Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 3011 2023-03-30 09:18:57 |
2 format Meta information modification 3011 2023-03-31 04:01:33 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Frati, L.; Brunialti, G. Lichen Biomonitoring in Forests. Encyclopedia. Available online: https://encyclopedia.pub/entry/42633 (accessed on 01 July 2024).
Frati L, Brunialti G. Lichen Biomonitoring in Forests. Encyclopedia. Available at: https://encyclopedia.pub/entry/42633. Accessed July 01, 2024.
Frati, Luisa, Giorgio Brunialti. "Lichen Biomonitoring in Forests" Encyclopedia, https://encyclopedia.pub/entry/42633 (accessed July 01, 2024).
Frati, L., & Brunialti, G. (2023, March 30). Lichen Biomonitoring in Forests. In Encyclopedia. https://encyclopedia.pub/entry/42633
Frati, Luisa and Giorgio Brunialti. "Lichen Biomonitoring in Forests." Encyclopedia. Web. 30 March, 2023.
Lichen Biomonitoring in Forests
Edit

Forest ecosystems are often located in remote areas, far from direct sources of air pollution. Nonetheless, they may be affected by different types of atmospheric deposition, which can compromise their health and inner balance. Epiphytic lichens respond to air pollution and climate change, and they have been widely adopted as ecological indicators, mainly in urban and industrial areas, while forest ecosystems are still underrepresented. 

air pollution climate change lichen diversity functional traits

1. Lichen Communities—Lichen Diversity Indices

Biomonitoring methods based on the study of epiphytic lichen communities represent excellent tools for monitoring the effects of air pollutants over time, especially sulfur and nitrogen compounds and atmospheric particulate matter (for a review, see, for example, [1][2]). Most of them detect lichen diversity within a sampling grid placed on tree trunks and consider richness and abundance indices, such as the index of atmospheric purity (IAP) [3][4][5][6] and the lichen diversity value (LDV) [7][8][9]. The IAP combines the number of species at the site with their sensitivity towards environmental stressors, primarily air pollution. The LDV is the most recent methodology, and it is strongly standardized to allow easier comparisons throughout Europe; it is not related to any specific pollutant but can be considered an indicator of general environmental quality. Although most of the research has been conducted in urban and industrial areas (for a review, see [10]) where air pollution represents one of the biggest threats to human health, there are also numerous examples of scientific studies and monitoring programs that adopt the LDV or IAP in forest ecosystems. Most of them concern sites in Europe [11][12][13][14][15][16][17][18][19][20][21], North America [22][23], and South America [24].
Forests are usually far from direct emissions sources and, therefore, they are subject to lower pollution conditions than urban and industrial areas. Thus, while in the latter, atmospheric pollution is the main limiting factor for lichen communities (see, for example, [13][14][18], in forest environments, habitat-related variables may play a major role in lichen diversity, leading to the possibility of incorrect interpretation of diversity indices [14][16][18]. Some authors agree that the IAP may be a suitable air pollution indicator even in forests and with low pollution levels. A study conducted in Finnish and Russian spruce- and birch-dominated forests in Finland and Russia [12] showed a clear correlation between modeled air pollution and IAP values based on epiphytic communities. The authors demonstrated that the IAP also does not seem to be influenced by forest structure characteristics or forest type in areas with low air pollution and can detect fine gradients in sulfur and nitrogen air pollutants. Similarly, Gibson et al. [22] showed that the IAP can reveal sites where ecological degradation occurs, which may be associated with air pollution, even at low levels. However, many authors suggest carefully interpreting lichen diversity data in terms of the direct effects of pollution in forest areas. For example, in the Western Carpathians forests, IAP values were negligibly related to air pollution depositions and better reflected forest ecological conditions (Tanona and Czarnota [25]). In a study carried out in the province of Genova (Italy), Giordani [14] showed that, in forested areas, harvesting and forest fires have the predominant effects on lichen diversity, suggesting the need to develop a more defined sampling protocol to estimate atmospheric pollution in such environments. In forested areas lacking air pollution, Brunialti et al. [26] found that lichen diversity strictly depends on the structural characteristics of the managed forests, mainly tree substrates, forest types, and forest management. These results confirm the strong influences of land use and forest management on LDVs, which may represent confounding effects for data interpretation in the context of atmospheric pollution assessment. For this reason, the authors suggested adopting interpretative scales for LDV scores in different environmental conditions (forested and non-forested areas). Similarly, Svoboda et al. [18] found that forest age and fragmentation strongly influence lichen diversity, and, in some cases, several natural factors can obscure the effect of human influence.
