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Evaluation of lichen species resistance to atmospheric metal pollution by coupling diversity and bioaccumulation approaches: A new bioindication scale for French forested areas

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To link to this article : DOI: 10.1016/j.ecolind.2016.08.006
To cite this version : Agnan, Yannick and Probst, Anne and Séjalon-
Delmas, Nathalie Evaluation of lichen species resistance to
atmospheric metal pollution by coupling diversity and
bioaccumulation approaches: A new bioindication scale for French
forested areas. (2017) Ecological Indicators, vol. 72. pp. 99-110.
ISSN 1470-160X
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concerning this service should be sent to the repository
Evaluation of lichen species resistance to atmospheric metal pollution
by coupling diversity and bioaccumulation approaches: A new
bioindication scale for French forested areas
Y. Agnana,b,, A. Probsta, N. Séjalon-Delmasa,c,
aECOLAB, Université de Toulouse, CNRS, INPT, UPS, France
bMilieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), UMR 7619, Sorbonne Universités UPMC-CNRS-EPHE, 4
place Jussieu, F-75252 Paris, France
cLaboratoire de Recherche en Sciences Végétales (LRSV), Université de Toulouse, UPS, CNRS, 31326 Castanet-Tolosan, France
Resistance scale
Atmospheric purity
ab s t r a c t
In order to evaluate the metal resistance or sensitivity of lichen species and improve the bioindication
scales, we studied lichens collected in eight plottings in French and Swiss remote forest areas. A total of
92 corticolous species was sampled, grouped in 54 lichen genera and an alga. Various ecological variables
were calculated to characterize the environmental quality – including lichen diversity, lichen abundance,
and Shannon index –, as well as lichen communities. Average ecological features were estimated for each
study site and each of the following variables – light, temperature, continentality, humidity, substrate
pH, and eutrophication – and they corresponded to lichen communities. Based on lichen frequencies, we
calculated the index of atmospheric purity (IAP) and lichen diversity value (LDV). These two bioindication
indices were closely related to lichen diversity and lichen abundance, respectively, due to their calcula-
tion formula. It appeared that LDV, which measures lichen abundance, was a better indicator of metal
pollution than IAP. Coupling lichen diversity and metal bioaccumulation in a canonical correspondence
analysis, we evaluated the resistance/sensitivity to atmospheric metal pollution for the 43 most frequent
lichen species. After validation by eliminating possible influences of acid and nitrogen pollutions, we
proposed a new scale to distinguish sensitive species (such as Physconia distorta,Pertusaria coccodes,
and Ramalina farinacea) from resistant species (such as Lecanactis subabietina,Pertusaria leioplaca, and
Pertusaria albescens) to metal pollution, adapted to such forested environment.
1. Introduction
Atmospheric deposition of chemicals impacts natural ecosys-
tems over a long-term, and biological species are more or less
susceptible to these pollutants (Schulze et al., 1989; Tyler, 1989).
Lichens are considered sensitive organisms because of their biolog-
ical features. The absence of protective cuticle or root system results
in a high sensitivity to anthropogenic disturbances, such as atmo-
spheric pollutants (Bajpai et al., 2010; Conti and Cecchetti, 2001;
Shukla et al., 2014; Szczepaniak and Biziuk, 2003). The loss of lichen
diversity constitutes one of the main markers of atmospheric pol-
lution on the biosphere, as revealed since the first observations in
Corresponding authors at: ECOLAB, Université de Toulouse, CNRS, INPT, UPS,
E-mail addresses: (Y. Agnan), (N. Séjalon-Delmas).
the late 19th century in Paris (Nylander, 1866). Because assessment
of atmospheric pollution is complex and expensive, biomonitoring
is a helpful support technique. Several biomonitoring approaches
are used to evaluate the level of atmospheric pollution, in relation
to lichen diversity (i.e., bioindication; Geiser and Neitlich, 2007;
Pinho et al., 2004) or accumulation of pollutants (i.e., bioaccumu-
lation; Conti et al., 2011; Hissler et al., 2008). Lichens are relatively
good candidates frequently used to monitor atmospheric depo-
sition in various environmental contexts: e.g., forested (Gauslaa,
1995; Giordani et al., 2012), rural (Bosch-Roig et al., 2013; Vonarb
et al., 1990), and urban (Gombert et al., 2004; Loppi et al., 2004)
Atmospheric acid deposition in Europe several decades ago,
linked to man-made SO2and NOx emissions, was responsible
for several disturbances on forest diversity (Schulze et al., 1989).
More specifically, many authors reported that some lichen species
have disappeared because of their susceptibility to acid pollu-
tants (Piervittori et al., 1997; Sigal and Johnston, 1986). In this
context, a first biomonitoring scale was developed in England
and Wales by Hawksworth and Rose (1970), associating common
lichen species for different atmospheric SO2concentrations. More
recently, in Germany, Wirth (1991) developed a toxitolerance index
for more than 750 lichen species also based on the acid pollu-
tion criteria. With the generalized decrease of SO2concentration
in the atmosphere since the 1980s (Berge et al., 1999), a change
in biomonitoring scale was needed. Several scales were devel-
oped following the relative importance of nitrogen compounds in
the atmosphere (i.e., NOx and NH4;Lallemant et al., 1996; van
Haluwyn and Lerond, 1993). Nevertheless, these various scales
do not take into account other pollutants such as metals (e.g.,
lead, zinc, cadmium) or organic pollutants (e.g., polycyclic aromatic
hydrocarbons [PAH] and polychlorinated biphenyl [PCB]), and lit-
tle is known about the sensitivity or resistance to such pollutants
for lichen species commonly found in northern countries. Conse-
quently, the development of new scales integrating these changes
in sulfur and nitrogen compounds as background levels and the
occurrence of emerging pollutants is therefore required.
In the meantime, several indices of atmospheric air quality were
established based on lichen richness and abundance, such as the
lichen diversity value (LDV; Asta et al., 2002) and the index of
atmospheric purity (IAP; LeBlanc and Sloover, 1970). These indices
attempt to evaluate a general degree of atmospheric pollution. The
limit of such indices, however, is that they do not point to the exact
pollutants caused by disturbance. A qualitative ecological char-
acterization of lichen occurrence should also be employed as an
additional tool to complete the quantitative evaluation, as being
more frequently done.
In this study, we sampled lichen species in open forest sites
from various remote regions of France and neighboring country
to characterize the current degree of recent atmospheric pollution
based on several approaches of lichen biomonitoring. Assuming a
response to a gradient of metal bioaccumulation on lichen richness
and abundance, our main objective was to evaluate the resis-
tance/sensitivity of lichen species to atmospheric metal pollution
by coupling both lichen diversity and bioaccumulation of metals in
a multivariate analysis, and to propose a new resistance/sensitivity
scale adapted to present-day environmental conditions to further
assess the critical loads using lichens.
2. Materials and methods
2.1. Study area
Eight unmanaged open-forested sites were monitored, of which
seven sites from various regions of France, and one site located
in Switzerland (Fig. 1). The French sites (SP 11, EPC 63, EPC 74,
HET 54a, EPC 08, PM 72, and CHS 35) belong to the French moni-
toring network of forest ecosystems RENECOFOR (Réseau National
de suivi des Écosystèmes Forestiers), which is part of the Inter-
national Cooperative Programme Forest network (ICP-Forest). The
sites included both coniferous forests (Abies alba Mill. in SP 11, Picea
abies (L.) H. Karst in EPC 63, EPC 74, and EPC 08, and Pinus pinaster
Aiton in PM 72) and hardwood forests (Quercus petraea (Matt.) Liebl.
in CHS 35 and Fagus sylvatica L. in BEX and HET 54a). Despite the
dominant trees, a mixed of species were found with generally both
coniferous and hardwood trees in each study site.
The sites considered various environmental conditions (Table 1).
The elevation was from 80 m a.s.l for CHS 35–1210 m a.s.l for EPC
74. The Northwestern sites (PM 72 and CHS 35) were influenced
by an oceanic climate with low annual precipitation (<840 mm),
while the Northeastern (HET 54a and EPC 08) and central (EPC 63)
ones were under semi-continental climate. The climate was more
of mixed influences for the mountainous sites (SP 11, EPC 74, and
Fig. 1. Location of the study sites sampled for lichen diversity: seven sites are located
in various regions of France and one in nearby Switzerland.
BEX). Several types of bedrock were concerned, from sedimentary
(limestone or sandstone) to magmatic (basalt) substratum.
Metal atmospheric pollution has already been studied for these
sites through surface horizons of soils (Gandois et al., 2010a;
Hernandez et al., 2003), bulk atmospheric deposition (Gandois
et al., 2010b), and lichen bioaccumulation (Agnan et al., 2015).
