Use of lichens as bioindicators of contamination by agrochemicals and metals

Use of lichens as bioindicators of contamination by agrochemicals and metals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Use of lichens as bioindicators of contamination by agrochemicals and metals Shirley Gómez, Maria Vergara, Bryan Rivadeneira, Joan Rodríguez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4103676/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jul, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract The presence or absence of lichens serves as an indicator of the condition of an ecosystem and the degree to which it is contaminated by various agents, such as agrochemicals and metals. Evaluating the use of lichens as bioindicators of agrochemical contamination could provide a more comprehensive perspective on current contamination levels. Monitoring was, therefore, carried out over a four-month period in two study areas: a well-conserved control area and another treated area surrounded by agricultural crops. Data on the presence and abundance of lichens in each study area were recorded at 10 sampling points, a procedure that was repeated 16 times (every 15 days), and concentrations of heavy metals and “organophosphate” agrochemicals in the lichens collected were measured by means of gas chromatography. Generalized linear mixed models were used to assess abundance and richness, while general linear mixed models were used to attain Shannon diversity and Simpson dominance indices. Moreover, a multivariate analysis was performed in order to compare the lichen communities in both areas. The results indicated differences between the control and treated areas in terms of abundance and Simpson's dominance index, while no differences were found for the richness and diversity models. The PERMANOVA analysis also showed differences between the lichen communities in the two areas. The results also demonstrated that “ Canoparmelia caroliniana ” bioaccumulated metals in both areas. Finally, the concentrations of agrochemicals were higher in the treated area, and included toxic substances such as Methyl Parathion and Parathion, which are prohibited in Ecuador. In conclusion, the research underscores the importance of lichens as precise indicators of environmental health and contamination by agrochemicals and metals. Air quality environmental pollution gas chromatography epiphytes bioaccumulation organophosphates Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Air pollution is considered one of the most important environmental problems (Mateos et al., 2018 ). The large amounts of pollutants released into the atmosphere have a negative impact on air quality, thus making air pollution studies a crucial research area (Parviainen et al., 2019 ). The decrease in air quality as a result of the increase in atmospheric pollutants has various effects on living organisms, and has become an environmental problem at a global level (Saib et al., 2023 ). Of these, emissions of air pollutants from road traffic are, along with heavy metals (Cicek et al., 2001 ), widely recognized as the main sources of air pollution in cities around the world (Fioravanti et al., 2018; Hankey & Marshall 2017). These polluting compounds are also widely distributed in the environment owing to industrial and mining activities and domestic emissions, but particularly as the result of intensive agricultural activities (De Lurdes & Fiuza, 2011). The growing need to assess pollution levels has led researchers to seek versatile and reliable indicators that can reflect the complex dynamics of airborne pollutants (Parviainen et al. 2019 ). The health of ecosystems on which anthropic interactions have had an impact necessitates innovative approaches with which to monitor pollution levels (Mateos et al., 2018 ). There is a growing demand for the implementation of such approaches to monitor the air pollution caused by agrochemicals and metals (Yatawara and Dayananda, 2019 ). One alternative to this is the use of bioindicators, which are commonly employed to qualitatively identify and determine levels of air pollution (Tonneijck and Posthumus, 1987). They provide a suitable tool with which to assess these levels (Smodiš & Parr, 1999 ) by using organisms as bioindicators (Parviainen et al., 2019 ). These organisms absorb environmental pollutants and can serve as indicators of the bioavailability of specific pollutants over time, in some cases allowing comparisons between pollution levels in different geographic regions (Conti and Cecchetti, 2001 ). Bioindicators have been prominently used in air quality monitoring in both urban and rural settings (Boamponsem et al., 2010 ). Some of the organisms whose capacity as bioindicators has been demonstrated are epiphytic lichens, which are capable of bioaccumulating contaminants for prolonged periods and above their own physiological needs, thus making them an ideal means to evaluate the presence and bioavailability of specific contaminants. They could, therefore, become a key tool in pollution studies (Saib et al. 2023 ). Although their use as bioindicators is of the utmost importance, few studies apply them to issues concerning contamination by agrochemicals (Yatawara & Dayananda 2019 ). The accumulation of metals and agrochemicals in lichens is a relatively accurate indicator when evaluating chronic exposure to pollution in urban areas (Bosch-Roig et al., 2013 ). Unlike the physicochemical instruments at weather stations that provide instantaneous measurements, lichens can record long-term exposures owing to the long periods of time that they reside in the substrate (Parviainen et al., 2019 ). The objective of this study was to explore the role played by the lichen species “Canoparmelia caroliniana” as a toxitolerant bioindicator in an area of “natural” elemental composition. It has proven to be a valuable organism with which to detect pollution patterns, highlighting its ability to absorb heavy metals and agrochemicals. This capability provides a comprehensive view of environmental pollution. In this respect, we stress the importance of integrating field and laboratory methodologies that highlight favorable results in air pollution studies. All of the aforementioned issues led us to conceive the following objectives: i) to evaluate the use of lichens as bioindicators of contamination with agrochemicals; ii) to compare the richness, abundance, dominance and diversity of lichens in two study areas (control vs. a treated area); iii) to evaluate the role played by the lichen species “Canoparmelia caroliniana” as a bioindicator of concentrations of agrochemicals and metals, and iv) to identify the agrochemicals and metals bioaccumulated by the species “Canoparmelia caroliniana”. Material and methods Study area The present study was conducted in the La Pila Parish of the Montecristi Canton, covering an area of 98.68 km 2 . This region is situated in the southeast of the Manabí province on the Ecuadorian coast and is located along the E15 road, also known as the Pacific Road (Fig. 1 ). The area is characterized by an average annual temperature ranging between 23 and 26 C°, with an average annual precipitation averaging between 300 and 350 mm. According to the cartography provided by the IGM (the Geographic Military Institute), the Pila rural Parish includes the communities of Cerro Aguas Nuevas and Las Lagunas (GAD 2015). Two study areas were selected, each with ten monitoring points. Zone 1, known as the control zone, is well preserved and is located 4,890m away from agricultural areas. It is called “Cerro Aguas Nuevas”. Zone 2, known as the treated zone, is close to agricultural crops at a distance of 100 m and contains agrochemicals; It is called “Finca Virgen del Pilar”. Lichen monitoring The monitoring process was conducted as follows: test trees were selected on the basis of the criteria outlined in the guide “Forest species of the dry forests of Ecuador” published by Aguirre Mendoza (2012) and the characteristics proposed by Canseco et al. ( 2006 ), which led to the selection of the species Canoparmelia caroliniana . Following the suggestions of the aforementioned authors, trees with trunk diameters ranging from 20 to 50 cm and covered with lichens were chosen, while damaged trees were excluded from the research. Meshes were placed on each phorophyte (the tree supporting the lichen) at specified locations. Monitoring was carried out every 15 days over a four-month period spanning May, June, July and August 2022. This resulted in total of 8 monitoring sessions in each study area, thus yielding a sample size of 16 field visits, with 10 trees examined during each visit (n = 160). Species were identified by carrying out the following steps: i) a photographic record was taken on each monitoring date; ii) a field magnifying glass, field guides and lichen identification keys were employed in order to categorize lichen species into morphological groups, including crustacean, foliaceous and fruticulose, and iii) a database was compiled in which the lichen species were recorded and quantified. These data were used to calculate diversity and dominance indices. Samples in the field were selected by focusing on the species “ Canoparmelia caroliniana” , because it was a predominant species in the study areas. This species has a leafy thallus, which makes it moderately resistant to the presence of contaminants, signifying that it is possible to analyze concentrations of metal and “organophosphate” agrochemicals. A total of 20 samples of the selected species were collected. A CEM MARS 6 closed vessel acid digestion system was used in this study. The MARS 6 equipment has the capability of processing up to 40 samples simultaneously and of closely monitoring the temperature of each vessel using two highly sensitive IR internal temperature sensors, which are NIST traceable. The microwave program consisted of three steps: i) a 25 minute ramp to 180°C; ii) maintenance at 180°C for an additional 25 minutes, and iii) a cooling cycle. After completing the microwave digestion, the samples were transferred to test tubes and allowed to cool to below 40°C. Subsequently, they were subsequently transferred to 50 ml Fischer Scientific centrifuge tubes and adjusted to a final volume of 25 ml using ultrapure water for ICP chromatography Thermo Scientific. After the completion of the pipetting process, the samples underwent centrifugation in Eppendorf Centrifuge 5804/5804 R equipment for 5 minutes at 25 ºC and 5,000 RPM so as to eliminate any excess solids. Finally, the prepared samples were injected into the ICP Emission Spectrometer Thermo Scientific iCAP 6000 Series. The metals considered for this study included: Aluminum (Al), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Iron (Fe), Potassium (K), Magnesium (Mg), Manganese (Mn), Sodium (Na), Nickel (Ni) and Lead (Pb). Preparation of Gas Chromatography Samples We used 5g of the selected species, which was homogenized with a solution of methanol and H2O. The resulting mixture was then filtered using a Buchner funnel and filter paper. The residue obtained after filtration was washed three times with methanol and H2O and then alkalinized with NaOH before undergoing evaporation. The evaporation process was performed using a SyncorePlus apparatus at a temperature of 60ºC, 200 atm and 200 RPM, resulting in an aqueous residue. This residue was transferred to a 250 ml separating funnel with the help of 50 ml of distilled water. The extraction was carried out in two phases, and 12.5 ml of dichloromethane was used in each phase. After shaking the funnel, the samples were left undisturbed for 10 minutes to allow for separation into distinct phases. The samples collected were injected into the Thermo Scientific TRACE Series 1300 Gas Chromatograph for analysis, using an organophosphate kit for measurements. Chromatographics conditions The gas chromatograph was equipped with a TraceGOLD™ TG-ALC Plus Pesticides II column (30 m x 0.25 mm x 0.25 µm), a splitless injector and a flame ionization detector (FID). The initial column temperature was 100°C, but the temperature was subsequently ramped to 280°C at a rate of 10°C-min-1 and held for 4 minutes. Both the injector temperature and the detector temperature were set at 280°C using DCM as an internal standard for quantification of the components. The EPA 8270 Organophosphorus Pesticide Mix 2 kit was used, and all analytes were separated and quantified according to EPA METHOD 8141B. The retention times for each analyte are shown in the chromatogram provided in the supplemental materials information. Data analysis The total number of species was employed in order to determine recorded richness data, while the number of individuals identified was used to determine abundance. Moreover, the Shannon Diversity Index was calculated so as to simplify the specific biodiversity, and the Simpson dominance index was calculated by taking into account the most important species. In order to evaluate the effect of agrochemicals (control area vs. treated area) on lichen richness, diversity, dominance and abundance, we employed generalized linear mixed models (GzLMM) and general linear mixed models (GLMM). The area (treated vs. control) was included as a fixed factor in all models, that is, the abundance model (Model 1), the richness model (Model 2), the diversity model (Model 3) and the dominance model (Model 4). The tree (ten levels) was considered as a random factor. Statistical analyses were performed using InfoStats software. The dissimilarity and differences between species composition in the two areas (control and treated) were tested using a permutational multivariate analysis of variance (PERMANOVA). The Type III Sum of Squares was used, as this is appropriate in the case of an unbalanced design. All tests were performed with 9999 permutations with the aim of increasing the power and precision of the analysis (Anderson et al.2008) of the residuals in a reduced model (Anderson & Ter Braak 2003 ). We also performed a SIMPER analysis similarity percentage, (Clarke1993) in order to determine which species explained the greatest proportion of the differences between community composition in the two areas. SIMPER was used to identify those lichen species that were responsible for more than 90% of the dissimilarity between the two study areas and rank-abundance diagrams for each study area using the number of observed species (Sobs). Analyses were performed using PRIMERv6 software (Clarke & Gorley2006), including the PERMANOVA + add-on package (Anderson, Gorley & Clarke2008). In order to compare the values of metals and agrochemicals in the treated areas and the control areas, a Student's t-test for independent