![List of the common species in the dataset, with their relative values of agreement among the different surveys and the pairwise comparison between the three team categories. The names of the species with significant differences among teams are reported in bold. Nomenclature according to Nimis [43].](https://www.researchgate.net/publication/331881225/figure/tbl2/AS:738338155802626@1553044942482/List-of-the-common-species-in-the-dataset-with-their-relative-values-of-agreement-among_Q320.jpg)
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diversity
Article
Do Different Teams Produce Different Results in
Long-Term Lichen Biomonitoring?
Giorgio Brunialti 1, Luisa Frati 1, Cristina Malegori 2, Paolo Giordani 2, * and Paola Malaspina 2
1TerraData srl environmetrics, Spin-off dell’Universitàdi Siena, 58025 Monterotondo Marittimo (GR), Italy;
brunialti@terradata.it (G.B.); frati@terradata.it (L.F.)
2DIFAR, University of Genova, 16148 Genova, Italy; malegori@difar.unige.it (C.M.);
paola.malaspina@edu.unige.it (P.M.)
*Correspondence: giordani@difar.unige.it
Received: 20 February 2019; Accepted: 14 March 2019; Published: 19 March 2019
Abstract:
Lichen biomonitoring programs focus on temporal variations in epiphytic lichen
communities in relation to the effects of atmospheric pollution. As repeated surveys are planned at
medium to long term intervals, the alternation of different operators is often possible. This involves
the need to consider the effect of non-sampling errors (e.g., observer errors). Here we relate the trends
of lichen communities in repeated surveys with the contribution of different teams of specialists
involved in sampling. For this reason, lichen diversity data collected in Italy within several ongoing
biomonitoring programs have been considered. The variations of components of gamma diversity
between the surveys have been related to the composition of the teams of operators. As a major
result, the composition of the teams significantly affected data comparability: Similarity (S), Species
Replacement (R), and Richness Difference (D) showed significant differences between “same” and
“partially” versus “different” teams, with characteristics trends over time. The results suggest a more
careful interpretation of temporal variations in biomonitoring studies.
Keywords: lichens; air pollution; Lichen Diversity Value (LDV); gamma diversity
1. Introduction
Given their strict dependence on the atmosphere for water and mineral supply [
1
], lichens are
extremely sensitive to substances that alter the atmospheric composition. Their occurrence is therefore
modulated by pollution levels, thus justifying their wide use as bioindicators of air pollution [
2
].
Among other biomonitoring methods, the Lichen Diversity Value (LDV) grounds on the assessment
of any change in the frequency and abundance of all epiphytic lichen species [
3
–
5
]. Though it was
originally developed for investigating the effects of phytotoxic gases, such as SO
2
and NO
x
[
5
–
8
],
methods based on the assessment of lichen diversity have also been extensively applied for detecting
the sustainability of forest management [
9
–
13
], estimating the impact of disturbances related to land
use change [14–16], and monitoring local- and large-scale effects of climate change [17–21].
Although the disturbances reported above have different drivers, they all cause changes in the
diversity, abundance, and composition of epiphytic lichen communities. As lichens are slow-growing
organisms, they can be used as long-term biomonitors and the potential trends of the biological
effects caused by environmental changes can be monitored by repeated measures over time [
22
,
23
].
The extent, and spatial and temporal range of these changes are related to the level and type of
impact produced, considering environmental background. Regardless of the cause, the changes can be
assessed by monitoring variations in lichen communities in subsequent samplings, taking into account
the situation observed at a reference time. Although these changes are classically measured in terms
of diversity and abundance (e.g., Lichen Diversity Value—LDV [
5
]), recent works have shown that
Diversity 2019,11, 43; doi:10.3390/d11030043 www.mdpi.com/journal/diversity
Diversity 2019,11, 43 2 of 17
variations can also be described by components of the gamma diversity, such as species replacement,
richness difference, and similarity [24,25].
The temporal trend of the diversity of the epiphytic lichen communities is however determined
by various factors that can interact in an additive or multiplicative way, often making a robust
interpretation of the observed data difficult [
26
]. Among them: (1) changes induced by the temporal
variation of the disturbing factors that affect the study area (e.g., increase or decrease in atmospheric
pollution) [
2
]; (2) changes in composition and specific abundance due to the natural succession of the
communities [
27
]; (3) apparent variations in the diversity due to sampling errors [
22
,
26
,
28
] (i.e., due to
the fact that in subsequent surveys the same sampling units and / or the same trees are not always
detected); (4) changes in perceived diversity due to non-sampling errors, including the operator effect
(i.e., due to the fact that, under the same conditions, different people can identify different species or
even overlook some lichen species) [29].
Normally the first factor of variation (effects caused by disturbances) is the target of biomonitoring
studies, while natural succession is often intrinsically taken into account by the assumption that natural
variations can develop randomly throughout a study area. As far as the sampling error is concerned, it
is often minimized by the maintenance of the same sampling units in long term studies [6].
