46 R e search ec o.m ont – Volum e 10, Num ber 1, Ja n u a r y 2 0 1 8 I SSN 2 0 7 3 -1 0 6 X print v ersion – ISSN 2073-1558 online v ersion: http:/ / epub.oeaw.ac .a t / e co .mo n t https:/ / dx.doi.org/ 10.1553/ ec o.mo n t-1 0 -1 s4 6 From natural forest to cultivated land: Lichen species diversity along land-use gradients in Kanchenjunga, Eastern Nepal Til Bikram Chongbang, Christine Keller, Michael Nobis, Christoph Scheidegger & Chitra Bahadur Baniya Keywords: canopy openness, Canonical Correspondence Analysis, Ghunsa valley, hemispherical photography, land-use change, lichen diversity Profile A b s t r a ct This study aimed to evaluate the effects of elevation, land use and canopy openness on species richness and composition of lichens in Ghunsa valley of Kanchenjunga Conservation Area, Eastern Nepal. At five elevational levels, from 2 200 m to 3 800 m, transects were established in four land-use types – cultivated land, meadows, exploited and natural forests. Detrended Correspondence Analysis (DCA) and Canonical Correspondence Analysis techniques were used to explore the lichen species distribution patterns. Generalized linear models were applied to analyse the impact of elevation and canopy openness on lichen species richness. Canopy openness was measured by hemispherical photography. A total of 229 species belonging to 71 genera were recorded. The length of the first DCA axis of 8.01 SD units indicated a complete species turnover and high beta diversity along the elevation gradient. Exploited forests with lower canopy openness supported higher lichen diversity than open meadows and cultivated areas. Significant differences in lichen species richness were found for different land-use types, along the elevation gradient, and with varying canopy openness. A gradual increase of lichen species richness from cultivated land to forests was observed. We concluded that substrate types that depend on land-use types as well as canopy openness significantly affect the distribution of lichen communities. Introduction Lichen diversity along elevational gradients has been analysed intensively in recent years (Bruun et al. 2006; Grytnes et al. 2006; Pinokiyo et al. 2008; Cobanoglu & Sevgi 2009; Baniya et al. 2010; Rai et al. 2011; Baniya et al. 2012) as well as lichen diversity along land-use gradients (Bergamini et al. 2005; Motiejûnaitë & Faùtynowicz 2005; Stofer et al. 2006; Wolseley et al. 2006; Giordani et al. 2010). Similarly, some recent studies are concerned with the influence of canopy openness on species richness, diversity and distribution of lichens (Li et al. 2011; Marmor et al. 2012; Li et al. 2013b, 2013a). However, effects of landuse related canopy openness on species richness and composition of lichens have rarely been studied. Land-use change determines vegetation cover, species composition and distribution patterns of plant communities (Tasser & Tappeiner 2002) and, consequently, the variation in key characteristics of host tree species, like their density, age and diameter, which all influence the composition and distribution of epiphytic lichen communities (Löbel et al. 2006; Mežaka et al. 2008; Cobanoglu & Sevgi 2009; Li et al. 2011; Mežaka et al. 2012; Odor et al. 2013). Land-use changes, habitat loss and degradation often decline lichen populations (Scheidegger & Werth 2009). Compared with other factors, changing light and moisture Protected area Kanchenjunga Conservation Area Mountain range Himalaya Country Nepal conditions are often the dominant factors to explain differences in lichen diversity and abundance (Li et al. 2013a). Nepal is a mountainous country in the central Himalayas with an area of 147 181 km2. It is situated between China in the north and India in the east, south and west. The elevation ranges from 60 m above sea level in Terai to 8 848 m at Mt Everest, the highest peak in the world (Chaudhary 1998). In Nepal, lichens are found in all climatic zones. However, floristic and ecological studies on lichens are largely missing. The latest physiographic data of Nepal showed 29 % of the total land area covered by forests, 10 % by shrubs and degraded forests and 21 % by cultivated land (MFSC 2009). Land-use and land-cover change are substantial in Nepal; especially the forest cover shows a drastic decline – even in protected areas. For the Kanchenjunga Conservation Area (KCA), for instance, Gautam and Watanabe (2004) found