samples (normal variables) or a Mann Whitney U-test (for non-normal variables) was used. Results Descriptive results A total of 26 growth thallus biotypes were identified, with 21 in the control zone comprising 13 crustacean thallus, 4 leafy thallus and 4 fruticulous thallus. We identified 19 biotypes in the treated zone, including 13 crustacean thallus and 6 leafy thallus. Notably, species with fruticulous thallus growth were observed in the treated area. A total of 3,405 individuals were identified and counted, with 2,120 individuals being recorded in the control area and 1,285 individuals in the treated area (Table S1 ). The Shannon index in the control zone showed that there was a mean diversity value of 2.559, while that of the treated zone was 2.180. According to this index, the control zone consequently had a higher diversity. The Simpson index showed that the dominance value in the control zone was 6.539. The species contributing most to this dominance in the control zone were Chrysothrix xanthina and Trentepohlia aurea. In contrast, the treated zone had a dominance value was 7.688, with the greatest dominance attributed to Xanthoria parietina and Flavoparmelia caperata (Table S2). Effects of zone on the abundance, richness, dominance and diversity of species of lichen The results obtained for Models 1, 2, 3 and 4 are detailed in (Table 1 and Fig. 2 ). The abundance model for individuals showed that there were differences for the factor zone (control and treated area), which were higher for the control area (Table 1 and Fig. 2 a). However, the species richness model did not show a difference between the control and treated areas (Table 1 and Fig. 2 b). Similarly, the Shannon diversity index model did not reveal differences for the control and treated areas (Table 1 and Fig. 2 c). In contrast, the Simpson dominance index model showed significant differences for this factor (control and treated areas), which were greater in the treated area (Table 1 and Fig. 2 d). Table 1 F-values and coefficients of the variables included in the full models to explain lichen abundance (Model 1), lichen species richness (Model 2), lichen diversity (model 3) and lichen dominance (model 4) Variables F-value Coefficient ± SE Lichen abundance (Model 1) Intercept 21.70*** 5.08 ± 0.23 Treatment 40.23*** Treated=-0.52 ± 0.01 Lichen richness (Model 2) Intercept 25.64*** 2.3 ± 0.09 Treatment 0.02 Treated=-1.2E-03 ± 0.05 Lichen diversity (Model 3) Intercept 33.79*** 2.14 ± 0.06 Treatment 0.03 Treated = 2.4E-03 ± 0.09 Lichen dominance (Model 4) Intercept 134.10*** 6.34 ± 0.05 Treatment 17.68*** Treated = 1.18 ± 0.07 Coefficients for the level of fixed factor were calculated using reference value of “Control” in the variable “treatment”. *p < .05.; **p < .01.; ***p < .001 Effects of zone on the composition of the lichen community The permutational multivariate analysis of variance (PERMANOVA) revealed significant differences between the communities according to the zones (p = 0.001) (Table 2 ). The SIMPER indicated that the average dissimilarity between both zones was 68.10%. The species that made the greatest contribution to dissimilarity between the control and treated zones were Chrysothrix xanthina (12.94%), Trentepohlia aurea (11.89%), Cryptothecia striata (6.61%) and Punctelia subrudecta (6.50%) (Table S3). Moreover, an NMDS (Non-Metric Multidimensional Scaling) analysis demonstrated that the sampling points in the study areas were well-separated as regards space, thus indicating distinct communities (Fig. 3 ). Table 2 Permutational multivariate analysis of variance of lichen species composition based on zone (control and treated) Main test Variable df SS MS Pseudo-F Zo 1 54103 54103 31.102*** Res 158 2.74E5 1739.5 Total 158 3.28E5 ***p ≤ .001. Bioaccumulation of metals by the lichen species " Canoparmelia caroliniana " The Wilcoxon Test showed significant differences between the control and treated areas for the following metals: barium (p = 0.0030); cadmium (p = 0.0044) and sodium (p = 0.0130), whose levels were higher in the control area (Table 3 ). Moreover, there were higher concentrations of chromium (p = 0.0054) and copper (p = 0.0202) in the treated area (Fig. 4 ). Table 3 Wilcoxon test used to compare the means of metals bioaccumulated by the species “ Canoparmelia caroliniana ” Variable W P (2 colas) Al 82.00 0.0821 Ba 144.00 0.0030 Cd 141.50 0.0044 Cr 123.00 0.0054 Cu 102.00 0.0202 Fe 107.00 0.8798 K 81.00 0.0696 Mg 94.00 0.1180 Mn 101.00 0.7624 Na 130.00 0.0130 Ni 85.00 0.1306 Pb 91.00 0.2899 Bioaccumulation of organophosphates by the lichen species " Canoparmelia caroliniana " The bioaccumulation of organophosphates by the species 'Canoparmelia caroliniana' yielded results indicating significant differences (p < 0.05) between the control and treated areas, as presented in Table 4 . The sole organophosphate identified in the control zone was Thionazin, while the treated zone contained a broader spectrum of identified organophosphates, including Thionazin, Famphur, Methylparathion, Parathion, Dimethoate, and Sulfoted (Fig. S1 ). Table 4 Mann Whitney U test used to compare the average concentrations of each organophosphate in the areas (control and treated) Variable W p(2 colas) Thionazin 76.00 0.0283 Famphur 55.00 0.0001 Methylparathion 60.00 0.0002 Parathion 85.00 0.0305 Dimethoate 75.00 0.0052 Sulfoted 65.00 0.0006 Discussion Species of lichens identified in the study areas Lichen monitoring carried out for a period of 4 months in the control and treated areas allowed the identification of 26 species of lichens. In this context, it should be noted that thallus lichens with crustaceous growth have a high resistance to the presence of contaminants, while those with foliaceous thallus have a medium resistance and those with fruticulous thallus have a very low resistance. Some of the tolerant species identified in this research have already been reported in other previous studies (Fernández & Terrón 2003; Fontecha & Burgaz 2018 ), as is the case of Physcia aipolia , Phaeophyscia orbicularis and Xanthoria parietina , which were also identified in the treated area “Finca Virgen del Pilar”. Cohn Berger & Quezada ( 2016 ) mention that some of the toxitolerant leafy species are Physcia aipolia; Xanthoria parietina and Phaeophyscia orbicularis , while Fernández & Terrón (2003) state that the species mentioned above have optimal growth and are resistant in areas that show signs of contamination. Diploicia canescens was similarly one of the species that did not show any signs of necropsy during monitoring and was considered toxitolerant. This is supported by Mendoza (2018a), who reported that it was a resistant and tolerant species to contamination. Species of crustacean thallus were also identified, such as Lecanora muralis, Bacidia rosella , along with species that proved to be sensitive to fruticulous thallus such as Ramalina farinacea, Ramalina asahinae , Teloschistes exilis and Trentepohlia aurea , which were identified in the control zone “Cerro Aguas Nuevas”. Dahlberg ( 2015 ) identified Bacidia rosella as a species that grows in open and closed forests, which is why this species was identified only in the control area. Gutierrez (2020) deduced that the species Teloschistes exilis has symptoms and undergoes effects of atmospheric pollution that do not allow it to grow properly. The present study accordingly demonstrated that said species did not grow in the treated area during the monitoring period, as also occurred with the species Ramalina farinacea and Ramalina asahinae. With regard to the species that were identified in the control and treated areas, the following were found: Flavoparmelia caperata and Punctelia subrudecta growing on a foliaceous thallus, and Caloplaca cupulifera , Canoparmelia caroliniana , Pertusaria leioplaca and Cryptothecia striata growing on a crustacean thallus, all of which have been identified as good bioindicators owing to their degree of resistance to the presence of contaminants originating from anthropogenic activities such as agriculture. One of the species that had the greatest impact on this research was Canoparmelia caroliniana , since a gas chromatography laboratory analysis showed that it is a species that has medium-high sensitivity and is a good bioindicator of the presence of contaminants (Hale 1976 ; Nash & Elix 2002 ; Spielmann & Marcelli 2008 ). Species such as Pertusaria leioplaca and Cryptothecia striata can be identified in many scenarios owing to their high resistance to atmospheric pollutants (Gonzales 2018). Effects on the abundance, richness, dominance and diversity of species in the control and treated area The differences between the abundance of lichen individuals observed in the control and treated areas can be attributed to variations in vegetation cover, which became evident during the monitoring period. The control zone, which is characterized by its dense vegetation, provided an environment that is conducive to the thriving of epiphytic lichen species. For instance, Bacidia rosella , a species known to flourish in areas with robust plant cover, was more abundant in this zone. Haro (2017) mentions that the species composition changes along the alteration gradient. Deforestation stands out as the primary cause of epiphyte loss, particularly among shade-dependent epiphytes. This loss is attributed to changes in forest cover and microclimates within primary forests, leading to reduced environmental humidity and an increased exposure to light (Hawksworth et al.2005). The growth of epiphytic lichens in the treated area was clearly and significantly influenced by farming practices. Moreover, there is a notable disparity between the number of tree species in the two areas. Acevedo & Charry (2018) noted a close relationship between dominance and the diversity index (Shannon), where lower dominance corresponds to greater diversity. According to the aforementioned authors, the diversity results were similar in both areas (control and treated), but the dominance was higher in the treated area owing to the species Xanthoria parietina , which is highly tolerant and resistant to the growth of thallus crustacean. The species Flavoparmelia caperata , which has moderate tolerance with its foliaceous thallus growth, also contributes to this pattern. Bioaccumulation of metals by the lichen species " Canoparmelia caroliniana " Lichens are capable of indiscriminately accumulating a wide range of pollutants, including metals, which are often accumulated at levels above their own physiological needs (Bačkor & Loppi2009). Said tolerance has been related to the occurrence of successive phases of accumulation and the loss of particles over time until the concentrations of contaminants in the thallus reach equilibrium with the average levels of environmental contamination (Kularatne & De Freitas2013). The chemical composition of lichens consequently reflects the availability of elements present in the environment (Bargagli & Nimis2002) and provides us with information regarding their spatial and temporal variations (Paoli et al. 2015 ). Agricultural practices, which are characterized by the extensive use of pesticides, fertilizers and soil amendments, may inadvertently introduce metals into the environment as impurities (Nziguheba & Smolders2008). The repetitive application of these agricultural inputs can lead to the accumulation of toxic substances in the environment over time (Wuana & Okieimen2011). The analysis of metal concentrations in the lichen species " Canoparmelia caroliniana " within two distinct study areas (control and treated) is, therefore, a relevant aspect of this study. The analyses of metals such as barium (Ba), cadmium (Cd) and sodium (Na) showed that there were differences between zones, and higher concentrations in the control zone. In this respect, the concentrations of (0.16 ppm) barium in the control area and (0.04 ppm) in the treated area are related to the combustion of automobiles (Giampaoli et al. 2016 ). The concentration of Cadmium (Cd) found in the control area was 0.02 ppm, which was attributed to emissions from internal combustion vehicles, as mentioned by Chaparro et al. ( 2010 ). In contrast, the concentration of cadmium in the treated area was 1.50E-03 ppm, which can be linked to the use of agrochemicals such as insecticides and herbicides. Sodium (Na) was identified only in the control area, with a concentration of 1137.6 ppm, thus showing significant differences between the areas. Its presence is likely related to the geological conditions of the control zone, specifically "Cerro Aguas Nuevas". These findings highlight the influence of various sources, including automotive emissions and agricultural practices, on concentrations of metal in the lichen species " Canoparmelia caroliniana " within the study areas. Moreover, the analyses carried out in order to discover metals such as chromium (Cr) and copper (Cu) also showed that there were differences between zones but higher concentrations in the treated zone. In the control zone, these concentrations are attributed to vehicular traffic (abrasion from car engines, normal wear suffered by tires and combustion of cars), while in the treated area the concentrations are associated with the use of agrochemicals, since this area is surrounded by numerous crops. Roig et al. ( 2010 ), cites that these metals are present in numerous pesticides (fungicides and insecticides) and fertilizers used in agriculture, which are employed by farmers on their farms. All the results showed how the bioaccumulation of metals by the lichen species " Canoparmelia caroliniana " varies widely, and although the concentrations of metals are generally below toxic levels, Kothe et al. ( 2010 ) mention that the long-term risk results in the addition of more toxins to the environment. Bioaccumulation of organophosphates by the lichen species " Canoparmelia caroliniana " Agrochemicals encompass a wide range of chemical substances, which are typically categorized on the basis of their effects on pests. These categories include insecticides, fungicides, herbicides and rodenticides, with over 90% of pesticides falling in the organosynthetic category (Vásquez2015). In recent years, insecticides have accounted for 27% of all agrochemical imports in Ecuador. This group is often considered the most hazardous, as it includes substances that are highly toxic for humans and persist in the environment (Valarezo & Muñoz2011). Interviews conducted with farmers in the treated area revealed that insecticides and herbicides are frequently used for pest control during the cultivation of corn. The gas chromatography analyses performed in this study revealed the presence and concentrations of