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REPLACE WITHCORRECT JOURNAL
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BLACK ON WHITE
Cutting-Edge Research on Environmental Processes & Impacts
Journal of Environmental Monitoring
ISSN 1464-0325
www.rsc.org/jem Volume 11 | Number 4 | April 2009 | Pages 693–896
Volume 11 | N
umber 4 | 2009
Journal of Environmental M
onitoring Them
ed Issue: Quality A
ssurance in Ecological Monitoring
Pages 693–896
Giordani et al. Rapid biodiversity assessment
Marchetto et al. QA/QC monitoring in forests
Poole et al.Modelling sorption of VOCs
Oller et al.Workplace nickel exposures 1464-0325(2009)11:4;1-5
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Photosynthetic energy conversion: natural and artificialJames Barber
Single Nanowire photovoltaicsBozhi Tian, Thomas J. Kempa and Charles M. Lieber
Multifunctional 3D nanoarchitectures for energy storage and conversionDebra R. Rolison, Jeffrey W. Long, Justin C. Lytle, Anne E. Fischer, Christopher P. Rhodes, Todd M. McEvoy, Megan E. Bourg and Alia M. Lubers
Photodriven heterogeneous charge transfer with transition-metal compounds anchored to TiO2 semiconductor surfacesShane Ardo and Gerald J. Meyer
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Themed Issue: Quality Assurance in Ecological Monitoring
PAPER www.rsc.org/jem | Journal of Environmental Monitoring
Rapid biodiversity assessment in lichen diversity surveys: implications forquality assurance†‡
Paolo Giordani,*a Giorgio Brunialti,b Renato Benesperi,c Guido Rizzi,a Luisa Fratib and Paolo Modenesia
Received 14th October 2008, Accepted 5th February 2009
First published as an Advance Article on the web 3rd March 2009
DOI: 10.1039/b818173j
Rapid Biodiversity Assessments (RBAs) of lichen communities, obtained by means of simplified
sampling lists based on morphospecies, showed good correlations with Lichen Diversity Values
(LDVs), based on the complete identification of lichen species only when performed by operators with
high levels of taxonomic knowledge. Furthermore, the use of highly simplified sampling lists did not
lead to significant advantages in terms of time needed for field operations. This approach proved to be
especially unreliable in high diversity ecological contexts where variation of morpho-structural
composition within lichen communities is frequent (i.e. co-occurring crustose- and foliose-dominated
communities); it may also lead to weak results if applied for conservation purposes. Hence, the use of
simplified RBA sampling lists in lichen monitoring has to be carefully evaluated and, in any case,
should be based on sound taxonomic knowledge on the part of those in charge of data collection. The
proper assessment of descriptors of lichen abundance and/or frequency, however, strictly depends on
the skill, taxonomic knowledge, and willingness to learn of the lichenologist-in-training.
Introduction
The concept of biodiversity includes numerous structures and
processes interacting at different spatial and temporal scales and
at different levels of functional organization, from genetic to
ecosystemic. In general, the higher the level of detail required, the
greater the need to use experienced personnel for taxa determi-
nation. For these reasons, those in charge of data collection can
influence the quality of such data, most of all in methods where
high levels of taxonomic knowledge are required.1 This influence
depends both on the taxonomic accuracy desired for (bio)-
monitoring programs, and on their spatial scale, since familiarity
with local biodiversity may affect the results.2
Especially in the case of large-scale biodiversity assessments,
which require large numbers of people for data sampling or post-
sampling data processing, it is not always possible to have an
adequate number of specialists at one’s disposal.3,4
In this regard, Wilkie et al.3 observed that in large-scale
invertebrate biodiversity assessments, the post-sampling
processing requires considerable effort for the sorting, the iden-
tification and the organisation of the material. The time demand
aBotanic Centre ‘Hanbury’, DIP.TE.RIS., University of Genova, corsoDogali 1M, I-16136 Genova, Italy. E-mail: [email protected];Fax: +390102099363; Tel: +390102099362bTerraData environmetrics, Department of Environmental Sciences,University of Siena, Via P.A. Mattioli 4, I-53100 Siena, Italy. E-mail:[email protected]; [email protected]; Web: www.terradata.it; Fax:+390577232896; Tel: +390577235415c3 Department of Plant Biology, University of Firenze, Via La Pira 4,I-50121 Firenze, Italy. E-mail: [email protected]
† Presented at TerraData Environmetrics 2008, a recent workshop onQuality Assurance in Ecological Monitoring held on the 7 March 2008,Siena, Italy.