a decline in forest land cover by 14.9 % and grazing land cover by 77.9 % between 1979 and 1992. This was the result of an increase in cultivated land by 4.9 % and shrubland by 19.7 %. KCA is a community-managed protected area established in 1997 and handed over to the KCA Management Council by the government of Nepal in 2006. The shifting cultivation is a common traditional farming system practiced in this protected area by the local ethnic groups as their traditional oc- Cho ng ba ng e t a l . Nepal 0 45 90 180 270 360 km Afghanistan China Nepal Bhutan Pakistan India Bangladesh protected areas 0 rivers 5 10 20 km Kanchenjunga conservation area transect points Figure 1 – Map of the study area showing the locations of the study sites. cupation and livelihood. It also falls within the Sacred Himalayan Landscape being developed by WWF Nepal (Aryal et al. 2010). The main objective in the present study, therefore, is to evaluate the effects of different land-use types, canopy openness on species richness and composition of lichens along the elevational gradient in KCA, Eastern Nepal. We hypothesized that (a) lichen diversity generally decreases from forests to open land and (b) highest lichen diversity is reached in forests under intermediate canopy openness. Materials and methods Study area This study was carried out in Ghunsa of Eastern Nepal between 2 200 m and 3 800 m (Figure 1). Ghunsa lies towards the north-eastern part of Nepal in the KCA. KCA is located between 27º 24’–27º 57’ N latitudes and 87º 39’–88º 12’ E longitudes, close to the boarders of China in the North and India in the East. KCA covers an area of 2 035 km2 between the Middle Mountains and the high Himalayas, with an elevational range from 1 200 m (Thiwa Khola) to Mt Kanchenjunga (8 586 m), the third-highest peak in the world. The area includes three river valleys: Simbua, Ghunsa, and Tamur (Anonymous 2011). KCA has diverse climatic zones, including subtropical monsoon at 1 200 m to alpine forests (above 4 000 m), where June to August are the warmest months, with monthly maximums of 24.73°C to 24.81°C, and January is the coldest month, with a maximum temperature of 13.8°C (Shrestha & Ghimire 1996). KCA receives a good amount of monsoon rainfall from April / May to September / Octo- ber, with a mean annual precipitation of 2 013 mm / yr (Anonymous 2009). Field methods and data collection Land-use types were classified according to land cover, disturbance frequency and intensity. At each elevational level, land-use gradients were stratified into four land-use types (Scheidegger et al. 2010). 1. Natural forest: Forested area with very little or no human disturbance. It includes mainly broad-leaved trees and pine trees. This land-use type is often several hours walking distance away from human settlements. 2. Exploited forest: Disturbed and / or exploited forests used for extensive grazing and / or the collection of fodder and firewood, which are close to human settlements. 3. Meadow: Areas dominated by grasses and scattered trees and shrubs. Grazed by domestic livestock like sheep, goats, buffaloes, cows, yaks, and horses. 4. Cultivated land: Land extensively used for cultivation and including terraced fields. These arable fields are often irrigated and fertilized. Fieldwork was carried out in April 2012. Five elevation levels, from 2 200–3 800 m, with an interval of approximately 400 m were selected for the study. At each level, the four land-use types were selected on both sides of the Ghunsa river valley and two transects of 2.5 m × 25 m each were studied at each land-use type on both sides of the valley, which showed southeast and north-west facing aspects. A total of 72 of 80 planned transects were established, because not all land-use types were found at each elevation level. The 47 48 R esearch distance between two transects within the same landuse type was at least ten meters. On each transect, elevation was recorded by Global Positioning System (Garmin, GPSmap60CSx) and slope, and the direction of the slope was recorded by a clinometer (Silva, Ranger). The growth form and substrate types were recorded. We considered the growth forms crustose, foliose, fruticose and leprose, and the substrate types corticolous (on bark), saxicolous (on rock), muscicolous (on moss) and terricolous (on soil) (Hale 1983). Hemispherical photographs were taken using a digital camera (Coolpix995 Nikon) and