Many studies have provided interesting biomonitoring results regarding the relationships between lichen communities’ compositions and modeled or measured values of nitrogen and sulfur deposition within forest national monitoring networks in several different European countries (Estonia [27][28], the UK [29], Portugal [30][31], Finland [32], Italy [33], and the USA [34][35][36][37][38][39]). Forests are remote ecosystems of less interest from a risk assessment point of view because of their low population density. However, studying the effects of atmospheric pollution in forest environments may be a good approach for obtaining a reference for the background values in remote areas. Moreover, these studies can provide information on the long-range dispersion of pollutants. For this purpose, the Convention on Long-range Transboundary Air Pollution (the Air Convention, formerly CLRTAP) of the United Nations Economic Commission for Europe (UNECE) launched the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) in 1985. This network monitors forest conditions at two monitoring levels to gain insight into the spatial and temporal variations in forest conditions (level I) and to clarify cause–effect relationships (level II). In this context, the study of lichen diversity was also introduced with the adoption of a specific survey manual [7]. The protocol refers to the European standard [8][9], but the sampling plan (selection of trees within the plot) is adapted to the ICP Forests context. The method was applied in the Forest Biota project [40] with the objectives of (i) monitoring the richness and frequencies of lichen species in EU/ICP Forests level II plots; (ii) evaluating the relationship between lichen diversity and influencing factors (e.g., stand structure and composition, deposition); (iii) testing a methodology for biodiversity assessment specifically for EU/ICP Forests level II plots; and (iv) setting a baseline for monitoring future changes at the plot level. In total, 83 plots (1155 trees) across ten European countries were considered. The results showed that species’ richness and evenness were not significantly dependent on sulfur and nitrogen depositions but highly correlated with stand structure, geographical location, and altitude. Functional traits, such as growth form, are more effective in describing pollutant depositions; specifically, nitrogen input [11]. In addition, the data collected in the Italian plots confirmed that lichen species’ richness is influenced by, among other factors, longitude and latitude. In contrast, functional traits (growth forms, types of photobiont, types of reproductive strategy) are more related to factors that may be independent of geographic position [20].
Recently, to respond to EU Directive 2016/2284 (NEC), the Italian NEC network was set up, and it includes several sites from the ICP Forests network. This directive commits the Member States to: (i) reducing anthropogenic emissions of sulfur, nitrogen, non-methane volatile organic compounds, ammonia, and particulate matter (PM) through the elaboration and implementation of national air pollution control programs; and (ii) monitoring the effects of air pollutants on ecosystems. The network currently comprises six sites. The first two surveys (2019 and 2020) found the lowest LDVs for the sites in the Po and the pre-Alpine belt, which have historically been more subject to high levels of air pollutants. In contrast, the Apennine plots from central and southern Italy showed the highest values [33][41]. The LIFE MODERnNEC Project (project website: https://lifemodernec.eu/) was recently funded to improve this monitoring system, both by introducing new sites belonging to different bioclimatic regions, as required by the NEC guidelines [42], and by adopting new methods based on functional traits and the presence of indicator species.

2. Lichen Functional Groups

Functional diversity is a component of biodiversity that generally concerns the range of functions organisms have in communities and ecosystems [43]. It can provide a mechanistic link between changes in ecosystems and the functional roles of specific groups of organisms (functional groups) distinguished according to their functional traits. Monitoring of the functional traits of animals, plants, and fungi has increased considerably in recent decades [43]. Concerning epiphytic lichens, functional traits such as growth forms, photosynthetic partners, reproductive strategies, and sensitivity to pollutants (e.g., oligotrophic and nitrophytic species) have been extensively studied in the context of forest monitoring (e.g., [27][28][29][31][36][44]).
In general, most studies suggest that functional diversity can provide additional information in the study of lichen diversity, allowing early-warning responses to forest environmental changes; above all, in response to nitrogen compounds, which are now the predominant air pollutants [11]. One example is the composition and ratio of oligotrophic and nitrophytic species in the lower trunks of forest trees. Several studies suggest that the proportions of the two functional groups may be a suitable indicator of the impact of oxidized and reduced nitrogen compounds [17][19][29][30][45].