certain organophosphates in samples from the treated area. Thionazin (0.27 mg/L), Famphur (0.02 mg/L), Methyl Parathion (0.07 mg/L), Parathion (4.4E-03 mg/L), Dimethoate (0.03 mg/L), and Sulfoted (0.04 mg/L) were identified in the treated area, while only Thionazin (0.17 mg/L) was present in the control area (Fig. S1 ). These findings highlight significant differences between the agrochemical management carried out in the two zones, with a more extensive use of agrochemicals in the treated area known as "Finca Virgen del Pilar". It is worth noting that Parathion and Methyl Parathion, both of which were identified in the treated area, are on the list of prohibited insecticides in Ecuador (Valarezo & Muñoz 2011 ). Moreover, Methomyl, which is classified as extremely toxic (Category Ib), is reportedly used extensively in the treated area, although it was not part of the organophosphate kit used for gas chromatography analysis. Another highly toxic pesticide in Category II, Dimethoate, was identified solely in the treated area. The contamination stemming from agrochemicals can lead to the dispersion of their residues into the environment, posing risks to both biotic (animals and plants) and abiotic (air, water, and soil) components. These contaminants can potentially come into contact with humans through various exposure routes, including dermal, respiratory and digestive pathways (Gavidia 2020 ). In this context, insecticides, which are one of the primary tools in agriculture, raise ecological concerns owing to their potentially adverse effects on non-target organisms, such as soil nutrient recyclers, plant pollinators and pest predators. Moreover, they can impact on food products at higher trophic levels (Devine et al. 2008 ). Lichens are highly valuable as bioindicators with which to assess air pollution owing to their sensitivity and rapid response to variations in environmental conditions. However, most studies employing lichen bioaccumulation have focused on urban or heavily industrialized areas, while limited research has been carried out in remote regions (Bergamaschi et al. 2004 ). It is for this reason that the present study took place in an area with a seemingly natural elemental composition, since this would allow meaningful comparisons between an area with a presumed lower contamination and one with higher levels of contamination. Conclusion In conclusion, our study underscores that the lichen species " Canoparmelia caroliniana " has the potential to be used as a robust bioindicator for the bioaccumulation of metals and agrochemicals. It is worth noting that our findings are derived from observations of a single lichen species over a specific exposure period. It is, therefore, imperative that this information be taken into account by researchers and policymakers alike when addressing environmental concerns. These results provide valuable insights into the development of effective research and monitoring strategies, which can, in turn, inform evidence-based decisions and policies regarding metal and agrochemical contamination. As we continue to advance in our understanding of the impact of these contaminants on our ecosystems, lichen species such as " Canoparmelia caroliniana " prove to be invaluable allies in the pursuit of a healthier and more sustainable environment. Declarations Ethical Approval 'Not applicable' Consent to Participate 'Not applicable' Consent to Publish 'Not applicable' Authors Contributions AJC, JMR and SPG conceived and designed the study. SPG and MBV performed the data collection. SPG and BR performed laboratory analysis. AJC, SPG and MBV analyzed the data and wrote the first draft of the manuscript. All authors read and approved the final version of the manuscript. Funding The authors would like to thank: Sally Newton for her favorable comments; Luis Zambrano for his support in the laboratory, and Alex Quimis and Yamel Alvarez for their support as regards locating meshes for monitoring. AJC is supported by a 'Juan de la Cierva' contract (IJC2020-042629-I) funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR. He is also supported by research project PROG-015-DIP-PROYE-001-2019 and the Andalusian Agency for International Development Cooperation AACID (INVODES 2021UC001 projects). Our thanks also go to the Departamento de Química Orgánica, Universidad de Córdoba, Edificio Marie Curie (C-3), Campus de Rabanales, Ctra. Nnal. IV-A, Km 396, E14014, Córdoba, Spain and the Laboratorio de Análisis Químicos y Biotecnológicos, Instituto de Investigación, Universidad Técnica de Manabí, S/N, Avenida Urbina y Che Guevara, Portoviejo, 130104, Ecuador. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper. Conflicts of interest The authors declare that they have no conflict of interest. References Agnan, Y., Probst, A., & Séjalon Delmas, N. (2017). Evaluation of lichen species resistance to atmospheric metal pollution by coupling diversity and bioaccumulation approaches: A new bioindication scale for French forested areas. Ecological Indicators, 72, 99–110. https://doi.org/10.1016/j.ecolind.2016.08.006 Aguirre, Z. (2013). Guide to methods for measuring biodiversity. Anderson, M.J. & Ter Braak, C.J.F. (2003) Permutation tests for multi-factorial analysis of variance. Journal of Statistical Computation and Simulation, 73, 85–113. Anderson, M.J., Gorley, R.N. & Clarke, K.R. (2008) PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. PRIMER-E Ltd, U.K. Antonucci, A., Vitali, M., Avino, P., Manigrasso, M., & Protano, C. (2016). Sensitive multiresidue method by HS-SPME/GC-MS for 10 volatile organic compounds in urine matrix: a new tool for biomonitoring studies on children. Analytical and Bioanalytical Chemistry, 408(21), 5789–5800. https://doi.org/10.1007/S00216-016-9682-X/METRICS Attanayaka, A. N. P. M., & Chandrani Wijeyaratne, S. (2013). Corticolous lichen diversity, a potential indicator for monitoring air pollution in tropics. J.Natn.Sci.Foundation Sri Lanka, 41(2), 131–140. Bačkor, M., & Loppi, S. (2009). Interactions of lichens with heavy metals. In Biologia Plantarum (Vol. 53, Issue 2, pp. 214–222). https://doi.org/10.1007/s10535-009-0042-y Bargagli, R., & Nimis, P. L. (2002). Guidelines for the use of epiphytic lichens as biomonitors of atmospheric deposition of trace elements. Monitoring with lichens—monitoring lichens, 295-299. Bergamaschi, L., Rizzio, E., Giaveri, G., Profumo, A., Loppi, S., & Gallorini, M. (2004). Determination of baseline element composition of lichens using samples from high elevations. Chemosphere, 55(7), 933-939. Berner, K., & Berner, R. A. (2012). Global environment: Water, air, and geochemical cycles. In Global Environment: Water, Air, and Geochemical Cycles (Second Edition). https://doi.org/10.1080/00207233.2012.753739 Boamponsem, L. K., Adam, J. I., Dampare, S. B., Nyarko, B. J. B., & Essumang, D. K. (2010). Assessment of atmospheric heavy metal deposition in the Tarkwa gold mining area of Ghana using epiphytic lichens. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 268(9), 1492–1501. https://doi.org/10.1016/J.NIMB.2010.01.007 Boonpeng, C., Sangiamdee, D., Noikrad, S., & Boonpragob, K. (2023). Lichen biomonitoring of seasonal outdoor air quality at schools in an industrial city in Thailand. Environmental Science and Pollution Research, 30(21), 59909–59924. https://doi.org/10.1007/S11356-023-26685-Z/METRICS Bosch-Roig, P., Barca, D., Crisci, G. M., & Lalli, C. (2013). Lichens as bioindicators of atmospheric heavy metal deposition in Valencia, Spain. Journal of Atmospheric Chemistry, 70(4), 373–388. https://doi.org/10.1007/S10874-013-9273-6/METRICS Brotas, V., Sim-Sim, M., Gordo, L., Bloisé, C., Garcia, C., Martins, A., Neves, A. M., & Tracana, A. (2016). Relatório técnico sobre o Programa de Monitorização dos Ecossistemas Terrestre e Estuarino na Envolvente à CTRSU de S. João da Talha. Bungartz, Frank, Søchting, U., & Arup, U. (2020). Teloschistaceae (lichenized Ascomycota) from the Galapagos Islands: A phylogenetic revision based on morphological, anatomical, chemical, and molecular data. Plant and Fungal Systematics, 65(2), 515–576. https://doi.org/10.35535/pfsyst-2020-0030 Canseco, A., Anze, R., & Franken, M. (2006). Comunidades de líquenes : indicadores de la calidad del aire en la ciudad de La Paz , Bolivia. Acta Nova, 3, 286–307. http://www.scielo.org.bo/pdf/ran/v3n2/v3n2_a09.pdf Cicek, A., Koparal, A. S., Catak, S., & Ugur, S. (2001). The levels of some heavy metals and nutritional elements in the samples from soils and tree-leaves growing in the vicinity of Seyitomer Thermal Power Plant in Kutahya (Turkey). In Proceedings of 2nd international symposium on air quality management at urban regional and global scales, Istanbul (pp. 157-162). Cislaghi, C., & Nimis, P. L. (1997). Lichens, air pollution and lung cancer. Nature 1997 387:6632, 387(6632), 463–464. https://doi.org/10.1038/387463a0 Clarke, K. R. (1993). Non-parametric multivariate analyses of changes incommunity structure. Australian Journal of Ecology, 18, 117–143. Clarke, K.R. & Gorley, R.N. (2006) PRIMER v6: User Manual/Tutorial. PRIMER-E, U.K. Cohn Berger, G., & Quezada, M. (2016). Líquenes como bioindicadores de contaminación aérea en el corredor metropolitano de la ciudad de Guatemala. Revista Científica de La Facultad de Ciencias Químicas y Farmacia, 26(1), 20–39. Conti, M. E., & Cecchetti, G. (2001). Biological monitoring: lichens as bioindicators of air pollution assessment--a review. Environmental Pollution (Barking, Essex : 1987), 114(3), 471–492. https://doi.org/10.1016/S0269-7491(00)00224 . Chaparro, M. A., Marié, D. C., Gogorza, C. S., Navas, A., & Sinito, A. M. (2010). Magnetic studies and scanning electron microscopy—X-ray energy dispersive spectroscopy analyses of road sediments, soils and vehicle-derived emissions. Studia Geophysica et Geodaetica, 54, 633-650. Dahlberg, A. (2015). Bacidia rosella . The Global Fungal Red List Initiative. http://iucn.ekoo.se/iucn/species_view/378392/ De Lurdes Dinis, M., Fiúza, A. (2011). Exposure Assessment to Heavy Metals in the Environment: Measures to Eliminate or Reduce the Exposure to Critical Receptors. In: Simeonov, L., Kochubovski, M., Simeonova, B. (eds) Environmental Heavy Metal Pollution and Effects on Child Mental Development. NATO Science for Peace and Security Series C: Environmental Security, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0253-0_2 Devine, G. J., Eza, D., Ogusuku, E., & Furlong, M. J. (2008). Uso de insecticidas:contexto yconsecuencias ecológicas. Rev. Peru. Med. Exp. Salud Publica, 25(1),74–100. http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1726-46342008000100011&lng=es&nrm=iso&tlng=es Ferencova, Z. (2012). Estudio morfológico comparado de los caracteres generativos en relación con linajes monofiléticos de la familia Parmeliaceae (Lecanorales, Ascomycota) (Doctoral dissertation, Universidad Complutense de Madrid).https://eprints.ucm.es/id/eprint/22628/1/T34425.pdf Fernández-Salegi, A., & Terrón Alfonso, A. (2003). Biomonitorización de la calidad del aire en los alrededores de La Robla (León). Ecosistemas, 12(2). Recuperado a partir de https://www.revistaecosistemas.net/index.php/ecosistemas/article/view/349 Fontecha, A., & Burgaz, A. R. (2018). Uso de los líquenes como bioindicadores de la calidad del aire: estado de la Ciudad Universitaria (Madrid, España). Botanica Complutensis, 42, 57–68. https://doi.org/10.5209/bocm.61368 Gavidia, P. (2020). Plaguicidas: efectos dañinos en el medio ambiente y la salud. Tiempo.Com, 1–1. https://www.tiempo.com/noticias/actualidad/plaguicidas-medio-ambiente-y-salud.html Gerault, A. (2019). Phaeophyscia Orbicularis. http://www.lichensmaritimes.org/index.php?task=fiche&lichen=543&lang=en Giampaoli, P., Wannaz, E. D., Tavares, A. R., & Domingos, M. (2016). Suitability of Tillandsia usneoides and Aechmea fasciata for biomonitoring toxic elements under tropical seasonal climate. Chemosphere, 149, 14-23. Gobierno Autonomo Descentralizado de la Parroquia La Pila. (2015). Plan de Desarrollo y Ordenamiento Territorial de la Parroquia “La Pila” del cantón Montecristi Provincia de Manabí. http://app.sni.gob.ec/sni link/sni/PORTAL_SNI/data_sigad_plus/sigadplusdocumentofinal/1360025700001_03 PDOT - GR LA PILA_12-12-2016_16-42-12.pdf Hale, M. E. 1976. A monograph of the lichen genus Pseudoparmelia Lynge (Parmeliaceae). Smithsonian Contr. Bot. 31: 1-62. Hawksworth, D. L., Iturriaga, T., & Crespo, A. (2005). Lichens as rapid bioindicators of pollution and habit disturbance in the tropics. In Revista Iberoamericana de Micologia (Vol. 22, Issue 2, pp. 71–82). Elsevier Doyma. https://doi.org/10.1016/s1130-1406(05)70013-9 Herrero, J., Herranz, J. M., & Martínez, M. J. (2004). Quejigares Manual de Gestión Natura 2000 (Primera ed). https://www.castillalamancha.es/sites/default/files/documentos/pdf/20140226/rn2000_quejigares.pdf Koch, N. M., Matos, P., Branquinho, C., Pinho, P., Lucheta, F., Martins, S. M. de A., & Vargas, V. M. F. (2019). Selecting lichen functional traits as ecological indicators of the effects of urban environment. Science of The Total Environment, 654, 705–713. https://doi.org/10.1016/J.SCITOTENV.2018.11.107 Kothe, E., Dimkpa, C., Haferburg, G., Schmidt, A., & Schmidt, A. (2010). Streptomycete heavy metal resistance: extracellular and intracellular mechanisms. I. Sherameti, A. Varma (Eds.), , Soil Biology, Springer, Berlin/Heidelberg, 225–235. Kularatne, K. I. A., & De Freitas, C. R. (2013). Epiphytic lichens as biomonitors of airborne heavy metal pollution. Environmental and experimental botany, 88, 24-32.https://www.sciencedirect.com/science/article/abs/pii/S0098847212000494 Loppi, S. (2014). Lichens as sentinels for air pollution at remote alpine areas (Italy). Environmental Science and Pollution Research, 21, 2563-2571. Loppi, S. (2019). May the Diversity of Epiphytic Lichens Be Used in Environmental Forensics? Diversity 2019, Vol. 11, Page 36, 11(3), 36. https://doi.org/10.3390/D11030036 Loppi, S., Pirintsos, S. A., & De Dominicis, V. (1999). Soil contribution to the elemental composition of epiphytic lichens (Tuscany, central Italy). Environmental Monitoring and Assessment, 58(2), 121–131. https://doi.org/10.1023/A:1006047431210 Marié, D. C., Chaparro, M. A., Gogorza, C. S., Navas, A., & Sinito, A. M. (2010). Vehicle-derived emissions and pollution on the road autovia 2 investigated by rock-magnetic parameters: A case study from Argentina . Studia Geophysica et Geodaetica, 54, 135-152. Mateos, A. C., Amarillo, A. C., Carreras, H. A., & González, C. M. (2018). Land use and air quality in urban environments: Human health risk assessment due to inhalation of airborne particles. Environmental Research, 161, 370–380. https://doi.org/10.1016/J.ENVRES.2017.11.035 Matusmoto, M. & Mizoguchi, T. (1995). A