fish-eye lens (Fish-eye converter FC-E8 Nikon). The camera was mounted at a height of 1.5 m above the ground on a tripod and levelled with a bubble level. with downweighting of rare species and found a gradient length 8.01 standard unit (SD) for the first axis. This indicated the use of Canonical Correspondence Analysis (CCA) (Lepš & Šmilauer 2003) and its implied unimodal response model over a linear model like in Redundancy Analysis (RDA) to analyse the relationships between species co-occurrence and environmental variables (i. e., elevation, land-use type and canopy openness). All environmental variables were permuted 199 times during CCA to test for significant environmental variables. Direct correlations of environmental predictors with CCA axes were also performed. All statistical analyses were performed using the vegan 2.4-0 package (Oksanen et al. 2016) under the free statistical software environment R version 3.3.1 (R Core Team 2016). Lichen identification and image analysis Collected lichen specimens were examined at the Laboratory of the Central Department of Botany, Tribhuvan University, Kathmandu, Nepal, and at the Swiss Federal Research Institute, WSL, Switzerland. Identification of lichens was carried using the relevant keys and checklists (Awasthi 1991; Sharma 1995; Awasthi 2007; Singh & Sinha 2010). Identified specimens were deposited at the Swiss Federal Research Institute WSL, Switzerland. Lichen species were categorized according to family, growth forms, substrate type and photobiont types, i. e. cyanobacteria or green algae, following the recent updated taxonomical classification (Lücking et al. 2016). Data were organized in a relational database (MS Access). Hemispherical photographs were converted to binary (black and white pixels) following the image analysis manual described by Frazer et al. (1999). All image analyses were performed using image-processing software, Gap Light Analyzer (GLA Version 2.0). Statistical analysis We calculated Pearson correlation coefficients between variables such as total lichen species richness, growth forms, substrate types and photobiont types (i. e. green algal and cyanobacterial lichen species richness) and canopy openness. TukeyHSD multiple comparison tests were used to test the effect of particular land-use types on species richness of lichens. Generalized Linear Models (GLMs; McCullagh & Nelder 1989) with quasi-poisson error distribution were performed for modelling lichen richness. We build models with linear only and linear and quadric predictor terms and chose the final model parameterization according to the significance of the quadratic term. Graphics were made only for statistically significant models by using GLM. GLMs were not built for species richness of leprose, terricolous and muscicolous lichens because of the scarcity of occurrence data. Detrended Correspondence Analysis (DCA; Hill & Gauch 1980) was used to determine the lengths of the main gradient in species composition based on the sample by species data matrix. We performed DCA Results A total of 518 lichen specimens were collected from 72 transects, which included 229 lichen species of 71 genera (Appendix 1). 95 species belonged to the foliose growth form, 87 species were crustose, 44 species fruticose and 3 species were leprose. With regard to the substrate preference, 157 species were corticolous, 55 saxicolous, 14 muscicolous and 3 terricolous species. Green algal photobionts were associated with 205 lichen species, while the remaining 24 lichen species were associated with cyanobacteria. A TukeyHSD test showed significant differences in lichen species richness between cultivated and other land-use types (p < 0.05) (Appendix 2a). Species richness between land-use types According to land-use types, 174 species were recorded from exploited forests with the highest number of foliose lichens (77 species), followed by 172 species on natural forests, dominated again by foliose lichens (70 species). Likewise, the highest number of corticolous species (151 species) was recorded from natural forests followed by exploited forests with 135 species. Species richness and canopy openness Total species richness showed a significant monotonic decline with canopy openness (Figure 2a). Such a monotonic decline of species richness was also found for specific growth forms, specific photobiont species richness and species richness of