This line of research includes the study by Gadsdon et al. [29], which supports the use of lichen communities growing on the twigs of acid-barked tree species as sensitive indicators of NH3 and NO2 air pollution. This approach is based on the fact that the bark pH of the twigs is naturally higher than that of the trunk, and lichens on twigs are more sensitive to these compounds than those living on the tree trunk [46][47][48][49]. These results are further supported by Marmor et al. [27], who studied the relationship between the vertical distribution of macrolichens on trunks and alkaline dust pollution. Considering a subset of ten lichen species, they showed that their composition in the upper canopy was a more informative indicator of air pollution in forests compared to the lichen composition in the lower part of the trunk on trees (first 2 m). However, the authors pointed out that the sampling of canopy lichens is complicated due to methodological difficulties. For this reason, most studies have focused on lichens growing on the lower trunk, which are easier to detect and can give equally promising results. A long-term study (1993–2010) carried out at a remote site of the European Alps (Austria) showed a significant decrease in epiphytic lichen diversity in the presence of high levels of S and N transported by fog and rain due to the decrease in oligotrophic species rather than the increase in nitrophytic ones [32]. The authors explained these results in terms of the differential time lag in the responses: nitrogen-sensitive species could have already vanished, while nitrophytic ones had not yet started to colonize the monitoring plot. In addition, in the USDA Forest Service Air Program, Root et al. [50] confirmed that, when nitrogen deposition increases, nitrogen-loving eutrophic lichens become dominant over the oligotrophic species that thrive in nutrient-poor habitats.
Among the functional groups, macrolichens have been widely considered in both European [12][51][52] and United States [34][35][36][37][39][53] forest-monitoring programs. The objectives of these studies were to determine the presence and abundance (from rare to abundant, four-class ordinal scale) of epiphytic macrolichen species (i.e., foliose and fruticose) on living and dead trees (including recently fallen branches and logs) in each plot. Compared to the assessment of the total lichen diversity (including crustose species), the main advantage of this method was that the crew members were required to collect lichen samples and send them to be identified by lichen specialists. Since expert lichenologists were not directly involved in the field, this made it possible to survey many plots (e.g., the USDA Forest Service surveyed approximately 1500 sampling sites). Furthermore, this simplified method is reliable and suitable for explaining air pollution trends, as the results of several field surveys carried out in recent decades highly correlate with N and S depositions [36][38][39]. In particular, the abundance of nitrophilous lichens correlates with the deposition of nitrogen compounds in both reduced and oxidized forms [34][45]. In this context, functional groups have been widely used to establish the nitrogen and sulfur critical loads for epiphytic lichen communities in North America and Europe [11][19][45]. Such lichen-based critical loads, tested by means of multiple linear regression models, are important tools that can help managers, regulators, and policymakers meet the goals of protecting biodiversity and sustaining the health and productivity of forests [36][38][39]. Similar results were obtained in Europe [11][19], confirming the relations between macrolichens and the main nitrogen compounds. In the context of the ICP Forests monitoring program (Forest Biota project), Giordani et al. [11] showed that the percentage of macrolichens is the most effective indicator of nitrogen compounds, with 57% of the variation explained by nitrogen depositions.
The assessment of lichen functional groups may also represent an interesting tool in the field of climate change monitoring. Indeed, several studies have underlined the influence of climate change on lichen communities and the potential application of bioclimatic models to lichen species as indicators of climate change risk [54]. Geiser and Neitlich [35] modeled epiphytic macrolichen community responses to air quality and climate gradients at 1416 forested plots in western Oregon and Washington. They showed that the gradients were responsive to regionally increasing nitrogen availability and to temperature changes predicted by climate models. A study carried out in the interior forested mountain ecosystems of the Pacific Northwest (USA) offered a different picture, highlighting that climatic moisture deficit and continentality, independently of air quality impacts, are the main drivers for macrolichens [50].
Among macrolichens, hair lichens represent a promising functional group for the monitoring of climate change and air pollution depositions, particularly in the context of high-elevation mountain areas, as reported in a recent literature review by Nascimbene et al. [55]. This morpho-functional group includes pendulous epiphytic lichens with fruticose-filamentose thalli mainly belonging to the genera Alectoria, Bryoria, Evernia, Ramalina, and Usnea. The authors provide a starting point for the development of predictive tools to study the effects of global change in the Alps.
Other functional traits can be considered suitable indicators for monitoring forest pollution and climate change (e.g., [56][57][58]). In a study carried out in Italy, Marini et al. [45] suggested that the impacts of global change on lichens are likely influenced by the photobiont type (i.e., the synthetic partner of the lichen symbiosis), on which the differential responses in lichen species’ richness to various environmental drivers depend. In Polish forests, Łubek et al. [59] considered a broader set of functional traits, including growth form, photobiont type, structures for sexual (ascomata type and pigmentation) and asexual reproduction (vegetative propagules, such as pycnidia, sporodochia, isidia, and soredia), and secondary metabolites. Furthermore, these researchers also examined the ecological requirements for temperature, moisture, light, continentalism, and eutrophication among lichen species. They suggested that the concurrent study of the functional diversity and the ecological optima of species, rather than considering shifts in species composition alone, can help in better monitoring directions in lichen biota changes over time.