simple and simultaneous measurement method of sulphur dioxide in atmosphere using molecular diffusion sampler, International Seminar on the Simple Measuring and Evaluation Method on Air Pollution, Japan Society of Air Pollution (JSAP) and Environmental Research and Training Center (ERTC), Pathumthani, Thailand. Mikhaylov, A. (2020). Lichens as indicators of atmospheric pollution in urban ecosystems. Israel Journal of Ecology and Evolution, 67(1–2), 60–68. https://doi.org/10.1163/22244662-BJA10016 Mohamed, A. H., M’hamed, M., Hichem, M. F. and B. M., Mohamed, A. H., M’hamed, M., & Hichem, M. F. and B. M. (2016). Air Pollution Mapping with Bio‐Indicators in Urban Areas. Air Quality - Measurement and Modeling. https://doi.org/10.5772/65299 Nash I. I. , T. H. & J. A. Elix. 2002. Canoparmelia. En: Nash III, T. H., B. D. Ryan, C. Gries & F. Bungartz (eds.), Lichen Flora of the Greater Sonoran Desert Region , Arizona State University, Vol. 1: 122-125. Lichens Unlimited, Arizona State University, Arizona. Nziguheba, G., & Smolders, E. (2008). Inputs of trace elements in agricultural soils via phosphate fertilizers in European countries. https://www.sciencedirect.com/science/article/abs/pii/S0048969707010030 OchoaJ, DA, Cueva, A., Prieto, M., Aragón, G., & Benitez, Á. (2015). Changes in the composition of epiphytic lichens related to air quality in the city of Loja (Ecuador). Caldasia , 37 (2), 333-343.https://doi.org/10.15446/caldasia.v37n2.53867 Paoli, L., Munzi, S., Guttová, A., Senko, D., Sardella, G., & Loppi, S. (2015). Lichens as suitable indicators of the biological effects of atmospheric pollutants around a municipal solid waste incinerator (S Italy). Ecological Indicators, 52, 362–370. https://doi.org/10.1016/j.ecolind.2014.12.018 Parviainen, A., Casares-Porcel, M., Marchesi, C., & Garrido, C. J. (2019). Lichens as a spatial record of metal air pollution in the industrialized city of Huelva (SW Spain). Environmental Pollution, 253, 918–929. https://doi.org/10.1016/J.ENVPOL.2019.07.086 Qu, C., Doherty, A. L., Xing, X., Sun, W., Albanese, S., Lima, A., Qi, S., & De Vivo, B. (2018). Polyurethane Foam-Based Passive Air Samplers in Monitoring Persistent Organic Pollutants: Theory and Application. Environmental Geochemistry: Site Characterization, Data Analysis and Case Histories: Second Edition, 521–542. https://doi.org/10.1016/B978-0-444-63763-5.00021-5 Roig, PB, Barca, D., Crisci, GM, & Lalli, C. (2010). Study on Lichens as bioindicators of heavy metal content in the environment of the Church of Saints Johns of Valencia. Conservation and Restoration Studies, (2), 21-37. https://orcid.org/0000-0003-2019-399X Rubiano L. (1987). Delimitación de áreas de isocontaminación en Cali y Medellín utilizando líquenes como bioindicadores. Pérez Arbelaezia; 1(4): pp.7-41. Saib, H., Yekkour, A., Toumi, M., Guedioura, B., Benamar, M. A., Zeghdaoui, A., Austruy, A., Bergé-Lefranc, D., Culcasi, M., & Pietri, S. (2023). Lichen biomonitoring of airborne trace elements in the industrial-urbanized area of eastern algiers (Algeria). Atmospheric Pollution Research, 14(1), 101643. https://doi.org/10.1016/J.APR.2022.101643 Seppänen, A. (toim.), Laatikainen, T., Piispanen, J., Poikolainen, J., Karhu, J., Seppänen, R., & Kubin, E. (2017). Kainuun bioindikaattoriselvitys. https://www.doria.fi/handle/10024/147661 Smodiš, B., & Parr, R. M. (1999). Biomonitoring of air pollution as exemplified by recent IAEA programs. Biological Trace Element Research, 71–72(1), 257–266. https://doi.org/10.1007/BF02784211/METRICS Spielmann, AA, & Marcelli, MP (2008). Parmeliaceae (lichenized Ascomycota) in the ravines and peraus of the Serra Geral slope, Vale do Rio Pardo, Rio Grande do Sul, Brazil. II. Genera Canoparmelia, Hypotrachyna, Myelochroa, Parmelinopsis and Relicina. Iheringia, Botanical Series. , 63 (2), 193-212. Torres, D., & Capote, T. (2004). Agrochemicals a global environmental problem: use of chemical analysis as a tool for environmental monitoring. Ecosystems, 13(3), 2–6. Valarezo, O., & Muñoz, X. (2011). Insecticides for Agricultural use in Ecuador. INiAP, 3–6. https://repositorio.iniap.gob.ec/bitstream/41000/1253/1/INIAP boletín divulgativo 401.pdf Varrica, D., Lo Medico, F., & Alaimo, M. G. (2022). Air Quality Assessment by the Determination of Trace Elements in Lichens (Xanthoria calcicola) in an Industrial Area (Sicily, Italy). International Journal of Environmental Research and Public Health 2022, Vol. 19, Page 9746, 19(15), 9746. https://doi.org/10.3390/IJERPH19159746 Vásquez, M. (2015). Study of the effects of atmospheric pollution on lichen bioindicators and their degradation.460. Wuana, R. A., & Okieimen, F. E. (2011). Heavy Metals in Contaminated Soils: A Review of Sources, Chemistry, Risks and Best Available Strategies for Remediation. ISRN Ecology, 2011, 1–20. https://doi.org/10.5402/2011/402647 Yatawara, M., & Dayananda, N. (2019). Use of corticolous lichens for the assessment of ambient air quality along rural–urban ecosystems of tropics: a study in Sri Lanka. Environmental Monitoring and Assessment, 191(3), 1–14. https://doi.org/10.1007/S10661-019-7334-2/METRICS Supplementary Files SUPPLEMENTARYMATERIALPamelaAnt.Carp.Mar.24.docx Cite Share Download PDF Status: Published Journal Publication published 25 Jul, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 01 May, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers invited by journal 05 Apr, 2024 Editor invited by journal 04 Apr, 2024 Editor assigned by journal 25 Mar, 2024 First submitted to journal 20 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4103676","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287906821,"identity":"85d567f5-27d5-4931-9d83-8d4c9a53a965","order_by":0,"name":"Shirley Gómez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYPCCAwxs7EDqAxCDGcRpYWZgYJwB0sJMrBYGoEpmHhCbkBZz9tOJD79U3JHnY2Y+9tjm1zYgg4Hxw8cc3Fose3I3G8uceWbYxsyWbpzbdxvIYGCWnLkNtxaDA7nbpCXbDjO2MfOYSef23AYygN7hxafl/Nvtv4Fa7MFaLHtu2xPWciN3G+PHtsOJYC0MP24nEtRiOePtZmmGM8+SgX5Jk+xtuA1kMDbj9Ys5f+7Gjz8q7tjOb28+JvHjz20Q4+CHj/gcxgCLDhBgbAOTDbjVQ7Uw/oBz/+BVPApGwSgYBSMUAAAx40+cRJDRuAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7032-6223","institution":"Universidad Estatal del Sur de Manabi","correspondingAuthor":true,"prefix":"","firstName":"Shirley","middleName":"","lastName":"Gómez","suffix":""},{"id":287906822,"identity":"7fb80125-f684-49d2-b57b-865b94b57492","order_by":1,"name":"Maria Vergara","email":"","orcid":"","institution":"Universidad Estatal del Sur de Manabi","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Vergara","suffix":""},{"id":287906823,"identity":"e6fef1a7-786c-4f2d-a522-264a4aed6868","order_by":2,"name":"Bryan Rivadeneira","email":"","orcid":"","institution":"Universidad Técnica de Manabí: Universidad Tecnica de Manabi","correspondingAuthor":false,"prefix":"","firstName":"Bryan","middleName":"","lastName":"Rivadeneira","suffix":""},{"id":287906824,"identity":"0763fcc6-939c-4b6d-a4af-40f48b317e59","order_by":3,"name":"Joan Rodríguez","email":"","orcid":"","institution":"Universidad Técnica de Manabí: Universidad Tecnica de Manabi","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Rodríguez","suffix":""},{"id":287906825,"identity":"2b1bb885-eac7-45dc-a272-41f4a5498335","order_by":4,"name":"Antonio Carpio","email":"","orcid":"","institution":"Universidad de Córdoba: Universidad de Cordoba","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Carpio","suffix":""}],"badges":[],"createdAt":"2024-03-14 23:01:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4103676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4103676/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-024-34450-z","type":"published","date":"2024-07-25T16:16:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54443389,"identity":"d880efe1-dd51-4b8e-b27a-52c3bdb36019","added_by":"auto","created_at":"2024-04-10 15:44:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31162,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic map of the study areas, monitoring and sampling points.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4103676/v1/b5b9d47fe60beffc290cfa3b.png"},{"id":54443390,"identity":"a2b141f4-81ad-4f19-9768-276d558ad55f","added_by":"auto","created_at":"2024-04-10 15:44:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157812,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted mean values (±S.E.) of the a) abundance, b) richness, c) diversity and d) dominance of lichens between zones (control and treated). Bars indicate the standard error. Different letters indicate significant differences among groups according to Fisher´s LSD post-hoc tests (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4103676/v1/0b35bf16cb9590e4abafb3b7.jpg"},{"id":54443388,"identity":"ed911ebb-62fe-4aa7-a14b-1782eaef6bbb","added_by":"auto","created_at":"2024-04-10 15:44:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147972,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) for sample representation (control and treated).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4103676/v1/9f8e502dea7bd32f84bfa934.jpg"},{"id":54443387,"identity":"d7adaf8d-6d24-4e50-bc3c-1041dd8f18c1","added_by":"auto","created_at":"2024-04-10 15:44:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249244,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation map and box plots for metal concentrations in the control and treated areas.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4103676/v1/2f1743bb310f6a6601ecc42a.jpg"},{"id":61596535,"identity":"f71505aa-c0e9-4670-899e-ecf72ab9b067","added_by":"auto","created_at":"2024-08-01 17:28:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1276611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4103676/v1/223501d9-74e4-4272-824f-a7bfec226a28.pdf"},{"id":54443391,"identity":"bc93dbeb-c1de-4701-82a8-e9b776580042","added_by":"auto","created_at":"2024-04-10 15:44:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":100217,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALPamelaAnt.Carp.Mar.24.docx","url":"https://assets-eu.researchsquare.com/files/rs-4103676/v1/2a1ebfc60ebc024456a71dba.docx"}],"financialInterests":"","formattedTitle":"Use of lichens as bioindicators of contamination by agrochemicals and metals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAir pollution is considered one of the most important environmental problems (Mateos et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The large amounts of pollutants released into the atmosphere have a negative impact on air quality, thus making air pollution studies a crucial research area (Parviainen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The decrease in air quality as a result of the increase in atmospheric pollutants has various effects on living organisms, and has become an environmental problem at a global level (Saib et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Of these, emissions of air pollutants from road traffic are, along with heavy metals (Cicek et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), widely recognized as the main sources of air pollution in cities around the world (Fioravanti et al., 2018; Hankey \u0026amp; Marshall 2017). These polluting compounds are also widely distributed in the environment owing to industrial and mining activities and domestic emissions, but particularly as the result of intensive agricultural activities (De Lurdes \u0026amp; Fiuza, 2011). The growing need to assess pollution levels has led researchers to seek versatile and reliable indicators that can reflect the complex dynamics of airborne pollutants (Parviainen et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe health of ecosystems on which anthropic interactions have had an impact necessitates innovative approaches with which to monitor pollution levels (Mateos et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There is a growing demand for the implementation of such approaches to monitor the air pollution caused by agrochemicals and metals (Yatawara and Dayananda, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). One alternative to this is the use of bioindicators, which are commonly employed to qualitatively identify and determine levels of air pollution (Tonneijck and Posthumus, 1987). They provide a suitable tool with which to assess these levels (Smodiš \u0026amp; Parr, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) by using organisms as bioindicators (Parviainen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These organisms absorb environmental pollutants and can serve as indicators of the bioavailability of specific pollutants over time, in some cases allowing comparisons between pollution levels in different geographic regions (Conti and Cecchetti, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Bioindicators have been prominently used in air quality monitoring in both urban and rural settings (Boamponsem et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome of the organisms whose capacity as bioindicators has been demonstrated are epiphytic lichens, which are capable of bioaccumulating contaminants for prolonged periods and above their own physiological needs, thus making them an ideal means to evaluate the presence and bioavailability of specific contaminants. They could, therefore, become a key tool in pollution studies (Saib et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although their use as bioindicators is of the utmost importance, few studies apply them to issues concerning contamination by agrochemicals (Yatawara \u0026amp; Dayananda \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The accumulation of metals and agrochemicals in lichens is a relatively accurate indicator when evaluating chronic exposure to pollution in urban areas (Bosch-Roig et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Unlike the physicochemical instruments at weather stations that provide instantaneous measurements, lichens can record long-term exposures owing to the long periods of time that they reside in the substrate (Parviainen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe objective of this study was to explore the role played by the lichen species \u003cem\u003e\u0026ldquo;Canoparmelia caroliniana\u0026rdquo;\u003c/em\u003e as a toxitolerant bioindicator in an area of \u0026ldquo;natural\u0026rdquo; elemental composition. It has proven to be a valuable organism with which to detect pollution patterns, highlighting its ability to absorb heavy metals and agrochemicals. This capability provides a comprehensive view of environmental pollution. In this respect, we stress the importance of integrating field and laboratory methodologies that highlight favorable results in air pollution studies.