The operator effect (non-sampling error) has been tackled in numerous studies that have
tried to evaluate it on the basis of intercalibration tests between individual operators or groups
of operators
[29–33]
. These tests represent basic activities for the assessment of quality assurance [
34
].
As lichen biomonitoring is based on the identification of all epiphytic lichen species within a sampling
grid, it requires very high levels of taxonomic knowledge [
6
,
29
–
32
,
35
,
36
]. In this regard, it has been
shown that the effect of the operator can sometimes be relevant, even when expert lichenologists are
involved in the sampling procedure. For example, during an intercalibration ring test conducted in
Italy, only a small number of skilled operators reached the Measurement Quality Objective (MQO)
for accuracy of taxonomic identification [
29
]. Consequently, the periodic and frequent comparison
between operators through intercalibration procedures is a fundamental step to guarantee the quality
and comparability of the data collected in different areas or in the context of repetitive surveys in the
same area. European guidelines for assessing lichen diversity [
3
] take into account Quality Assurance
procedures and field checks on data reproducibility, establishing that the personnel involved in
biomonitoring studies must fulfill high levels of taxonomic accuracy and precision to guarantee the
reliability and consistency of data collected by different teams.
Data reproducibility is crucial, especially in the case of large-scale, mid-term, or long-term
biomonitoring programs or when a before–after approach is foreseen. In fact, in both cases the
repetitions of field surveys over time are supposed to be or may be conducted by different teams
of specialists.
This is a current topic because in recent years biomonitoring results have been used in the context
of environmental forensics [
37
,
38
]. Even more so, it is fundamental to provide robust and defensible
data, and quality assurance procedures related to the control and evaluation of non-sampling errors
play a major role.
Despite several studies on intercalibration between operators having been conducted, no
one has yet evaluated the effect of the taxonomic expertise of a team on real data of repeated
biomonitoring surveys.
In this paper, we analyzed lichen diversity data collected in Italy from 2007 to 2016 within several
ongoing biomonitoring programs. We aimed to study the temporal variations of lichen diversity
between repeated investigations in relation to the team composition, distinguishing between surveys
carried out by the same or by different teams. By providing information on the variability associated
with the operator effect, the results of our work can contribute to improving the interpretative
framework of biomonitoring data.
Diversity 2019,11, 43 3 of 17
2. Materials and Methods
2.1. Survey Selection and Sampling Design
We analyzed lichen diversity data collected in Italy from 2007 to 2016 within six ongoing
biomonitoring programs (Table 1). The study areas are located all over Italy, from the dry
Mediterranean to the humid sub-Mediterranean phytoclimatic belts. The landscape is generally hilly,
with elevation ranging from 0 to 800 m. All sites are characterized by urban, industrial, agricultural,
and forest areas, within Mediterranean oak vegetation. On average, lichen gamma diversity among
the study areas was similar (Table 1), although the range of variation within each study area was
considerable. Lichen diversity has been sampled only on trees with similar bark characteristics
(sub-acid bark, Quercus and Tilia). For the purpose of this study, we define “biomonitoring program”
as a set of lichen biomonitoring surveys repeated in the same study area over time. For every single
program, all possible comparisons between subsequent biomonitoring surveys carried out at different
times were considered (from here on we refer to these as “survey pairs”). Overall, the time elapsed
between repeated surveys ranged from a minimum of 1 to a maximum of 8 years. Biomonitoring
programs were always carried out by teams of two skilled and qualified lichenologists. In particular,
all operators had an extensive experience in lichen taxonomy and lichen biomonitoring (from a
minimum of 15 to a maximum of 25 years of experience). They all have been qualified by specialist
training courses where they have reached the requested quality objectives. However, the team
composition was not always constant over time; in some cases the study repetition was conducted by
the same two operators (subsequently we will refer to this category of team as “same”), in other cases
by two different operators (subsequently called “different”), and in a third case only one of the two
operators took part in both investigations (subsequently called “partially”).
Conforming to the standards described by Asta et al. [
5
] and the Italian guidelines [
4
], plots
were selected by systematic sampling, and 3 to 12 trees were selected within each plot. Epiphytic
lichen diversity was sampled on trees belonging to species with sub-acid bark (Quercus spp. and
Tilia spp.) and with the following characteristics; tree circumference > 60 cm; bole inclination <10
◦
;
absence of damage and decorticated areas on the trunk and moss cover <25% of the observation grid.
The abundance of each lichen species was sampled on the bole of each tree. For each sampled tree,
the Lichen Diversity Value (LDV) was obtained by the sum of the abundance of all lichen species
occurring within a 10
×
50 cm observation grid, divided into 5 squares of 10
×
10 cm, placed at each
of the four cardinal points of the trunk (N, S, E, W) at a height of 100 cm above the ground. In the
repeated surveys within each biomonitoring program, the teams always sampled the same individual
trees in the same plots.
Diversity 2019,11, 43 4 of 17
Table 1.
Descriptive statistics of the biomonitoring surveys considered within the six study areas. Average Similarity (S), Species Replacement (R), and Richness
Difference (D) at tree level for each pair of surveys in the same area are reported, together with the total number of species found.