3. The Focus on Single Indicator Species

The study of single indicator species, such as air pollution-sensitive or more tolerant lichens, can provide useful information on air pollution and climate change impacts on forest sites. This approach builds on the assumption that air pollutants can cause physical and physiological alterations to lichens, resulting in visible injuries on their thalli (e.g., damage or discoloration, reduction in the production of apothecia and soredia; for a review, see [60]).
In this context, the Finnish epiphytic lichen method aims to monitor air quality by focusing on the presence of a set of macro lichen species and the abundance of and degree of damage suffered by two species with known pollution sensitivity: the highly sensitive Bryoria spp. and the more tolerant Hypogymina physodes [52]. The abundances of the two species are obtained by counting the numbers of dots in a sampling grid placed on tree bark. A five-class scale of damage is used to assess to the most damaged individuals observed between heights of 50 and 200 cm above ground. However, when adopting this method in southern Finland and northwestern Russia, Mayer et al. [12] showed that the responses of single species may not be reliable bioindicators at low air pollution concentrations. Indeed, the sensitive lichen Bryoria spp. were rare in their forest sites, thus not providing information, while the abundance of and damage suffered by H. physodes were correlated with forest structure but not with air pollution, as already suggested in a previous study by Will-Wolf et al. [61].
Another lichen widely used in forest monitoring is Lobaria pulmonaria. This large foliose species is very sensitive to air pollution and declining heavily throughout Europe. Several studies have demonstrated its suitability both as a flagship and as an umbrella species for nature conservation, and it is associated with many other rare and endangered forest-dwelling organisms (e.g., [62][63][64][65][66]). Further, being easy to identify in the field, it can be successfully sampled by non-expert lichenologists, such as personnel trained in appropriate training courses. These features make L. pulmonaria an excellent indicator of air quality and forest continuity, especially when setting up rapid biodiversity assessments (RBAs) in monitoring programs carried out over large forest areas. In particular, studying its abundance and the viability of its populations in forest plots may represent a suitable method for obtaining early-warning responses to environmental changes in the medium- to long-term periods. In this respect, many authors not only consider the occurrence and abundance of L. pulmonaria but also assess its conservation status and health in terms of active growth (presence of meristematic lobes) and dispersion capacity (presence of juvenile thalli, vegetative propagules, and fruiting bodies) (e.g., [66][67][68]). However, most of these studies investigate the effects of forest management and old-growth structural attributes. Although promising, the use of this lichen as an air pollution bioindicator is still under-explored. In a recent study by Paoli et al. [69], L. pulmonaria was adopted as a model to test if the translocation of this sensitive species is only effective under low-pollution conditions. They transplanted fragments and whole thalli in beech and oak forests in Central and Southern Europe (Slovakia and Italy). The transplantation was successful in remote areas with low or negligible metal contamination. On the other hand, the translocation of the thalli in the most polluted sites did not ensure their survival. These results show that air quality still limits the recolonization of L. pulmonaria in the sites where it disappeared in the past.
Therefore, using single species in forest monitoring can provide additional information on the overall framework of air pollution in these ecosystems.
Furthermore, these species are usually easy to identify in the field, even by non-expert personnel, so they can be adopted in large-scale monitoring networks using simplified detection methods. However, studies have shown that focusing on a single species can also have some drawbacks. Indeed, the distribution of single forest lichen species, as well as being affected by air pollution, may also be affected by microclimatic and/or biogeographic variables, thus reducing their effectiveness as bioindicators (e.g., [66][67]).

References

  1. Nimis, P.L.; Scheidegger, C.; Wolseley, P.A. Monitoring with lichens-monitoring lichens: An introduction. In Monitoring with Lichens-Monitoring Lichens; Nato science program-IV, Nimis, P.L., Scheidegger, C., Wolseley, P.A., Eds.; Kluwer Academic Publisher: Amsterdam, The Netherlands, 2002; Volume VII, pp. 1–4.
  2. Giordani, P.; Brunialti, G. Sampling and interpreting lichen diversity data for biomonitoring purposes. In Recent Advances in Lichenology; Upreti, D.K., Divakar, P.K., Shukla, V., Bajpai, R., Eds.; Springer: Delhi, India, 2015; pp. 19–46.
  3. LeBlanc, S.C.F.; De Sloover, J. Relation between industrialization and the distribution and growth of epiphytic lichens and mosses in Montreal. Can. J. Bot. 1970, 48, 1485–1496.
  4. Ammann, K.; Herzig, R.; Liebendoerfer, L.; Urech, M. Multivariate correlation of deposition data of 8 different air pollutants to lichen data in a small town in Switzerland. In Advances in Aerobiology; Boehm, G., Leuschner, R.M., Eds.; Birkhäuser: Basel, Switzerland, 1987; Volume 51, pp. 401–406.