\u003c/p\u003e \u003cp\u003eAll of the aforementioned issues led us to conceive the following objectives: i) to evaluate the use of lichens as bioindicators of contamination with agrochemicals; ii) to compare the richness, abundance, dominance and diversity of lichens in two study areas (control vs. a treated area); iii) to evaluate the role played by the lichen species \u003cem\u003e\u0026ldquo;Canoparmelia caroliniana\u0026rdquo;\u003c/em\u003e as a bioindicator of concentrations of agrochemicals and metals, and iv) to identify the agrochemicals and metals bioaccumulated by the species \u003cem\u003e\u0026ldquo;Canoparmelia caroliniana\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy area\u003c/h2\u003e\n\u003cp\u003eThe present study was conducted in the La Pila Parish of the Montecristi Canton, covering an area of 98.68 km\u003csup\u003e2\u003c/sup\u003e. This region is situated in the southeast of the Manab\u0026iacute; province on the Ecuadorian coast and is located along the E15 road, also known as the Pacific Road (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The area is characterized by an average annual temperature ranging between 23 and 26 C\u0026deg;, with an average annual precipitation averaging between 300 and 350 mm. According to the cartography provided by the IGM (the Geographic Military Institute), the Pila rural Parish includes the communities of Cerro Aguas Nuevas and Las Lagunas (GAD 2015).\u003c/p\u003e\n\u003cp\u003eTwo study areas were selected, each with ten monitoring points. Zone 1, known as the control zone, is well preserved and is located 4,890m away from agricultural areas. It is called \u0026ldquo;Cerro Aguas Nuevas\u0026rdquo;. Zone 2, known as the treated zone, is close to agricultural crops at a distance of 100 m and contains agrochemicals; It is called \u0026ldquo;Finca Virgen del Pilar\u0026rdquo;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eLichen monitoring\u003c/h2\u003e\n\u003cp\u003eThe monitoring process was conducted as follows: test trees were selected on the basis of the criteria outlined in the guide \u0026ldquo;Forest species of the dry forests of Ecuador\u0026rdquo; published by Aguirre Mendoza (2012) and the characteristics proposed by Canseco et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), which led to the selection of the species \u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e. Following the suggestions of the aforementioned authors, trees with trunk diameters ranging from 20 to 50 cm and covered with lichens were chosen, while damaged trees were excluded from the research.\u003c/p\u003e\n\u003cp\u003eMeshes were placed on each phorophyte (the tree supporting the lichen) at specified locations. Monitoring was carried out every 15 days over a four-month period spanning May, June, July and August 2022. This resulted in total of 8 monitoring sessions in each study area, thus yielding a sample size of 16 field visits, with 10 trees examined during each visit (n\u0026thinsp;=\u0026thinsp;160). Species were identified by carrying out the following steps: i) a photographic record was taken on each monitoring date; ii) a field magnifying glass, field guides and lichen identification keys were employed in order to categorize lichen species into morphological groups, including crustacean, foliaceous and fruticulose, and iii) a database was compiled in which the lichen species were recorded and quantified. These data were used to calculate diversity and dominance indices.\u003c/p\u003e\n\u003cp\u003eSamples in the field were selected by focusing on the species \u0026ldquo;\u003cem\u003eCanoparmelia caroliniana\u0026rdquo;\u003c/em\u003e, because it was a predominant species in the study areas. This species has a leafy thallus, which makes it moderately resistant to the presence of contaminants, signifying that it is possible to analyze concentrations of metal and \u0026ldquo;organophosphate\u0026rdquo; agrochemicals. A total of 20 samples of the selected species were collected.\u003c/p\u003e\n\u003cp\u003eA CEM MARS 6 closed vessel acid digestion system was used in this study. The MARS 6 equipment has the capability of processing up to 40 samples simultaneously and of closely monitoring the temperature of each vessel using two highly sensitive IR internal temperature sensors, which are NIST traceable. The microwave program consisted of three steps: i) a 25 minute ramp to 180\u0026deg;C; ii) maintenance at 180\u0026deg;C for an additional 25 minutes, and iii) a cooling cycle. After completing the microwave digestion, the samples were transferred to test tubes and allowed to cool to below 40\u0026deg;C. Subsequently, they were subsequently transferred to 50 ml Fischer Scientific centrifuge tubes and adjusted to a final volume of 25 ml using ultrapure water for ICP chromatography Thermo Scientific. After the completion of the pipetting process, the samples underwent centrifugation in Eppendorf Centrifuge 5804/5804 R equipment for 5 minutes at 25 \u0026ordm;C and 5,000 RPM so as to eliminate any excess solids. Finally, the prepared samples were injected into the ICP Emission Spectrometer Thermo Scientific iCAP 6000 Series.\u003c/p\u003e\n\u003cp\u003eThe metals considered for this study included: Aluminum (Al), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Iron (Fe), Potassium (K), Magnesium (Mg), Manganese (Mn), Sodium (Na), Nickel (Ni) and Lead (Pb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003ePreparation of Gas Chromatography Samples\u003c/h2\u003e\n\u003cp\u003eWe used 5g of the selected species, which was homogenized with a solution of methanol and H2O. The resulting mixture was then filtered using a Buchner funnel and filter paper. The residue obtained after filtration was washed three times with methanol and H2O and then alkalinized with NaOH before undergoing evaporation. The evaporation process was performed using a SyncorePlus apparatus at a temperature of 60\u0026ordm;C, 200 atm and 200 RPM, resulting in an aqueous residue. This residue was transferred to a 250 ml separating funnel with the help of 50 ml of distilled water. The extraction was carried out in two phases, and 12.5 ml of dichloromethane was used in each phase. After shaking the funnel, the samples were left undisturbed for 10 minutes to allow for separation into distinct phases. The samples collected were injected into the Thermo Scientific TRACE Series 1300 Gas Chromatograph for analysis, using an organophosphate kit for measurements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eChromatographics conditions\u003c/h2\u003e\n\u003cp\u003eThe gas chromatograph was equipped with a TraceGOLD\u0026trade; TG-ALC Plus Pesticides II column (30 m x 0.25 mm x 0.25 \u0026micro;m), a splitless injector and a flame ionization detector (FID). The initial column temperature was 100\u0026deg;C, but the temperature was subsequently ramped to 280\u0026deg;C at a rate of 10\u0026deg;C-min-1 and held for 4 minutes. Both the injector temperature and the detector temperature were set at 280\u0026deg;C using DCM as an internal standard for quantification of the components. The EPA 8270 Organophosphorus Pesticide Mix 2 kit was used, and all analytes were separated and quantified according to EPA METHOD 8141B. The retention times for each analyte are shown in the chromatogram provided in the supplemental materials information.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eThe total number of species was employed in order to determine recorded richness data, while the number of individuals identified was used to determine abundance. Moreover, the Shannon Diversity Index was calculated so as to simplify the specific biodiversity, and the Simpson dominance index was calculated by taking into account the most important species.\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the effect of agrochemicals (control area vs. treated area) on lichen richness, diversity, dominance and abundance, we employed generalized linear mixed models (GzLMM) and general linear mixed models (GLMM). The area (treated vs. control) was included as a fixed factor in all models, that is, the abundance model (Model 1), the richness model (Model 2), the diversity model (Model 3) and the dominance model (Model 4). The tree (ten levels) was considered as a random factor. Statistical analyses were performed using InfoStats software.\u003c/p\u003e\n\u003cp\u003eThe dissimilarity and differences between species composition in the two areas (control and treated) were tested using a permutational multivariate analysis of variance (PERMANOVA). The Type III Sum of Squares was used, as this is appropriate in the case of an unbalanced design. All tests were performed with 9999 permutations with the aim of increasing the power and precision of the analysis (Anderson et al.2008) of the residuals in a reduced model (Anderson \u0026amp; Ter Braak \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe also performed a SIMPER analysis similarity percentage, (Clarke1993) in order to determine which species explained the greatest proportion of the differences between community composition in the two areas. SIMPER was used to identify those lichen species that were responsible for more than 90% of the dissimilarity between the two study areas and rank-abundance diagrams for each study area using the number of observed species (Sobs). Analyses were performed using PRIMERv6 software (Clarke \u0026amp; Gorley2006), including the PERMANOVA\u0026thinsp;+\u0026thinsp;add-on package (Anderson, Gorley \u0026amp; Clarke2008).\u003c/p\u003e\n\u003cp\u003eIn order to compare the values of metals and agrochemicals in the treated areas and the control areas, a Student's t-test for independent samples (normal variables) or a Mann Whitney U-test (for non-normal variables) was used.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eDescriptive results\u003c/h2\u003e\n\u003cp\u003eA total of 26 growth thallus biotypes were identified, with 21 in the control zone comprising 13 crustacean thallus, 4 leafy thallus and 4 fruticulous thallus. We identified 19 biotypes in the treated zone, including 13 crustacean thallus and 6 leafy thallus. Notably, species with fruticulous thallus growth were observed in the treated area. A total of 3,405 individuals were identified and counted, with 2,120 individuals being recorded in the control area and 1,285 individuals in the treated area (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Shannon index in the control zone showed that there was a mean diversity value of 2.559, while that of the treated zone was 2.180. According to this index, the control zone consequently had a higher diversity. The Simpson index showed that the dominance value in the control zone was 6.539. The species contributing most to this dominance in the control zone were \u003cem\u003eChrysothrix xanthina\u003c/em\u003e and \u003cem\u003eTrentepohlia aurea.\u003c/em\u003e In contrast, the treated zone had a dominance value was 7.688, with the greatest dominance attributed to \u003cem\u003eXanthoria parietina\u003c/em\u003e and \u003cem\u003eFlavoparmelia caperata\u003c/em\u003e (Table S2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eEffects of zone on the abundance, richness, dominance and diversity of species of lichen\u003c/h2\u003e\n\u003cp\u003eThe results obtained for Models 1, 2, 3 and 4 are detailed in (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The abundance model for individuals showed that there were differences for the factor zone (control and treated area), which were higher for the control area (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). However, the species richness model did not show a difference between the control and treated areas (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). Similarly, the Shannon diversity index model did not reveal differences for the control and treated areas (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). In contrast, the Simpson dominance index model showed significant differences for this factor (control and treated areas), which were greater in the treated area (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eF-values and coefficients of the variables included in the full models to explain lichen abundance (Model 1), lichen species richness (Model 2), lichen diversity (model 3) and lichen dominance (model 4)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoefficient\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLichen abundance (Model 1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.70***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.23***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreated=-0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLichen richness (Model 2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.64***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreated=-1.2E-03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLichen diversity (Model 3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.79***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreated\u0026thinsp;=\u0026thinsp;2.4E-03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLichen dominance (Model 4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134.10***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.68***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreated\u0026thinsp;=\u0026thinsp;1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eCoefficients for the level of fixed factor were calculated using reference value of \u0026ldquo;Control\u0026rdquo; in the variable \u0026ldquo;treatment\u0026rdquo;. *p \u0026lt; .05.; **p \u0026lt; .01.; ***p \u0026lt; .001\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eEffects of zone on the composition of the lichen community\u003c/h2\u003e\n\u003cp\u003eThe permutational multivariate analysis of variance (PERMANOVA) revealed significant differences between the communities according to the zones (p\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The SIMPER indicated that the average dissimilarity between both zones was 68.10%. The species that made the greatest contribution to dissimilarity between the control and treated zones were \u003cem\u003eChrysothrix xanthina\u003c/em\u003e (12.94%), \u003cem\u003eTrentepohlia aurea\u003c/em\u003e (11.89%), \u003cem\u003eCryptothecia striata\u003c/em\u003e (6.61%) and \u003cem\u003ePunctelia subrudecta\u003c/em\u003e (6.50%) (Table S3). Moreover, an NMDS (Non-Metric Multidimensional Scaling) analysis demonstrated that the sampling points in the study areas were well-separated as regards space, thus indicating distinct communities (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePermutational multivariate analysis of variance of lichen species composition based on zone (control and treated)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eMain test\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003edf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePseudo-F\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.102***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.74E5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1739.