‡ Electronic supplementary information (ESI) available: Appendices 1(simplified sampling lists) and 2 (glossary of the main lichenologicalterms). See DOI: 10.1039/b818173j
730 | J. Environ. Monit., 2009, 11, 730–735
on specialists for these tasks would have severe financial impli-
cations for any project.4 Thus, it is possible to reduce costs in this
phase of the project by reducing the role of specialists. This is
why several authors5–8 have proposed the use of Rapid Biodi-
versity Assessment (RBA) methods that imply a reduction of
taxonomic determination to simplified groups of species,
providing a possible short cut in assessing total species richness.
A variety of strategies5,6 have been developed for rapid post-
sampling processing of data, such as sorting to higher taxonomic
level only (‘taxonomic sufficiency’) or employing non-specialist
technicians to separate specimens into informal groups based on
obvious external characteristics (morphospecies). Oliver and
Beatty9 found that the results obtained by technicians using
a simplified approach were very similar to those obtained using
species data, and concluded that identification errors were
insufficient to affect results and conclusions.
Among highly specialized methods, the recently standardized
lichen biomonitoring method10,11 is based on the well known
sensitivity of epiphytic lichens to environmental changes, mainly
atmospheric pollution, but also forest continuity and habitat
fragmentation (for a review see ref. 12). In order to improve data
quality, European guidelines for assessing lichen diversity10,11,13
have gone through a standardization process, during which two
main goals have spawned a number of changes in sampling
procedures:
– Reduce sampling errors: new sampling grid, to reduce
subjectivity and sample the entire tree trunk; studies have been
carried out in order to evaluate the most cost-effective sampling
density for estimating population parameters as well as for map
reliability;14 standardized method for the selection of trees on
which the sampling is to be carried out.
– Reduce non-sampling errors (or observer errors): training
and harmonization of the personnel in charge of data collection,
QA procedures and field checks on reproducibility of data.15
This journal is ª The Royal Society of Chemistry 2009
This method requires very high levels of taxonomic knowledge
of personnel in charge of field work, since the protocol’s
sampling design is based on the assessment of frequency and
abundance of all lichen species, including groups of lichens that
are difficult to identify in the field, such as crustose lichens.10 As
a consequence, this approach is very expensive in terms of time
employed in species determination.
The aim of this work is to compare the results obtained by
non-specialists through simplified methods based on morpho-
species (Rapid Biodiversity Assessment), with the data obtained
by specialists using the Lichen Diversity Value (LDV) method,10
and thus identifying all the species in the sampling grid. Twenty-
four non-specialists were submitted to ring tests in which they
assessed lichen diversity within twelve sampling stations located
in a natural area and characterized by high lichen diversity.16,17
Study area
The field study was performed in Aveto Regional Park, located
in the Ligurian Apennines (NW Italy), within the sub-Mediter-
ranean vegetation zone. The area has no sources of anthropo-
genic impact such as air pollution17 and it is mainly characterized
by highly fragmented deciduous forests of Castanea sativa,
Fraxinus excelsior, Quercus cerris and Q. pubescens within an
agricultural landscape.
Topography is hilly to mountainous with elevations ranging
from ca. 600 m to the higher peaks of the Ligurian Apennines
such as Mount Maggiorasca (1,799 m) and Mount Penna
(1,735 m).
The area is among the rainiest of the Apennines, with average
annual rainfall around 2,300 mm; snowfall starts towards the end
of October and continues until the following year. The average
annual temperature is 9.2 �C; during the year there are no periods
of dryness, and fog is rather frequent. Overall, this is a typical
Apennine temperate climate, with strong suboceanic to oceanic
characteristics.
Materials and methods
A total of twelve sampling sites was selected within three habitats
(Table 1): rural areas, oak woods and mature chestnut woods.
Four trees were sampled in each station (with the exception of
station 11 with only 3 trees); the trees belonged to species
with subacid bark (mainly Castanea sativa, Fraxinus excelsior
and Tilia spp., but also on two specimens of Juglans regia and
Aesculus hippocastanus), with trunks that were neither damaged
nor irregular, and having a circumference greater than 70 cm, in
accordance with the guidelines.18,10,11
Sampling of lichen diversity was carried out using four 10 �50 cm grids, split up into 5 squares, placed at the four cardinal
points (N, S, E, W), according to ref. 10. Sampling grids were
positioned at a height of 100 cm above ground level. The sum of
the frequencies of the lichen species occurring within the four
grids is the Lichen Diversity Value (LDV).