Study Area N Trees N Plots
Gamma
Diversity
(N Species)
Survey Pair (Years
of Surveys)
Team
Composition in
the Surveys
Delta Years Av. Similarity
(S)
Av. Richness
Difference
(D)
Av. Species
Replacement
(R)
A78 26 84
2008 versus 2009 same 1 72 12 15
2008 versus 2011 partially 3 52 20 28
2008 versus 2012 partially 4 48 22 30
2008 versus 2014 partially 6 47 23 31
2008 versus 2015 different 7 46 20 34
2009 versus 2011 partially 2 63 17 20
2009 versus 2012 partially 3 56 19 25
2009 versus 2014 partially 5 53 18 29
2009 versus 2015 different 6 52 20 28
2011 versus 2012 same 1 74 11 15
2011 versus 2014 same 2 64 14 22
2011 versus 2015 partially 3 61 18 21
2012 versus 2014 same 2 69 15 17
2012 versus 2015 partially 3 65 18 17
2014 versus 2015 partially 1 82 12 6
B108 36 119
2007 versus 2009 different 2 72 11 17
2007 versus 2012 same 5 62 14 24
2007 versus 2015 different 8 49 18 34
2009 versus 2012 different 3 70 11 19
2009 versus 2015 different 6 54 15 30
2012 versus 2015 different 3 55 16 29
C 135 39 55 2012 versus 2016 partially 4 58 25 17
D73 21 98
2010 versus 2013 different 3 57 21 22
2010 versus 2016 same 6 62 14 24
2013 versus 2016 different 3 53 18 30
E 71 24 83 2009 versus 2012 same 3 71 13 15
F 135 42 94 2014 versus 2016 partially 2 81 9 10
Diversity 2019,11, 43 5 of 17
2.2. Data Analysis
For each survey pair, we analyzed pairwise comparisons between lichen communities sampled
on the same trees. The species presence/absence data matrix was analyzed with SDR Simplex software
using the Simplex method—SDR Simplex (Similarity, richness Difference, species Replacement) [
39
].
For all pairs of the same trees sampled in different surveys, we evaluated the relative contributions
of the components of gamma diversity, i.e., Similarity, S; Richness Difference, D; and Species
Replacement, R.
Particularly, S corresponds to the Jaccard coefficient of similarity:
S = (a/n) ×100, (1)
where a is the number of species shared by two surveys and n is the total number of species.
D was calculated as the ratio of the absolute difference between the species numbers of each tree
(b, c) and the total number of species, n:
D = (|b −c|/n) ×100, (2)
R was calculated as:
R = (2 min {b,c}/n) ×100 (3)
Generalized Linear Mixed Models (GLMM) were applied for analyzing the relationships between
R, D, and S and predictor variables. In particular, we took into account the effects on gamma diversity
components of the following predictors: (1) composition of the teams (“same”, “partially” and
“different”); (2) Lichen Diversity Value sampled in the first survey of each program (“LDV at T
0
”;
(3) time elapsed between survey pairs of the same programs (“delta years”). Plot ID, nested within
the Study area, was considered as random effect. A Gaussian error distribution and an identity link
function were considered for the models. The Akaike Information Criterion (AIC) [
40
] was calculated
for each model, using the lme4 package [41] in R version 3.5.2 [42].
To analyze the taxonomic agreement between subsequent samplings, for each species in each
survey pair the percentage agreement was calculated as follows:
% Agreement = (CP/(CP + SR)) ×100, (4)
where CP (co-presence) is the number of trees on which the species was found in both surveys; and
SR (species replacement) is the number of trees where the species was found in only one of the two
surveys under comparison. A 1-way ANOVA analysis was carried out to detect significant differences
in taxonomic agreement according to categories of team composition. LSD Fisher post-hoc test was
applied to check significant differences between each pair of team categories.
Furthermore, we have taken into account the level of rarity of the species, to verify whether
the results were consistent regardless of the distributional characteristics of the species considered.
With reference to our dataset, we have defined “common” as those species that fulfill the following
criteria: identified by all the team categories, present in at least four of the six study areas and with an
occurrence on
≥
15% of the trees. In contrast, we have defined “rare” as species that comply with the
first two criteria mentioned above but occurring on < 15% of the trees. For the purpose of this analysis,
we excluded species found in less than four areas.
3. Results
Overall, the gamma diversity of the six study areas ranged from 55 to 119 (Table 1). The average
Species Replacement was between 6% and 34%, with the lowest values observed for the “same” teams
(from 15% to 24%), even when the interval between two surveys was rather long (5 and 6 years).
Diversity 2019,11, 43 6 of 17
The opposite trend was evident for the “different” teams, showing the highest values of replacement
(from 17% to 34%), while the “partially” teams showed a greater variability (6% to 31%).
Average Richness Differences ranged from 9% to 25% (Table 1). In this case the highest and most
variable values were observed for the “partially” teams (from 9% to 25%) and the other two team
categories were characterized by lower values (“same” teams: from 11% to 15%; “different” teams:
from 11% to 21%).