The metal concentrations registered in lichens collected on the
trees considered for bioindication are given in Table 2. Differences
between sites were observed with a higher anthropogenic influ-
ence in the North-Eastern part of the country, particularly for Pb
and Cd in EPC 08, while a greater dust deposition was observed
in the Southern regions (e.g., in SP 11). The availability of lichen
bioaccumulation data (i.e., metal concentrations in lichens though
accumulation from the environment) was a central part of this
study to determine both lichen resistance and lichen sensitivity
in coupling lichen diversity to the degree of metal concentrations.
2.2. Sampling procedure
Because microclimate and bark properties are known to influ-
ence lichen diversity (Ellis, 2012; Giordani, 2006), each study
site encompassed a representative area of about 250000 m2in
open field at the edge of a forest to both maximize the number
of sampling species and preserve the forest influence (Poliˇ
et al., 2008). Twelve trees avoiding young and disturbed specimens
for lichen sampling (i.e., circumference > 40 cm, inclination < 10 ,
trunk without mosses and damages) of various species were sam-
pled (Bargagli and Nimis, 2002; Giordani et al., 2011), including
both deciduous and coniferous trees (Table 1) to improve the repre-
sentativeness of local lichen diversity (Daillant et al., 2007; Deruelle
and Garcia Schaeffer, 1983). We followed the standardized Euro-
pean protocol (EN 16413, 2014), leaving the random sampling to
maximize the number of lichen species by increasing the tree diver-
sity (Moreau et al., 2002). Since we aimed to evaluate the metal
resistance and sensitivity of lichens by combining bioaccumulation
and diversity approaches, we thus followed the same procedure
as for bioaccumulation study (Agnan et al., 2015). The four cardi-
nal points of the tree trunks were sampled using a ladder grid of
five vertical squares of 10 cm ×10 cm to cover an area of 500 cm2
per tree side and a total area of 24000 cm2(i.e., 240 squares) for
Table 1
Summary of geographical and environmental characteristics for each study sites.
site coordinates elevation (m) annual
lithology tree species sampled
SP 11 20540’E/ 425215’N 990 1200 limestome/marble Abies alba Mill., Corylus avellana L.,
Fagus sylvatica L., Fraxinus excelsior L.,
Malus pumila Mill.
EPC 63 25805’E/ 454500’N 950 1100 basalt Crataegus monogyna Jacq., Fraxinus excelsior L.,
Picea abies (L.) Karst., Pinus sp.
EPC 74 62100’E/ 461330’N 1210 1300 sandstone/schist Abies alba Mill., Acer sp., Fagus sylvatica L.,
Picea abies (L.) Karst.,
Prunus avium L., Salix sp., Sorbus aucuparia L.
BEX 65830’E/ 461300’N 945 1000 limestone/schist Acer sp., Betula pendula Roth, Fagus sylvatica L.,
Fraxinus excelsior L., Salix sp.
HET 54a 64310’E/ 483050’N 320 900 limestone Fagus sylvatica L., Fraxinus excelsior L., Quercus sp.
EPC 08 44750’E/ 495700’N 475 1300 clay loam Betula pendula Roth, Corylus avellana L.,
Fagus sylvatica L., Picea abies (L.) Karst.,
Prunus avium L., Quercus sp., Rhus hirta (L.) Sudw.,
Salix caprea L., Syringa vulgaris L.
PM 72 02000’E/ 474425’N 155 800 schist Castanea sativa Mill., Pinus pinaster Ait.,
Quercus petraea (Mattus.) Liebl., Quercus rubra L.
CHS 35 13250’W/481010’N 80 840 clay Fagus sylvatica L., Pinus pinaster Ait.,
Quercus petraea (Mattus.) Liebl.
Table 2
Summary of metal bioaccumulation (mean ±standard deviation, in mg g1) in three foliose lichen species (i.e., X. parietina,P. sulcata, and H. physodes) from the investigated
forest areas (from Agnan et al., 2015).
element SP 11 EPC 63 EPC 74 BEX HET 54a EPC 08 PM 72 CHS 35
Al 2364.1 ±1054.3 988.3 ±299.4 1126.7 ±412.0 1157.9 ±277.8 192.4 ±603.1 1072.7 ±591.1 426.1 ±46.0 397.7 ±62.2
As 0.7 ±0.2 0.7 ±0.4 0.3 ±0.1 0.3 ±0.0 0.3 ±0.1 0.6 ±0.2 0.2 ±0.0 0.2 ±0.0
Cd 0.1 ±0.0 0.1 ±0.0 0.7 ±0.7 0.1 ±0.1 0.2 ±0.2 0.6 ±0.1 0.4 ±0.1 0.1 ±0.1
Co 0.4 ±0.2 0.3 ±0.1 0.4 ±0.1 0.3 ±0.1 0.3 ±0.1 0.4 ±0.1 0.1 ±0.0 0.2 ±0.0
Cr 3.7 ±1.3 2.2 ±1.0 1.8 ±0.6 2.8 ±1.0 1.8 ±0.7 2.5 ±0.9 0.8 ±0.1 0.7 ±0.1
Cs 0.3 ±0.1 0.3 ±0.2 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1 0.2 ±0.1 0.1 ±0.0 0.1 ±0.0
Cu 4.7 ±0.9 6.9 ±2.1 10.4 ±3.2 7.1 ±2.4 7.9 ±3.1 7.3 ±1.0 7.2 ±1.0 5.1 ±1.5
Fe 1347.1 ±595.6 759.1 ±262.2 618.5 ±208.4 687.0 ±158.2 617.6 ±334.8 631.0 ±328.5 278.6 ±35.7 240.9 ±36.4
Mn 29.3 ±14.6 25.1 ±4.0 142.0 ±126.8 102.7 ±74.7 69.5 ±73.2 45.7 ±11.3 45.3 ±4.4 346.8 ±117.7
Ni 1.7 ±0.5 1.3 ±0.5 2.1 ±0.7 2.1 ±0.7 1.7 ±0.6 2.0 ±0.3 0.6 ±0.1 1.4 ±0.3
Pb 2.3 ±1.5 2.5 ±1.1 7.3 ±3.5 5.1 ±2.7 16.1 ±13.4 5.2 ±1.1 1.5 ±0.3 6.2 ±10.4
Sb 0.1 ±0.1 0.1 ±0.0 0.1 ±0.0 0.2 ±0.0 0.2 ±0.1 0.3 ±0.1 0.2 ±0.0 0.1 ±0.0
Sn 0.4 ±0.2 0.3 ±0.2 0.5 ±0.1 0.6 ±0.2 0.4 ±0.2 0.7 ±0.1 0.3 ±0.0 0.2 ±0.0
Sr 8.5 ±3.4 44.4 ±29.8 16.9 ±9.3 16.8 ±5.6 10.4 ±6.3 10.7 ±2.0 4.4 ±0.4 30.7 ±22.5
Ti 187.9 ±82.9 123.2 ±51.2 62.6 ±20.5 80.3 ±19.8 85.6 ±43.8 73.3 ±42.5 32.3 ±2.6 33.6 ±5.4
V 4.1 ±2.0 2.4 ±0.5 2.4 ±0.7 2.2 ±0.5 2.6 ±1.3 2.4 ±0.4 1.0 ±0.1 1.3 ±0.2
Zn 22.1 ±9.6 30.0 ±18.1 69.0 ±43.4 35.1 ±14.4 47.9 ±20.8 108.4 ±11.8 72.8 ±16.2 30.0 ±4.2
each study site (Asta et al., 2002;Fig. 2). The ladder was placed
at minimum 1 m above the ground level to avoid soil influence
(Bargagli and Nimis, 2002). We determined the presence of lichen
species in each 100 cm2noticed in a sampling sheet: 0 if absent, 1
if present. This allowed obtaining the frequency of each species by
site, averaging all values: from 0 (totally absent in the study site)
to 1 (present in every 10 cm ×10 cm squares). The average values
are given in Table 3. We used a 10- or 30-fold hand lens to identify
all the species. Lichen specimens were collected using a knife, and
preserved in a plastic bag until complete identification.
2.3. Species identification
Lichen species identification was performed in laboratory using
a stereomicroscope (from 20- to 60-fold) and microscope (100-
fold). Determination guides (Clauzade and Roux, 1985; Dobson,
2011; Smith et al., 2009; van Haluwyn and Lerond, 1993), and
chemicals – potassium hydroxide 10% (K), sodium hypochlorite
(C), and paraphenylenediamine (P) – were used to distinguish the
different genera and/or species. Only genera were identified for
immature specimens. Conversely, we identified the sub-species
when possible. The nomenclature used was based on Roux (2012).
2.4. Index calculations and statistical treatment
For each site, we determined the number of species found and
the abundance of each species calculated by adding each frequency,
determined using the field ladder grid (see above). We also cal-
culated the Shannon’s diversity index H’ based on the following
H’ = −
where piis the proportion of characters of the species i, and R is the
species richness.