Quality control and evaluation
Twenty-four non-specialists sampled eight trees each (2 sampling
stations), using two sampling lists with different simplification
levels (Appendix 1‡): RBA-A with 22 morphospecies and
This journal is ª The Royal Society of Chemistry 2009
RBA-B with 47 morphospecies. Both sets of morphospecies were
defined on the basis of the following macroscopic lichen char-
acters (a brief glossary is provided in Appendix 2.‡ For further
details see Purvis et al.,19):
– growth form (foliose, fruticose or crustose thallus);
– thallus colour;
– presence of sexual (apothecia and perithecia) and/or vege-
tative (soralia and isidia) reproductive structures.
Non-specialists followed a one month training course on
lichen taxonomy, which included identifications in lab and in the
field with a final test. The sampling grids were placed by a group
of expert lichenologists on the bole of selected trees. Each
operator sampled the trees without any diagnostic key or
chemical test.
Sampling operations were repeated by a group of four expert
lichenologists (control group), using both the two simplified
sampling lists and the Lichen Diversity Value method.10
Results obtained by each of the non-specialists were compared
with the scores of the experts. Differences in LDV counts
between the two were assessed using accuracy,15 defined as the
percentage deviation of data collected by the non-specialists from
that of the control group. It is calculated as:20
accuracy % ¼ 100 � [100 � (1 � non spec/exp)] (1)
where ‘‘non spec’’ is the LDV value scored by the non-specialist
and ‘‘exp’’ the count scored by the group of experts on the same
tree.
Besides quantitative accuracy, calculated on LDV values,
taxonomic accuracy was also assessed, using the number of
morphospecies recognized in the grid.
Statistical analysis was carried out using STATISTICA 6.0.21
Pearson r correlation coefficient was used to check relationships
between simplified lists vs. LDV and between specialist and
non-specialists. Levels for statistical significance were set at p <
0.05 and p < 0.01. Specimens of critical species, which are diffi-
cult to recognize in the field, were collected for later identifica-
tion. Nomenclature, ecological and distributional information
on lichen species follow the on-line checklist of Italian lichens.22
Results and discussion
The use of guilds or morphological groups as indicators of
changes in ecosystem function has been considered by several
authors23,24 as a good compromise between the need for speci-
alised knowledge and rapid field procedures employing non-
specialist technicians. In particular, McCune et al.,2 observed
that random subsamples of species (community level) tend to
produce the same patterns as the complete data set. If, on the
other hand, groups of species are defined by their growth forms
(e.g. foliose, fruticose, crustose lichens) or their ecology
(epiphytic, epilythic lichens), then different patterns of biodi-
versity and compositional gradients will be observed.
Estimating lichen diversity with RBA
Use of RBA by specialists. The results of lichen diversity
sampling by expert lichenologists using RBA-A and RBA-B lists
(Table 1) show a high predictivity of the ‘true’ LDV value
J. Environ. Monit., 2009, 11, 730–735 | 731
Table1
Res
ult
so
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7)
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(n¼
12
)
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732 | J. Environ. Monit., 2009, 11, 730–735 This journal is ª The Royal Society of Chemistry 2009
Table 2 Correlation between lichen diversity (LDV) and diversity recorded by morphospecies using simplified sampling lists in three different habitats
Habitat nRBA-A RBA-B
RBA-A,macrolichensonly
RBA-B,macrolichensonly RBA-A RBA-B
RBA-A,macrolichensonly
RBA-B,macrolichensonly
Control (expert) Non expert
Rural areas 19 0.953b 0.971b 0.493a 0.411 0.678b 0.702b 0.256 0.150Oak woods 12 0.942b 0.996b 0.850b 0.872b 0.727b �0.069 0.800b 0.288Mature chestnut woods 16 0.933b 0.971b 0.656b 0.744b 0.281 �0.169 0.403 �0.157
a p < 0.05. b p < 0.01.
(Pearson r; p < 0.01), with correlation values always higher than
0.90 (Table 2). Hence, the use of the two sampling lists could be
recommended, being representative of the total lichen diversity
on the selected trees. However, when considering macrolichens
alone the correlation sharply decreases with a considerable lack
of information (predictivity < 40%), depending on the percentage
of crustose lichens in the sampled lichen communities.