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.28E5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e***p \u0026le; .001.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBioaccumulation of metals by the lichen species \"\u003c/strong\u003e \u003cstrong\u003eCanoparmelia caroliniana\u003c/strong\u003e \u003cstrong\u003e\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Wilcoxon Test showed significant differences between the control and treated areas for the following metals: barium (p\u0026thinsp;=\u0026thinsp;0.0030); cadmium (p\u0026thinsp;=\u0026thinsp;0.0044) and sodium (p\u0026thinsp;=\u0026thinsp;0.0130), whose levels were higher in the control area (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, there were higher concentrations of chromium (p\u0026thinsp;=\u0026thinsp;0.0054) and copper (p\u0026thinsp;=\u0026thinsp;0.0202) in the treated area (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eWilcoxon test used to compare the means of metals bioaccumulated by the species \u0026ldquo;\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\u0026rdquo;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eW\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP (2 colas)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0821\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e141.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0044\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e123.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0054\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e102.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8798\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e81.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0696\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1180\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7624\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e130.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0130\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1306\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e91.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2899\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBioaccumulation of organophosphates by the lichen species \"\u003c/strong\u003e \u003cstrong\u003eCanoparmelia caroliniana\u003c/strong\u003e \u003cstrong\u003e\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bioaccumulation of organophosphates by the species \u003cem\u003e'Canoparmelia caroliniana'\u003c/em\u003e yielded results indicating significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the control and treated areas, as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The sole organophosphate identified in the control zone was Thionazin, while the treated zone contained a broader spectrum of identified organophosphates, including Thionazin, Famphur, Methylparathion, Parathion, Dimethoate, and Sulfoted (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMann Whitney U test used to compare the average concentrations of each organophosphate in the areas (control and treated)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eW\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep(2 colas)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThionazin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0283\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamphur\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMethylparathion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParathion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0305\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDimethoate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSulfoted\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSpecies of lichens identified in the study areas\u003c/h2\u003e \u003cp\u003eLichen monitoring carried out for a period of 4 months in the control and treated areas allowed the identification of 26 species of lichens. In this context, it should be noted that thallus lichens with crustaceous growth have a high resistance to the presence of contaminants, while those with foliaceous thallus have a medium resistance and those with fruticulous thallus have a very low resistance. Some of the tolerant species identified in this research have already been reported in other previous studies (Fern\u0026aacute;ndez \u0026amp; Terr\u0026oacute;n 2003; Fontecha \u0026amp; Burgaz \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as is the case of \u003cem\u003ePhyscia aipolia\u003c/em\u003e, \u003cem\u003ePhaeophyscia orbicularis\u003c/em\u003e and \u003cem\u003eXanthoria parietina\u003c/em\u003e, which were also identified in the treated area \u0026ldquo;Finca Virgen del Pilar\u0026rdquo;.\u003c/p\u003e \u003cp\u003eCohn Berger \u0026amp; Quezada (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) mention that some of the toxitolerant leafy species are \u003cem\u003ePhyscia aipolia; Xanthoria parietina\u003c/em\u003e and \u003cem\u003ePhaeophyscia orbicularis\u003c/em\u003e, while Fern\u0026aacute;ndez \u0026amp; Terr\u0026oacute;n (2003) state that the species mentioned above have optimal growth and are resistant in areas that show signs of contamination. \u003cem\u003eDiploicia canescens\u003c/em\u003e was similarly one of the species that did not show any signs of necropsy during monitoring and was considered toxitolerant. This is supported by Mendoza (2018a), who reported that it was a resistant and tolerant species to contamination. Species of crustacean thallus were also identified, such as \u003cem\u003eLecanora muralis, Bacidia rosella\u003c/em\u003e, along with species that proved to be sensitive to fruticulous thallus such as \u003cem\u003eRamalina farinacea, Ramalina asahinae\u003c/em\u003e, \u003cem\u003eTeloschistes exilis\u003c/em\u003e and \u003cem\u003eTrentepohlia aurea\u003c/em\u003e, which were identified in the control zone \u0026ldquo;Cerro Aguas Nuevas\u0026rdquo;.\u003c/p\u003e \u003cp\u003eDahlberg (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) identified \u003cem\u003eBacidia rosella\u003c/em\u003e as a species that grows in open and closed forests, which is why this species was identified only in the control area. Gutierrez (2020) deduced that the species \u003cem\u003eTeloschistes exilis\u003c/em\u003e has symptoms and undergoes effects of atmospheric pollution that do not allow it to grow properly. The present study accordingly demonstrated that said species did not grow in the treated area during the monitoring period, as also occurred with the species \u003cem\u003eRamalina farinacea\u003c/em\u003e and \u003cem\u003eRamalina asahinae.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWith regard to the species that were identified in the control and treated areas, the following were found: \u003cem\u003eFlavoparmelia caperata\u003c/em\u003e and \u003cem\u003ePunctelia subrudecta\u003c/em\u003e growing on a foliaceous thallus, and \u003cem\u003eCaloplaca cupulifera\u003c/em\u003e, \u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e, \u003cem\u003ePertusaria leioplaca\u003c/em\u003e and \u003cem\u003eCryptothecia striata\u003c/em\u003e growing on a crustacean thallus, all of which have been identified as good bioindicators owing to their degree of resistance to the presence of contaminants originating from anthropogenic activities such as agriculture.\u003c/p\u003e \u003cp\u003eOne of the species that had the greatest impact on this research was \u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e, since a gas chromatography laboratory analysis showed that it is a species that has medium-high sensitivity and is a good bioindicator of the presence of contaminants (Hale \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Nash \u0026amp; Elix \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Spielmann \u0026amp; Marcelli \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Species such as \u003cem\u003ePertusaria leioplaca\u003c/em\u003e and \u003cem\u003eCryptothecia striata\u003c/em\u003e can be identified in many scenarios owing to their high resistance to atmospheric pollutants (Gonzales 2018).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects on the abundance, richness, dominance and diversity of species in the control and treated area\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe differences between the abundance of lichen individuals observed in the control and treated areas can be attributed to variations in vegetation cover, which became evident during the monitoring period. The control zone, which is characterized by its dense vegetation, provided an environment that is conducive to the thriving of epiphytic lichen species. For instance, \u003cem\u003eBacidia rosella\u003c/em\u003e, a species known to flourish in areas with robust plant cover, was more abundant in this zone.\u003c/p\u003e \u003cp\u003eHaro (2017) mentions that the species composition changes along the alteration gradient. Deforestation stands out as the primary cause of epiphyte loss, particularly among shade-dependent epiphytes. This loss is attributed to changes in forest cover and microclimates within primary forests, leading to reduced environmental humidity and an increased exposure to light (Hawksworth et al.2005). The growth of epiphytic lichens in the treated area was clearly and significantly influenced by farming practices. Moreover, there is a notable disparity between the number of tree species in the two areas.\u003c/p\u003e \u003cp\u003eAcevedo \u0026amp; Charry (2018) noted a close relationship between dominance and the diversity index (Shannon), where lower dominance corresponds to greater diversity. According to the aforementioned authors, the diversity results were similar in both areas (control and treated), but the dominance was higher in the treated area owing to the species \u003cem\u003eXanthoria parietina\u003c/em\u003e, which is highly tolerant and resistant to the growth of thallus crustacean. The species \u003cem\u003eFlavoparmelia caperata\u003c/em\u003e, which has moderate tolerance with its foliaceous thallus growth, also contributes to this pattern.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBioaccumulation of metals by the lichen species \"\u003c/b\u003e \u003cb\u003eCanoparmelia caroliniana\u003c/b\u003e \u003cb\u003e\"\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLichens are capable of indiscriminately accumulating a wide range of pollutants, including metals, which are often accumulated at levels above their own physiological needs (Bačkor \u0026amp; Loppi2009). Said tolerance has been related to the occurrence of successive phases of accumulation and the loss of particles over time until the concentrations of contaminants in the thallus reach equilibrium with the average levels of environmental contamination (Kularatne \u0026amp; De Freitas2013). The chemical composition of lichens consequently reflects the availability of elements present in the environment (Bargagli \u0026amp; Nimis2002) and provides us with information regarding their spatial and temporal variations (Paoli et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgricultural practices, which are characterized by the extensive use of pesticides, fertilizers and soil amendments, may inadvertently introduce metals into the environment as impurities (Nziguheba \u0026amp; Smolders2008). The repetitive application of these agricultural inputs can lead to the accumulation of toxic substances in the environment over time (Wuana \u0026amp; Okieimen2011). The analysis of metal concentrations in the lichen species \"\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\" within two distinct study areas (control and treated) is, therefore, a relevant aspect of this study. The analyses of metals such as barium (Ba), cadmium (Cd) and sodium (Na) showed that there were differences between zones, and higher concentrations in the control zone. In this respect, the concentrations of (0.16 ppm) barium in the control area and (0.04 ppm) in the treated area are related to the combustion of automobiles (Giampaoli et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The concentration of Cadmium (Cd) found in the control area was 0.02 ppm, which was attributed to emissions from internal combustion vehicles, as mentioned by Chaparro et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, the concentration of cadmium in the treated area was 1.50E-03 ppm, which can be linked to the use of agrochemicals such as insecticides and herbicides. Sodium (Na) was identified only in the control area, with a concentration of 1137.6 ppm, thus showing significant differences between the areas. Its presence is likely related to the geological conditions of the control zone, specifically \"Cerro Aguas Nuevas\". These findings highlight the influence of various sources, including automotive emissions and agricultural practices, on concentrations of metal in the lichen species \"\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\" within the study areas. Moreover, the analyses carried out in order to discover metals such as chromium (Cr) and copper (Cu) also showed that there were differences between zones but higher concentrations in the treated zone.\u003c/p\u003e \u003cp\u003eIn the control zone, these concentrations are attributed to vehicular traffic (abrasion from car engines, normal wear suffered by tires and combustion of cars), while in the treated area the concentrations are associated with the use of agrochemicals, since this area is surrounded by numerous crops. Roig et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), cites that these metals are present in numerous pesticides (fungicides and insecticides) and fertilizers used in agriculture, which are employed by farmers on their farms.