Average Similarity was between 46% and 82% (Table 1). The majority of the lowest values (<70%)
was observed for the “different” (from 46% to 70%) and “partially” (from 47% to 82%) teams, especially
with long time intervals among surveys (from 4 to 7 years). In contrast, the highest Similarity in the
species communities (> 70%) was related to the surveys carried out by the “same” teams.
We explored the effects of delta years, team composition, LDV and their interactions on the
variations in S, D, and R components (Table 2). Delta years and team composition consistently showed
significant effects on S, D, and R. Particularly, both the “same” and “partially” teams showed significant
differences of the three gamma diversity components compared with the “different” teams. The effect
of LDV at T
0
was significant for D and S, but not for R. The interaction between delta years and team
composition showed significant differences when comparing the “different” versus “partially” teams
(Table 2).
Table 2.
Generalized Linear Mixed Models describing the effects of delta years, team composition, LDV
at T
0
and their interactions on the gamma diversity components S (Similarity), D (Richness Difference),
and R (Species Replacement). * p< 0.05; ** p< 0.01.
S D R
AIC −
1441.79
−
2532.52
−
1485.97
Estimates
Std Error
t-value
Estimates
Std Error
t-value Estimates
Std Error
t-value
Random effect
(Plot/Area) St. dev. 0.099 0.036 0.055
Residuals 0.166 0.137 0.169
Fixed effects
(Intercept) 0.653 0.021 31.472 ** 0.176 0.015 11.872 ** 0.173 0.019 9.155 **
DeltaYears −0.029 0.003 −9.710 ** 0.008 0.002 3.261 ** 0.021 0.003 7.169 **
Team ‘partially’ (versus
‘different’) 0.159 0.025 6.480 ** −0.048 0.018 −2.692 ** −0.126 0.023 −5.524 **
Team ‘same’ (versus
‘different’) 0.091 0.027 3.347 ** −0.025 0.019 −1.294 * −0.063 0.025 −2.554 *
LDV at T0 0.001 0.000 6.772 ** −0.001 0.000 −6.260 ** 0.000 0.000 −1.078
DeltaYears:Team
‘partially’ (versus
different)
−0.036 0.005 −6.999 ** 0.017 0.004 4.204 ** 0.023 0.005 4.561 **
DeltaYears:Team ‘same’
(versus ‘different’) −0.003 0.006 −0.505 0.000 0.005 0.051 0.003 0.006 0.488
When considering the whole dataset independently from the team composition, the SDR analysis
revealed that the structure of lichen communities was characterized by a reduction of the values of
similarity (S) from 75% to 50% in 8 years, with a more marked reduction in the first 4 years (Figure 1a).
The differences in richness (D) among years showed a more regular trend, with values lower than
25% (Figure 1b). An increasing gradient in species replacement (R) from 10% to 35% in the time-span
considered was evident (Figure 1c).
Diversity 2019,11, 43 7 of 17
Diversity 2019, 11, x FOR PEER REVIEW 7 of 16
(a)
(b)
(c)
Figure 1. Fitted modeled relationships between delta years and diversity components, according to
the Generalized Linear Mixed Models (GLMM) of Table 2: (a) Similarity; (b) Richness Difference;
and (c) Species Replacement.
We explored the effect of the team composition on the three components of diversity (Figure 2).
The decrease in Similarity (S) over time was more marked for the "partially" teams (Figure 2a),
ranging from 75% to values lower than 50%, while a more constant trend was evident for the
"different" and "same" teams in the repeated surveys, with values ranging respectively from 70% to
50% and from 75% to 65%. Richness Differences (D; Figure 2b) and Species Replacement (R; Figure
2c) in lichen communities showed similar increasing patterns, with a more variable trend for the
"partially" teams. R values ranged from 10% to 30%, while D values were always lower than 25%
0
25
50
75
100
2 4 6 8
Delta years
Similarity (S)
Predicted values
0
25
50
75
2 4 6 8
Delta years
Richness Difference (D)
Predicted values
0
25
50
75
100
2 4 6 8
Delta years
Species replacement (R)
Predicted values
Figure 1.
Fitted modeled relationships between delta years and diversity components, according to
the Generalized Linear Mixed Models (GLMM) of Table 2: (
a
) Similarity; (
b
) Richness Difference;
and (c) Species Replacement.
We explored the effect of the team composition on the three components of diversity (Figure 2).
The decrease in Similarity (S) over time was more marked for the “partially” teams (Figure 2a), ranging
from 75% to values lower than 50%, while a more constant trend was evident for the “different” and
“same” teams in the repeated surveys, with values ranging respectively from 70% to 50% and from
75% to 65%. Richness Differences (D; Figure 2b) and Species Replacement (R; Figure 2c) in lichen
communities showed similar increasing patterns, with a more variable trend for the “partially” teams.