As a matter of fact, the exclusion of crustose species from RBA
sampling lists would save some time in terms of fieldwork and,
especially, of post-sampling procedures such as the identification
of critical species. However, the trade-off between the deriving
loss of information and the predictivity of simplified sampling
lists RBA-A and RBA-B (with macrolichens only) is not optimal,
as the outcomes are not significantly better then those obtained
taking into account all growth forms.
Use of RBA by non experts. Very low values of correlation
among RBA-A and RBA-B, and the ‘true’ value of the LDV
were found for non-experts, showing that the simplified method
does not yield good estimates of LDVs. Higher correlations with
LDVs were obtained in rural areas (both RBA-A and RBA-B
lists) and in oak woods (RBA-A only). These results could be
explained by the predominance, in these two habitats, of
common species, easily recognisable by non-experts.
The overall results suggest that the use of simplified methods
for the assessment of lichen diversity should be used only by
expert lichenologists, who easily distinguish species from one
another and can obtain reliable estimations of diversity.
These results are confirmed by the low correlations among
RBA-A/B considering macrolichens alone and LDV for most of
the sampled trees (Pearson r < 0.500; p > 0.05). High correlations
were observed in oak wood stations only (Pearson r ¼ 0.800;
p < 0.01), probably for the same reasons put forth for the
complete sampling lists.
Hence, in this study, despite the considerable level of simpli-
fication, RBA leads to inconsistent assessments of lichen diver-
sity through gradients of taxonomic knowledge of those in
charge of data collection, and of environmental complexity, so
Table 3 Percentage accuracy (equation 1) of LDV and RBA values(RBA-A ¼ rapid biodiversity assessment by means of simplified samplinglist A;. RBA-B ¼ rapid biodiversity assessment by means of simplifiedsampling list B). Values were calculated at tree level (n ¼ 47)
Sampling list % Quantitative accuracy % Taxonomic accuracy
LDV 68.6 � 14.8 28.9 � 14.9RBA-A 67.4 � 10.4 47.4 � 16.5RBA-B 55.7 � 19.0 20.4 � 9.6
This journal is ª The Royal Society of Chemistry 2009
that it seems scarcely reliable for both spatial and, especially,
long term monitoring of lichen diversity.
Data quality
Percentage accuracy of the non-specialists was calculated for the
three sampling lists (Table 3). Quantitative accuracy is similar for
all of these, ranging from 55.7% for RBA-B to 68.6% for LDV.
Although it is worth noting that the best result was obtained
with the complete species list, only a few operators reached
the Measurement Quality Objectives (MQO) for quantitative
accuracy, set at 75%,15 and scored a satisfactory result. This can
probably be explained by the non-specialists’ lack of field expe-
rience, and their consequent inability to distinguish similar
species; these results are nonetheless in line with the average
accuracy obtained in similar biomonitoring intercalibration
tests, not only for the LDV technique,15 but also for the visual
assessment of leaf damage injury induced by ozone in indicator
tobacco plants.25 The average percentage accuracy in taxonomic
identification was rather low, ranging from 20.4% (RBA-B) to
47.4% (RBA-A). The MQO, set at 65%15 was not reached in any
test and, again, a clear trend of increasing accuracy along the
gradient of increasing complexity of the sampling lists was not
detectable.
Time needed for fieldwork
Non-expert personnel took more than 1 hour to sample each grid
with the detailed RBA list (RBA-B), and about 36 minutes with
the simplified one (RBA-A). On average, the LDV assessment
was carried out by the same operators in more than 85 minutes
(Table 4).
On average, the experts’ group took more or less 10 minutes
for each sampling grid, regardless of the sampling list.
Furthermore, for both groups and regardless of the sampling
list, the differences between sampling high vs. low diversity sites
were non-significant.
The experts had good knowledge of taxonomy and were able
to easily identify at least the most common species: in many cases
it was harder for them to locate a known scientific binomium in
an artificial morpho-functional group (i.e. RBA sampling lists).
On the other hand, a minimum of time for basic fieldwork
(e.g. filling in the field sampling list) was needed for any kind of
taxonomic level of assessment.