Two bioindication indices were calculated: the lichen diversity
value (LDV; Asta et al., 2002), which represents the sum of frequen-
cies, and the index of atmospheric purity (IAP; LeBlanc and Sloover,
1970) as follows:
IAP =1
where n is the number of species, Qiis the ecological index of each
species i (corresponding to the total number of companion species
present at all studied sites), and fiis the frequency of species i.
Table 3
Site and average (avg.) frequencies for each lichen species.
species code SP 11 EPC 63 EPC 74 BEX HET 54a EPC 08 PM 72 CHS 35 avg.
Acrocordia gemmata
(Ach.) A. Massal.
Age 0.092 0.071 0.067 0.021 0.031
Alyxoria varia
(Pers.) Ertz et Tehler
Ava 0.025 0.003
Amandinea punctata
(Hoffm.) Coppins et Scheid.
Apu 0.200 0.008 0.242 0.058 0.021 0.066
Anisomeridium biforme
(Borrer) R. C. Harris
Abi 0.054 0.007
Arthonia atra
(Pers.) A. Schneid.
Aat 0.063 0.008
Arthonia radiata
(Pers.) Ach.
Ara 0.171 0.021 0.054 0.021 0.033
Aspicilia coronata
(A. Massal.) Anzi
Aco 0.013 0.002
Buellia disciformis
(Fr.) Mudd
Bdi 0.104 0.046 0.019
Calicium salicinum
Csa 0.029 0.046 0.009
Caloplaca cerina
(Ehrh. ex Hedw.) Th. Fr.
Cce 0.004 0.001
Caloplaca ferruginea
(Hudson) Th. Fr.
Cfe 0.008 0.029 0.005
Candelaria concolor
(Dicks.) Stein
Cco 0.154 0.019
Candelariella reflexa
(Nyl.) Lettau
Cre 0.025 0.003
Candelariella vitellina
(Hoffm.) Müll. Arg.
Cvi 0.046 0.006
Chaenotheca ferruginea
(Turner ex Sm.) Mig.
Chf 0.075 0.009
Chrysothrix candelaris
(L.) J. R. Laundon
Cca 0.117 0.242 0.042 0.133 0.029 0.063 0.067 0.086
Cladonia fimbriata
(L.) Fr.
Cfi 0.050 0.317 0.171 0.046 0.073
Dendrographa decolorans
(Turner et Borrer ex Sm.) Ertz
et Tehler
Dde 0.117 0.025 0.046 0.023
Enterographa crassa
(DC.) Fée
Ecr 0.196 0.024
Evernia prunastri
(L.) Ach.
Epr 0.025 0.238 0.104 0.108 0.058 0.042 0.072
Fuscidea cyathoides subsp. corticola
(Fr.) Cl. Roux comb. nov.
Fcy 0.033 0.004
Graphis elegans
(Borrer ex Sm.) Ach.
Gel 0.175 0.022
Graphis scripta
(L.) Ach.
Gsc 0.042 0.242 0.042 0.041
Haematomma ochroleucum
(Neck.) J. R. Laundon
Hoc 0.042 0.005
Hypocenomyce scalaris
(Ach.) M. Choisy
Hsc 0.013 0.002
Hypogymnia physodes
(L.) Nyl.
Hph 0.067 0.358 0.313 0.017 0.075 0.104
Hypotrachyna laevigata
(Sm.) Hale
Hla 0.008 0.001
Lecanactis subabietina
Coppins et P. James
Lsu 0.008 0.038 0.050 0.012
Lecanora albella
(Pers.) Ach.
Lab 0.067 0.008
Lecanora allophana
Lal 0.054 0.033 0.011
Lecanora argentata
(Ach.) Malme
Lar 0.113 0.254 0.017 0.033 0.052
Lecanora barkmaniana
Aptroot et Herk
Lba 0.063 0.038 0.013
Lecanora carpinea
(L.) Vain.
Lca 0.088 0.004 0.008 0.013
Lecanora chlarotera
Lch 0.150 0.008 0.246 0.788 0.054 0.071 0.004 0.165
Lecanora compallens
van Herk et Aptroot
Lcm 0.133 0.017
Lecanora conizaeoides
Nyl. ex Cromb.
Lcn 0.025 0.004 0.021 0.006
Table 3 (Continued)
species code SP 11 EPC 63 EPC 74 BEX HET 54a EPC 08 PM 72 CHS 35 avg.
Lecanora dispersa
(Pers.) Sommerf.
Ldi 0.025 0.003
Lecanora expallens
Lex 0.008 0.033 0.005
Lecanora hagenii
(Ach.) Ach.
Lha 0.042 0.005
Lecanora horiza
(Ach.) Linds.
Lho 0.021 0.003
Lecanora intumescens
(Rebent.) Rabenh.
Lit 0.075 0.009
Lecanora leptyrodes
(Nyl.) Degel.
Lle 0.008 0.001
Lecanora subcarpinea
Lsc 0.025 0.003
Lecanora subrugosa
Lsr 0.025 0.003
Lecidea sp. Lec 0.021 0.003
Lecidella elaeochroma
(Ach.) M. Choisy
Lel 0.013 0.692 0.038 0.113 0.107
Lepraria incana
(L.) Ach.
Lic 0.450 0.238 0.483 0.142 0.679 0.458 0.625 0.671 0.468
Leptogium teretiusculum
(Wallr.) Arnold
Lte 0.163 0.020
Melanelixia glabratula
(Lamy) Sandler et Arup
Mgl 0.121 0.179 0.042 0.442 0.121 0.142 0.174
Melanohalea exasperata
(DeNot.) O. Blanco, A. Crespo, Divakar,
Essl., D. Hawksw. et Lumbsch
Mea 0.008 0.001
Melanohalea exasperatula
(Nyl.) O. Blanco, A. Crespo, Divakar,
Essl., D. Hawksw. et Lumbsch
Meu 0.138 0.008 0.018
Melanohalea laciniatula
(Flagey ex H. Olivier) O.Blanco, A.
Crespo, Divakar, Essl., D. Hawksw. et
Mla 0.004 0.001
Micarea prasina
Mpr 0.021 0.003
Naetrocymbe punctiformis
(Pers.) R. C. Harris
Npu 0.013 0.013
Ochrolechia androgyna
(Hoffm.) Arnold
Oan 0.013 0.021 0.004
Ochrolechia pallescens
(L.) A. Massal.
Opa 0.025 0.003
Ochrolechia pallescens subsp. parella
Opp 0.004 0.029 0.004
Ochrolechia turneri
(Sm.) Hasselr.
Och 0.033 0.029 0.008
Ochrolechia sp. Otu 0.029 0.004
Opegrapha rufescens
Oru 0.038 0.005
Parmelia sulcata
Psl 0.075 0.442 0.213 0.579 0.488 0.454 0.050 0.008 0.289
Parmelina carporrhizans
(Taylor) Poelt et Vˇ
Pca 0.096 0.113 0.026
Parmeliopsis ambigua
(Wulfen) Nyl.
Pab 0.004 0.001
Pertusaria albescens
(Huds.) M. Choisy et Werner
Pal 0.033 0.175 0.021 0.083 0.039
Pertusaria amara
(Ach.) Nyl.
Paa 0.125 0.046 0.058 0.029
Pertusaria coccodes
(Ach.) Nyl.
Pco 0.008 0.488 0.071 0.071
Pertusaria flavida
(DC.) J. R. Laundon
Pfl 0.013 0.002
Pertusaria hemisphaerica
(Flörke) Erichsen
Phe 0.013 0.002
Pertusaria leioplaca
Pli 0.013 0.025 0.005
Pertusaria pertusa
(Weigel) Tuck.
Ppe 0.088 0.011
Phaeographis smithii
(Leight.) B. de Lesd.
Psm 0.154 0.019
Phlyctis argena
(Spreng.) Flot.
Par 0.004 0.117 0.221 0.046 0.048
Physcia adscendens
(Fr.) H. Olivier
Pad 0.025 0.417 0.192 0.154 0.098
Table 3 (Continued)
species code SP 11 EPC 63 EPC 74 BEX HET 54a EPC 08 PM 72 CHS 35 avg.
Physcia clementei
(Turner) Lynge
Pcl 0.267 0.033
Physcia leptalea
(Ach.) DC.
Plp 0.004 0.001
Physcia tenella
(Scop.) DC.