corticolous of specific substrate types (Figure 2, Appendix 3). As an exception, a significant monotonic increase was found for saxicolous species richness towards higher canopy openness (Figures 2e, Appendix 3). An optimum of total lichen richness was found at low canopy openness with 20.1 species predicted at 10 % canopy openness, with a gradual decline towards higher canopy openness (Figure 2a). Similarly, species numbers of crustose and fruticose lichens showed a decline towards higher canopy openness, with a predicted species number of 6.4 and 5.2 species at 10 % canopy openness respec- Cho ng ba ng e t a l . Figure 2 – Relationship between lichen species richness and canopy openness. a–g: a) total species richness; b & c) species richness of specific grwoth forms; d & e) species richness of specific substrate types and f & g) specific photobiont species richness. The fitted regression lines represent Model 1 (Appendix 3). tively (Figures 2b & c). Regression analysis was not performed for the leprose growth form because only three species presented this feature. Regarding the four substrate categories, corticolous lichen richness also showed a gradual decline with increasing canopy openness, with an average of 19.8 species at 10 % canopy openness (Figure 2d). In contrast, saxicolous lichen richness had a positive trend with increasing canopy openness with an average of 7.4 species at 85% canopy openness (Figure 2e). GLM was not performed for muscicolous and terricolous species as their number was too low (14 and 3 species respectively). With respect to photobiont type, both cyanolichens and green algal lichens exhibited a significant decrease with canopy openness, with an average of 2.9 species of cy- anolichens, 17.2 species of green algal lichens at 10 % canopy openness (Figures 2f & g) respectively. Species richness along elevation There is a significant correlation of the total lichen species richness with the elevation and canopy openness (p ≤ 0.05). Total species richness of lichens and species richness of specific growth forms, specific substrate types and specific photobiont types, except species richness of leprose, muscicolous, terricolous lichens, showed a significant (p ≤ 0.05) monotonic increase with elevation (Figures 3a-g, Appendix 3). A total richness of 21.9 species was predicted at 3 800 m with a predicted species richness of 6.3 crustose, 10.3 foliose, 5.2 fruticose, 16.5 corticolous and 2.4 cyanoli- 49 50 R esearch Figure 3 – Relationship between lichen species richness and elevation. a–g: a) total species richness; b–d) species richness of specific grwoth forms; e) species richness of specific substrate types and f & g) specific photobiont species richness. The fitted regression lines represent Model 1 (Appendix 3). chens and 19.5 green algal lichen species at 3 800 m (Figures 3a–g). The regression analysis results showing the best selected model for each response variable is shown in Appendix 3. Species composition The length of the first DCA axis was 8.01 SD units (Table 2) that indicated a high beta diversity with almost complete species turnover between transects. The first two DCA axes explained 12.3 % of the total variance in the data matrix. In CCA, the environmental variables elevation, canopy openness and land-use explained 21 % of the total species variation variance (Table 3). CCA axis I was significantly correlated with elevation, while CCA axis II was highly correlated with canopy openness and land-use types (Figure 4, Appendix 2b). Along the CCA axis I, the highest abundance of Aspicilia contorta, Chaenotheca chrysocephala, Evernia mesomorpha, Leptogium burnetiae, Umbilicaria indica var. indica and Usnea longissima showed more preference towards high elevation, while species such as Cladonia scabriuscula, Heterodermia comosa, Lecanora cenisia showed high preference towards low elevation. Likewise, along the CCA axis II, species composition of Aspicilia caesiocinerea, Coccocarpia erythroxyli, Phaeophyscia ciliata, Umbilicaria badia, Xanthoria fallax showed higher abundance towards higher canopy openness, while species like Caloplaca farinosa, Hypogymnia vittata, Cladonia crispata var. cetrariiformis, Usnea himalayana, Chaenotheca chryso- Cho ng ba ng e t a l . Table 1 – Environmental correlation coefficient matrix (Pearson correlation) among variables used during the study (p ≤ 0.05). elv = elevation, cano = canopy