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Revista de Biología Tropical, ISSN: 2215-2075, Vol. 69(3): 1107-1123, July-September 2021 (Published Sep. 30, 2021)

Spatial distribution of lichen communities and air pollution mapping

in a tropical city: Medellín, Colombia

Mauricio Andres Correa-Ochoa

; https://orcid.org/0000-0003-3666-0767

Leidy Catalina Vélez-Monsalve

; https://orcid.org/0000-0003-2349-1044

Julio César Saldarriaga-Molina

; https://orcid.org/0000-0002-9395-5417

1. Grupo de Investigación y Laboratorio de Monitoreo Ambiental-GLIMA, Grupo de Ingeniería y Gestión Ambiental-

GIGA, Escuela de Ingeniería, Universidad de Antioquia, Medellín, Colombia; mandres.correa@udea.edu.co

(Correspondence*), lcatalina.velez@udea.edu.co, julio.saldarriaga@udea.edu.co

Received 10-V-2021. Corrected 12-VII-2021. Accepted 29-IX-2021.

ABSTRACT

Introduction: Enough scientific evidence is available on the harmful effects of air pollution on the health of

human beings, fauna, flora, and ecosystems in general. The mechanical and electronical monitoring networks are

the first option for the air quality diagnosis, but they do not allow a direct and precise assessment of the impacts

in living organisms that may result from the exposure to air pollutants.

Objective: To evaluate the changes in the composition of corticulous lichen communities as a response to vari-

ous stress factors in areas with different levels of air quality to diagnose the state of pollution or intervention in

an area with a more complete option.

Methods: Air quality contrasts and changes in richness and coverage of corticulous lichens in response to dif-

ferent stress factors, such as land use and distance to roads, in three different biomonitoring areas, were evaluate

using GIS, and the data are presented in an easy-to-understand grey scale coded isoline map.

Results: Indicators such as lichen coverage (R= -0.4) and richness (R= -0.7) are inverse correlated with PM

2.5

concentrations in each area. A total of 110 lichen species were identified, being Phaeophyscia chloantha (Ach.)

Moberg and Physcia poncinsii Hue the most frequent species (present in 38 and 33 % of the 86 sampled phoro-

phytes, respectively). The intra-area relationships of lichen richness exhibit significant relationships with regards

to the land use and distance to roads (with correlations coefficients greater than 0.5) and the Simpson index was

higher than 0.9, in places with better conditions in terms of air quality and microenvironments, likewise the

resistance factors calculated suggest that the most sensitive species can be found in environments with a lesser

degree of disturbance.

Conclusion: These evaluations represent more criteria elements for the diagnosis of the environmental health

in the biomonitoring areas.

Key words: biomonitoring; air quality; corticulous lichens; resistance factors; mapping lichens; lichens

diversity.

Correa-Ochoa, M. A., Vélez-Monsalve, L. C., & Saldarriaga-

Molina, J. C. (2021). Spatial distribution of lichen

communities and air pollution mapping in a tropical city:

Medellín, Colombia. Revista de Biología Tropical, 69(3),

1107-1123. https://doi.org/10.15517/rbt.v69i3.46934

https://doi.org/10.15517/rbt.v69i3.46934

Worldwide, air pollution represents one

of the greatest concerns because of its proven

adverse effects on people’s health (WHO,

2016a). These have been evidenced by a sig-

nificant number of epidemiological studies

developed in recent decades in different parts

of the world, which have established a close

relationship between the levels of pollutants

(gases and particles) concentration in the air

and increased mortality as well as hospital

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admissions for respiratory and cardiovascular

conditions, among others (Aguiar-Gil et al.,

2020; Cakmak et al., 2018; Li et al., 2018;

WHO, 2016b).

One of the mandatory strategies that envi-

ronmental authorities responsible for ensur-

ing good air quality in their jurisdiction must

undertake is the implementation of monitoring

networks for atmospheric pollutants (Green

& Sánchez, 2012). The data reported by the

measurement stations are the input for the

formulation of the Integrated Management

Plans for Air Quality (planning, implementa-

tion, and evaluation (AMVA & Clean Air

Institute, 2017). These plans will help achieve

acceptable and safe standards for the wel-

fare of inhabitants and ecosystems in general.