R values ranged from 10% to 30%, while D values were always lower than 25%
Diversity 2019,11, 43 8 of 17
Diversity 2019, 11, x FOR PEER REVIEW 8 of 16
(a)
(b)
(c)
Figure 2. Fitted modeled relationships between delta years and diversity components, with respect to the
composition of the teams, according to the GLMM models of Table 2: (a) Similarity; (b) Richness Difference; (c)
Species Replacement.
When considering the SDR values in relation to LDV at T0 (Figure 3), the three team categories
showed increasing values of Similarity (S) ranging from lower to higher values of LDV (from 50% to
75% for the "different" teams; 60% to 80% for the "partially" teams; and from 65 to 75% for the "same"
teams). The three team categories showed more marked differences for lower LDV rather than
higher ones.
0
25
50
75
100
2 4 6 8
Delta years
Similarity (S)
Team
different
partially
same
Predicted values
0
25
50
75
2 4 6 8
Delta years
Richness Difference (D)
Team
different
partially
same
Predicted values
0
25
50
75
100
2 4 6 8
Delta years
Species replacement (R)
Team
different
partially
same
Predicted values
Figure 2.
Fitted modeled relationships between delta years and diversity components, with respect to
the composition of the teams, according to the GLMM models of Table 2: (
a
) Similarity; (
b
) Richness
Difference; (c) Species Replacement.
When considering the SDR values in relation to LDV at T
0
(Figure 3), the three team categories
showed increasing values of Similarity (S) ranging from lower to higher values of LDV (from 50%
to 75% for the “different” teams; 60% to 80% for the “partially” teams; and from 65 to 75% for the
“same” teams). The three team categories showed more marked differences for lower LDV rather than
higher ones.
Diversity 2019,11, 43 9 of 17
Diversity 2019, 11, x FOR PEER REVIEW 9 of 16
(a)
(b)
(c)
Figure 3. Fitted modeled relationships between Lichen Diversity Value (LDV) measured at the
beginning of the biomonitoring program and diversity components, according to the GLMM models
of Table 2: (a) Similarity; (b) Richness Difference; (c) Species Replacement.
Table 3 reports a list of the 30 most common species in the dataset, with their relative values of
agreement among the different surveys and the pairwise comparison between the three categories of
teams. Average agreement ranged from 39% to 87%, with 9 species showing values lower than 50%.
Ten species showed significant differences among teams. Five of these showed the lowest values in
the trees sampled by the "different" teams (p < 0.05) with respect to the other two team categories.
Table 3. List of the common species in the dataset, with their relative values of agreement among the
different surveys and the pairwise comparison between the three team categories. The names of the
species with significant differences among teams are reported in bold. Nomenclature according to
Nimis [43].
Species
Average percentage agreement
Total
Team
"different"
Team
"partially"
Team
"same"
Ramalina fraxinea (L.) Ach.
39
19a
48b
51b
Amandinea punctata (Hoffm.) Coppins & Scheid
40
22a
48b
49b
Candelariella xanthostigma (Ach.) Lettau
42
41a
43a
44a
Caloplaca ferruginea (Huds.) Th. Fr.
45
50a
44a
39a
0
25
50
75
100
050 100 150 200
LDV at T0
Similarity (S)
Team
different
partially
same
Predicted values
0
25
50
75
050 100 150 200
LDV at T0
Richness Difference (D)
Team
different
partially
same
Predicted values
0
25
50
75
100
050 100 150 200
LDV at T0
Species Replacement (R)
Team
different
partially
same
Predicted values
Figure 3.
Fitted modeled relationships between Lichen Diversity Value (LDV) measured at the
beginning of the biomonitoring program and diversity components, according to the GLMM models of
Table 2: (a) Similarity; (b) Richness Difference; (c) Species Replacement.
Table 3reports a list of the 30 most common species in the dataset, with their relative values of
agreement among the different surveys and the pairwise comparison between the three categories of
teams. Average agreement ranged from 39% to 87%, with 9 species showing values lower than 50%.
Ten species showed significant differences among teams. Five of these showed the lowest values in the
trees sampled by the “different” teams (p< 0.05) with respect to the other two team categories.
Diversity 2019,11, 43 10 of 17
Table 3.
List of the common species in the dataset, with their relative values of agreement among the
different surveys and the pairwise comparison between the three team categories. The names of the
species with significant differences among teams are reported in bold. Nomenclature according to
Nimis [43].
