapha atra Pers. 3.2* C 115

Opegrapha culmigena Lib. < 2 116

Opegrapha varia Pers. < 2 117

Pachyphiale arbuti (Bagl.) Arnold < 2 118

Pannaria conoplea (Ach.) Bory < 2 119

Parmelia saxatilis (L.) Ach. 7.9* A 120

Parmelia submontana Hale < 2 121

Parmelia sulcata Taylor 3.8* B 122

Parmeliella triptophylla (Ach.) Mull. Arg. < 2 123

Parmelina pastillifera (Harm.) Hale 7.8* A 124

Parmelina quercina (Willd.) Hale 2.5* A 125

Parmelina tiliacea (Hoffm.) Hale 2.6* B 126

Parmelinopsis horrescens (Taylor) Elix & Hale < 2 127

Parmeliopsis ambigua (Wulfen) Nyl. 4.9* A 128

Parmeliopsis hyperopta (Ach.) Arnold < 2 129

Parmotrema chinense (Osbeck) Hale & Ahti 8.2* B 130

Parmotrema crinitum (Ach.) M. Choisy < 2 131

Parmotrema reticulatum (Taylor) M. Choisy 8.4* C 132

Parmotrema stuppeum (Taylor) Hale < 2 133

Plant Ecol (2008) 195:257–272 263

123

Table 1 continued

Taxon logB Cluster Code

Pertusaria albescens (Huds.) M. Choisy & Werner 4.2* A 134

Pertusaria amara (Ach.) Nyl. 3.1* B 135

Pertusaria coccodes (Ach.) Nyl. < 2 136

Pertusaria coronata (Ach.) Th. Fr. < 2 137

Pertusaria flavida (DC.) J. R. Laundon 3.3* A 138

Pertusaria hemisphaerica (Florke) Erichsen 4.7* A 139

Pertusaria hymenea (Ach.) Schaer. 4.2* C 140

Pertusaria leioplaca DC. 2.7* A 141

Pertusaria pertusa (Weigel) Tuck. 2.4* B 142

Pertusaria pustulata (Ach.) Duby < 2* 143

Phaeophyscia chloantha (Ach.) Moberg < 2 144

Phaeophyscia endophoenicea (Harm.) Moberg < 2 145

Phaeophyscia hirsuta (Mereschk.) Essl. 2.8* C 146

Phaeophyscia nigricans (Florke) Moberg < 2 147

Phaeophyscia orbicularis (Neck.) Moberg 3.8* B 148

Phaeophyscia pusilloides (Zahlbr.) Essl. < 2 149

Phlyctis agelaea (Ach.) Flot. < 2 150

Phlyctis argena (Spreng.) Flot. 6.0* B 151

Physcia adscendens (Fr.) H. Olivier 3.1* B 152

Physcia aipolia (Humb.) Furnrh. < 2 153

Physcia biziana (A. Massal.) Zahlbr. var. biziana < 2 154

Physcia clementei (Turner) Maas Geest. 3.3* C 155

Physcia leptalea (Ach.) DC. < 2 156

Physcia stellaris (L.) Nyl. 4.3* A 157

Physcia tenella (Scop.) DC. < 2* 158

Physcia tribacioides Nyl. < 2 159

Physcia vitii Nadv. 2.8* C 160

Physconia distorta (With.) J. R. Laundon 2.2* C 161

Physconia grisea (Lam.) Poelt subsp. grisea 2.4* C 162

Physconia servitii (Nadv.) Poelt < 2 163

Pleurosticta acetabulum (Neck.) Elix & Lumbsch < 2 164

Porina borreri (Trevis.) D. Hawksw. & P. James < 2 165

Pseudevernia furfuracea (L.) Zopf var. furfuracea < 2 166

Punctelia borreri (Sm.) Krog 3.1* C 167

Punctelia perreticulata (Rasanen) G. Wilh. & Ladd < 2 168

Punctelia subrudecta (Nyl.) Krog 5.7* B 169

Punctelia ulophylla (Ach.) van Herk & Aptroot < 2 170

Pyrenula chlorospila Arnold < 2 171

Pyrenula nitida (Weigel) Ach. 3.2* C 172

Pyrenula nitidella (Schaer.) Mull. Arg. < 2 173

Pyxine subcinerea Stirt. < 2 174

Ramalina farinacea (L.) Ach. < 2 175

Ramalina fraxinea (L.) Ach. < 2* 176

Rinodina nimisii Giralt & H. Mayrhofer < 2 177

264 Plant Ecol (2008) 195:257–272

123

A (Fig. 3A) show a mainly montane distribution in

the survey area (Eastern Ligurian Apennines and

Maritime Alps). Cluster B (Fig. 3B) includes lichens

that are mainly located in hinterland areas of the

Eastern part of the region. The species of cluster C

are distributed along a narrow coastal belt both in the

Eastern and in the Western Riviera (Fig. 3C).