However, a high percentage of the monitoring

networks use expensive technology equipment

that require periodic repair, maintenance, ser-

vices, and specialized calibration procedures.

In addition to the high acquisition costs, the

operation, transportation, and custody (secu-

rity) may condition the implementation in areas

with difficult access and limited resources,

such as energy services and stable communica-

tion (INE, 2019).

On the other hand, the complexity in

the comprehensive assessment of air quality

through the use of these technologies repre-

sents a strong limitation, since they are limited

to certain chemical compounds and measure

pollutants in isolation (WHO, 2006), and they

do not allow the establishment of an intrinsic

relationship with the biological effect of air

pollutants and their long-term consequences on

organisms, their communities, and the ecosys-

tem in general (Castro et al., 2014).

Bioindication has been a tool used as a

methodological alternative to contribute to the

solution of the complex situation that arises in

the field of environmental assessment. More-

over, it enables the answering of the questions

or uncertainties that stem from the information

generated by the conventional monitoring sys-

tems on the real physiological and behavioral

effects to which organisms are subjected, both

in space and time (Augusto et al., 2013; Kienzl

et al., 2003; Van Dijk et al., 2015). Therefore,

the use of different groups of organisms as

indicators has been consolidated as an adequate

alternative to evaluate the effect that may be

derived from the deterioration of air quality in

a territory under study. Due to their anatomical,

morphological, and physiological characteris-

tics, the lichen has been used as a bioindicator

with successful results. Moreover, as they are

widespread worldwide, lichens allow the deter-

mination of the involvement degree when they

are exposed to dangerous substances, as in the

case air pollutants, allowing the classification

of some species as sensitive and others as tol-

erant (Anze et al., 2007; Cleavitt et al., 2015;

Käffer et al., 2011; Jovan, 2008).

Lichens are intimate and long-lasting sym-

bioses of photosynthetic algae or cyanobacteria

and heterotrophic fungi (Piercey-Normore &

DePriest, 2001). This symbiotic relationship

is related to their tolerance and / or sensitivity,

so they can be used as bioindicators (Llop et

al., 2012). Since the techniques for the imple-

mentation of their use as bioindicators can be

considered simple and low-cost maintenance,

lichens have great strengths as an instrument

for the diagnosis of air quality (Correa-Ochoa

et al., 2020; Hawksworth et al., 2005; Salcedo

et al., 2014). Furthermore, they define air qual-

ity levels with greater precision, establishing

limits that are difficult to be detected by con-

ventional systems and allowing the determina-

tion of the degree of impact on humans, given

that they are exposed to the same complex

mixture of air pollutants (Castro et al., 2014).

The excellent results that the different

biomonitoring programs with lichens have pro-

duced have served as an argument to integrate

them into environmental assessment strate-

gies of national protocols in countries such

as England, Germany, the United States and

the Netherlands (Anze et al., 2007; Käffer et

al., 2011; Monge-Nájera et al., 2002). In this

regard, the evaluation of the spatial behav-

ior patterns of these air quality bioindicators

has allowed the establishment of associations

between their diversity, such as their bioac-

cumulation capacity, their relationship with the

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state of contamination in some areas, as well as

their relationship with some diseases (Cislaghi

& Nimis, 1997; Pollard et al., 2015; Ribeiro et

al., 2016; Yatawara & Dayananda, 2019).

The articulation of current systems for

measuring air quality and the use of bioindi-

cators, represents an alternative that allows a

better knowledge of pollution, especially in

cities like Medellín that register areas with sig-

nificant air quality alterations and that have a

robust monitoring network for the diagnosis of

air quality but do not have qualitative tools to

assess its impact on living beings. In this sense,

this study aims to evaluate the changes in the

composition of corticulous lichen communities

as a response to various stress factors in areas

with different levels of air quality. This was

achieved through the quantification of lichen

richness and coverage, mapmaking, and the

determination of relationships among these

parameters, land uses, and distance to roads

in the different biomonitoring areas, located in

Medellín city.