openness, spn = total species number, cru = crustose species number, fol = foliose species number, fru = fruticose species number, lep = leprose species number, cort = corticolous species, musc = muscicolous species, saxi = saxicolous species, terr = terricolous species, blgrn = blue green algae and grnal = green algae Elev Canopy Spn Cru Fol Fru Lep Cort Musc Saxi Terr Blgrn Grnal 0.00 1.00 0.06 0.85 0.06 0.47 1.00 0.26 0.65 1.00 1.00 0.81 0.07 Canopy 0.29 0.00 1.00 1.00 1.00 1.00 1.00 0.06 1.00 0.09 1.00 0.28 1.00 Spn 0.00 0.05 0.00 0.00 0.00 0.00 1.00 0.00 0.06 1.00 1.00 0.00 0.00 Cru 0.02 0.05 0.00 0.00 0.00 0.00 1.00 0.00 0.78 1.00 1.00 0.00 0.00 Fol 0.00 0.18 0.00 0.00 0.00 0.00 1.00 0.00 0.61 1.00 1.00 0.00 0.00 Fru 0.01 0.05 0.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 0.00 0.00 Lep 0.41 0.64 0.21 0.10 0.47 0.50 0.00 1.00 1.00 1.00 1.00 1.00 1.00 Cort 0.01 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.61 1.00 1.00 0.00 0.00 Musc 0.02 0.29 0.00 0.02 0.01 0.00 0.67 0.01 0.00 1.00 1.00 0.37 0.07 Saxi 0.27 0.00 0.16 0.33 0.04 0.84 0.32 0.55 0.36 0.00 1.00 1.00 1.00 Terr 0.68 0.09 0.58 0.31 0.97 0.38 0.60 0.44 0.83 0.34 0.00 1.00 1.00 Blgrn 0.02 0.01 0.00 0.00 0.00 0.00 0.93 0.00 0.01 0.56 0.51 0.00 0.00 Grnal 0.00 0.09 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.09 0.61 0.00 0.00 Elev Table 3 – DCA analysis summary of the study site. Table 2 – DCA summary of the study site. DCA Axis I Eigenvalues 0.63 II 0.50 III 0.40 IV 0.33 Axis lengths 8.01 6.30 4.28 3.77 Total inertia 9.16 Cumulative % vari6.87 12.32 16.71 20.30 ance of species data cephala showed a higher abundance towards low canopy openness (Figure 4). Similarly, species like Phaeophyscia endococcina, Lecanora polytropa, Umbilicaria badia, Parmotrema subarnoldii showed high abundance towards open habitats and cultivated land, while species like Aspicilia cinerea, Hypotrachyna scytophylla, Parmelina quercina, Heterodermia obscurata, Stereocaulon paradoxum, Rhizoplaca chrysoleuca showed high abundance in meadows. Likewise, Hypotrachyna cirrhata, Hypotrachyna nepalensis, Cetrelia cetrarioides, Parmotrema pseudonilgherrense, Usnea compressula showed high abundance towards exploited forest and species like Lobaria retigera, Cladonia crispata var. cetrariiformis, Hypogymnia vittata, Caloplaca farinosa, Nephromopsis ahtii showed high abundance towards natural forest landscapes (Figure 4). Inertia Proportion Rank Total 9.15 1.00 Constrained 1.92 0.21 6 Unconstrained 7.23 0.79 29 Our study revealed a monotonic decrease in total lichen species richness with increasing canopy openness. In the present study, low canopy openness of about 10 % still supported a high number of lichen species. In the steep Himalayan mixed forests, canopy openness of 10 % and more is likely to provide sufficient light into the forest stand and, in general, light limitation does not seem to be a major limiting factor for lichen species richness in the mountain forests of KCA. Discussion Lichen species richness and composition along gradients of land use, canopy openness and elevation Our study indicated distinct effects of elevation, land use and canopy openness on lichen species richness and composition. We found a considerable variation in lichen species richness among the four selected land-use types, with decline of species richness from forest to cultivated land. These findings are in accordance with other studies like Stofer et al. (2006), who also observed decreasing lichen species richness from natural forest landscape to open agricultural landscape in a large-scale study covering several European biogeographic zones. Figure 4 – Canonical Correspondence Analysis (CCA) of lichen composition constraint by elevation, canopy openness and land-use types; C = Cultivated, M = Meadow, E = Exploited, and N = Natural. Arrow indicates the direction of increasing values and their length is proportional to the correlation between the variable and the plot scores (not shown) on the two ordination axes. Land-use types are shown as centroids. For full names of species see Appendix 1. 