These three local areas could be intended as

bioclimatic homogeneous units once it is shown that

species grouped on the basis of their local distribution

do have similar ecological response to climatic

variables.

SpOcc results, modelling the ecological response

to macroclimatic factors, are shown in Fig. 4 for case-

species of each group of the cluster analysis. The

NPMR model of Amandinea punctata (Fig. 4A) is

also reported, as an example for species not distrib-

uted in accordance with the pattern of macroclimatic

variables. In this case the probability of occurrence of

the species is relatively high throughout the ecolog-

ical space, so that it is not likely to give relevant

information on the bioclimatic variability in the

survey area.

Model for Buellia griseovirens is representative

for the ecological response of species of cluster A

Fig. 4B). It shows the higher probability of occur-

rence for average yearly temperature ranging

between 108C and 118C and for average yearly

rainfall higher than 1600 mm. These ecological

ranges do correspond to the montane distributional

pattern (Fig. 3A).

Cluster B includes a more ecologically heteroge-

neous subset of species. Some of them (e.g.,

Pertusaria amara—Fig. 4C) show high probability

of occurrence for meso- to warm conditions (from

118C to 158C) and high yearly average rainfall (more

than 1800 mm). Other species (e.g., Flavoparmelia

caperata—Fig. 4D) have a broader ecological range.

This corresponds to a distribution pattern mainly

located in sub-mediterranean areas of the survey area

(Fig. 3B).

All the taxa of cluster C resulted associated with

warm conditions (yearly average temperature ranging

from 13 to 158C), but two sub-cluster may be found.

The former one includes species related to low yearly

average rainfall, e.g., Flavoparmelia soredians,

showing the higher probability of occurrence for

average yearly rainfall lower than 1000 mm

(Fig. 4E), whilst the latter one groups together

thermophilous species related to very humid condi-

tions (e.g., Heterodermia obscurata—Fig. 4F), cor-

responding to average yearly rainfall higher than

Table 1 continued

Taxon logB Cluster Code

Rinodina pyrina (Ach.) Arnold 3.7* A 178

Rinodina sophodes (Ach.) A. Massal. < 2 179

Schismatomma decolorans (Sm.) Clauzade & Vezda 4.2* B 180

Schismatomma graphidioides (Leight.) Zahlbr. < 2 181

Scoliciosporum chlorococcum (Stenh.) Vezda < 2 182

Scoliciosporum umbrinum (Ach.) Arnold 8.5* A 183

Tephromela atra (Huds.) Hafellner var. torulosa (Flot.) Hafellner 4.3* A 184

Thelopsis rubella Nyl. < 2 185

Usnea florida (L.) F. H. Wigg. < 2 186

Usnea hirta (L.) F. H. Wigg. < 2 187

Vulpicida pinastri (Scop.) J. E. Mattsson & M. J. Lai < 2 188

Xanthoria fallax (Hepp) Arnold < 2 189

Xanthoria parietina (L.) Th. Fr. 2.6* C 190

Nomenclature follows Nimis (2003). log B column indicates the evaluation of the NPMR model for species abundance in the

sampling sites. Values for species with distribution strongly related to the pattern of predictive climatic variables (log B > 2, see text)

are in bold

* P (log B) < 0.05. For each taxon the local distributional cluster and an identifying code are also reported (see Fig. 2)

Plant Ecol (2008) 195:257–272 265

123

1600 mm. Irrespectively to their water requirement,

the ecological response of the species of cluster C do

correspond to a mediterranean distributional pattern,

along the coasts of the survey area (Fig. 3C).

Discussion

Though epiphytic lichens distribution depends on a

complex set of environmental and substrate-related

explanatory variables, acting from the tree to the

landscape level (Nimis et al. 2002), many authors

pointed out the role of climatic factors (McCune et al.

1997; Goward and Spribille 2005; Hauck and Spri-

bille 2005; Giordani 2006, 2007).