MATERIALS AND METHODS

Study site: Medellín city is located in

the central mountain range of the Colombian

Andes (Fig. 1A and Fig. 1B) at an average

height of 1 450 m above sea level; it is cen-

tered in a deep (1 km) and narrow (10 km on

average) valley called Valle de Aburrá. The

city has a temperate-dry climate, with average

Fig. 1. Biomonitoring area. A. Study area location in Colombia, B. Study area location in the City of Medellín, C.

Biomonitoring areas, D. Area 1, E. Area 2, F. Area 3. (Coordinate system – WGS_1984).

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annual temperature values of 22.5 °C, precipi-

tation of 1 685 mm, relative humidity of 67 %

(IDEAM, 2018a), and a predominant direc-

tion of the winds from North to South in the

axis of the valley (IDEAM, 2018b). During

recent years, the levels of air pollution in the

valley have generated great concern; espe-

cially the PM

2.5

particulate material (Gaviria

et al., 2011; MADS, 2012). This, in addition

to the valley´s topography and the local and

regional meteorological conditions that vary

throughout the year, favors episodes of atmo-

spheric stability (mainly in March–April and

October–November) that influence the pollut-

ants dynamic dispersion (Correa et al., 2009).

Considering the risk that air pollution has on

people’s health, Valle de Aburrá has one of the

most robust and advanced air quality networks

in the region (BID, 2016) that is integrated into

the Sistema de Alerta Temprana del Valle de

Aburrá (SIATA).

Biomonitoring areas: This study was

conducted in three biomonitoring areas (Fig.

1C) that present differences in their sources of

air pollutant emission as well as physical and

environmental conditions. Area 1 is located in

the center of the city in a commercial and insti-

tutional sector, comprises emissions mainly

due to vehicular traffic with roads where urban,

inter-municipal, and private and public trans-

port converge in the city. It has little vegetation

with isolated trees, located mainly on platforms

that are part of the urban planning of the area

(Fig. 1D). On the other hand, Area 2 is charac-

terized by heterogeneous conditions, combin-

ing exposure to high vehicular traffic and large

vegetation areas (Fig. 1E). This area is located

inside the Universidad Nacional de Colombia,

which has a great number and diversity of tree

species. Finally, Area 3 is located in an area

with different characteristics from the previous

ones (Fig. 1F). It is defined by predominant

vegetation and scarce emissions of pollutants

of industrial origin. This is an area of important

contrast with land uses mainly forestry.

General air quality of the biomonitor-

ing areas: The annual average concentrations

of PM

2.5

reported by SIATA for the year 2018

were inserted using Spline, a tool of ESRI’s

ArcGis® software to assign the air quality

associated with the biomonitoring areas to

generate isoconcentration maps of this pollut-

ant. Subsequently, an estimated average value

was obtained for this pollutant in each of the

areas from the maps obtained and using the

spatial analyst tools (Extract values to points)

of ESRI’s ArcGis® software.

Lichen species mapping, collection,

identification and area determination: In

each of the three biomonitoring areas, 28 to

30 phorophytes were sampled (without species

differentiation). The field work focused on the

identification of the number of lichen species

and on the mapping of the specific coverage of

lichens. Moreover, it followed the methodol-

ogy proposed by Monge-Nájera et al. (2002), in

which a transparent film of polyethylene tere-

phthalate (acetate) of 50 cm × 100 cm

and 0.4

mm thickness was fixed on the bark of the tree

at a height of 50 cm, measured from the ground

level up to the lower edge of the acetate on the

side where the greatest presence of lichens was

evident (Fig. 2A).

Subsequently, the outer contour of the

lichen spot (no matter how type of thallus they

have) was outlined with a fine-tipped marker

using a different color for each lichen species

(Fig. 2B). Each acetate was digitized at a 1:1

scale in the presence of a metric pattern and

was stored in digital “.tiff” format. Then, each

digitized image was processed using ARLIQ®

software that determines the inventory of lichen

cover (Fig. 2C and Fig. 2D) and the number of

lichen species (lichen richness per phorophyte).