51 52 R esearch As trees are an important factor explaining lichen species composition and richness (Mežaka et al. 2008; Odor et al. 2013), meadows and natural forests seemed to provide lichen-rich habitats because of a high diversity and abundance of trees. The exploited forest type with varying disturbance intensity still maintained a reasonable diversity of microhabitats for epiphytic lichens, but some species that depend on semi-shaded habitats and high moisture in natural forests, such as corticolous lichens, are declining in exploited forests. Pinokiyo et al. (2008) also found the maximum number of corticolous lichens in dense forest. In the present study, we found high saxicolous lichen richness in meadows, because a high abundance of rocks and boulders are exposed on meadows, where litter does not continuously cover their surface. Exposed rock surfaces can support more saxicolous lichens than in closed forests, where slightly inclined rock surfaces are often covered with litter. On cultivated lands, slightly inclined rock surfaces are often disturbed by human influence to remove them or to use them for various activities related to farming. Frequent and intense disturbance of rock surfaces in agricultural land is a significant difference to European land-use gradients, where Wolseley et al. (2006) recorded high saxicolous richness in farmland including cultivated land. The saxicolous species richness revealed a gradual increase of species richness with increasing canopy openness and reached an average of 7.4 species per transect at 85 % openness, which corresponds to meadows and open cultivated land. Rocks and boulders inside forest landscapes are primarily covered by litter or mosses and also have a low exposition to solar radiation. However, corticolous species richness showed a decline with increasing openness and reached an average of 19.8 species at 10 % openness. Because corticolous lichens in the studied land-use gradients form a more species-rich species pool than saxicolous species, the observed decline of saxicolous species richness is overcompensated by a stronger increase of epiphytic lichens. As the lichen diversity is related to tree diversity, density (Baniya et al. 1999; Li et al. 2011; Li et al. 2013b, 2013a) and humidity (Pinokiyo et al. 2008), cultivated landscapes bear a limited number of trees, shrubs and fewer rocks and boulders as well as less humidity. The resulting lower epiphytic lichen species richness cannot be compensated by an increased density of boulders and bare rocks, which are generally covered with lichen vegetation under an open sky receiving direct solar radiation. The lichen richness pattern is also closely related to the management practices of the particular landscape, e. g. the protected area. The traditional shifting cultivation practice common to this area (Aryal et al. 2010) is significantly explained after finding of declining lichen richness pattern with open canopy. The shifting cultivation practice opens up a landscape which seems not to support lichen richness and its diversity pattern. Further, the shifting cultivation practice is also common to Makalu-Barun areas of East Nepal. Thus, future diversity of lichen seems in a difficult situation. Conservation of lichen will automatically conserve the landscape. In addition to the differences between land-use types, our study clearly indicated a distinct variation in species richness along the elevational gradient studied. We found a linear relationship with increasing elevation. Cobanoglu and Sevgi (2009) reported a similar pattern for epiphytic lichens with elevations from 1 300 m to 1 900 m in Turkey. However, a majority of former studies reported an unimodal relationship (Bruun et al. 2006; Grytnes et al. 2006; Baniya et al. 2010, 2012). Unlike these studies, which generally covered long elevational gradients, our study was more closely confined to a local scale, with an elevational gradient covering temperate to subalpine forests, but not reaching areas above the timberline. Therefore our linear relationship can be interpreted as part of an unimodal relationship on larger scales. Lichen species composition showed a strong species turnover along CCA axis I (elevation) and CCA axis II (land use-types). Natural and exploited forests supported diverse lichen vegetation which decreased towards meadows and cultivated land. These results confirm findings from European land-use gradients from forest to agricultural land-use types (Stofer et al. 2006). Conclusion We conclude that besides elevation as a general climate proxy, differences in land use, which directly affect canopy openness, are the two main general factors of both lichen species richness and composition in this area