Non-expert personnel spent much time trying to understand
the differences and the similarities among taxa and a comparable
amount of time trying to improve the result. Probably because
of scarce taxonomic knowledge, this high attention in the
observation of specimens did not necessarily imply a better result
J. Environ. Monit., 2009, 11, 730–735 | 733
Table 4 Time (minutes) required for each 10 � 50 cm sampling grid, using different sampling lists
Expert Non-expert
LDV RBA-A RBA-B LDV RBA-A RBA-B
Whole dataset a 10 � 4 7 � 3 9 � 4 85 � 15 36 � 9 64 � 19Low diversity samples b 10 � 4 5 � 2 7 � 4 80 � 16 36 � 11 60 � 23High diversity samples c 11 � 4 8 � 3 11 � 4 91 � 12 37 � 8 68 � 13
a (n ¼ 47). b (LDV < median; n ¼ 23). c (LDV > median; n ¼ 24).
in terms of data quality: in fact, quantitative and taxonomic
accuracy of RBA-A and RBA-B by the non-specialists are
severely lacking and, in some cases, comparable with LDV
accuracy (Table 3).
Theoretical and applicative issues
The results of this case-study point to three important goals
for future research and application in the field of lichen
biomonitoring:
Preventing information loss
Even basic monitoring surveys should not just be routine studies,
but should provide new data, to be effective in suggesting proper
management strategies of environmental resources.26 The risk in
using artificial morphospecies is to lose a (probably still
unknown) quantity of information on the variations of lichen
community composition under dynamic ecological scenarios.
Several works on lichen flora shifts due to decreasing SO2
concentrations and increasing nitrogen pollution confirm the
relevance of such information (see e.g. van Herk27). Because of
lack of knowledge about the autoecology of target species all the
available information should be gathered, if sufficient confidence
is to be placed in present and future assessments of the effects
of anthropogenic disturbances, using biomonitors. Bergamini
et al.28 provided interesting results in this sense. Looking at
a large range of land-use intensity throughout Europe, they
obtained significant predictions of total lichen species richness
using estimates of macrolichen diversity. They argued that both
macrolichen and crustose species assemblages responded to the
same main land-use gradient and habitat alterations. Neverthe-
less, they agree with the results of this study when they noted that
the relationships between the species richness of macrolichens
and crustose lichens explained a rather low amount of variation
and the predictions were highly imprecise.
Preserving data quality
This study shows that the budget of non-sampling error due to
the uneven taxonomic knowledge of those carrying out the
sampling is considerably high; the crucial importance of allowing
for Quality Assurance programmes in each biomonitoring survey
seems thus confirmed, while the simplistic equation ‘‘less taxo-
nomic detail equals greater data quality’’ seems far from true.
According to Beattie and Oliver6 a basic advantage of taxonomic
minimalism is that resources can be allocated to replication
rather than identification, relieving researchers of the trade-off
between paying for taxonomic expertise and paying for
734 | J. Environ. Monit., 2009, 11, 730–735
additional sampling. In this study, despite the considerable level
of simplification, RBAs led to inconsistent assessments of lichen
diversity between different levels of taxonomic knowledge and
of environmental complexity, so that it seems scarcely reliable
for spatial and, especially, long term temporal monitoring
programmes, even if a better performance of RBA was often
reported in areas with high anthropogenic impact.28
Data quality and cost-effectiveness do not depend solely on
taxonomic detail. Beattie and Oliver6 argued that if formal
identification is not required, resources may be concentrated on
replication and increasing sample sizes. Although different
groups of organisms may call for different sampling techniques,
this statement may be thus amended: when assessing lichen
diversity, a formal identification is strongly recommended, and
resources may be saved by optimizing replications and sample
size, on the basis of proper investigation on data variability (see
e.g. Ferretti et al.14).
Reducing the gap between specialists and non-specialists
As observed by several authors,29–31 the morphospecies approach
appears to be successful where taxonomy is poorly known and
keys to species are inadequate. In the case of epiphytic lichens,
some other good points support the choice of an all-inclusive,
classic systematic approach vs. Rapid Biodiversity Assessment.
– Even if identification is often difficult (many microscopical
characters are requested, especially for crustose species), the
number of species is rather low (e.g. about 2,500 for Italy), if
compared with other crucial groups of organisms (e.g. insects).
– The number of species one must be able to recognise is
greatly reduced in the disturbed areas where biomonitoring
surveys are usually carried out, since their local epiphytic floras
rarely exceed 60 species; these may indeed be managed by non-
specialists with a sufficient level of quality. Previous studies15,32
have shown how after a one-week introductory course and some
months of fieldwork, both quantitative and taxonomic accura-
cies of non-experts increase and reach the MQOs.