\u003c/p\u003e \u003cp\u003eAll the results showed how the bioaccumulation of metals by the lichen species \"\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\" varies widely, and although the concentrations of metals are generally below toxic levels, Kothe et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) mention that the long-term risk results in the addition of more toxins to the environment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBioaccumulation of organophosphates by the lichen species \"\u003c/b\u003e \u003cb\u003eCanoparmelia caroliniana\u003c/b\u003e \u003cb\u003e\"\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAgrochemicals encompass a wide range of chemical substances, which are typically categorized on the basis of their effects on pests. These categories include insecticides, fungicides, herbicides and rodenticides, with over 90% of pesticides falling in the organosynthetic category (V\u0026aacute;squez2015). In recent years, insecticides have accounted for 27% of all agrochemical imports in Ecuador. This group is often considered the most hazardous, as it includes substances that are highly toxic for humans and persist in the environment (Valarezo \u0026amp; Mu\u0026ntilde;oz2011). Interviews conducted with farmers in the treated area revealed that insecticides and herbicides are frequently used for pest control during the cultivation of corn.\u003c/p\u003e \u003cp\u003eThe gas chromatography analyses performed in this study revealed the presence and concentrations of certain organophosphates in samples from the treated area. Thionazin (0.27 mg/L), Famphur (0.02 mg/L), Methyl Parathion (0.07 mg/L), Parathion (4.4E-03 mg/L), Dimethoate (0.03 mg/L), and Sulfoted (0.04 mg/L) were identified in the treated area, while only Thionazin (0.17 mg/L) was present in the control area (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings highlight significant differences between the agrochemical management carried out in the two zones, with a more extensive use of agrochemicals in the treated area known as \"Finca Virgen del Pilar\".\u003c/p\u003e \u003cp\u003eIt is worth noting that Parathion and Methyl Parathion, both of which were identified in the treated area, are on the list of prohibited insecticides in Ecuador (Valarezo \u0026amp; Mu\u0026ntilde;oz \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, Methomyl, which is classified as extremely toxic (Category Ib), is reportedly used extensively in the treated area, although it was not part of the organophosphate kit used for gas chromatography analysis. Another highly toxic pesticide in Category II, Dimethoate, was identified solely in the treated area. The contamination stemming from agrochemicals can lead to the dispersion of their residues into the environment, posing risks to both biotic (animals and plants) and abiotic (air, water, and soil) components. These contaminants can potentially come into contact with humans through various exposure routes, including dermal, respiratory and digestive pathways (Gavidia \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, insecticides, which are one of the primary tools in agriculture, raise ecological concerns owing to their potentially adverse effects on non-target organisms, such as soil nutrient recyclers, plant pollinators and pest predators. Moreover, they can impact on food products at higher trophic levels (Devine et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Lichens are highly valuable as bioindicators with which to assess air pollution owing to their sensitivity and rapid response to variations in environmental conditions. However, most studies employing lichen bioaccumulation have focused on urban or heavily industrialized areas, while limited research has been carried out in remote regions (Bergamaschi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It is for this reason that the present study took place in an area with a seemingly natural elemental composition, since this would allow meaningful comparisons between an area with a presumed lower contamination and one with higher levels of contamination.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study underscores that the lichen species \"\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\" has the potential to be used as a robust bioindicator for the bioaccumulation of metals and agrochemicals. It is worth noting that our findings are derived from observations of a single lichen species over a specific exposure period. It is, therefore, imperative that this information be taken into account by researchers and policymakers alike when addressing environmental concerns. These results provide valuable insights into the development of effective research and monitoring strategies, which can, in turn, inform evidence-based decisions and policies regarding metal and agrochemical contamination. As we continue to advance in our understanding of the impact of these contaminants on our ecosystems, lichen species such as \"\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\" prove to be invaluable allies in the pursuit of a healthier and more sustainable environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAJC, JMR and SPG conceived and designed the study. SPG and MBV performed the data collection. SPG and BR performed laboratory analysis. AJC, SPG and MBV analyzed the data and wrote the first draft of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank: Sally Newton for her favorable comments; Luis Zambrano for his support in the laboratory, and Alex Quimis and Yamel Alvarez for their support as regards locating meshes for monitoring. AJC is supported by a \u0026apos;Juan de la Cierva\u0026apos; contract (IJC2020-042629-I) funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR. He is also supported by research project PROG-015-DIP-PROYE-001-2019 and the Andalusian Agency for International Development Cooperation AACID (INVODES 2021UC001 projects). Our thanks also go to the Departamento de Qu\u0026iacute;mica Org\u0026aacute;nica, Universidad de C\u0026oacute;rdoba, Edificio Marie Curie (C-3), Campus de Rabanales, Ctra. Nnal. IV-A, Km 396, E14014, C\u0026oacute;rdoba, Spain and the Laboratorio de An\u0026aacute;lisis Qu\u0026iacute;micos y Biotecnol\u0026oacute;gicos, Instituto de Investigaci\u0026oacute;n, Universidad T\u0026eacute;cnica de Manab\u0026iacute;, S/N, Avenida Urbina y Che Guevara, Portoviejo, 130104, Ecuador.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgnan, Y., Probst, A., \u0026amp; S\u0026eacute;jalon Delmas, N. (2017). Evaluation of lichen species resistance to atmospheric metal pollution by coupling diversity and bioaccumulation approaches: A new bioindication scale for French forested areas. \u003cem\u003eEcological Indicators, 72, 99\u0026ndash;110. \u003c/em\u003ehttps://doi.org/10.1016/j.ecolind.2016.08.006\u003c/li\u003e\n\u003cli\u003eAguirre, Z. (2013). Guide to methods for measuring biodiversity. \u003c/li\u003e\n\u003cli\u003eAnderson, M.J. \u0026amp; Ter Braak, C.J.F. (2003) Permutation tests for multi-factorial analysis of variance. \u003cem\u003eJournal of Statistical Computation and Simulation, 73, 85\u0026ndash;113.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAnderson, M.J., Gorley, R.N. \u0026amp; Clarke, K.R. (2008) PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. PRIMER-E Ltd, U.K. \u003c/li\u003e\n\u003cli\u003eAntonucci, A., Vitali, M., Avino, P., Manigrasso, M., \u0026amp; Protano, C. (2016). Sensitive multiresidue method by HS-SPME/GC-MS for 10 volatile organic compounds in urine matrix: a new tool for biomonitoring studies on children. \u003cem\u003eAnalytical and Bioanalytical Chemistry, 408(21), 5789\u0026ndash;5800. https://doi.org/10.1007/S00216-016-9682-X/METRICS\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAttanayaka, A. N. P. M., \u0026amp; Chandrani Wijeyaratne, S. (2013). Corticolous lichen diversity, a potential indicator for monitoring air pollution in tropics. \u003cem\u003eJ.Natn.Sci.Foundation Sri Lanka, 41(2), 131\u0026ndash;140.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBačkor, M., \u0026amp; Loppi, S. (2009). Interactions of lichens with heavy metals. \u003cem\u003eIn Biologia Plantarum (Vol. 53, Issue 2, pp. 214\u0026ndash;222). https://doi.org/10.1007/s10535-009-0042-y\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBargagli, R., \u0026amp; Nimis, P. L. (2002). Guidelines for the use of epiphytic lichens as biomonitors of atmospheric deposition of trace elements. Monitoring with lichens\u0026mdash;monitoring lichens, 295-299.\u003c/li\u003e\n\u003cli\u003eBergamaschi, L., Rizzio, E., Giaveri, G., Profumo, A., Loppi, S., \u0026amp; Gallorini, M. (2004). Determination of baseline element composition of lichens using samples from high elevations. Chemosphere, 55(7), 933-939.\u003c/li\u003e\n\u003cli\u003eBerner, K., \u0026amp; Berner, R. A. (2012). Global environment: Water, air, and geochemical cycles. \u003cem\u003eIn Global Environment: Water, Air, and Geochemical Cycles (Second Edition). https://doi.org/10.1080/00207233.2012.753739\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBoamponsem, L. K., Adam, J. I., Dampare, S. B., Nyarko, B. J. B., \u0026amp; Essumang, D. K. (2010). Assessment of atmospheric heavy metal deposition in the Tarkwa gold mining area of Ghana using epiphytic lichens. \u003cem\u003eNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 268(9), 1492\u0026ndash;1501. https://doi.org/10.1016/J.NIMB.2010.01.007\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBoonpeng, C., Sangiamdee, D., Noikrad, S., \u0026amp; Boonpragob, K. (2023). Lichen biomonitoring of seasonal outdoor air quality at schools in an industrial city in Thailand. \u003cem\u003eEnvironmental Science and Pollution Research, 30(21), 59909\u0026ndash;59924. https://doi.org/10.1007/S11356-023-26685-Z/METRICS\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBosch-Roig, P., Barca, D., Crisci, G. M., \u0026amp; Lalli, C. (2013). Lichens as bioindicators of atmospheric heavy metal deposition in Valencia, Spain. \u003cem\u003eJournal of Atmospheric Chemistry, 70(4), 373\u0026ndash;388. https://doi.org/10.1007/S10874-013-9273-6/METRICS\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBrotas, V., Sim-Sim, M., Gordo, L., Blois\u0026eacute;, C., Garcia, C., Martins, A., Neves, A. M., \u0026amp; Tracana, A. (2016). Relat\u0026oacute;rio t\u0026eacute;cnico sobre o Programa de Monitoriza\u0026ccedil;\u0026atilde;o dos Ecossistemas Terrestre e Estuarino na Envolvente \u0026agrave; CTRSU de S. Jo\u0026atilde;o da Talha.\u003c/li\u003e\n\u003cli\u003eBungartz, Frank, S\u0026oslash;chting, U., \u0026amp; Arup, U. (2020). Teloschistaceae (lichenized Ascomycota) from the Galapagos Islands: A phylogenetic revision based on morphological, anatomical, chemical, and molecular data. \u003cem\u003ePlant and Fungal Systematics, 65(2), 515\u0026ndash;576. https://doi.org/10.35535/pfsyst-2020-0030\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eCanseco, A., Anze, R., \u0026amp; Franken, M. (2006). Comunidades de l\u0026iacute;quenes : indicadores de la calidad del aire en la ciudad de La Paz , Bolivia. \u003cem\u003eActa Nova, 3, 286\u0026ndash;307. http://www.scielo.org.bo/pdf/ran/v3n2/v3n2_a09.pdf\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eCicek, A., Koparal, A. S., Catak, S., \u0026amp; Ugur, S. (2001). The levels of some heavy metals and nutritional elements in the samples from soils and tree-leaves growing in the vicinity of Seyitomer Thermal Power Plant in Kutahya (Turkey). In Proceedings of 2nd international symposium on air quality management at urban regional and global scales, Istanbul (pp. 157-162).\u003c/li\u003e\n\u003cli\u003eCislaghi, C., \u0026amp; Nimis, P. L. (1997). Lichens, air pollution and lung cancer.\u003cem\u003e Nature 1997 387:6632, 387(6632), 463\u0026ndash;464. https://doi.org/10.1038/387463a0\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eClarke, K. R. (1993). Non-parametric multivariate analyses of changes incommunity structure. \u003cem\u003eAustralian Journal of Ecology, 18, 117\u0026ndash;143.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eClarke, K.R. \u0026amp; Gorley, R.N. (2006) PRIMER v6: User Manual/Tutorial. PRIMER-E, U.K.\u003c/li\u003e\n\u003cli\u003eCohn Berger, G., \u0026amp; Quezada, M. (2016). L\u0026iacute;quenes como bioindicadores de contaminaci\u0026oacute;n a\u0026eacute;rea en el corredor metropolitano de la ciudad de Guatemala. \u003cem\u003eRevista Cient\u0026iacute;fica de La Facultad de Ciencias Qu\u0026iacute;micas y Farmacia, 26(1), 20\u0026ndash;39.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eConti, M. E., \u0026amp; Cecchetti, G. (2001). Biological monitoring: lichens as bioindicators of air pollution assessment--a review. \u003cem\u003eEnvironmental Pollution (Barking, Essex : 1987), 114(3), 471\u0026ndash;492. https://doi.org/10.1016/S0269-7491(00)00224\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eChaparro, M. A., Mari\u0026eacute;, D. C., Gogorza, C. S., Navas, A., \u0026amp; Sinito, A. M. (2010). Magnetic studies and scanning electron microscopy\u0026mdash;X-ray energy dispersive spectroscopy analyses of road sediments, soils and vehicle-derived emissions. \u003cem\u003eStudia Geophysica et Geodaetica,\u003c/em\u003e 54, 633-650.\u003c/li\u003e\n\u003cli\u003eDahlberg, A. (2015). \u003cem\u003eBacidia rosella\u003c/em\u003e. The Global Fungal Red List Initiative. http://iucn.ekoo.se/iucn/species_view/378392/\u003c/li\u003e\n\u003cli\u003eDe Lurdes Dinis, M., Fi\u0026uacute;za, A. (2011). Exposure Assessment to Heavy Metals in the Environment: Measures to Eliminate or Reduce the Exposure to Critical Receptors. In: Simeonov, L., Kochubovski, M., Simeonova, B. (eds) Environmental Heavy Metal Pollution and Effects on Child Mental Development. NATO Science for Peace and Security Series C: Environmental Security, vol 1. Springer, Dordrecht. \u003cem\u003ehttps://doi.org/10.1007/978-94-007-0253-0_2\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eDevine, G. J., Eza, D., Ogusuku, E., \u0026amp; Furlong, M. J. (2008). Uso de insecticidas:contexto yconsecuencias ecol\u0026oacute;gicas. \u003cem\u003eRev. Peru. Med. Exp. Salud Publica, 25(1),74\u0026ndash;100. http://www.scielo.org.pe/scielo.php?script=sci_arttext\u0026amp;pid=S1726-46342008000100011\u0026amp;lng=es\u0026amp;nrm=iso\u0026amp;tlng=es\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eFerencova, Z. (2012). Estudio morfol\u0026oacute;gico comparado de los caracteres generativos en relaci\u0026oacute;n con linajes monofil\u0026eacute;ticos de la familia Parmeliaceae (Lecanorales, Ascomycota) (Doctoral dissertation, Universidad Complutense de Madrid).https://eprints.ucm.es/id/eprint/22628/1/T34425.pdf\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Salegi, A., \u0026amp; Terr\u0026oacute;n Alfonso, A. (2003). Biomonitorizaci\u0026oacute;n de la calidad del aire en los alrededores de La Robla (Le\u0026oacute;n). Ecosistemas, 12(2). Recuperado a partir de https://www.revistaecosistemas.net/index.php/ecosistemas/article/view/349\u003c/li\u003e\n\u003cli\u003eFontecha, A., \u0026amp; Burgaz, A. R. (2018). Uso de los l\u0026iacute;quenes como bioindicadores de la calidad del aire: estado de la Ciudad Universitaria (Madrid, Espa\u0026ntilde;a). \u003cem\u003eBotanica Complutensis, 42, 57\u0026ndash;68. https://doi.org/10.5209/bocm.61368\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eGavidia, P. (2020). Plaguicidas: efectos da\u0026ntilde;inos en el medio ambiente y la salud. Tiempo.Com, 1\u0026ndash;1. https://www.tiempo.com/noticias/actualidad/plaguicidas-medio-ambiente-y-salud.html\u003c/li\u003e\n\u003cli\u003eGerault, A. (2019). Phaeophyscia Orbicularis. http://www.lichensmaritimes.org/index.php?task=fiche\u0026amp;lichen=543\u0026amp;lang=en\u003c/li\u003e\n\u003cli\u003eGiampaoli, P., Wannaz, E. D., Tavares, A. R., \u0026amp; Domingos, M. (2016). Suitability of Tillandsia usneoides and Aechmea fasciata for biomonitoring toxic elements under tropical seasonal climate. Chemosphere, 149, 14-23.