Species
Average Percentage Agreement
Total Team
“different”
Team
“partially”
Team
“same”
Ramalina fraxinea (L.) Ach. 39 19 a48 b51 b
Amandinea punctata (Hoffm.) Coppins & Scheid 40 22 a48 b49 b
Candelariella xanthostigma (Ach.) Lettau 42 41 a43 a44 a
Caloplaca ferruginea (Huds.) Th. Fr. 45 50 a44 a39 a
Evernia prunastri (L.) Ach. 45 52 a40 a46 a
Physcia biziana (A. Massal.) Zahlbr. var. biziana 46 20 a73 b39 a
Candelariella reflexa (Nyl.) Lettau 46 44 a50 a43 a
Ramalina fastigiata (Pers.) Ach. 47 50 a51 a34 a
Phlyctis argena (Spreng.) Flot. 47 63 b34 a51 ab
Pertusaria pustulata (Ach.) Duby 54 32 a59 b61 b
Lecanora expallens Ach. 54 41 a65 b53 ab
Normandina pulchella 56 46 a68 a47 a
Lepra amara (Ach.) Hafenller 59 60 a58 a57 a
Flavoparmelia soredians (Nyl.) Hale 60 46 a67 a66 a
Candelaria concolor (Dicks.) Stein 61 56 a63 a61 a
Physconia grisea (Lam.) Poelt 64 54 a75 b58 a
Melanelixia subaurifera (Nyl) O. Blanco, A. Crespo, Divakar,
Essl., D. Hawksw. & Lumbsch 66 60 a71 a67 a
Physcia aipolia (Humb.) Fürnr 67 55 a73 b71 b
Punctelia subrudecta (Nyl.) Krog 68 68 a68 a67 a
Parmelina tiliacea Taylor 70 73 a67 a70 a
Lecanora chlarotera Nyl. 71 69 a68 a77 a
Parmotrema perlatum (Huds.) M. Choisy 73 69 a76 a71 a
Parmelia sulcata (Taylor) 74 77 a70 a75 a
Pertusaria albescens (Huds.) M. Choisy & Werner 75 68 a100 b82 ab
Lecidella elaeochroma (Ach.) M. Choisy 75 73 a74 a81 a
Physconia distorta (With.) J.R. Laundon 76 75 a75 a77 a
Xanthoria parietina (L.) Th. Fr. 78 73 a82 a76 a
Hyperphyscia adglutinata (Flörke) H. Mayrhofer & Poelt 78 64 a88 b79 b
Flavoparmelia caperata (L.) Hale 82 85 a79 a84 a
Physcia adscendens H. Oliver 87 83 a89 a87 a
ab Same letters correspond to homogeneous groups (p> 0.05) according to an LSD Fischer post-hoc test.
Similar results were also evident for the group of rare species (Table 4; 33 species), with average
agreement ranging from 10% to 98%. Twenty-three of them showed values lower than 50%.
Twelve species showed significant differences among teams. Six of these had significantly lower
values (
p< 0.05
) in the “different” teams. Among them, were four crustose lichens (Lecanora argentata,
Tephromela atra,Pertusaria flavida, and Chrysothrix candelaris), and two narrow-lobed foliose lichens
(Phaeophyscia orbicularis and Heterodermia obscurata).
Diversity 2019,11, 43 11 of 17
Table 4.
List of the rare species in the dataset, with their relative values of agreement among the
different surveys and the pairwise comparison between the three team categories. The names of the
species with significant differences among teams are reported in bold. Nomenclature according to
Nimis [43].
Species Average Agreement
Total Team
“different”
Team
“partially”
Team
“same”
Caloplaca pyracea (Ach.) Zwackh. 10 10 a11 a8a
Physcia tenella (Scop.) DC. 14 0 a21 a8a
Buellia griseovirens (Sm.) Almb. 15 5 a24 a20 a
Leprocaulon microscopicum (Vill.) Gams 16 20 a19 a0a
Physcia leptalea (Ach.) DC. 17 23 a11 a22 a
Naetrocymbe punctiformis (Pers.) R.C. Harris 20 14 a25 a16 a
Physconia perisidiosa (Erichsen) Moberg 24 25 a20 a32 a
Lecanora argentata (Ach.) Malme 27 5 a32 b39 b
Gyalecta truncigena (Ach.) Hepp 27 38 b11 a44 ab
Tephromela atra (Huds.) Hafellner 27 13 a33 b37 b
Melanelixia fuliginosa (Duby) O. Blanco, A. Crespo, Divakar,
Essl., D. Hawksw. and Lumbsch 30 38 a28 a25 a
Lecanora hagenii (Ach.) Ach. 30 20 a41 a28 a
Ramalina farinacea (L.) Ach. 31 47 b17 a29 ab
Pertusaria hymenea (Ach.) Schaer 31 40 a17 a47 a
Bacidia rubella (Hoffm.) A. Massal 35 37a 40 a21 a
Physconia servitii (Nádv.) Poelt 35 18 a47 b39 ab
Phaeophyscia orbicularis (Neck.) Moberg 36 17 a46 b47 b
Lecanora horiza (Ach.) Linds. 39 28 a44 a43 a
Phaeophyscia hirsuta (Mereschk.) Moberg 40 45 b27 a58 b
Collema furfuraceum Du Rietz 48 40 a49 a58 a
Pertusaria pertusa (L.) Tuck. 48 46 a47 a53 a
Caloplaca cerinelloides (Erichsen) Poelt 49 29 a62 a50 a
Physcia clementei (Turner) Lynge 49 27 a53 a58 a
Pertusaria flavida (DC.) J.R. Laundon 50 27 a56 b71 b
Lecanora carpinea (L.) Vain. 50 48 a56 a45 a
Dendrographa decolorans (Sm.) Ertz and Tehler 52 35 a58 b48 ab
Pleurosticta acetabulum (Neck.) Elix & Lumbsch 53 45 a67 a39 a
Diploicia canescens (Dicks.) A. Massal. 55 38 a60 a74 a
Lecanora symmicta (Ach.) Ach. 55 39 a67 b58 ab
Heterodermia obscurata (Nyl.) Trevis. 60 33 a76 b63 b
Chrysothrix candelaris (L.) J.R. Laundon 61 35 a80 b67 b
Parmotrema reticulatum (Taylor) M. Choisy 64 66 a61 a68 a
Opegrapha niveoatra (Borrer) J.R. Laundon 98 100 a97 a100 a
ab Same letters correspond to homogeneous groups (p> 0.05) according to a LSD Fischer post-hoc test.