The design of this software emerged as a tool

to conduct a research project by GIGA and

GEPAR groups of the Facultad de Ingeniería

de la Universidad de Antioquia (UdeA).

Representative specimens were collected

according to the methodology proposed by

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Chaparro & Aguirre (2002) and then the char-

acterization of the lichen species was performed

using the methodology of Orange et al. (2001).

Simpson Index and resistance factor:

The Simpson Index (Simpson, 1949) was cal-

culated for different intervals of distances (0-50

m, 50-100 m, 100-150 m, >150 m) in each

area, with respect to the roads (Area 1 and 2)

or respect to the forest area (Area 3). Likewise,

the resistance factor, were calculated for the

same distance intervals. This factor establishes

the degree of sensitivity of the species, assum-

ing that contamination reduces its diversity and

the sensitivity of a specie depends on its repre-

sentativeness in the environment in which it is

found (Jaramillo & Botero, 2010).

Data analysis: Kruskal–Wallis test were

used to evaluate whether statistically signifi-

cant differences were present between the

richness and coverage medians of the mapped

phorophytes in the biomonitoring areas and

Mann–Whitney U were used to do paired

comparisons between richness and coverage

medias for different levels of these factors.

Furthermore, the Moran index was also used to

determine the possible existence of spatial clus-

tering patterns of richness and coverage values.

The richness and lichen coverage values

obtained for each sampling unit were inter-

polated using two mathematical algorithms:

Inverse Distance Weight (IDW) and Radial

Basis Function (Spline). Geostatistical Ana-

lyst® tool of ESRI’s ArcGis® software was

used to perform all the analyses. The algorithm

with the best performance was chosen to repre-

sent the distribution of lichens.

The performance of each interpolation

algorithms was performed using the leave

one out cross validation method (James et

al., 2013), and then calculating the index

Fig. 2. Lichen mapping in phorophytes, and counting and inventory of lichen areas A. Acetate location in each phorophyte

B. Acetate digitized with a metric scale C. Color identification. D. Quantification of the area.

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of agreement (IOA), root mean square error

(RMSE), and the RMSE standardized (RMSS).

The best mathematical performance algorithm

comprised the lowest RMSE and an RMSS and

IOA closer to 1 (Chai & Oceanic, 2015; Szpiro

et al., 2007; Willmott et al., 2012).

The relationships between intra-area lichen

richness and cover, land use, and roads dis-

tances were evaluated using the Spearman cor-

relation coefficient. The information regarding

the road network and land use was downloaded

from the official public website GeoMedellín

(Alcaldía de Medellín, 2021). The phorophytes

distances to the roads and soil types were cal-

culated using the Euclidean Distance tool of

ArcGis® Software; the number of lichen spe-

cies was calculated for different intervals of

distances (0-50 m, 50-100 m, 100-150 m, >150

m) in each area, with respect to the roads (Area

1 and 2) or respect to the forest area (Area 3).

Likewise, the Simpson index (Simpson, 1949)

were calculated for the same interval distances

and the resistance factor of each lichen specie

(Correa-ochoa et al., 2020) were calculated for

the same distance intervals. Finally, a Kruskal-

Wallis test was used to determine the existence

of statistically significant differences in the

resistance factor between distance intervals,

and the Mann–Whitney U were used to do

paired comparisons between the resistance

factor medias in the different distance intervals.

All analyses were carried out using software

RStudio version 3.6.3 (RStudio Team, 2020).

RESULTS

Air quality in biomonitoring areas: Fig.

3A shows the air pollution patterns associated

with the annual average of PM

2.5

for the study

area in 2018. The highest estimated concentra-

tion is in Area 1 with 26.93 µg/m

, followed by

Area 2 with 21.29 µg/m

, and finally, Area 3

with 18.84 µg/m

(Fig. 3B).