Pte 0.013 0.229 0.030
Physconia distorta
(With.) J. R. Laundon
Phy 0.025 0.008 0.004
Physconia enteroxantha
(Nyl.) Poelt
Pdi 0.042 0.005
Physconia sp. Pen 0.013 0.002
Pleurosticta acetabulum
(Neck.) Elix et Lumbsch
Pac 0.025 0.196 0.028
Pseudevernia furfuracea
(L.) Zopf
Pfu 0.063 0.304 0.046
Punctelia subrudecta
(Nyl.) Krog
Psb 0.021 0.003
Pyrenula laevigata
(Pers.) Arnold
Pla 0.083 0.010
Ramalina farinacea
(L.) Ach.
Rfr 0.175 0.329 0.017 0.033 0.069
Ramalina fastigiata
(Pers.) Ach.
Rfs 0.004 0.001
Schismatomma cretaceum
(Hue) J. R. Laundon
Scr 0.117 0.075 0.024
Tephromela atra
(Huds.) Hafellner
Tcr 0.046 0.006
Thelotrema lepadinum
(Ach.) Ach.
Tat 0.021 0.003
Usnea sp. Usn 0.004 0.046 0.006
Xanthoria parietina
(L.) Th. Fr.
Xpa 0.013 0.025 0.017 0.075 0.025 0.019
Zwackhia viridis
(Pers. ex Ach.) Poetsch et Schied.
Zvi 0.071 0.009
Pleurococcus viridis
Pvi 0.188 0.092 0.146 0.304 0.188 0.063 0.017 0.124
all 2.854 3.313 3.596 3.200 3.767 2.171 2.067 1.867
Fig. 2. Sampling procedure using a 10 cm ×50 cm grid on the tree trunk in the four
cardinal directions.
A Student t-test was applied on lichen diversity between each
tree genus (a= 0.05). The lichen frequencies did not follow a normal
distribution (Shapiro-Wilk test); then, data were log-transformed
for the multivariate analyses. Principal component analysis (PCA)
was performed on ecological and environmental data (Dobson,
2011; Nimis and Martellos, 2008; Smith et al., 2009; Wirth, 2010)
based on lichen species frequency. Canonical correspondence anal-
ysis (CCA) was used to evaluate the resistance or sensitivity of the
43 most abundant lichen species to metal atmospheric pollution
based on species frequency. Statistical analyses were carried out
using RStudio 0.98 (RStudio Inc., Boston, Massachusetts, USA) and
ade4 package (Dray and Dufour, 2007).
3. Results
3.1. Ecological indices
3.1.1. Lichen and tree diversities
The identified lichen species and their respective frequency for
each study site are reported in Table 3. A total of 54 lichen gen-
era, distributed in 92 corticolous species, and an alga (Pleurococcus
viridis Ag.) were sampled (Fig. 3a). The most abundant species
were Lepraria incana (L.) Ach. (observed in 8 sites with a total fre-
quency of 3.75), Parmelia sulcata Taylor (8 sites, frequency of 2.31),
Lecanora chlarotera Nyl. (7 sites, frequency of 1.32), and Melanelixia
glabratula (Lamy) Sandler & Arup (6 sites, frequency of 1.05). Some
species were found in only one site with a very low frequency
(<0.005): e.g., Ramalina fastigiata (Pers.) Ach., Physcia leptalea (Ach.)
DC, and Parmeliopsis ambigua (Wulfen) Nyl. Overall, the lichen
species were distributed into 64 crustose, 20 foliose, 6 fruticose,
and one squamulose morphologies (Fig. 3b). The foliose/crustose
thallus ratios were from 0.05 to 1 and decreased as follows: CHS
35 < SP 11 < HET 54a < PM 72 < BEX < EPC 74 < EPC 63 < EPC 08.
Biological richness and abundance (sum of frequencies) showed
a high heterogeneity among the study sites: from 13 to 35 species
encountered by individual site and the abundances ranged between
1.87 and 3.77 (Table 4). SP 11, PM 72, and CHS 35 showed a
Fig. 3. Lichen diversity found in the eight study sites: average abundance of each lichen species (a) and relative proportion of each type of morphology (b).
Table 4
Summary of main ecological, bioindication indices, and values of the six environmental variable from Wirth, 2010 of each plotting area.
study site ecological indices bioindication
Wirth, 2010s environmental indices (%)
IAP LDV light temperature continentality humidity pH eutrophication
SP 11 35 2.85 4.43 241 57 55.8 52.3 44.8 26.3 32.6 43.2
HET 54a 33 3.77 4.30 263 75 62.7 51.1 42.9 21.8 37.6 47.5
EPC 74 30 3.60 4.27 227 72 76.4 50.1 45.8 18.5 36.5 46.0
PM 72 26 2.02 3.71 159 40 69.0 50.2 38.5 26.2 25.8 38.6
EPC 63 25 3.31 3.66 157 66 75.1 50.0 41.9 26.6 37.1 51.8
CHS 35 23 1.87 3.52 137 37 45.2 51.3 39.1 37.2 25.9 23.0
BEX 20 3.20 3.02 117 64 64.5 49.9 50.0 14.1 37.0 57.1
EPC 08 13 2.17 3.16 94 43 77.3 50.0 50.0 15.6 32.4 56.5
relatively low lichens abundance for a same range of richness (rich-
ness/abundance ratio from 12.3 to 12.9) compared to the other sites
(ratio from 6.0 to 8.8). The Shannon index, ranged between 3.02 and
4.43. It followed the lichen diversity values with the exception of
BEX site, which may be due to a higher abundance (Table 4).
The main lichen communities observed in the study sites were
commonly found in France (Coste, 2001; van van Haluwyn and
Lerond, 1993; van Haluwyn et al., 2009): Leprarion incanae Almborn
1948 (except in BEX and PM 72), including sciaphilous species (Lep-
raria incana), and Lecanorion carpinae (Ochsn.) Barkm, 1958 (except
in EPC 63 and CHS 35), including heliophilous, nitrophilous and
toxitolerant species (such as Lecanora carpinea,Lecanora chlarotera,
and Lecidella elaeochroma). Parmelion acetabuli Barkman 1958 was
found in four sites (BEX, HET 54a, EPC 08, and PM 72), including
Parmelia sulcata,Melanelixia glabratula, as well as Melanohalea exas-
peratula and Physcia adscendens, mainly heliophilous and slightly
neutrophilous and toxitolerant species. Other nitrophobous and
poleophobous communities were found locally: Graphidion scriptae
Oschner 1928 (with Arthonia,Graphis,Enterographa and Opegrapha;
in HET 54a and CHS 35), Cladonion coniocraeae Duvigneaud ex
James, Hawksworth & Rose, 1977 (with Cladonia fimbriata; in EPC
08 and PM 72), and Calicion viridis ˇ
Cernh. & Hadaˇ
c 1944 (with
Chrysothrix candelaris; in BEX).
The sampling procedure, including both hardwood and conifer
trees as far as possible (Table 1), attempted to reduce the tree bark
influence by limiting to only sample the main representative tree
species in each site (i.e., fir in SP11, spruce in EPC 63, beech for HET
54a, oak in CHS 35, etc.), and thus, the lichen communities adapted
to these tree species. We collected lichen samples on a total of 21
different tree species, from 3 to 9 by site. Considering dominant
tree species (n 5, Fig. 4), lichen richness observed on hardwood
trees was usually greater compared to richness on conifers, except
for Abies: p < 0.05 (Student test). Fraxinus was the tree species
with the greater lichen richness (9.6 species on average). Also, the
lichen communities found on deciduous trees (Lecanorion carpinae
and Parmelion acetabuli associated with other foliose and fruti-
cose species) differed from those on conifers (generally Leprarion
3.1.2. Bioindication indices
The highest IAP (>200) were found in HET 54a, SP 11, and EPC 74,
while EPC 08 and BEX showed the lowest values (<120), following
asimilar trend as lichen richness (Table 4). The different sampling
and/or calculation methods may limit the data comparison (Scerbo
et al., 1999). Lichen diversity values were also highest (>70) in HET
54a and EPC 74, but the lowest values (40) were for the two West-
ern stations (CHS 35 and PM 72), following the lichen abundance
3.1.3. Ecological features
For each lichen species, we studied ecological features through
six environmental parameters described by Wirth (2010): light,
temperature, continentality, humidity, pH, and eutrophication.
When ecological data were absent (i.e., for 25 species), we
used data from Nimis and Martellos (2008) database, as well
as other references (Clauzade and Roux, 1985; Dobson, 2011;
Fig. 4. Lichen diversity by tree-support species (n indicates the number of individuals for each tree genus).
Smith et al., 2009; van Haluwyn and Lerond, 1993). An aver-
age ecological value of each parameter was calculated for each
station based on individual value and frequency of each lichen
species. To better homogenize the indices between these differ-
ent references and to reduce the wide ranges (generally nine
levels are reported by Wirth, 2010), we introduced a new scale
of three levels (e.g., xerophytic/mesophytic/hygrophytic species,
acid/neutral/basic substrate pH, etc.). The results were expressed
using the frequency of each lichen species (Table 4).