  5. Kricke, R.; Loppi, S. Bioindication: The I.A.P. approach. In Monitoring with Lichens—Monitoring Lichens; Nimis, P.L., Scheidegger, C., Wolseley, P.A., Eds.; Kluwer: Dordrecht, The Netherlands, 2002; pp. 21–37.
  6. VDI. Richtlinie 3799, Blatt 1: Ermittlung und Beurteilung Phytotoxischer Wirkungen von Immissionswirkungen mit Flechten: Flechtenkartierung zur Ermittlung des Luftgütewertes (LGW); VDI/DIN-Kommission Reinhaltung der Luft (KRdL)-Normenausschuss: Düsseldorf, Germany, 1995.
  7. Stofer, S.; Calatayud, V.; Giordani, P.; Neville, P. Assessment of epiphytic lichen diversity. In Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests; UNECE ICP Forests Programme Co-ordinating Centre, Ed.; Thünen Institute of Forest Ecosystems: Eberswalde, Germany, 2016; Part VII.2; p. 13 + Annex. Available online: http://www.http://icp-forests.net/page/icp-forests-manual (accessed on 31 January 2023).
  8. Asta, J.; Erhardt, W.; Ferretti, M.; Fornasier, F.; Kirschbaum, U.; Nimis, P.L.; Purvis, O.W.; Pirintsos, S.; Scheidegger, C.; Van Haluwyn, C.; et al. Mapping lichen diversity as an indicator of environmental quality. In Monitoring with Lichens—Monitoring Lichens; Nimis, P.L., Scheidegger, C., Wolseley, P.A., Eds.; Kluwer: Dordrecht, The Netherlands, 2002; pp. 273–279.
  9. EN 16413. Ambient air-Biomonitoring with Lichens-Assessing Epiphytic Lichen Diversity; CEN-CENELEC Management Center: Brussels, Belgium, 2014.
  10. Abas, A. A systematic review on biomonitoring using lichen as the biological indicator: A decade of practices, progress and challenges. Ecol. Indic. 2021, 121, 107197.
  11. Giordani, P.; Calatayud, V.; Stofer, S.; Seidling, W.; Granke, O.; Fischer, R. Detecting the nitrogen critical loads on European forests by means of epiphytic lichens. A signal-to-noise evaluation. For. Ecol. Manag. 2014, 311, 29–40.
  12. Mayer, A.L.; Vihermaa, L.; Nieminen, N.; Luomi, A.; Posch, M. Epiphytic macrolichen community correlates with modeled air pollutants and forest conditions. Ecol. Indic. 2009, 9, 992–1000.
  13. Giordani, P. Variables influencing the distribution of epiphytic lichens in heterogeneous areas: A case study for Liguria, NW Italy. J. Veg. Sci. 2006, 17, 195–206.
  14. Giordani, P. Is the diversity of epiphytic lichens a reliable indicator of air pollution? A case study from Italy. Environ. Pollut. 2007, 146, 317–323.
  15. Svoboda, D. Evaluation of the European method for mapping lichen diversity (LDV) as an indicator of environmental stress in the Czech Republic. Biologia 2007, 62, 424–431.
  16. Poličnik, H.; Simončič, P.; Batič, F. Monitoring air quality with lichens: A comparison between mapping in forest sites and in open areas. Environ. Pollut. 2008, 151, 395–400.
  17. Cristofolini, F.; Giordani, P.; Gottardini, E.; Modenesi, P. The response of epiphytic lichens to air pollution and subsets of ecological predictors: A case study from the Italian Prealps. Environ. Pollut. 2008, 151, 308–317.
  18. Svoboda, D.; Peksa, O.; Veselá, J. Epiphytic lichen diversity in central European oak forests: Assessment of the effects of natural environmental factors and human influences. Environ. Pollut. 2010, 158, 812–819.
  19. Pinho, P.; Bergamini, A.; Carvalho, P.; Branquinho, C.; Stofer, S.; Scheidegger, C.; Maguas, C. Lichen functional groups as ecological indicators of the effects of land-use in Mediterranean ecosystems. Ecol. Indic. 2012, 15, 36–42.
  20. Giordani, P.; Brunialti, G.; Bacaro, G.; Nascimbene, J. Functional traits of epiphytic lichens as potential indicators of environmental conditions in forest ecosystems. Ecol. Indic. 2012, 18, 413–420.
  21. Agnan, Y.; Probst, A.; Séjalon-Delmas, N. Evaluation of lichen species resistance to atmospheric metal pollution by coupling diversity and bioaccumulation approaches: A new bioindication scale for French forested areas. Ecol. Indic. 2017, 72, 99–110.