Lichen diversity, coverage, and rich-

ness in the biomonitoring areas: 110 species

of lichens were identified. The most frequent

was Phaeophyscia chloantha (Ach.) Moberg,

that was present in 33 of the 86 phorophytes

evaluated in all areas, followed by the Physcia

poncinsii Hue that was in 28 phorophytes. The

highest number of species was in Area 1 with

84, followed by Area 2 with 44, and finally, Area

3 with 23. In Area 1, Phaeophyscia chloantha

(Ach.) Moberg (21 of the 30 phorophytes) and

Candelaria concolor (Dicks.) Arnold (20 of the

30 phorophytes) were dominant and for Area 3,

Candelaria fibrosa (Fr.) Müll. Arg (12 of the 26

phorophytes) was dominant.

Fig. 3. A. Modeled Annual Concentration of PM

2.5

(general error of Spline interpolator with an error of ± 4.97 µg/m

) B.

Modeled Annual Concentration in the biomonitoring areas.

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According to the Kruskall-Wallis test, sta-

tistically significant differences were present

in at least one of the richness (P < 0.05) and

coverage (P= 0.00) medians in the biomonitor-

ing areas, and the paired comparisons (Mann-

Whitney U test) for richness values in the

biomonitoring areas, the test indicates statisti-

cally significant differences in the medians

for all the pair comparisons evaluated. Also,

2.5

values are negatively correlated with

richness (R= -0.7) and coverage (R= -0.4) in

the biomonitoring zones (Fig. 4A). Similarly,

for coverage values in the biomonitoring areas

indicated significant differences between areas

1-2 and 1-3 (P < 0.05 in both cases); while

no statistically significant differences were

found for this variable in areas 2-3 (P= 0.88)

(Fig. 4B).

Performance of spatial interpolation

algorithms and spatial patterns of lichen

richness and lichen coverage: The results

of the Moran Index indicate that clustering

patterns (With z values > 0) are presented in

the observations of richness (z= 6.7, P= 0.00)

and coverage (z= 3.65, P= 0.00) and there is

spatial autocorrelation of the observations with

P-values < 0.05, indicating that there is a prob-

ability of less than 1 % that these clusters are

the result of chance.

The Spline model was the chosen model,

since, in the case of richness, it presented a

lower RMSE and an RMSS and IOA closer to

one in relation to the IDW model. In the case

of coverage, it presented an RMSS closer to

one and a lower RMSE (Fig. 5A). The errors

associated with the interpolation model of the

richness and coverage values can be observed

in Fig. 5B and Fig. 5C, where the error associ-

ated with the interpolation model is 2.3 for

richness and 0.18 cm

of lichen/cm

of mapped

phorophyte for coverage.

Fig. 6 shows the land uses and pathways

in each biomonitoring area (Fig. 6A, Fig. 6B

and Fig. 6C). Spatial distribution patterns of

the lichen richness (Fig. 6D, Fig. 6E and Fig.

6F) and coverage values (Fig. 6G, Fig. 6H and

Fig. 6I) using the Spline spatial interpolation

algorithm. According to the scales of the color

patterns in the figure, it can be observed that

the richness and coverage of lichens was lower

in zone 1 compared to zones 2 and 3. Likewise,

the zone that presented a higher value of rich-

ness and coverage was zone 3.

Fig. 4. A. Lichen richness B. Lichen cover.

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Lichen coverage and richness intra

areas: A positive correlation was found

between richness and distance to main roads

(Fig. 7A) in Area 1 (R= 0.58). In the case of

coverage, no significant relationship was found

with respect to the distance to main roads (R=

0.1338, P= 0.47) in this area (Fig. 7B). In Area

2 we found a positively correlation (R= 0.5)

between lichen richness and distance to roads

(Fig. 7C). In the case of coverage (Fig. 7D), no

significant relationship was found with respect

to the distance to roads at this site (R= 0.04).

Finally, in Area 3 richness values were nega-

tively correlated (R= -0.5) with the distance to

the forest (Fig. 7E). For the case of the cover-

age (Fig. 7F), a moderate-low relationship was

found (R= -0.39). Note: For Area 3 richness

and cover values were correlated with distance

to forest zona, given that the phorophytes

sampled in this area were located on the sides

of the roads (distance to roads = 0 m) and not

inside the forest area to avoid edge effects that

could generate erroneous interpretations in the

data analysis.