4. Discussion
Lichen biomonitoring is a standardized method that requires high levels of taxonomic knowledge.
Therefore, lichenologists in charge of data collection can influence the quality of the results [32].
At a small scale of observation, as that used for single plots in lichen biomonitoring, observer
error is expected to be high and might overcome the variance related to the target environmental signal
(e.g., pollution) [44].
Although several tests [
30
–
32
] evaluated the accuracy of single operators, none have assessed the
results obtained in cases of rotation or partial change of team composition in long-term biomonitoring
programs. To fill this gap of knowledge, in this work we investigated temporal variations of epiphytic
lichen diversity in relation to the composition of the teams involved in repeated biomonitoring surveys.
In general, our study highlighted significant effects on diversity assessments due to team
composition. These effects will have to be taken into due consideration because they could potentially
lead to errors in the interpretation of the data obtained. However, detailed analysis of these effects
will allow targeted activities to be planned to mitigate this risk. Furthermore, the observed effects are
Diversity 2019,11, 43 12 of 17
diversified according to whether quantitative (e.g., diversity) or qualitative (e.g., species composition
and species ecology) aspects are taken into account.
4.1. Quantitative Aspects of Lichen Diversity
Probabilistic sampling based on the location of plots and sub-plots within a survey area can
provide reliable estimates of the overall diversity, even though a given number of rare species are often
unrecorded [
44
]. Among several descriptors of lichen diversity, Giordani et al. [
25
] showed that the
components of gamma diversity are important to highlight temporal and spatial variation in epiphytic
lichen communities and to follow their progress over time. In the present work, we compared pairs
of subsequent surveys carried out at time intervals ranging from 1 to 8 years. In these situations,
our results showed an increase over time in the Species Replacement (R), corresponding to a decrease
in Similarity (S) and a constant trend of the Richness Difference (D). Taking into consideration the
composition of the teams in more detail, it has emerged that R and D increased over time both for
the “different” and “same” teams with a comparable trend. In contrast, for the “partially” teams
the trend was significantly different compared with the other team categories, determining a less
controllable non-sampling error. Correspondingly, the decrease of S after 6 years was more marked for
the “different” and “partially” teams (about 50%), while, at the same time, the samplings undertaken
by the “same” teams were more similar to each other.
It should be carefully taken into account that SDR values describe variations in lichen communities
ascribable to at least four major factors, including natural succession of communities [
27
], variations
due to the increase/decrease of pollution [
2
,
45
], and sampling and non-sampling errors (operator
effect) [
26
,
33
]. As the evaluation of the effects of environmental changes is based on these variations,
it is fundamental to understand the contribution of each factor. Several studies have addressed the
first three aspects, whereas less is known about the last one. Our results do not allow us to discern the
effect of population dynamics or pollution, but we can still observe significant variations according to
the time elapsed between two surveys and depending on the team composition.
In contrast to what we would have expected, the differences between teams were more evident on
trees with low Lichen Diversity Values (S = 70% in the “same” teams versus S = 50% in the “different”
teams). The differences between teams were reduced in case of high diversity, with values of S
approximately equal to 70% for all types of teams. This is probably due to the fact that in conditions of
high alteration the lichen thalli are often wrecked and scarcely recognizable even to skilled operators.
Errors of identification and/or overlook of these species could lead to important underestimates of
diversity. This is in accordance with what observed by Ellis and Coppins [
44
]. These authors noted
that the confirmation of atypical specimens is particularly difficult when using small subsamples (as in
the case of lichen biomonitoring). As altered study areas are often the main targets of biomonitoring
programs, it is a priority to implement countermeasures to increase the similarity in such conditions
(see paragraph 4.3). In contrast, the positive aspect is that, under conditions of high LDV values,
greater homogeneity was observed between the results produced by the different categories of teams.
Based on our results we cannot provide a direct assessment of the taxonomic accuracy reached in the
surveys considered. However, high similarity values guarantee good data comparability and reduce
the risk of misinterpretation of results [26].