Interpretation of SpOcc models

About 30% of the epiphytic lichen flora (59 species

out of 190) resulted significantly related to yearly

average temperature and rainfall patterns. The sig-

nificance of climate-dependence for the selected

species is based on two main considerations: (a)

all these taxa showed significantly high log B (> 2),

and (b) log B values resulted significant (P < 0.05)

when evaluated with Monte–carlo test. Under these

conditions, it is assumed that the distribution of all

these taxa does not depend significantly on factors

other than climatic, a part from those covariating:

some nitrophitic species of cluster C (e.g., Phaeo-

physcia hirsuta, Physcia vitii, Physconia distorta,

A005A078A139A070A068A124A157A134A138A178A014A183A081A059A128A048A105A120A184A084A071A141A125B151B088B049B130B102B122B126B135B169B033B142B057B152B148B089B091B113B180C017C021C160C039C146C050C161C190C162C029C140C167C056C132C155C032C115C172

100

3.8e-02

75

4.1e+00

50

8.2e+00

25

1.2e+01

0

1.6e+02

Information Remaining (%)

Distance (ObjectiveFunction)

A

B

C

Fig. 2 Dendrogram of the

species based on their

abundance in the sampling

sites. Species are indicated

with the cluster code (from

A to C) followed by the

identifying codes of Table 1

266 Plant Ecol (2008) 195:257–272

123

Physconia grisea and Xanthoria parietina) are mostly

distributed in the south part of the survey area, where

both temperature and NOx pollution are higher.

A complete analysis of the non climatic factors

affecting epiphytic lichen distribution in the survey

area, such as substrate- or pollution-related ones, is

not an aim of the present work and it is addressed

elsewhere (Giordani 2007).

Selecting guilds of indicator species to assess

bioclimatic features of a territory

Whilst several authors provided results on the

relationship between total epiphytic lichen diversity

and bioclime (Fos 1998; Loppi et al. 2002), in the

present work we focussed the attention on distribu-

tional and ecological features of guilds of species,

statistically selected with reference to their relation to

climatic variables.

The use of guilds of species instead of diversity as

a whole was suggested by Will-Wolf et al. (2002) as a

valid tool for assessing peculiar ecological condi-

tions. A similar approach was recently applied to

various organisms in different regions (e.g., Pyke and

Fischer 2005). Some authors select guilds a priori, on

the basis of well-known ecological requirements or

distribution (Goward and Spribille 2005). We con-

sider that this approach could be biassed by an

incorrect (subjective) use of ‘well-known’ informa-

tion, which should be referred to the specific context

in which it was acquired. The use of an a posteriori

methodology (Karlsen and Elvebakk 2003; Karlsen

et al. 2005), in which a guild is defined as a group of

taxa whose distribution is significantly related to the

pattern of one or more climatic factors, is comple-

mentary to the first approach: in our work, it lays on a

probability-based sampling dataset and, conse-

quently, the level of uncertainty and the statistic

significance of the results are known and the inter-

pretative step is free from the level of floristic

investigation of the area. Nevertheless other con-

straints of the ‘guild approach’ are not completely

solved by our method, including the lack of detailed

information about the complex eco-physiology of the

selected species, which may result in a weak

correlation between ‘indicators’ and response vari-

able (Elzinga et al. 2001).

Ecological and ecophysiological requirements

of the selected species

The guilds derived from SpOcc models includes

some of the most studied species in lichen ecology

and eco-physiology (e.g., Jensen 2002). The out-

comes referred to those species can be used to better

understand the relationship between lichen and

climatic factors distribution.

Honegger (1995) pointed out the remarkable

regenerative capacity of Xanthoria parietina (cluster

C: species of warm areas) vs. Parmelia sulcata

(cluster B: mesothermic and humid), in relation to

drought and temperature stress. Baruffo and Tretiach

(2005) showed that Parmelia saxatilis (cluster A—

100%50%10%

Sum of frequencies

100%50%10%

Sum of frequencies

100%50%10%

Sum of frequencies

A

B

C

Fig. 3 Distributional pattern of the species in the Study Area.

Each map (A–C) shows the joint distribution and the relative

abundance of all the species of the corresponding cluster in Fig.

1. The abundance is measured as the sum of frequencies of all

the species in each sampling site, expressed in percentage on

the maximum observed value

Plant Ecol (2008) 195:257–272 267

123

cold-humid), showed best photosynthetic parameters

in beech forests, whilst Parmotrema chinense (cluster

B—mesothermic-humid) had its optimal performance

in Quercus forests, rapidly decreasing at medium to

high elevations.

According to Caviglia et al. (2001) and Caviglia

and Modenesi (1993) Flavoparmelia soredians and

Parmotrema reticulatum (cluster C) are more reactive

than F. caperata (cluster B) to seasonal fluctuations

of oxidative stresses, showing an increased produc-

tion of antioxidative lichen substances during the

warmest periods, suggesting that these species, in

their bioclimatic optimum, are able to promptly react

to climate-related stresses.