The most important gradient were found for eutrophication
(from low, i.e., CHS 35 and PM 72, to moderate eutrophic species,
i.e., BEX and EPC 08) and light (with high proportions of helio-
philous species, i.e., EPC 08, EPC 74, and EPC 63, and species
with moderate light affinity, i.e., CHS 35 and SP 11). In contrast,
mesophytic species were dominant indicating a low difference in
temperature among sites. On overall, lichen species were, on aver-
age, mostly acidophilic, xerophilic and moderately oceanic in all
the stations.
3.2. Coupling ecological and biogeochemical approaches
To determine the resistance or sensitivity of each lichen species
to metal atmospheric pollution, we performed multivariate sta-
tistical analyses including the three diversity variables previously
studied (lichen richness, lichen abundance, and Shannon index), the
six ecological parameters mentioned above, the two bioindication
indices (IAP and LDV), and metal bioaccumulation data measured
in foliose lichen species (i.e., Xanthoria parietina,Parmelia sulcata,
or Hypogymnia physodes) estimated using the sum of enrichment
factors (EF) for 17 metals (Al, As, Cd, Co, Cr, Cs, Cu, Fe, Mn, Ni, Pb,
Sb, Sn, Sr, Ti, V, and Zn; see Agnan et al. (2015)). A PCA was then
performed and the first two components (81% of the data variance)
were represented (Fig. 5).
The first component (45% of the data variance) was influenced by
lichen abundance and LDV with negative scores. It was associated to
lichen species living on basic bark and eutrophic, continental, and
bright environments, as illustrated by EPC 74, BEX, HET 54a, and
EPC 63 sites. The positive scores were characterized by hydrophilic
species and metal EF data from bioaccumulation in lichen, influ-
encing the two Western sites (CHS 35 and PM 72). The second
component (36% of the data variance) grouped the two diversity
indices (lichen richness and Shannon index), as well as IAP and
lichen species living in warmer environments. The temperature
could not explain this component due to the lack of ecological con-
trast in the study sites. This component distinguished SP 11 and
HET 54a with positive scores, and EPC 08, and to a lesser extent
BEX, with negative scores.
A CCA was performed on metal bioaccumulation data and
lichen species frequencies found for each study site (Fig. 6a,b).
This method was already used for lichen sensitivity to nitrogen by
Glavich and Geiser (2008). Only lichen species presented in at least
two different study sites were included in the CCA. We added in
the analysis the sum of EF of the 17 metals previously cited (Agnan
et al., 2014) and the two bioindication indices (IAP and LDV). The
IAP was explained by the first axis (26% of the data variance), while
the second axis (21% of the data variance) evidenced an opposite
pattern between LDV and EF (Fig. 6a). Each lichen species was repre-
sented by a three letter code on Fig. 6b (see Table 5 for the species
correspondence). Since IAP was a diversity index (Fig. 5), it was
proved difficult to classify the lichen species following the first axis.
Using the EF position in the first plot as factor of metal pollution
(Fig. 6a), however, we determined the degree of metal influence for
each lichen species depending on the position of the species in the
second plot (Fig. 6b). To scale this influence, we applied a geomet-
ric rotation using EF as the new y axis (y’). The rotated coordinates
allowed differentiation of sensitive vs resistant species based on the
EF values (i.e., projection on the EF gradient, y= 2.28 x;Fig. 6b). The
lowest and negative y’ indicated a resistant species to metal atmo-
spheric pollution and the highest and positive y’ a sensitive species
(Table 5). Given the range of y’ values of 3 (between 1.5 to +1.5),
we determined three groups of identical ranges as follows: y’ < 0.5
for resistant species, 0.5 < y’ < 0.5 for intermediate species, y’ > 0.5
for sensitive species. The list of resistant species included various
crustose lichens, while only two crustose species were present in
the sensitive list (Pertusaria coccodes and Caloplaca ferruginea). Two
foliose (Melanohalea exasperatula and Physcia tenella) and one fruti-
cose (Cladonia fimbriata) species, however, were found as resistant
species. The number of sites where each lichen species was present
was given as confidence information of y’.
4. Discussion
4.1. Lichen diversity and communities
The diversity of corticose lichen species observed in the eight
forest study sites was generally lower on coniferous trees com-
pared to hardwood trees (Fig. 4), confirming literature observations
(Selva, 1994). Lichen communities were likewise different between
Fig. 5. Principal component analysis (PCA) including ecological characteristics (normal), ecological indices (italic), bioindication indices (bold), and the sum of enrichment
factors of 17 metals (EF, bold and italic).
Fig. 6. Canonical correspondence analysis based on frequency of the 43 main lichen species (presented in more than two different sites, with a three letter code, see Table 4
for the species correspondence), bioindication (IAP and LDV) and bioaccumulation (sum of enrichment factors [EF] of 17 metals) indices for each study site.
these two types of trees with mostly sciaphilous communities on
conifers and heliophilous species on hardwood trees (e.g., Leprarion
incanae vs Lecanorion carpinae in EPC 74, respectively). Our sam-
pling method in open areas bordering forests allowed therefore
maximizing lichen diversity and communities: both sciaphilous
and heliophilous lichens were found as dominant species (Lepraria
incana and Parmelia sulcata, respectively; Fig. 3).
Overall, the lichen diversity observed in the study sites was
high. The number of lichen species was in the same range as those
observed in other European forests: e.g., in Italy (Giordani, 2007),
Slovenia (Poliˇ
cnik et al., 2008), or Portugal (Pinho et al., 2004);
the Shannon index, however, showed higher values compared to
other European and North American forested sites (Mulligan, 2009;
Peterson and McCune, 2001). But, this range was higher than in
boreal environments (Kuusinen, 1996), probably in relation to spe-
cific climate conditions in cold regions. Indeed, the diversity data
from the literature are not always comparable since the sampling
methods used can sometimes lead to discrepancies between the
observations (e.g., Kuusinen and Siitonen, 1998; Selva, 1994).
Based on the indices of Nimis and Martellos (2008), 12% of the
overall taxa were pioneer species, with the maximum proportion
for BEX and SP 11 (25 and 20%, respectively) and the minimum
for CHS 35 (4%). In BEX, two common lichen species (Lecanora
chlarotera and Lecidella elaeochroma) were responsible for 98% of
the pioneer frequency, but these species can also be found in
non-pioneer environments (Pirintsos et al., 1995). The pioneer fre-
quency was not directly positively correlated with lichen richness
(Table 4), as suggested by Selva (1994). This can be explained either
by our sampling protocol in open field limiting forest, or by inad-
equate Nimis and Martellos (2008) pioneer index applied in our
study sites.
Differences were, conversely, observed among study sites
regarding ecological characteristics. For example, SP 11 and EPC 08
showed both nitrophilous and poleotolerant communities, while
nitrophobous species were found in CHS 35 and HET 54a. This
agreed with observations in atmospheric deposition sometimes
different from modeled estimates, particularly under-estimated in
the Pyrenees (SP 11) and over-estimated in the Armorican Mas-
Table 5
List of resistant, intermediate, and sensitive lichen species relative to atmospheric metal pollution based on a bioaccumulation–lichen diversity coupling method. y’ value
indicates the new scale of lichen resistance/sensitivity to metals. The number of sites where each lichen species was present gives a confidence information of y’.
lichen species number of sites code y’
resistant species Lecanactis subabietina 3 Lsu 1.442
Pertusaria leioplaca 2 Pli 1.402
Pertusaria albescens 4 Pal 1.093
Graphis scripta 3 Gsc 0.919
Cladonia fimbriata 4 Cfi 0.893
Melanohalea exasperatula 2 Meu 0.854
Dendrographa decolorans 3 Dde 0.799
Ochrolechia pallescens subsp. parella 2 Opp 0.781
Ochrolechia androgyna 2 Oan 0.656
Pertusaria amara 3 Paa 0.620
Lepraria incana 8 Lic 0.615
Lecanora allophana 2 Lal 0.608
Physcia tenella 2Pte 0.555
Calicium salicinum 2 Csa 0.551
Acrocordia gemmata 4 Age 0.537
Schismatomma cretaceum 2 Scr 0.525
Arthonia radiata 4 Ara 0.513
intermediate species Lecidella elaeochroma 4 Lel 0.494
Chrysothrix candelaris 7 Cca 0.482
Lecanora chlarotera 7 Lch 0.428
Melanelixia glabratula 6 Mgl 0.389
Lecanora conizaeoides 3 Lcn 0.284
Lecanora expallens 2 Lex 0.234
Parmelia sulcata 8 Psl 0.187
Lecanora argentata 4 Lar 0.021
Ochrolechia turneri 2 Otu 0.018
Amandinea punctata 5 Apu 0.066
Lecanora barkmaniana 2 Lba 0.080
Buellia disciformis 2 Bdi 0.124
Lecanora carpinea 3 Lca 0.170
Parmelina carporrhizans 2 Pca 0.204
Xanthoria parietina 5 Xpa 0.244
Phlyctis argena 4 Par 0.493
sensitive species Pleurosticta acetabulum 2 Pac 0.519
Caloplaca ferruginea 2 Cfe 0.524
Pseudevernia furfuracea 2 Pfu 0.590
Hypogymnia physodes 5 Hph 0.706
Evernia prunastri 6 Epr 0.732
Usnea sp. 2 Usn 0.739
Physcia adscendens 4 Pad 0.771
Ramalina farinacea 4 Rfr 0.824
Pertusaria coccodes 3 Pco 1.256
Physconia distorta 2 Pdi 1.405
sif (CHS 35; Boutin et al., 2015;Pascaud et al., 2016). Even though
no obvious correlation was observed with lichen richness, lichen
abundance, or foliose/crustose thallus ratio, lichen communities
agreed with the ecological features described by Wirth (2010):
e.g., CHS 35 had a low percentage of eutrophic species, unlike EPC
08. These ecological observations were therefore a complementary
description to assess environmental quality that cannot be illus-
trated by lichen richness or abundance only.