4.2. Taxonomic Agreement in Relation to Team Composition
From what was discussed in the previous paragraph, we can state that the results of biomonitoring
programs are generally comparable when considering quantitative aspects (e.g., gamma diversity
components or LDV). However, would we obtain the same information if we took into account
species ecological requirements (e.g., nitrophilous and acidophilus species)? As shown in Table 3,
some species with low agreement were very common species, but ones that can be frequently confused
with similar species that differ in their ecology (e.g., Flavoparmelia caperata versus Flavoparmelia soredians,
Amandinea punctata versus Lecidella elaeochroma, or Parmotrema perlatum versus Parmotrema reticulatum).
Diversity 2019,11, 43 13 of 17
In general, species with low agreement in the “different” team category are difficult to identify
in the field when present on tree trunks with poorly developed thalli. Most of them are crustose
lichens (e.g., Pertusaria pustulata,Lecanora argentata), or foliose narrow-lobed ones (e.g., Physcia aipolia,
Phaeophyscia orbicularis). This fact can lead to erroneous considerations about community composition
(e.g., acidophitic versus nitrophytic) and about the factors that might have determined it [
46
–
51
].
This issue is crucial to prevent the loss of information related to the ecology of the species.
In modern biomonitoring, the analysis of ecological requirements of the lichen communities
is pivotal for a comprehensive interpretation of the drivers that might have caused observed
variations [
52
,
53
]. It has been demonstrated that species and/or functional traits react differently
to anthropogenic disturbances [
14
,
16
,
54
–
56
]. Several studies focused on lichen biota shifts due to
decreasing SO
2
concentrations and increasing nitrogen pollution confirmed the relevance of such
information [50,57–60].
Even though there may be disagreement in the identification of very common species, in our study,
we generally observed that the overall agreement was higher for common species compared with rare
ones (Tables 3and 4). In some cases, it has been observed that common species can be overlooked,
because the attention of the operators is often focused on infrequent species [
44
]. In ecological sampling
based on sub-samples, rare species are unrecorded due to their low probability of being included
within the sample units. In our study, for both common and rare species, we observed significant
differences between team categories. In fact, most of the common species in the dataset (70%) showed
levels of agreement > 50%, while the opposite is true for the rare ones, with only 30% of the species
exceeding this level of agreement. This is probably related to the fact that common species can be easily
identified in the field based on the acquired experience of the operators. In contrast, rare species are
more sporadic, and often small; many of them are critical taxa, often difficult to identify, less familiar
for operators, especially if they do not have sufficient knowledge of the local lichen biota [
32
,
33
].
Accordingly, it has been demonstrated that the familiarity with local diversity is a critical factor that
can influence the number of species detected by different operators [33,44,61].
4.3. Recommandations
Our results show that the surveys conducted by the “same” teams have the highest agreements.
However, as biomonitoring programs are open to public tenders and last several years, it is not always
possible that all the surveys are carried out by the same team composition. These results highlight the
importance of the continuous training of the personnel involved. Particularly, attention must be paid
to the following aspects:
•
Periodic ring test organization. The effectiveness of this activity is achieved only if the
intercalibration exercise is regularly repeated over time [
34
]. In fact, it has been shown that
a decrease, even if limited, of taxonomic accuracy can be observed even in taxonomist experts.
The effect of the loss of accuracy is obviously much more evident in trained personnel who have
not yet reached a high level of experience.
•
Calibrations of the operators within the same program. This calibration activity should be done
between the two operators composing the team carrying out the same survey and/or among teams
involved in different surveys of the same program. Additionally, external skilled personnel could
also be involved as a control team to provide a further level of quality assurance. This activity
would minimize the differences attributable to non-sampling errors within the same area of study
(e.g., between high and low diversity areas) and/or subsequent surveys of the same monitoring
program. The main problems in applying these interventions can be identified in the difficulty of
planning long-term activities and involving people who have worked at different times.
•
Preparatory training aimed at improving the knowledge of local lichen biota. In many cases,
the operators involved in the sampling of a study area may not have specific knowledge of the
local biota. This is particularly true in case of operators with low level of experience, but even
Diversity 2019,11, 43 14 of 17
skilled lichenologists may not be able to maintain a high level of taxonomic accuracy without
preparatory and intensive training on the local lichen biota.
•
Staff training on critical taxonomic groups. On the basis of the results reported in this study,
it is evident that specialized training on some critical groups of species (e.g., genera of crustose
lichens) can lead to a substantial improvement of the agreement between operators. Although
recommendable, the organization of advanced workshops involves a considerable logistical effort
in the retrieval of materials, laboratory equipment and the availability of experts able to clarify
doubts on critical species. As a further option, experts could be invited to participate in the
surveys of the monitoring program, even though this may lead to an increase of the total cost of
the program.
Author Contributions:
Conceptualization, G.B., L.F., P.G. and P.M.; Methodology, G.B., L.F., P.G. and P.M.; Formal
analysis, C.M. and P.G.; Resources, G.B., L.F., P.G. and P.M.; Data curation, L.F., P.G. and P.M.; Writing—original
draft preparation, G.B., L.F., P.G. and P.M.; Writing—review and editing, G.B., L.F., P.G. and P.M.; Visualization,
C.M. and P.G.; Supervision, P.G.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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