  22. Gibson, M.D.; Heal, M.R.; Li, Z.; Kuchta, J.; King, G.H.; Hayes, A.; Lambert, S. The spatial and seasonal variation of nitrogen dioxide and sulfur dioxide in Cape Breton Highlands National Park, Canada, and the association with lichen abundance. Atmos. Environ. 2013, 64, 303–311.
  23. McMullin, R.T.; Ure, D.; Smith, M.; Clapp, H.; Wiersma, Y.F. Ten years of monitoring air quality and ecological integrity using field-identifiable lichens at Kejimkujik National Park and National Historic Site in Nova Scotia, Canada. Ecol. Indic. 2017, 81, 214–221.
  24. Correa-Ochoa, M.A.; Vélez-Monsalve, L.C.; Saldarriaga-Molina, J.C.; Jaramillo-Ciro, M.M. Evaluation of the Index of Atmospheric Purity in an American tropical valley through the sampling of corticulous lichens in different phorophyte species. Ecol. Indic. 2020, 115, 106355.
  25. Tanona, M.; Czarnota, P. Index of Atmospheric Purity reflects the ecological conditions better than the environmental pollution in the Carpathian forests. J. Mt. Sci. 2020, 17, 2691–2706.
  26. Brunialti, G.; Frati, L.; Incerti, G.; Rizzi, G.; Vinci, M.; Giordani, P. Lichen Biomonitoring of air pollution: Issues for applications in complex environments. In Air Quality in the 21st Century; Romano, G.C., Conti, A.G., Eds.; Nova Science Publishers, Inc.: New York, NY, USA, 2009; pp. 211–259. ISBN 9781604567939.
  27. Marmor, L.; Tõrra, T.; Randlane, T. The vertical gradient of bark pH and epiphytic macrolichen biota in relation to alkaline air pollution. Ecol. Indic. 2010, 10, 1137–1143.
  28. Degtjarenko, P.; Matos, P.; Marmor, L.; Branquinho, C.; Randlane, T. Functional traits of epiphytic lichens respond to alkaline dust pollution. Fungal Ecol. 2018, 36, 81–88.
  29. Gadsdon, S.R.; Dagley, J.R.; Wolseley, P.A.; Power, S.A. Relationships between lichen community composition and concentrations of NO2 and NH3. Environ. Pollut. 2010, 158, 2553–2560.
  30. Pinho, P.; Theobald, M.R.; Dias, T.; Tang, Y.S.; Cruz, C.; Martins-Loução, M.A.; Máguas, C.; Sutton, M.; Branquinho, C. Critical loads of nitrogen deposition and critical levels of atmospheric ammonia for semi-natural Mediterranean evergreen woodlands. Biogeosciences 2012, 9, 1–11.
  31. Morillas, L.; Roales, J.; Cruz, C.; Munzi, S. Resilience of epiphytic lichens to combined effects of increasing nitrogen and solar radiation. J. Fungi 2021, 7, 333.
  32. Mayer, W.; Pfefferkorn-Dellali, V.; Türk, R.; Dullinger, S.; Mirtl, M.; Dirnböck, T. Significant decrease in epiphytic lichen diversity in a remote area in the European Alps, Austria. Basic Appl. Ecol. 2013, 14, 396–403.
  33. Papitto, G.; Quatrini, V.; Cindolo, C.; Cocciufa, C.; Brunialti, G.; Frati, L. Rilevamento della diversità dei licheni epifiti nell’ambito del monitoraggio in continuo dell’inquinamento atmosferico nei siti della rete CON.ECO.FOR. di Livello II ICP- Forests. Natura 2019, 111, 19.
  34. Jovan, S.E.; McCune, B. Air-quality bioindication in the greater Central Valley of California, with epiphytic macrolichen communities. Ecol. Appl. 2005, 15, 1712–1726.
  35. Geiser, L.H.; Neitlich, P.N. Air pollution and climate gradients in western Oregon and Washington indicated by epiphytic macrolichens. Environ. Pollut. 2007, 145, 203–218.
  36. Geiser, L.H.; Jovan, S.E.; Glavich, D.A.; Fenn, M.E. Predicting lichen-based critical loads for nitrogen deposition in temperate forests. In Nitrogen Deposition, Critical Loads and Biodiversity; Sutton, M., Mason, K., Sheppard, L., Sverdrup, H., Haeuber, R., Hicks, W., Eds.; Springer: Dordrecht, The Netherland, 2014; pp. 311–318.
  37. McDonough, A.M.; Watmough, S.A. Impacts of nitrogen deposition on herbaceous ground flora and epiphytic foliose lichen species in southern Ontario hardwood forests. Environ. Pollut. 2015, 196, 78–88.