Results of bioindication indices showed that IAP were largely
higher than data from French urban areas (Gombert et al., 2004),
and LDV were generally in the upper range compared to other forest
sites in Europe (Giordani, 2007; Pinho et al., 2004; Poliˇ
cnik et al.,
2008). These indices were closely related to lichen richness and
lichen abundance, respectively (Table 4), which was supported by
the PCA results (Fig. 5). This is most likely due to their calcula-
tion method: only frequencies were used in LDV whereas Qi (i.e.,
the number of companion species, largely influenced by lichen
diversity) is considered in IAP. Thereby, the difference of results
between IAP and LDV, already observed by Poliˇ
cnik et al. (2008),
can be attributed to the difference between lichen richness and
lichen abundance strongly highlighted with the Northwestern sites
(PM 72 and CHS 35) and SP 11, showing a high number of lichen
species weakly abundant. Each index was mainly influenced by one
principal component (Fig. 5): axis 1 for LDV (45% of the data vari-
ance) and axis 2 for IAP (36% of the data variance). Based on lichen
ecological features (Nimis and Martellos, 2008), the signs of envi-
ronmental alteration (e.g., acid or poor nutrient environment) were
mainly influenced by the positive scores of the first component,
i.e., opposed to the LDV. It is likely that IAP, and thus lichen diver-
sity, were mostly driven by climate variable (temperature) despite a
low gradient of temperature among lichen species. The sites PM 72
and CHS 35, both positively influenced by the first axis, may either
reflect an environmental alteration (i.e., more acid conditions), or
be driven by the continentality–humidity axis due to their location
with Atlantic influence.
4.2. Resistance and sensitivity of lichen species to metal
atmospheric pollution
As observed in the PCA (Fig. 5), the LDV was opposed to the sum
of metal enrichment factors in the axis 1 vs axis 2 plot. This implies
that, in addition to the response toward the general alteration of
environment, this index better responds to metal pollution as well.
Indeed, the three lowest LDV were observed in CHS 35, PM 72, and
EPC 08 (positive scores of the first axis of the PCA and negative
scores of the second axis), that correspond to the highest EF and
metal deposition (as observed in EPC 08; Gandois et al., 2010b).
The northeastern France is impacted by various activities (local
industries, metallurgy, and mining), while both energy and metal-
lurgy may explain such contamination in the northwestern France
(already observed in upper horizons; Hernandez et al., 2003). This
may be the dominant influence for CHS 35 and PM 72 in the PCA
toward other environmental variables. Thus, it can be supposed
that metal pollution affects more lichen abundance (illustrated by
LDV) than lichen richness (IAP). This is in agreement with results
from Jeran et al. (2002), who had previously observed that IAP was
not a good index for metal pollution.
Based on the CCA, we evaluated the resistance or sensitiv-
ity of each lichen species to metal pollution (Fig. 6 and Table 5).
Very few literature observations, however, allowed supporting our
results: Cladonia fimbriata (present in 4 sites, y’ = 0.893) is a well-
known species able to grow on cadmium, lead, and zinc enriched
substrates (Cuny et al., 2004; Tyler, 1989), whereas conversely,
Hypogymnia physodes (present in 5 sites, y’ = 0.706), is known as
a metal sensitive species, particularly for copper (Hauck and Zöller,
2003). To validate our results, we verified any correlations with
other pollutants: in both resistant and sensitive groups. There were
both acidophilic (e.g., Graphis scripta,Pertusaria albescens,Pertusaria
coccodes) and nitrophilic (Dendrographa decolorans,Physcia adscen-
dens,Physconia distorta;Gombert et al., 2004) species, as well as
both tolerant (Melanohalea exasperatula,Physcia adscendens) and
sensitive (Ochrolechia pallescens,Lecanora allophana,Physconia dis-
torta;Wirth, 1991) to SO2/NO2pollution species. This implies that
we cannot attribute the y’ values to sulfur and nitrogen pollution
influence, these elements being well known as major atmospheric
pollutants. In this way, our method allowed correct evaluation of
the influence of metal without other major disturbance. However,
organic pollutants also accumulated by lichens (Bajpai et al., 2010;
Harmens et al., 2013), were not investigated here. By applying the
frequencies of studied species to these indices, and comparing to
the enrichment factors from Agnan et al. (2015), we observed that
the four more polluted sites (i.e., HET 54a, EPC 08, CHS 35, and PM
72) as evidenced by bioindication, obtained negative scores (i.e.,
dominated by resistant lichen species), while several less contam-
inated sites (e.g., EPC 63 and EPC 74) obtained positive values (i.e.,
dominated by sensitive lichen species).
These preliminary data need to be completed and compared
with additional data from other European forest sites. Thus, it will
be possible to determine the maximum exposure of metal pollu-
tion without significant harmful effects (also called critical load) as
already done for nitrogen (Geiser et al., 2010).
5. Conclusions
This study aimed to evaluate the resistance or sensitivity of
lichen species to atmospheric metal pollution. We performed
eight lichen plottings in French and Swiss forested sites, and
used different biomonitoring approaches (lichen richness, lichen
abundances, lichen community description, ecological features,
bioindication indices, as well as metal bioaccumulation) for a com-
plete environmental description. Each method provided its own
contribution to this investigation; similar results were demon-
strated by lichen communities and ecological features. Ninety-two
corticolous species were sampled, including 70% of crustose
lichens. The abundance was higher on hardwood trees compared to
conifers. The lichen diversity value (LDV) showed a better response
to both ecological disturbances (largely influenced by light and
nutrient conditions, such as eutrophication and pH) and metal pol-
lution compared to the index of atmospheric purity (IAP).
Using a multivariate approach coupling frequencies of each
lichen species and metal bioaccumulation data, we performed an
innovative scale of resistance/sensitivity to metals for the 43 more
frequent lichen species, distinguishing sensitive, intermediate, and
resistant species to metal pollution. To validate these results, we
compared to the few data available in the literature, and checked
any correlation with sensitivity to acid and nitrogen pollution. This
approach constitutes a first insight into the investigation of resis-
tance and sensitivity of lichen species to metals in open forested
sites far from local pollution sources, which should be enhanced by
results with data from other European forests in future researches.
This project benefited from financial support number
1062C0019 from ADEME (French Agency for Environment).
The authors thank Clother Coste for his help in lichen determina-
tion. Yannick Agnan was funded with ADEME fellowship. Thanks
to four anonymous reviewers for their relevant comments that
improved this manuscript.
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... Although most of the research has been conducted in urban and industrial areas (for a review, see [9]) where air pollution represents one of the biggest threats to human health, there are also numerous examples of scientific studies and monitoring programs that adopt the LDV or IAP in forest ecosystems. Most of them concern sites in Europe [7,20,[27][28][29][30][31][32][33][34][35], North America [36,37], and South America [38]. ...
... Most of the lichen bioaccumulation studies carried out worldwide in forests focus on trace elements/metals (e.g., [9,39,87]. In general, relatively low concentrations of these pollutants were monitored in these studies, thus making the results useful for detecting background levels [35,89,92,100]. For example, Cecconi et al. [100], by collecting samples of Pseudevernia furfuracea at remote mountain sites, reported specific background element content (43 elements) for use in reference datasets for biomonitoring applications in the context of three macro-regions of Italy (the western Alps, the eastern Alps plus northern Apennines, and the central and southern Apennines). Similarly, Conti et al. [92] evaluated the bioaccumulation of 26 elements in the fruticose lichen Usnea barbata to define their background levels in the province of Tierra del Fuego (Southern Patagonia, Argentina). ...