– Powerful, tested electronic keys for lichens have been
recently developed, based on alternative criteria of selection of
core morphological characters (e.g. Nimis and Martellos22), and
may further reduce the gap between specialists and non-
specialists when using a classic taxonomic approach.
On the other hand, it seems likely that taxonomic minimalism
could help in some circumstances (e.g. large scale monitoring
programmes or biological conservation in poorly known areas),
in order to avoid biased diversity estimates due to uneven
experience among field workers.28 Nevertheless, in such cases, it
may be preferable to limit the investigation to well-known, easily
This journal is ª The Royal Society of Chemistry 2009
recognizable species, and still use the classic systematic approach,
rather than misuse simple but potentially misleading Rapid
Taxonomic Units. Bergamini et al.28 noted that for conservation
decisions neither richness of macrolichens nor richness of genera
may be an adequate parameter and it is best to optimize strategies
finding an acceptable trade-off between of representative samples
and their cost in resource-limited situations.
Conclusions
Lichen Rapid Biodiversity Assessments, obtained by means of
simplified sampling lists based on morphospecies, showed good
correlations with the results of a classical, systematic identifica-
tion of species only when performed by operators with high
taxonomic knowledge. Furthermore, the use of sampling lists
based on highly simplified morphospecies did not lead to
significant advantages in terms of time needed for field work.
This approach seems to be unreliable in high diversity ecological
contexts where the morpho-structural composition differs within
lichen communities (i.e. co-occurring crustose- and foliose-
dominated communities); it may also lead to weak results if
applied for conservation purposes. In fact, although lichen
bioindication methods are mainly used for the estimation of
atmospheric pollution, forest continuity and habitat fragmenta-
tion assessment are other frequent goals of lichen monitoring (for
a review see Nimis et al.12).
Hence, the use of simplified RBA sampling lists in lichen
monitoring must be carefully evaluated, and should always rely
on sufficient levels of taxonomic knowledge during field work.
The results show that the proper assessment of descriptors of
lichen abundance and/or frequency, strictly depends on the skill,
the taxonomic knowledge, as well as on the willingness to learn of
the non-specialist. Although some trainees are strongly moti-
vated, most of them need to feel that biodiversity assessment is
important.2 It is obvious that improving the taxonomic training
of field observers can significantly reduce the negative bias in
estimating species richness.
Even when only particularly critical groups (e.g. Lecanora
subfusca group) are treated at an over-specific level, the use of
simplified lists should nonetheless be carefully considered as the
loss of information may not be compensated by a significant
simplification of field work.
Given that the level of accuracy of a project strictly depends on
the final aim of the monitoring programme, the results of this
study suggest that robustness of lichen diversity data at within-
plot level should be maximised, while the required cost-effec-
tiveness should then be reached managing the sampling error at
between-plot level, with a careful and objective evaluation of the
sampling density.
References
1 M. Kov�acs, Herbaceous (flowering) plants, in Biological indicators inenvironmental protection, ed. M. Kov�acs, Ellis Horwood, New York,1992.
2 B. McCune, J. P. Dey, J. E. Peck, D. Cassel, K. Heiman, S. Will-Wolfand N. Neitlich, Repeatability of community data: species richnessversus gradient scores in large-scale lichen studies, Bryologist, 1997,100, 40–46.
This journal is ª The Royal Society of Chemistry 2009
3 L. Wilkie, G. Cassis and M. Gray, Biodivers. Conserv., 2003, 12,121.
4 S. A. Marshall et al., Bull. Entomol. Soc. Can., 1994, 12, 1.5 I. Oliver and A. J. Beattie, Conservation Biol., 1996, 10, 99–109.6 A. J. Beattie and I. Oliver, Trends Ecol. Evol., 1994, 9, 488–490.7 J. T. Kerr and M. Ostrovsky, Trends Ecol. Evol., 2003, 18, 299–
305.8 T. M. Caro and G. O’Doherty, Conservation Biol., 1998, 13, 805–814.9 I. Oliver and A. J. Beattie, Conservation Biol., 1993, 7, 562–568.
10 J. Asta, W. Erhardt, M. Ferretti, F. Fornasier, U. Kirschbaum,P. L. Nimis, W. Purvis, S. Pirintsos, C. Scheidegger, C. VanHaluwyn and V. Wirth, (2002). Mapping lichen diversity as anindicator of environmental quality. In P. L. Nimis, C. Scheidegger& P. Wolseley (eds.), Monitoring with Lichens–Monitoring Lichens,(273–281). Dordrecht: Netherlans.