\u003c/li\u003e\n\u003cli\u003eGobierno Autonomo Descentralizado de la Parroquia La Pila. (2015). Plan de Desarrollo y Ordenamiento Territorial de la Parroquia \u0026ldquo;La Pila\u0026rdquo; del cant\u0026oacute;n Montecristi Provincia de Manab\u0026iacute;. http://app.sni.gob.ec/sni link/sni/PORTAL_SNI/data_sigad_plus/sigadplusdocumentofinal/1360025700001_03 PDOT - GR LA PILA_12-12-2016_16-42-12.pdf\u003c/li\u003e\n\u003cli\u003eHale, M. E. 1976. A monograph of the lichen genus Pseudoparmelia Lynge (Parmeliaceae). Smithsonian Contr. Bot. 31: 1-62.\u003c/li\u003e\n\u003cli\u003eHawksworth, D. L., Iturriaga, T., \u0026amp; Crespo, A. (2005). Lichens as rapid bioindicators of pollution and habit disturbance in the tropics. \u003cem\u003eIn Revista Iberoamericana de Micologia (Vol. 22, Issue 2, pp. 71\u0026ndash;82). Elsevier Doyma. https://doi.org/10.1016/s1130-1406(05)70013-9\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eHerrero, J., Herranz, J. M., \u0026amp; Mart\u0026iacute;nez, M. J. (2004). Quejigares Manual de Gesti\u0026oacute;n Natura 2000 (Primera ed). https://www.castillalamancha.es/sites/default/files/documentos/pdf/20140226/rn2000_quejigares.pdf\u003c/li\u003e\n\u003cli\u003eKoch, N. M., Matos, P., Branquinho, C., Pinho, P., Lucheta, F., Martins, S. M. de A., \u0026amp; Vargas, V. M. F. (2019). Selecting lichen functional traits as ecological indicators of the effects of urban environment. \u003cem\u003eScience of The Total Environment, 654, 705\u0026ndash;713. https://doi.org/10.1016/J.SCITOTENV.2018.11.107\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eKothe, E., Dimkpa, C., Haferburg, G., Schmidt, A., \u0026amp; Schmidt, A. (2010). Streptomycete heavy metal resistance: extracellular and intracellular mechanisms. I. Sherameti, A. Varma (Eds.), ,\u003cem\u003e Soil Biology, Springer, Berlin/Heidelberg, 225\u0026ndash;235.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eKularatne, K. I. A., \u0026amp; De Freitas, C. R. (2013). Epiphytic lichens as biomonitors of airborne heavy metal pollution. Environmental and experimental botany, 88, 24-32.https://www.sciencedirect.com/science/article/abs/pii/S0098847212000494\u003c/li\u003e\n\u003cli\u003eLoppi, S. (2014). Lichens as sentinels for air pollution at remote alpine areas (Italy). \u003cem\u003eEnvironmental Science and Pollution Research, 21, 2563-2571.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eLoppi, S. (2019). May the Diversity of Epiphytic Lichens Be Used in Environmental Forensics? \u003cem\u003eDiversity 2019, Vol. 11, Page 36, 11(3), 36. https://doi.org/10.3390/D11030036\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eLoppi, S., Pirintsos, S. A., \u0026amp; De Dominicis, V. (1999). Soil contribution to the elemental composition of epiphytic lichens (Tuscany, central Italy). \u003cem\u003eEnvironmental Monitoring and Assessment, 58(2), 121\u0026ndash;131. https://doi.org/10.1023/A:1006047431210\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMari\u0026eacute;, D. C., Chaparro, M. A., Gogorza, C. S., Navas, A., \u0026amp; Sinito, A. M. (2010). \u003c/em\u003eVehicle-derived emissions and pollution on the road autovia 2 investigated by rock-magnetic parameters: A case study from Argentina\u003cem\u003e. \u003c/em\u003e\u003cem\u003eStudia Geophysica et Geodaetica, 54, 135-152.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMateos, A. C., Amarillo, A. C., Carreras, H. A., \u0026amp; Gonz\u0026aacute;lez, C. M. (2018). Land use and air quality in urban environments: Human health risk assessment due to inhalation of airborne particles. \u003cem\u003eEnvironmental Research, 161, 370\u0026ndash;380. https://doi.org/10.1016/J.ENVRES.2017.11.035\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMatusmoto, M. \u0026amp; Mizoguchi, T. (1995). A simple and simultaneous measurement method of sulphur dioxide in atmosphere using molecular diffusion sampler, International Seminar on the Simple Measuring and Evaluation Method on Air \u003cem\u003ePollution, Japan Society of Air Pollution (JSAP) and Environmental Research and Training Center (ERTC), Pathumthani, Thailand.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMikhaylov, A. (2020). Lichens as indicators of atmospheric pollution in urban ecosystems. \u003cem\u003eIsrael Journal of Ecology and Evolution, 67(1\u0026ndash;2), 60\u0026ndash;68. https://doi.org/10.1163/22244662-BJA10016\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMohamed, A. H., M\u0026rsquo;hamed, M., Hichem, M. F. and B. M., Mohamed, A. H., M\u0026rsquo;hamed, M., \u0026amp; Hichem, M. F. and B. M. (2016). Air Pollution Mapping with Bio‐Indicators in Urban Areas. Air Quality - \u003cem\u003eMeasurement and Modeling. https://doi.org/10.5772/65299\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eNash I. I. , T. H. \u0026amp; J. A. Elix. 2002. Canoparmelia. En: Nash III, T. H., B. D. Ryan, C. Gries \u0026amp; F. Bungartz (eds.), Lichen Flora of the Greater Sonoran Desert Region , Arizona State University, Vol. 1: 122-125. Lichens Unlimited, Arizona State University, Arizona.\u003c/li\u003e\n\u003cli\u003eNziguheba, G., \u0026amp; Smolders, E. (2008). Inputs of trace elements in agricultural soils via phosphate fertilizers in European countries. https://www.sciencedirect.com/science/article/abs/pii/S0048969707010030\u003c/li\u003e\n\u003cli\u003eOchoaJ, DA, Cueva, A., Prieto, M., Arag\u0026oacute;n, G., \u0026amp; Benitez, \u0026Aacute;. (2015). Changes in the composition of epiphytic lichens related to air quality in the city of Loja (Ecuador). Caldasia , 37 (2), 333-343.https://doi.org/10.15446/caldasia.v37n2.53867\u003c/li\u003e\n\u003cli\u003ePaoli, L., Munzi, S., Guttov\u0026aacute;, A., Senko, D., Sardella, G., \u0026amp; Loppi, S. (2015). Lichens as suitable indicators of the biological effects of atmospheric pollutants around a municipal solid waste incinerator (S Italy).\u003cem\u003eEcological Indicators, 52, 362\u0026ndash;370. https://doi.org/10.1016/j.ecolind.2014.12.018\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eParviainen, A., Casares-Porcel, M., Marchesi, C., \u0026amp; Garrido, C. J. (2019). Lichens as a spatial record of metal air pollution in the industrialized city of Huelva (SW Spain). \u003cem\u003eEnvironmental Pollution, 253, 918\u0026ndash;929. https://doi.org/10.1016/J.ENVPOL.2019.07.086\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eQu, C., Doherty, A. L., Xing, X., Sun, W., Albanese, S., Lima, A., Qi, S., \u0026amp; De Vivo, B. (2018). Polyurethane Foam-Based Passive Air Samplers in Monitoring Persistent Organic Pollutants: Theory and Application. \u003cem\u003eEnvironmental Geochemistry: Site Characterization, Data Analysis and Case Histories: Second Edition, 521\u0026ndash;542. https://doi.org/10.1016/B978-0-444-63763-5.00021-5\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eRoig, PB, Barca, D., Crisci, GM, \u0026amp; Lalli, C. (2010). Study on Lichens as bioindicators of heavy metal content in the environment of the Church of Saints Johns of Valencia. Conservation and Restoration Studies, (2), 21-37. https://orcid.org/0000-0003-2019-399X\u003c/li\u003e\n\u003cli\u003eRubiano L. (1987). Delimitaci\u0026oacute;n de \u0026aacute;reas de isocontaminaci\u0026oacute;n en Cali y Medell\u0026iacute;n utilizando l\u0026iacute;quenes como bioindicadores. P\u0026eacute;rez Arbelaezia; 1(4): pp.7-41.\u003c/li\u003e\n\u003cli\u003eSaib, H., Yekkour, A., Toumi, M., Guedioura, B., Benamar, M. A., Zeghdaoui, A., Austruy, A., Berg\u0026eacute;-Lefranc, D., Culcasi, M., \u0026amp; Pietri, S. (2023). Lichen biomonitoring of airborne trace elements in the industrial-urbanized area of eastern algiers (Algeria). \u003cem\u003eAtmospheric Pollution Research, 14(1), 101643. https://doi.org/10.1016/J.APR.2022.101643\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eSepp\u0026auml;nen, A. (toim.), Laatikainen, T., Piispanen, J., Poikolainen, J., Karhu, J., Sepp\u0026auml;nen, R., \u0026amp; Kubin, E. (2017). Kainuun bioindikaattoriselvitys. https://www.doria.fi/handle/10024/147661\u003c/li\u003e\n\u003cli\u003eSmodi\u0026scaron;, B., \u0026amp; Parr, R. M. (1999). Biomonitoring of air pollution as exemplified by recent IAEA programs. \u003cem\u003eBiological Trace Element Research, 71\u0026ndash;72(1), 257\u0026ndash;266. https://doi.org/10.1007/BF02784211/METRICS\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eSpielmann, AA, \u0026amp; Marcelli, MP (2008). Parmeliaceae (lichenized Ascomycota) in the ravines and peraus of the Serra Geral slope, Vale do Rio Pardo, Rio Grande do Sul, Brazil. II. Genera Canoparmelia, Hypotrachyna, Myelochroa, Parmelinopsis and Relicina. Iheringia, Botanical Series. , 63 (2), 193-212.\u003c/li\u003e\n\u003cli\u003eTorres, D., \u0026amp; Capote, T. (2004). Agrochemicals a global environmental problem: use of chemical analysis as a tool for environmental monitoring. Ecosystems, 13(3), 2\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eValarezo, O., \u0026amp; Mu\u0026ntilde;oz, X. (2011). Insecticides for Agricultural use in Ecuador. INiAP, 3\u0026ndash;6. https://repositorio.iniap.gob.ec/bitstream/41000/1253/1/INIAP bolet\u0026iacute;n divulgativo 401.pdf\u003c/li\u003e\n\u003cli\u003eVarrica, D., Lo Medico, F., \u0026amp; Alaimo, M. G. (2022). Air Quality Assessment by the Determination of Trace Elements in Lichens (Xanthoria calcicola) in an Industrial Area (Sicily, Italy). \u003cem\u003eInternational Journal of Environmental Research and Public Health 2022, Vol. 19, Page 9746, 19(15), 9746. https://doi.org/10.3390/IJERPH19159746\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eV\u0026aacute;squez, M. (2015). Study of the effects of atmospheric pollution on lichen bioindicators and their degradation.460.\u003c/li\u003e\n\u003cli\u003eWuana, R. A., \u0026amp; Okieimen, F. E. (2011). Heavy Metals in Contaminated Soils: A Review of Sources, Chemistry, Risks and Best Available Strategies for Remediation. \u003cem\u003eISRN Ecology, 2011, 1\u0026ndash;20. https://doi.org/10.5402/2011/402647\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eYatawara, M., \u0026amp; Dayananda, N. (2019). Use of corticolous lichens for the assessment of ambient air quality along rural\u0026ndash;urban ecosystems of tropics: a study in Sri Lanka. \u003cem\u003eEnvironmental Monitoring and Assessment, 191(3), 1\u0026ndash;14. https://doi.org/10.1007/S10661-019-7334-2/METRICS\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Air quality, environmental pollution, gas chromatography, epiphytes, bioaccumulation, organophosphates","lastPublishedDoi":"10.21203/rs.3.rs-4103676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4103676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe presence or absence of lichens serves as an indicator of the condition of an ecosystem and the degree to which it is contaminated by various agents, such as agrochemicals and metals. Evaluating the use of lichens as bioindicators of agrochemical contamination could provide a more comprehensive perspective on current contamination levels. Monitoring was, therefore, carried out over a four-month period in two study areas: a well-conserved control area and another treated area surrounded by agricultural crops. Data on the presence and abundance of lichens in each study area were recorded at 10 sampling points, a procedure that was repeated 16 times (every 15 days), and concentrations of heavy metals and \u0026ldquo;organophosphate\u0026rdquo; agrochemicals in the lichens collected were measured by means of gas chromatography. Generalized linear mixed models were used to assess abundance and richness, while general linear mixed models were used to attain Shannon diversity and Simpson dominance indices. Moreover, a multivariate analysis was performed in order to compare the lichen communities in both areas. The results indicated differences between the control and treated areas in terms of abundance and Simpson's dominance index, while no differences were found for the richness and diversity models. The PERMANOVA analysis also showed differences between the lichen communities in the two areas. The results also demonstrated that \u0026ldquo;\u003cem\u003eCanoparmelia caroliniana\u003c/em\u003e\u0026rdquo; bioaccumulated metals in both areas. Finally, the concentrations of agrochemicals were higher in the treated area, and included toxic substances such as Methyl Parathion and Parathion, which are prohibited in Ecuador. In conclusion, the research underscores the importance of lichens as precise indicators of environmental health and contamination by agrochemicals and metals.\u003c/p\u003e","manuscriptTitle":"Use of lichens as bioindicators of contamination by agrochemicals and metals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 15:44:30","doi":"10.21203/rs.3.rs-4103676/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-05-01T08:25:58+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-04-05T12:09:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-05T11:42:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-04-04T16:33:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-25T04:20:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-03-20T11:47:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c9c4479d-e999-4d08-a2e3-fc32a680ce97","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T17:11:48+00:00","versionOfRecord":{"articleIdentity":"rs-4103676","link":"https://doi.org/10.1007/s11356-024-34450-z","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-07-25 16:16:19","publishedOnDateReadable":"July 25th, 2024"},"versionCreatedAt":"2024-04-10 15:44:30","video":"","vorDoi":"10.1007/s11356-024-34450-z","vorDoiUrl":"https://doi.org/10.1007/s11356-024-34450-z","workflowStages":[]},"version":"v1","identity":"rs-4103676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4103676","identity":"rs-4103676","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}