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Currently, forest ecosystems are often located in remote areas, far from direct sources of air pollution. Nonetheless, they may be affected by different types of atmospheric deposition, which can compromise their health and inner balance. Epiphytic lichens respond to air pollution and climate change, and they have been widely adopted as ecological indicators, mainly in urban and industrial areas, while forest ecosystems are still underrepresented. However, in recent years, their use has become increasingly widespread, especially in the context of long-term monitoring programs for air pollution in forests. In this review, we provide a critical analysis of the topic from the point of view of the different methodological approaches based on lichen responses adopted in forest ecosystems. Further, we discuss the main challenges posed by the current global change scenario.
... Atmospheric pollution is a highlighted theme due to increasing emissions involving both organic and inorganic substances and particulate matter (PM) (Rai 2016a;Al-Thani et al. 2018;Xie et al. 2018;Swislowski et al. 2020). PM comprises an important class of pollutants that may affect both human health and ecosystems, while also influencing local or even global climate (Agnan et al. 2017;Chaligava et al. 2020;Beringui et al. 2021). Major air pollutant sources are located in urban environments and include industrial and vehicle emissions (Galal and Shehata 2015;Beringui et al. 2021). ...
... Trees are directly associated with air quality, as they act as particle filters and emitters (Terzaghi et al. 2013;Chen et al. 2016;Aponte-aponte et al. 2018). The ability of plants to filter particles make them useful tools concerning air pollution mitigation in urban environments (Dzierzanowski et al. 2011;Agnan et al. 2017;Qiu et al. 2018;He et al. 2020;Swislowski et al. 2020). Although several studies have sought to assess PM impacts on human health, few reports ecosystem effects. ...
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The increasing air pollutant emission, mainly in big cities, attracts significant attention to environmental sciences. Brazil boasts an important tropical forest, the Atlantic Rainforest, and the most important remnants areas are located in the state of Rio de Janeiro. In that regard, this study aimed to assess the impact of particulate matter (PM) emitted by traffic in ecosystems belonging to environmental protection areas (EPAs) near important highways with heavy traffic in southeastern Rio de Janeiro, Brazil. To the best of our knowledge, this is the most comprehensive study on air pollution and its impacts on EPAs in Brazil. PM concentrations (TSP, PM10, and PM2.5) from 14 air quality monitoring stations and meteorological parameters were obtained between 2014 and 2016 near EPAs. It was verified that CONAMA annual standard (Brazilian legislation) was overtake in five monitoring stations and in eight of them CONAMA daily standard was exceed. Wind direction was mainly from urban centers to EPAs, indicating that urban pollutants reach forest remnants in most cases, which may represent ecological risk. In order to guarantee environmental preservation, new studies should be performed to evaluate deeply the effect of air pollutants on fauna and flora of preserved areas.
... They are easy and inexpensive to use and provide results on which human health deductions can be based (Cislaghi and Nimis 1997). Considering the above-mentioned advantages, many countries use bioindication indices based on the diversity and spatial distribution of lichens to estimate air pollution levels (Agnan et al. 2017;Biazrov 2013;Belguidoum et al. 2021;Dron et al. 2016;Jayalal et al. 2016;Kirschbaum et al. 2012;Klymenko 2015;Kricke and Loppi 2002). ...
... These municipalities are located far from sources of pollution. Several authors (Agnan et al. 2017;Khastini et al. 2019) have reported the same observations. ...
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The use of living organisms in air quality monitoring has received increasing attention in recent years. Lichens, pioneer and colonizing organisms, are directly sensitive to environmental changes. This results in a loss of vitality or complete destruction of the thallus. The objective of this study is to assess and map the air quality of the Setif region, Algeria by lichens, using bio-indication indices. For air quality assessment, lichen species frequency, the Shannon-Wiener index, Atmospheric Purity Index (IAP), and Air Quality Index (IAQ) were used. Sixty stations were sampled across the region, representing the various level of landscape factors and anthropogenic activities. The study recorded 54 lichen species in urban areas of Setif belonged to 29 genera and 19 families, of which crustose and foliose, were the most common in the region. The pollution indicators (IAP, IAQ) showed an important correlation between them and a difference between rural and urban ecosystems. Anthropized urban areas showed very high air pollution. This is probably due to industrial and agricultural activities, but especially to gas emissions from vehicles. The majority of studied stations belonged to an area with very high atmospheric pollution. In addition, the presence of the species Xanthoria parietina in all the stations studied facilitates to use it as a reliable biomonitor of plant tolerance to pollutants in urban ecosystems.
... Arabic numbers refer to the sampling site coordinates presented in Table S1. reduce their abundance (Agnan et al., 2017). To keep the advantage of using lichen pollution biosensors in conditions and/or areas where these species are scarce or absent, alternative transplant techniques have been proposed Nannoni et al., 2015;Paoli et al., 2015). ...
This study established a comprehensive picture of airborne metal pollution in the industrial urbanized area of the East of Algiers (Algeria). Thalli of the epiphytic lichen Pseudevernia furfuracea were transplanted from a remote unpolluted forest (Theniet El-Had) to eighteen biomonitoring sites in the Rouiba−Reghaia region exhibiting contrasting anthropogenic activities, including the wooded Reghaia Nature Reserve. Thirty-three metals and rare earths, and Br in lichen thalli were determined after 3 months exposure by X-ray fluorescence and instrumental neutron activation analysis. All biomonitored element concentrations exhibited dramatic increases compared to the control region, and calculation of contamination factor index unveiled that Sb, Pb, Ti, V, Ce, La, Ga, Cr, Cs, Cu, and Cd had the highest contamination levels in almost all the study sites. The degree of ambient pollution was assessed using enrichment factor, pollution load index, cluster analysis, and principal components analysis. A multiple correspondence analysis showed Pb, Sb, and Ga to be highly enriched with heavy contamination in all study sites, even in the Reghaia Nature Reserve.
... Mosses were representative of local throughfall content, since they were enriched in elements from the accumulation of dry deposition inside the canopy, either due to leaching (Mn), direct uptake (Ni), or dry deposition dissolution (Pb, Cu, Cs) contrary to lichens growing on tree barks where transfer was observed only for major elements. Agnan et al. (2017) has improved the bioindication scale using lichens collected in eight distinct French and Swiss forest areas. This also enabled the evaluation of the metal resistance or sensitivity of lichens. ...
This chapter summarizes the current state of knowledge on the impacts of air pollution on terrestrial vegetation in general and in the Mediterranean region. These impacts occur either indirectly through changes in the physical state of the atmosphere, such as increase in the temperature (caused by greenhouse gases), and in the diffuse radiation (caused by aerosols) that reaches vegetation, or directly through phytotoxicity resulting from ozone, sulfur, nitrogen, and other pollutants’ stomatal and non-stomatal uptake by the plants, nutrient balance modification by atmospheric deposition, transfer of plant diseases by aerosols, and pollution by persistent pollutants and metals. Abiotic and biotic stresses can also alter the composition, amounts, and functioning of volatile organic compounds that are emitted by the plants and play known ecological roles. These impacts are summarized, and plant physiological responses to an excess of critical nutrient levels are presented and discussed.KeywordsAir pollution impact on terrestrial vegetationIndirect effectsEffect on solar radiationPhytotoxicityOzone (O3)Sulfur (S)Nitrogen (N)Nitrogen dioxide (NO2)Sulfur dioxide (SO2)Halogens (Cl2, HCl, HF)PesticidesStomatal uptakeNon-stomatal uptakeRoots uptakeNutrient balanceAtmospheric depositionAerosol-transmitted plant diseasesTrace metalsAbiotic stressesBiotic stressesVolatile organic compounds (VOC)EmissionsPhysiological responsesExposureSymptomatologyBiomonitoringEutrophicationAcidificationCritical loadsBiodiversity hotspotReactive oxygen species (ROS)
... Recently it has been demonstrated that lichens are exceptionally compelling life forms as biosensors to distinguish weighty metals in the climate. Detached bio observing with lichens is utilized worldwide as a natural estimating framework for checking air contamination (Agnan, Probst, & Sejalon-Delmas, 2017). Henceforth the variety of lichens has been perceived as a delicate pointer of the organic impacts of pollutants on human wellbeing. ...