11 ANPA (2001). I.B.L.: Indice di Biodiversit�a Lichenica. Serie Manualie Linee Guida, 2/2001. ANPA, Roma.
12 P. L. Nimis, C. Scheidegger and P. A. Wolseley, Monitoring withlichens-monitoring lichens: An Introduction, in Monitoring withLichens-Monitoring Lichens, ed. P. L. Nimis, C. Scheidegger, and P.A. Wolseley, Nato Science Program-IV, vol. VII, Kluwer AcademicPublisher, The Netherlands, 2002, pp. 408.
13 VDI, Biological measurement measure procedures for determining andevaluating the effects of ambient air pollutions by means of lichens(bioindication). Verein Deutscher Ingenieure – Richt LinienVDI3957-blatt 13. D€usseldorf, 2005.
14 M. Ferretti, E. Brambilla, G. Brunialti, F. Fornasier, C. Mazzali,P. Giordani and P. L. Nimis, Environ. Pollut., 2004, 249–256.
15 G. Brunialti, P. Giordani, D. Isocrono and S. Loppi, Environ. Monit.Assess., 2002, 13, 271–280.
16 G. Brunialti, P. Giordani and M. Tretiach, Not. Soc. Lich. Ital., 1999,12, 11–21.
17 P. Giordani, G. Brunialti and D. Alleteo, Environ. Pollut., 2002, 118,53–64.
18 P. L. Nimis, M. Castello and M. Perotti, Lichenologist, 1990, 22, 333–344.
19 O. W. Purvis, B. J. Coppins, D. L. Hawksworth, P. W. James andD. M. Moore, The lichen flora of Great Britain and Ireland, NaturalHistory Museum Publications, London, 1992.
20 Forest Health Monitoring 1994 Field Methods Guide, EPA/620/R-94/027, ed. N. G. Tallent-HassellU.S. Environmental ProtectionAgency, Washington D.C, 1994.
21 StatSoft Italia (2001). STATISTICA (sistema software di analisi deidati), versione 6. www.statsoft.it.
22 P. L. Nimis and S. Martellos, ITALIC – The information system onItalian lichens, 2008.
23 S. Will-Wolf, C. Scheidegger and B. McCune, Methods formonitoring biodiversity and ecosystem function, in Monitoring withLichens – Monitoring Lichens, ed. P. L. Nimis, C. Scheidegger andP. A. Wolseley, Kluwer Accademic Publishers, Dordrecht, 2002,147–162.
24 I. Oliver and A. J. Beattie, A possible method for the rapid assessmentof biodiversity, in Systematics and Conservation Evaluation, ed. P. L.Forey, C. J. Humphries, and R. L. Vane-Wright, Clarendon Press,Oxford, 1994, pp. 133–136.
25 G. Lorenzini, Environ. Monit. Assess., 2000, 62, 175.26 C. L. Elzinga, D. W. Salzer, J. W. Willoughby and J. P. Gibbs,
Monitoring Plant and Animal Population, Blackwell Science,Malden, Massachussets, USA, 2001.
27 C. M. van Herk, Monitoring van epifytische korstmossen in deprovincie Utrecht, 1979–2001, LON, Soest, 2002.
28 A. Bergamini, C. Scheidegger, S. Stofer, P. Carvalho, S. Davey,M. Dietrich, F. Dubs, E. Farkas, U. Groner, K. K€arkk€ainen,C. Keller, L. L€ok€os, S. Lommi, C. M�aguas, R. Mitchell, P. Pinho,V. J. Rico, G. Arag�on, A. M. Truscott, P. Wolseley and A. Watt,Conserv Biol, 2005, 19, 1051–1062.
29 A. V. Z. Brower, Trends Eco. Evol., 1995, 203.30 A. J. Beattie and I. Oliver, Trens Ecol. Evol., 1995, 488.31 I. C. Campbell, Trends Ecol. Evol., 1995, 203.32 G. Brunialti, P. Giordani and M. Ferretti, Discriminating between the
good and the bad: quality assurance is central in biomonitoringstudies, in Environmental Monitoring, ed. G.B. Wiersma, CRCPress, 2004, 443–464.
J. Environ. Monit., 2009, 11, 730–735 | 735