Species diversity estimators and resis-

tance factor by distance intervals: Fig. 8

shows de number of species and the Simpson

index for the areas in the different distance

intervals to roads (Area 1 and 2) or to the for-

estry area (Area 3). The obtained results for area

1 (Fig. 8A) showed an increase in the number

of lichen species as the distance intervals are

further from the roads, presenting the highest

number at distances > 150 m with a total of 18

lichen species. Likewise, the Simpson index

was higher as the distance to the road increases.

On the other hand, area 2 (Fig. 8B) presented

the highest number of species in the distance to

roads between 50 and 100 m with a total of 31

species; thereafter, the richness decreases when

the distance to the roads increases, going from

having 23 species between 100 and 150 m to

11 at distances > 150 m. Regarding the Simp-

son index, its highest values were presented in

the distance intervals between 50-100 m and

between 100-150 m. Finally, in area 3 (Fig.

8C), the highest number of species occurred

in the distance interval between 0 m and 50 m

from the forest zone with a total of 90 species

and in that same distance interval the highest

value of the Simpson index was presented.

According to the results of the Kruskal-

Wallis test there were statistically differences

in the resistance factor for the different distance

groups. For area 1 the resistance factors of

the species located at distances > 150 m were

higher (Fig. 9A) and the Mann-Whitney U

test established that they were different from

those reported for the other distance intervals.

Likewise, this test established that there are no

differences in the resistance factors of the spe-

cies located in the distances between 0-50 m,

50-100 m and 100-150 m.

On the other hand, for area 2, there were

statistically significant differences in the

Fig. 5. A. Spatial interpolators comparison Spline and IDW B. Predicted values adjustment vs. richness observed values C.

Predicted values adjustment vs. observed values for coverage (cm

of lichen/cm

mapped phorophyte).

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Fig. 6. Spatial distribution patterns of lichen cover and richness values using the Spline interpolation, and land uses and

roads in each biomonitoring area. A. Area 1 land use and roads B. Area 2 land use and roads C. Area 3 land use and roads

D. Area 1 richness and main roads distances E. Area 2 richness and main roads distances F. Area 3 richness and main roads

distances G. Area 1 coverage and main roads distances H. Area 2 coverage and main roads distances I. Area 3 coverage

and main roads distances.

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resistance factor for the different distance

groups (P= 0.000) and the Mann-Whitney

U test identified two different homogeneous

groups that establish that the resistance factor

registered for the distance groups of 0-50 m,

50-100 m and 100-150 m does not present dif-

ferences and that the resistance factor of the

lichen species at distances > 150 m was dif-

ferent from that of the other distance groups.

In this sense, for this distance interval it was

found that the resistance factors were lower

(Fig. 9B). Finally, the resistance factor calcu-

lated for the lichen species in the different dis-

tance intervals for area 1, present statistically

significant differences (P= 0.000), and three

different homogeneous groups were identi-

fied according to the Mann-Whitney U test.

Therefore, the resistance factor is statistically

different in the three distance ranges. In this

sense, the resistance factor was higher for the

species found closer to the forest zone (0-50

m) (Fig. 9C).

Fig. 7. Lichen richness and coverage in relation to the distance to roads or in relation to distance to forest region. A. Area 1

richness and main roads distances B. Area 1 cover and main roads distances C. Area 2 richness and main roads distances D.

Area 2 cover and main roads distances E. Area 3 richness and forest zone distances F. Area 3 cover and forest zone distances.

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DISCUSSION

The results showed the presence of lichen

species that indicate the state of intervention

of the ecosystems. Species like P. chloantha

has been documented in previous studies as a

resistant specie, commonly found in strongly

anthropogenically altered environments (Ski-

rina & Kozhenkova, 2018). On the other hand,

Gupta et al. (2017) found that some species

belonging to the genus Phaeophyscia are con-

sidered as indicator species in urban areas due

to their ability to bioaccumulate heavy metals.