Yearly average rainfall (mm)

Yea

rly

aver

age

tem

per

atu

re (

°C)

800 1000 1200 1400 1600 1800 2000

9

10

11

12

13

14

15

16

Yearly average rainfall (mm)

800 1000 1200 1400 1600 1800 2000

9

10

11

12

13

14

15

16 SpOcc-NPMR for Amandinea punctata SpOcc-NPMR for Buellia griseovirens

Probabilityof SpOcc

0.00

0.25

0.50

0.75

1.00

Probabilityof SpOcc

0.00

0.25

0.50

0.75

1.00

Yearly average rainfall (mm)

Yea

rly

aver

age

tem

per

atu

re (

°C)

800 1000 1200 1400 1600 1800 2000

9

10

11

12

13

14

15

16 SpOcc-NPMR for Heterodermia obscurata

Probabilityof SpOcc

0.00

0.25

0.50

0.75

1.00

Yearly average rainfall (mm)

Yea

rly

aver

age

tem

per

atu

re (

°C)

800 1000 1200 1400 1600 1800 2000

9

10

11

12

13

14

15

16SpOcc-NPMR for Flavoparmelia soredians

Probabilityof SpOcc

0.00

0.25

0.50

0.75

1.00

Yearly average rainfall (mm)

Yea

rly

aver

age

tem

per

atu

re (

°C)

800 1000 1200 1400 1600 1800 2000

9

10

11

12

13

14

15

16 SpOcc-NPMR for Flavoparmelia caperata

Probabilityof SpOcc

0.00

0.25

0.50

0.75

1.00

Yearly average rainfall (mm)

Yea

rly

aver

age

tem

per

atu

re (

°C)

Yea

rly

aver

age

tem

per

atu

re (

°C)

800 1000 1200 1400 1600 1800 2000

9

10

11

12

13

14

15

16 SpOcc-NPMR for Pertusaria amara

Probabilityof SpOcc

0.00

0.25

0.50

0.75

1.00

A

C

E F

D

B

Fig. 4 Non parametric multiregressive models (NPMR) for

species occurrence (SpOcc) in the ecological space defined by

the climatic predictive variables (yearly average temperature

and rainfall) in the Study Area. Models for Amandinea

punctata (A), Buellia griseovirens (B), Pertusaria amara(C), Flavoparmelia caperata (D), Flavoparmelia soredians (E)

and Heterodermia obscurata (F) are shown

268 Plant Ecol (2008) 195:257–272

123

Comparing different methods for delimitating

bioclimatic regions

After the results of this work, we were able to define

three bioclimatic units, on the basis of the joint

distribution of three guilds of species, significantly

sensitive to yearly average temperature and rainfall.

Their distributional patterns identify areas showing a

good accordance when compared with the biocli-

matic units sensu Nimis and Tretiach (1995) and

Nimis (2003), based on precipitations and altitudinal

belts (Fig. 1) and proposed at a national scale. The

following correspondences can be observed:

(1) Species of cluster A (e.g., B. griseovirens) are

distributed in an area corresponding to the

Montane bioclimatic unit (Fig. 1A);

(2) Distributional pattern of Cluster B fits rather

well with the Humid sub-Mediterranean unit

(Fig. 1B) sensu Nimis (2003);

(3) Species of cluster C generally show a good

accordance with the Mediterranean bioclimatic

unit (Fig. 1C). The heterogeneity of these

species in respect to rainfall requirement may

lead to a further localization of a Dry Mediter-

ranean unit, which was already proposed for

other Italian regions, but that was not described

by Nimis (2003) for Liguria.

(4) Our analysis does not evidence a cluster of

species related to the Dry sub-Mediterranean

unit sensu Nimis, being this area characterized

in Liguria by a very poor epiphytic lichen flora.

These results point out also some limits of small-

scale delimitation of bioclimatic units of Italy, which

can generate fictitious regions whose borderlines

cannot be easily defined (Nimis and Tretiach 2004):

the two Tyhrrenian units sensu Nimis do not strictly

correspond to the peninsular West-sided coasts: due

to local geographic, morphologic and climatic fea-

tures of the territory some ‘‘bioclimatic enclaves’’ are

likely to occur.