  38. Geiser, L.H.; Nelson, P.R.; Jovan, S.E.; Root, H.T.; Clark, C.M. Assessing ecological risks from atmospheric deposition of nitrogen and sulfur to US forests using epiphytic macrolichens. Diversity 2019, 11, 87.
  39. Geiser, L.H.; Root, H.T.; Smith, R.J.; Jovan, S.E.; St Clair, L.; Dillman, K.L. Lichen-based critical loads for deposition of nitrogen and sulfur in US forests. Environ. Pollut. 2021, 291, 118187.
  40. Stofer, S.; Catalayud, V.; Ferretti, M.; Fischer, R.; Giordani, P.; Keller, C.; Stapper, N.; Scheidegger, C. Epiphytic Lichen Monitoring within the EU/ICP Forests Biodiversity Test-Phase on Level II Plots; Forest Biota project, Technical Report. 2003. Available online: http://icp-forests.net/page/forestbiota (accessed on 3 February 2023).
  41. Brunialti, G.; Frati, L.; Giordani, P.; Nascimbene, J.; Canullo, R.; Cindolo, C.; Papitto, G. Rete NEC Italia: I risultati della prima campagna di monitoraggio della diversità dei licheni epifiti. Not. Soc. Lich. Ital. 2020, 33, 75–83.
  42. Landgrebe, R.; Best, A.; Hayes, F.; Stein, U.; Harker, G.; Schritt, H.; Duin, L.; Deacon, S. Guidance Note on Site Selection. Support to Member States Regarding the Monitoring of Effects of Air Pollution on Ecosystems according to Article 9(1) of the NEC Directive (Directive (EU) 2016/2284); Technical Report; Ecologic Institute: Berlin, Germany, 2022.
  43. Petchey, O.L.; Kevin, J.; Gaston, K.J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 2006, 9, 741–758.
  44. Ellis, C.J.; Asplund, J.; Benesperi, R.; Branquinho, C.; Di Nuzzo, L.; Hurtado, P.; Martínez, I.; Matos, P.; Nascimbene, J.; Pinho, P.; et al. Functional Traits in Lichen Ecology: A Review of Challenge and Opportunity. Microorganisms 2021, 9, 766.
  45. Jovan, S.; Riddell, J.; Padgett, P.E.; Nash, T.H. Eutrophic lichens respond to multiple forms of N: Implications for critical levels and critical loads research. Ecol. Appl. 2012, 22, 1910–1922.
  46. Wolseley, P.A.; Pryor, K.V. The potential of epiphytic twig communities on Quercus petraea in a welsh woodland site (Tycanol) for evaluating environ-mental changes. Lichenologist 1999, 31, 41–61.
  47. Pitcairn, C.E.R.; Leith, I.D.; Sheppard, L.J.; van Dijk, N.; Tang, Y.S.; Wolseley, P.; James, P.; Sutton, M. Field inter-comparison of different bio-indicator methods to assess the impacts of atmospheric nitrogen deposition. In Bioindicator and Biomonitoring Methods for Assessing the Effects of Atmospheric Nitrogen on Statutory Nature Conservation Sites; Sutton, M.A., Pitcairn, C.E.R., Whitfield, C.P., Eds.; JNCC Report 2004; Joint Nature Conservation Committee: Peterborough, UK, 2004; Number 356, Appendix 1; pp. 141–181.
  48. Wolseley, P.A.; Stofer, S.; Mitchell, A.-M.; Vanbergen, A.; Chimonides, J.; Scheidegger, C. Variation of lichen communities with land use in Aberdeenshire, UK. Lichenologist 2006, 38, 307–322.
  49. Wolseley, P.A.; Leith, I.D.; van Dijk, N.; Sutton, M.A. Macrolichens on twigs and trunks as indicators of ammonia concentrations across the UK—A practical method. In Atmospheric Ammonia; Sutton, M.A., Reis, S., Baker, S.M., Eds.; Springer: Dordrecht, The Netherlands, 2009; pp. 101–108.
  50. Root, H.T.; Geiser, L.H.; Jovan, S.; Neitlich, P. Epiphytic macrolichen indication of air quality and climate in interior forested mountains of the Pacific Northwest, USA. Ecol. Indic. 2015, 53, 95–105.
  51. Kinnunen, H.; Holopainen, T.; Kärenlampi, L. Sources of error in epiphytic lichen variables mapped as bioindicators: Needs to modify the Finnish standard. Ecol. Indic. 2003, 3, 1–11.
  52. SFS 5670; Ilmansuojelu. Bioindikaatio. Jäkäläkartoitus. Finnish Standards Association: Helsinki, Finland, 1990.
  53. USFS. Phase 3 Field Guide–Lichen Communities; Version 5.1 October, 2011; United States Forest Service: Washington, DC, USA, 2011. Available online: https://www.fia.fs.usda.gov/library/field-guides-methods-proc/index.php (accessed on 31 January 2023).