Still, the C. concolor species has been informed

as a tolerant species to contamination, as

reported in studies such as Ochoa-Jiménez et

al. (2015) and Saenz et al. (2007). Likewise, the

species C. fibrosa was dominant in Area 3; this

species was classified by Gonzales Vargas et al.

(2016) as a species sensitive to contamination.

Areas with better air quality had more

lichen richness and coverage, these findings

coincide with previous studies that found rela-

tionships between lichen diversity and air

Fig. 8. Lichen species richness and Simpson Index in relation to distance to roads for A. Area 1 and B. Area 2 or C. In

relation to distance at forest region for Area 3.

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quality in different areas (Gombert et al., 2004;

Käffer et al., 2011). Furthermore, the richness

and abundance of lichens significantly respond

to the concentrations of PM

2.5

in a linear and

negative way, so that the lichens in the study

areas are clear indicators of the concentra-

tion of particles, and according to Varela et

al. (2018), they can be used to estimate PM

2.5

concentrations in areas that do not have moni-

toring stations.

At the zonal level, patterns of relationships

were identified between observations of cover-

age and richness as well as the environmental

and pollution characteristics of the zones. In

Area 1, the contamination characteristics result

in low richness and coverage values, and

therefore, the establishment of lichens in the

area depends on the tolerance of the species,

their anatomy, their water retention capac-

ity, and their mechanisms for detoxification

of adverse effects, and according to experts, it

may be modified by different environmental

conditions and by the distribution area (Nimis

et al., 1990).

Area 2 presents a homogeneous environ-

ment in terms of land use. Hence, variations

and groups of coverage and richness could be

associated with roads as their main source of

pollution. For this zone, the richness increases

as the sampled phorophytes are further from

the surrounding pathways. This result is con-

sistent with reports such as those presented by

Perlmutter et al. (2017) and Will-Wolf et al.

(2015). Note that there are responses related

to changes in the composition of lichen com-

munities. Moreover, the spatial heterogeneity

of lichens in an area is greatly related to its land

uses; thus, the air quality indexes established

using lichens present higher values in garden

areas and lower values in the vicinity of vehic-

ular traffic. This coincides with the findings

found by Ribeiro et al. (2016). Likewise, the

Universidad Nacional de Medellín (located in

Area 2) is characterized by having green corri-

dors in its periphery, which makes the environ-

ment conducive to generating edge effects in

microclimate conditions for the establishment

of lichen communities (Aragón et al., 2015).

Furthermore, these green corridors help reduce

pollution levels (Hagler et al., 2011).

In area 3, richness values presented greater

responses for phorophytes that were within and

near the forest zone. These relationships can

be explained as the result of the adaptation of

lichens to the pollution gradients that occur as

the phorophytes sampled are located further

Fig. 9. Q Factor in relation to the distance to roads A. Area 1 and B. Area 2 or in relation to distance to forest region C.

Area 3.

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away from the urban–rural zone and approach

or locate in the forest zone. With respect to the

latter, Barreno and Pérez-Ortega (2003) state

that the proximity to forest mass structures is

a key factor that should be considered, given

that in or near forest areas, different types of

microenvironments can be created that are

immediately detected by the lichens and that

can contribute to the creation of adequate

conditions for the establishment of such com-

munities. In addition, the presence of green

areas increases humidity and is considered

a barrier to air pollutants (Barreno & Pérez-

Ortega, 2003).

In general, lichen richness presented better

relationships with respect to the environmental

and pollution conditions of the different areas.

This may be attributed to the fact that this indi-

cator may present a greater sensitivity to the

disturbances detected by lichens. In addition,

the richness of lichens is considered a good

element to determine for describing general

environmental state characteristics of an area

(Varela et al., 2018).

The obtained results indicate that the cor-

ticulous lichen communities present a response

to environmental factors and pollution in bio-

monitoring areas. Moreover, it yields the knowl-

edge of the quality of the environment with a

high spatial resolution. This knowledge can be

used as a warning to detect ecosystem damage

and can provide crucial information regarding

the generation of policies for land planning

and ecosystem conservation. Therefore, these

bioindicators can represent an environmental

pillar toward sustainable development. Bio-

indication can answer crucial questions about

risk without the need for an elaborate chemical

analysis. Lichens can be used as warning sys-

tems for environmental problems, evaluation

of the environmental health of an ecosystem,

among others, since the lichen species disap-

pearance may be more tangible than the “parts

per trillion” of a chemical (Kienzl et al., 2003).