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Microbial sensors tend to be systematic units proficient in realizing elements within the conditions owing to the actual explicit biological response from the microorganisms or even their alternatives. The creation of a microbial biosensor needs familiarity along with microbial reaction to the precise analyte. Connecting this particular reaction using the quantitative information, by using a transducer, is the vital part of the erection of the biosensor. Regarding the transducer choice, biosensors are split up into optical biosensors, electrochemical as well as microbial fuel cells. Microbially derived sensors are trouble-free to use, economical, squashed, and convenient. The distinctive | strength along with well-designed characteristics making these remarkably well suited for recognition and supervising associated with an assortment of eco-friendly relevant impurities. Considering the exploit of transgenic E. coli strains, bioluminescence or fluorescence-based biosensors are developed. Microbial fuel cells | allow their use of the heterogeneous microbial populations, separated through sewage. Various microorganisms bring diverse impurities like pesticides, heavy metals, phenolic compounds, organic waste, etc. As an emerging and serious threatening to the environment, these kinds of sensors are needed in this hour. Enzyme-based biosensor employs an enzyme besides dissimilar diagnosis approaches like optical, chemical, and electrical systems to sense ecological impurities. Molecularly imprinted polymers (MIPs) are painstaking like enhanced nanostructured components as advanced nanostructured materials promote the environmental monitoring of toxic pharmaceuticals, which are a serious threat toward the hygienic environment, biosafety, and human health, via highly developed sensing methods such as electrochemical, fluorescence, etc. Bioluminescence-dependent microbial biosensors bring the level of toxicity in addition to bioavailability screening. Biosensing permits the measurement of this attention and the toxic or genotoxic outcomes within the microbes and highly efficient sensing platforms. Biomonitoring of environmental health by biosensors has acquired very much recognition over the previous years. The surroundings are constantly filled with xenobiotics revealed through anthropogenic functions which contaminate ecosystems, getting their condition at an increased risk. Different microbes might act as biosensors to calculate the harmful effects of various pollutants. The following section will certainly have got acquired a lot of consideration within the last few years. The following section will certainly have got acquired a lot of consideration within the last few years. The surroundings are regularly set with xenobiotics introduced by anthropogenic actions which will contaminate ecosystems, placing their reliability at an increased risk. Different microbes could act as biosensors to calculate the adverse effects of assorted pollutants. This part will focus on a diverse group of biosensors and their development from bacteria, small mammals, plant species, and lichens or their counterparts as biosensors for effective environmental monitoring.
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This study attempts to document the lichen species and their distribution in different areas of Kathmandu valley, Nepal. Twenty sampling sites with different degrees of air pollution categorized as disturbed (industrial, heavy traffic and residential areas) and undisturbed areas (clean area) were selected for the study. Sampling was done using the quadrat method. To enumerate the total number of lichen species found in Kathmandu valley, lichen specimens were collected from inside as well as outside the quadrats. A total of 97 species of corticolous lichens belonging to 21 families and 44 genera were recorded from the study sites. Parmeliaceae was the largest family followed by Graphidaceae. The importance value analysis showed that Candelaria concolor (115.2), Dirinaria aegialita, Lepraria sp., Phaeophyscia hispidula var. hispidula and Physcia sorediosa (106.02) are the most common and dominant lichen species in Kathmandu valley. Among the most common and dominant lichen species, Candelaria concolor, Dirinaria aegialita, Phaeophyscia hispidula var. hispidula and Physcia sorediosa were found concentrated in heavy traffic areas whereas Lepraria sp. in the industrial areas. A higher number of lichen species (70%) was recorded in undisturbed areas than in disturbed areas (50%). These study confirm that the distribution of lichen flora is strongly influenced by degrees of pollution. This in turn suggests that lichens can be used as bio indicators of air quality in the Kathmandu valley.
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Air pollution is one of the most important environmental problems for rural, urban and industrial areas. This study assesses the concentrations, the possible interaction with the vegetation conditions and the sources of trace elements in atmospheric aerosol particles. To this aim, a novel holistic approach integrating biomonitoring techniques, satellite observations and multivariate statistical analysis was carried out in a semi-rural area before an on-shore reservoir (crude oil and gas) and an oil/gas pre-treatment plant identified as “Tempa Rossa” (High Sauro Valley—Southern Italy) were fully operative. The atmospheric trace element concentrations (i.e., Al, Ca, Cd, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, S, Ti and Zn) were assessed by “lichen-bag” monitoring. Satellite-derived normalized difference vegetation index (NDVI’) estimates were used to support the identification of environmental imbalances affecting vegetation conditions and linked to possible anthropogenic drivers. Principal component analysis (PCA) allowed identifying both natural and anthropogenic trace element sources, such as crustal resuspension, soil and road dust, traffic, biomass burning and agriculture practices. Empirical evidence highlighted an interaction between NDVI’ and S, Ni, Pb and Zn. The health risk impact of atmospheric trace elements on the exposed population, both adults and children, considering inhalation, ingestion and the dermal contact pathway, highlighted a possible non-carcinogenic risk concerning Ni and a not-negligible carcinogenic risk related to Cr(VI) for the adult population in the study area.
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The utilisation of biological organisms, especially lichens in the environmental biomonitoring approach, has been proven to be an effective and low-cost technique suitable for developing countries like Malaysia. Index of Atmospheric Purity (IAP) tracked compositional changes in lichen communities which correlate with changes in levels of atmospheric pollution. Gunung Jerai was formed during the Cambrian Period; thus, it is a biodiversity hotspot ideal for a diverse range of lichens. In the present work, a total of 44 corticolous lichen species were sampled and identified to evaluate the pollution status of Gunung Jerai using IAP, starting from 80 to 1200 m with 300 m intervals. The samples were collected within 10 × 50 cm sampling grids attached to 60 trees, bringing a total of 120 000 cm² of the sampling area. The air quality of the sampling area was determined by IAP score, a low score indicated by high levels of pollution. Results showed that the lowest IAP score was recorded at 300 m; meanwhile, the highest IAP score was recorded at 900 m elevation. Elevational gradient and pollution have a significant effect on the IAP score of Gunung Jerai. On average, Gunung Jerai is indicated as having a low pollution status. However, several elevations of the rainforest showed high and moderate pollution status. The IAP method is best to assess environmental pollution and provide quicker results than chemical monitoring methods. Further research could be done to evaluate the other sampling sites adjacent to other areas of Gunung Jerai.
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Lichens are symbiotic association between fungus and algae. Lichens have special characteristics such as absorption of gases, water, and nutrients directly from the air which makes this an ideal organism for air biomonitoring; a term used for application of biological organisms to monitor the air quality, and changes in surrounding environment. Absorption of metals in lichens especially occurs using modes such as; intracellular absorption through an exchange process, intracellular accumulation, and entrapments of particles that contain metals (Richardson 1995). During late 1960s and early 70s, a lichen-based mathematical formula (index) was introduced and applied as ‘index of atmospheric purity’ (IAP).
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The long-distance effect of atmospheric pollution on ecosystems has led to the conclusion of international agreements to regulate atmospheric emissions and monitor their impact. This study investigated variations in atmospheric deposition chemistry in France using data gathered from three different monitoring networks (37 stations) over the period from 1995 to 2007. Despite some methodological differences (e.g. type of collector, frequency of sampling and analysis), converging results were found in spatial variations, seasonal patterns and temporal trends. With regard to spatial variations, the mean annual pH in particular ranged from 4.9 in the north-east to 5.8 in the south-east. This gradient was related to the concentration of NO3- and non-sea-salt SO42- (maximum volume-weighted mean of 38 and 31 μeq l−1 respectively) and of acid-neutralising compounds such as non-sea-salt Ca2+ and NH4+. In terms of seasonal variations, winter and autumn pH were linked to lower acidity neutralisation than during the warm season. The temporal trends in atmospheric deposition varied depending on the chemical species and site location. The most significant and widespread trend was the decrease in non-sea-salt SO42- concentrations (significant at 65% of the stations). At the same time, many stations showed an increasing trend in annual pH (+0.3 on average for 16 stations). These two trends are probably due to the reduction in SO2 emissions that has been imposed in Europe since the 1980s. Temporal trends in inorganic N concentrations were rather moderate and not consistent with the trends reported in emission estimates. Despite the reduction in NOx emissions, NO3- concentrations in atmospheric deposition remained mostly unchanged or even increased at three stations (+0.43 μeq l−1 yr−1 on average). In contrast NH4+ concentrations in atmospheric deposition decreased at several stations located in western and northern areas, while the estimates of NH3 emissions remained fairly stable. The decrease in non-sea-salt SO42- and NH4+ concentrations was mainly due to a decrease in summer values and can in part be related to a dilution process since the precipitation amount showed an increasing trend during the summer. Furthermore, increasing trends in NO3- concentrations in the spring and, to a lesser extent, in NH4+ concentrations suggested that other atmospheric physicochemical processes should also be taken into account.
Literature on metals, particularly heavy metals, in lichens is reviewed including mechanisms of metal uptake, retention, toxicity and tolerance. Interspecies differences in sensitivity are discussed as well as the development and nature of extreme tolerance encountered in certain taxa.