of the Himalayas in Nepal. Forests with diverse habitats and relatively low canopy openness harbour more lichen species than meadows and cultivated land. However, elevation and canopy openness are not direct drivers. Canopy openness influences light intensity and relative moisture on the forest floor and tree trunks, which directly affect lichen diversity. In addition, elevation serves as a general climate proxy for temperature or precipitation, which more directly influences both species richness and composition of lichen communities. Highest species richness of lichens was reached at the highest altitudinal level of our survey, indicating that the maximum total species richness of lichens as well as the richness of most of the studied species groups is at or above 3 800 m in this part of the Himalayas. Acknowledgement We thank the Swiss National Science Foundation (JRP IZ70Z0_131338 to CS, MN and K. K. Shrestha) for financial support. We are also grateful to all members of the CDB-WSL Project, Prof. Dr. Krishna Kumar Shrestha, Prof. Dr. Khadga Basnet, Dr. Jyoti Pd. Gajurel, Mr. Shiva Devkota, Mr. Sanjeev Kumar Cho ng ba ng e t a l . Rai, Mr. Hem Bdr. Katuwal, Mr. Yam Aryal and Mr. Rajesh Tamang for their kind support. We thank all local people of Ghunsa, KCA, and Utra Kumar Rai for his help with collecting lichens in the field. 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Authors Til Bikram Chongbang – corresponding author holds an M.Sc. in Botany from Tribhuvan University, Kathmandu, Nepal. Now a member of the biology teaching staff at the National School of Sciences, Lainchour, Kathmandu, he is particularly interested in lichens and ethnobotany. E-mail: tbchongbang@ yahoo.com Christine Keller 1 is an expert in lichen taxonomy and ecology. She is a member of the scientific staff at the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf. E-mail: christine.keller@wsl.ch Michael Nobis 1 is a botanist and macroecologist with a research focus on trait-environment relationships, species distribution modelling, species migration and biological invasions. He is a member of the scientific staff at the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. E-mail: michael.nobis@wsl.ch Christoph Scheidegger 1 focuses in his research on the conservation biology of lichens and plants, population genetics and landscape ecology, and biodiversity assessment and evaluation at the landscape level. He recently coordinated a research project in Nepal. He is a senior scientist and group leader at the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. E-mail: christoph.scheidegger@wsl.ch Chitra Bahadur Baniya is Associate Professor of Ecology and Resource Management at the Central Department of Botany, Tribhuvan University, Kathmandu, Nepal. His major field of interest is quantitative ecology of plants and lichens. His present teaching and research mainly focus on open-source programs, such as R statistical packages and science in philosophy. E-mail: cbbaniya@ gmail.com 1 Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH8903 Birmensdorf. Cho ng ba ng e t a l . Appendices Appendix 1 – List of lichens, their family, growth forms, substrate groups, photobiont partner and frequency of occurrence along land use types in the study area. Cru – crustose, Fol – foliose, Fru – fruticose, Lep – leprose, Cort – corticolous, Musc – muscicolous, Saxi – saxicolous, Terr – terricolous, BGA – blue green alga, GA – green alga, C – cultivated land, M – meadow, E – exploited forest, F – natural forest. S.N. Name of Lichen species Short form Family Growth form Substrate Photobiont Frequency Land use group partner (Number) types 1 Amandinea punctata (Hoffm.) Coppins & Scheid. Ama pun Caliciaceae Cru Cort GA 2 F 2 Aspicilia caesiocinerea (Nyl.ex Malbr.) Arnold Asp cae Cru Saxi GA 12 C, E, M, F Megasporaceae 3 Aspicilia cinerea (L.) Körb. Asp cin Cru Saxi GA 4 C, E, F, M 4 Aspicilia contorta (Hoffm.) Körb. Asp con Cru Saxi GA 2 E, F 5 Aspicilia griseocinerea Räsänen Asp gri 6 Bacidia laurocerasi (Delise ex Duby) Zahlbr. Bac lau 7 Bacidia rubella (Hoffm.) A. Massal. Bac rub 8 Bryoria himalayensis (Motyka) Brodo & D. Hawksw. Bry him Cru Cort GA 1 C Ramalinaceae Cru Cort GA 1 F Cru Cort GA 2 F, E Parmeliaceae Fru Cort GA 1 F 9 Bryoria lactinea (Nyl.) Brodo & D. Hawksw. Bry lac Fru Cort GA 1 E 10 Bryoria smithii (Du Rietz) Brodo & D. Hawksw. Bry smi Fru Cort GA 3 M, F, E 11 Bryoria