Thus, establishing variations in the lichen com-

munities between and within areas that allowed

classifications and determining effects accord-

ing to their exposure to contamination sources

(routes) and land uses are possible; both are

important responses for the study of air quality

(Ulshöfer & Rosner, 2001).

Relationships in composition of lichen

communities associated to local pollution

sources exposure and its extent limits were

determined. This information may have great

relevance in public health studies, since human

beings located in these areas are exposed to

the same complex mixture of pollutants to

which lichens have been exposed to in this

study. Based on the above, studies such as

that of Cislaghi and Nimis (1997) highlight

the importance of associating the monitoring

of the diversity of lichens with the mortality

rates. This finding represents a valuable tool

for the knowledge of the environmental health

in cities like Medellín that register areas with

significant air quality alterations and that have

a robust monitoring network for the diagnosis

of air quality but do not have qualitative tools

to assess its impact on living beings.

Ethical statement: authors declare that

they all agree with this publication and made

significant contributions; that there is no con-

flict of interest of any kind; and that we fol-

lowed all pertinent ethical and legal procedures

and requirements. All financial sources are

fully and clearly stated in the acknowledge-

ments section. A signed document has been

filed in the journal archives.

ACKNOWLEDGMENTS

We thank to GIGA Research Group for

financing this project.

RESUMEN

Distribución espacial de las comunidades

de líquenes y mapeo de la contaminación del aire

en una ciudad tropical: Medellín, Colombia

Introducción: Existe suficiente evidencia científica de

los efectos nocivos de la contaminación atmosférica sobre

la salud de los seres humanos, fauna, flora y ecosistemas

en general. La primera opción para el diagnóstico de la

calidad del aire son las redes de monitoreo mecánicas

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o electrónicas, pero estas no permiten evaluar de forma

directa y precisa el impacto en los organismos vivos como

resultado de la exposición a contaminantes del aire.

Objetivo: Evaluar los cambios en la composición de las

comunidades de líquenes cortícolas como resultado a la

exposición de factores de estrés ambiental en áreas con

diferentes niveles de calidad del aire para diagnosticar el

estado de contaminación o intervención en una zona de una

manera más completa.

Métodos: Se determinaron los contrastes y cambios en la

calidad del aire, la riqueza y cobertura de líquenes cortíco-

las en respuesta a diferentes factores de estrés, como usos

del suelo y distancia a carreteras, en tres diferentes áreas de

biomonitoreo, las cuales fueron evaluadas usando GIS. Los

datos se presentan en un mapa de isolíneas con códigos en

escala de grises fácil de entender.

Resultados: Indicadores como cobertura (R= -0.4) y

riqueza (R= -0.7) de líquenes están inversamente correla-

cionados con las concentraciones de PM

2.5

en cada área.

Se identificaron un total de 110 especies de líquenes,

siendo Phaeophyscia chloantha (Ach.) Moberg y Physcia

poncinsii Hue las especies más frecuentes (presentes en 38

y 33 % de los 86 forófitos muestreados, respectivamente).

Las relaciones intra-área de riqueza de líquenes exhiben

relaciones significativas con respecto al uso del suelo y dis-

tancia a carreteras (con coeficientes de correlación mayores

a 0.5) y el índice de Simpson fue mayor a 0.9, en lugares

con mejores condiciones en términos de calidad del aire y

microambientes. Asimismo, los factores de resistencia cal-

culados sugieren que las especies más sensibles se pueden

encontrar en ambientes con menor grado de perturbación.

Conclusión: Estas evaluaciones representan más elemen-

tos de criterio para el diagnóstico de la salud ambiental en

las áreas de biomonitoreo.

Palabras clave: biomonitoreo; calidad del aire; líquenes

cortícolas; factores de resistencia; mapeo de líquenes;

diversidad de líquenes.

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