  54. Ellis, C.J. Climate Change, Bioclimatic Models and the Risk to Lichen Diversity. Diversity 2019, 11, 54.
  55. Nascimbene, J.; Benesperi, R.; Giordani, P.; Grube, M.; Marini, L.; Vallese, C.; Mayrhofer, H. Could hair-lichens of high-elevation forests help detect the impact of global change in the Alps? Diversity 2019, 11, 45.
  56. Marini, L.; Nascimbene, J.; Nimis, P.L. Large-scale patterns of epiphytic lichen species richness: Photobiont-dependent response to climate and forest structure. Sci. Total Environ. 2011, 409, 4381–4386.
  57. Hurtado, P.; Prieto, M.; De Bello, F.; Aragón, G.; López-Angulo, J.; Giordani, P.; Díaz-Peña, E.M.; Vicente, R.; Merinero, S.; Košuthová, A.; et al. Contrasting environmental drivers determine biodiversity patterns in epiphytic lichen communities along a European gradient. Microorganisms 2020, 8, 1913.
  58. Hurtado, P.; Prieto, M.; Aragón, G.; De Bello, F.; Martínez, I. Intraspecific variability drives functional changes in lichen epiphytic communities across Europe. Ecology 2020, 101, e03017.
  59. Łubek, A.; Kukwa, M.; Jaroszewicz, B.; Czortek, P. Shifts in lichen species and functional diversity in a primeval forest ecosystem as a response to environmental changes. Forests 2021, 12, 686.
  60. Nash, T.H., III. Lichen Biology, 2nd ed.; Cambridge University Press: Cambridge, UK, 2008.
  61. Will-Wolf, S.; Geiser, L.H.; Neitlich, P.; Reis, A.H. Forest lichen communities and environment—How consistent are relationships across scales? J. Veg. Sci. 2006, 17, 171–184.
  62. Nilsson, S.G.; Arup, U.; Baranowski, R.; Ekman, S. Tree-dependent lichens and beetles as indicators in conservation forests. Conserv. Biol. 1995, 9, 1208–1215.
  63. Campbell, J.; Fredeen, A.L. Lobaria pulmonaria abundance as an indicator of macrolichen diversity in Interior Cedar-Hemlock forests of east–central British Columbia. Canad. J. Bot. 2004, 82, 970–982.
  64. Nascimbene, J.; Brunialti, G.; Ravera, S.; Frati, L.; Caniglia, G. Testing Lobaria pulmonaria (L.) Hoffm as an indicator of lichen conservation importance of Italian forests. Ecol. Indic. 2010, 10, 353–360.
  65. Nascimbene, J.; Benesperi, R.; Brunialti, G.; Catalano, I.; Vedove, M.D.; Grillo, M.; Isocrono, D.; Matteucci, E.; Potenza, G.; Puntillo, D.; et al. Patterns and drivers of beta-diversity and similarity of Lobaria pulmonaria communities in Italian forests. J. Ecol. 2013, 101, 493–505.
  66. Di Nuzzo, L.; Giordani, P.; Benesperi, R.; Brunialti, G.; Fačkovcová, Z.; Frati, L.; Nascimbene, J.; Ravera, S.; Vallese, C.; Paoli, L.; et al. Microclimatic alteration after logging affects the growth of the endangered lichen Lobaria pulmonaria. Plants 2022, 11, 995.
  67. Brunialti, G.; Frati, L.; Ravera, S. Structural variables drive the distribution of the sensitive lichen Lobaria pulmonaria in Mediterranean old-growth forests. Ecol. Indic. 2015, 53, 37–42.
  68. Brunialti, G.; Frati, L.; Ravera, S. Ecology and conservation of the sensitive lichen Lobaria pulmonaria in Mediterranean old-growth forests. In Old-Growth Forests and Coniferous Forests. Ecology, Habitat and Conservation; Weber, P.R., Ed.; Nova Science Publisher: New York, NY, USA, 2015; pp. 1–20. ISBN 978-1-63482-369-2.
  69. Paoli, L.; Guttová, A.; Sorbo, S.; Lackovičová, A.; Ravera, S.; Landi, S.; Landi, M.; Basile, A.; Sanità di Toppi, L.; Vannini, A.; et al. Does air pollution influence the success of species translocation? Trace elements, ultrastructure and photosynthetic performances in transplants of a threatened forest macrolichen. Ecol. Indic. 2020, 117, 106666.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : ,
View Times: 460
Entry Collection: Environmental Sciences
Revisions: 2 times (View History)
Update Date: 31 Mar 2023
1000/1000
Video Production Service