These limits could be overcome by referring

bioclimatic observations to an higher geographic

scale such as at regional (as in this article) or local

level. We agree with Walther et al. (2002) that

regional changes, being spatially heterogeneous, are

relevant in the context of ecological response to

climatic change. Nevertheless, on the way of dispos-

ing of a much more detailed and precise bioclimatic

subdivision of Italy, a deeper knowledge of lichens

local distribution is needed, as showed by the

increasing number of floristic and vegetational stud-

ies recently carried out throughout the country at

regional scale (Brunialti and Giordani 2003; Nimis

and Tretiach 2004).

Baseline for forthcoming monitoring of the

effects of global change using epiphytic lichens

In a long-term perspective, our results provide a

baseline to investigate the possible effects on lichen

communities caused by global changes. Even if a

broad range of organisms were taken into account as

reliable indicators of climatic changes (Hughes 2000;

Walther et al. 2002), e.g., birds (Crick et al. 1997),

amphibians (Beebee 1995), flowering plants (Menzel

and Estrella 2001; Karlsen et al. 2005) and bryo-

phytes (Gignac 2001), there are few studies dealing

with possible applications with lichens (Insarov et al.

1999; Lucking 2003), thought the peculiar physiol-

ogy of these organisms, directly linked to atmosphere

for water supply (Rundel 1988; Nash 1996), should

increase the scientific interest in this sense. Our

outcomes, in particular, are in accord with those of

Van Herk et al. (2002) which observed fluctuations in

the epiphytic flora of The Netherlands from 1979 to

2001, related to an increasing of average temperature:

Five species of cluster A (Evernia prunastri, Hypo-

gymnia physodes, Lecanora argentata, Parmelia

saxatilis and Pertusaria albescens) and not those of

the other clusters showed in the Netherlands a

decreasing in the last years, whilst four species of

cluster C (Xanthoria parietina, Physconia grisea,

Micarea prasina and Punctelia borreri) and 8 of B

showed a significant (Flavoparmelia caperata, Lec-

idella elaeochroma, Punctelia subrudecta, Parmelia

sulcata, Physcia adscendens and Phaeophyscia or-

bicularis) and in some cases very rapid (Parmotrema

chinense and Hyperphyscia adglutinata) spreading.

As such, we can hypothesize that these species

could respond to climatic changes also at larger

scale than the one referring to Liguria region and in

relative short time framework. Nevertheless, we

should consider that the Netherlands’s original

lichen flora was nearly completely lost due to air

pollution (van Dobben et al. 2001; van Herk 2001),

so that, as Walther et al. (2002) pointed out, in

altered environments colonization of new species

Plant Ecol (2008) 195:257–272 269

123

well adapted to changed climatic conditions was

possible because of a low competitive pressure.

Probably this is not the case of our survey area,

where several ecosystems are still preserved.

Anyway the use of guild of epiphytic lichens as

climatic changes indicators could be tested only

providing a baseline of species occurrence at a given

time, once the species are selected on the basis of a

significant response to climate. In the case of Liguria,

for instance, one could plan to evaluate the future

increasing of warm-humid-related species of cluster

B in low diversity areas, as the urban and suburban

areas near the capital town Genoa or the dry sub-

Mediterranean areas in the western Riviera. Never-

theless, in our opinion, the reliability of epiphytic

lichens as tools for monitoring climatic changes in a

long-term perspective is still to demonstrate: future

studies should address the question if it would be

possible to obtain useful information about climatic

changes at ecosystem level, notwithstanding the

complex poikylohydric nature of these organisms,

which are probably related to micro- rather than

macroclimatic parameters.

Conclusions

The distribution of more than 30% of the releved

epiphytic species has been shown to be explained by

macroclimatic variables. A significant subset of

epiphytic lichens in the study area have been proved

to be efficient bioclimatic indicator for montane,

humid sub-Mediterranean and Mediterranean units,

whereas we have no evidences of a relation between a

dry sub-Mediterranean unit and epiphytic lichen

distribution, being this area characterized only by

the absence of indicator species.

Forthcoming studies will test at national scale this

quantitative approach for evaluating bioclimatic sub-

division of the country. It would be also possible to

model the likelihood of occurrence of indicator

species along the time by means of time series

analysis of macroclimatic variables. Furthermore,

applicative perspectives of this work include (a) a

better interpretation of biomonitoring data, thanks to

an improved knowledge of maximum potential

distribution and frequency of epiphytic lichens

throughout the region, and (b) a plan of long-term

monitoring survey to detect possible effects of global

changes on epiphytic vegetation.

Acknowledgement This work has been supported, in part, by

Ministero dell’Istruzione, dell’Universita e della Ricerca,

project F.I.S.R.—M.I.C.E.N.A., 2006.

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