tenuis (Dahl) Brodo & D. Hawksw. Bry ten 12 Buellia aethalea (Ach.) Th. Fr. Bue aet Caliciaceae Fru Cort GA 5 E, M, F Cru Saxi GA 1 F 13 Buellia inornata Zahlbr. Bue ino Cru Cort GA 1 E 14 Buellia montana H. Magn. Bue mon Cru Cort GA 2 M 15 Calicium subquercinum Asah. Cal sub 16 Caloplaca chlorina (Flot.) Sandst. Cal chl Teloschistaceae Cru Cort GA 4 F, E Cru Cort GA 1 M 17 Caloplaca citrina (Hoffm.) Th. Fr. Cal cit Cru Cort GA 1 F 18 Caloplaca encephalarti (Kremp.) Zahlbr. Cal enc Cru Cort GA 1 E 19 Caloplaca farinosa Poelt & Hinter. Cal far Cru Cort GA 3 F 20 Caloplaca holocarpa (Hoffm.) Wade Cal hol Cru Cort GA 1 F 21 Caloplaca holochracea (Nyl.) Zahlbr. Cal hol Cru Saxi GA 1 M 22 Caloplaca isabellina Poelt & Hinter. Cal isa Cru Saxi GA 5 M, C, E 23 Candelaria indica (Hue) Vain. Can ind 24 Candelariella vitellina (Hoffm.) Müll. Arg. Can vit 25 Candelariella xanthostigma (Pers. ex Ach.) Lettau Can xan 26 Cetrelia braunsiana (Müll.) W. Culb. & C. Culb. Cet bra Candelariaceae Parmeliaceae Fol Saxi GA 3 C, E, M Cru Saxi GA 1 M Cru Cort GA 2 E, M Fol Cort GA 4 E, F, M 27 Cetrelia cetrarioides (Delise) W. Culb. & C. Culb. Cet cet Fol Cort GA 10 E, F, M 28 Cetrelia olivetorum (Nyl.) W. Culb. & C. Culb. Cet oli Fol Cort GA 1 E 29 Cetrelia pseudolivetorum (Asahina) W. Culb. & C. Culb. Cet pse 30 Chaenotheca chrysocephala (Ach.) Th. Fr. Cha chr Coniocybaceae Fol Cort GA 2 E, F Cru Cort GA 3 F 31 Chaenotheca phaeocephala (Turner) Th. Fr. Cha pha Cru Cort GA 1 E 32 Chaenotheca trichialis (Ach.) Hellb. Cha tri Cru Cort GA 1 F 33 Chrysothrix candelaris (L.) Laundon Chr can 34 Chrysothrix chlorina (Ach.) Laundon Chr chl 35 Chrysothrix xanthina (Vain.) Kalb Chr xan 36 Cladoina chlorophaea (Flörke ex Sommerf.) Spreng. Cla chl Chrysotricaceae Cladoniaceae Lep Saxi GA 1 M Lep Cort GA 1 E Lep Cort GA 1 F Fru Musc GA 1 F 37 Cladoina corniculata Ahti & Kashiw. Cla cor Fru Musc GA 1 C 38 Cladonia coccifera (L.) Willd. Cla coc Fru Musc GA 6 E, M 39 Cladonia coniocraea (Flörke) Spreng. Cla con Fru Musc GA 4 E, F 40 Cladonia corymbescens Nyl. ex Leight. Cla cor Fru Musc GA 4 M, E E, F 41 Cladonia crispata var. cetrariiformis (Delise) Vain. Cla cri Fru Cort GA 3 42 Cladonia fimbriata (L.) Fr. Cla fim Fru Musc GA 1 E 43 Cladonia furcata (Huds.) Schrad. Cla fur Fru Cort GA 4 E, F, M 44 Cladonia macilenta Hoffm. Cla mac Fru Terr GA 1 M 45 Cladonia macroptera Räsänen Cla mac Fru Saxi GA 1 E 46 Cladonia ramulosa (With.) Laundon Cla ram Fru Musc GA 1 M 47 Cladonia scabriuscula (Delise) Nyl. Cla sca Fru Saxi GA 2 E, M 48 Cladonia stellaris (Opiz) Pouzar & Vězda Cla ste Fru Terr GA 1 E 49 Cladonia subconistea Asahina Cla sub Fru Cort GA 2 E 50 Cladonia subsquamosa Kremp. Cla sub Fru Cort GA 1 E 51 Cladonia subulata (L.) F.H. Wigg. Cla sub Fru Cort GA 1 M 52 Cladonia verticillata (Hoffm.) Schaer. Cla ver 53 Coccocarpia erythroxyli (Spreng.) Swinsc. & Krog Coc ery Coccocarpiaceae Fru Musc GA 1 C Fol Saxi BGA 2 M, C 54 Collema subconveniens Nyl. Col sub Collemataceae Fol Cort BGA 2 F 55 Dibaeis baeomyces (L. f.) Rambold & Hertel Dib bae Icmadophilaceae Cru Saxi GA 1 E 56 Coenogonium luteum (Dicks.) Kalb & Lücking Coe lut Coenogoniaceae Cru Cort GA 3 F, E 57 Diploschistes scruposus (Schreb.) Norman Dip scr Ghraphidaceae Cru Saxi GA 1 M Caliciaceae 58 Diplotomma alboatrum (Hoffm.) Flot. Dip alb 59 Diplotomma himalayense S. Singh & D.D. Awasthi Dip him 60 Diplotomma proximatum (Magn.) S. Singh & D.D. Awasthi Dip pro 61 Erioderma meiocarpum Nyl. Eri mei Pannariaceae Parmeliaceae Cru Cort GA 1 E Cru Cort GA 3 E, M Cru Cort GA 3 E, F Fol Cort BGA 3 F, M 62 Evernia mesomorpha Nyl. Eve mes Fru Cort GA 5 F, M, E 63 Hypotrachyna cirrhata (Fr.) Divakar, A. Crespo, Sipman, Elix & Lumbsch Hyp cir Fol Cort GA 5 E, M, F 64 Hypotrachyna nepalensis (Taylor) Divakar, A. Crespo, Sipman, Elix & Lumbsch Hyp nep Fol Cort GA 6 F, E, M 65 Flavoparmelia caperata (L.) Hale Fla cap Fol Saxi GA 1 M 55 56 R esearch S.N. Name of Lichen species Short form Family Graphidaceae 66 Glyphis cicatricosa Ach. Gly cic 67 Graphis nigroglauca Leight. Gra nig Growth form Substrate Photobiont Frequency Land use group partner (Number) types Cru Cort GA 1 F Cru Cort GA 1 F 68 Graphis pyrrhocheiloides Zahlbr. Gra pyr Cru Cort GA 3 E, F 69 Graphis rimulosa (Mont.) Trevis. Gra rim Cru Cort GA 1 F 70 Graphis scripta (L.) Ach. Gra scr Cru Cort GA 4 E, F 71 Graphis sikkimensis Nagarkar & Patw. Gra sik Cru Cort GA 5 F, E 72 Graphis sorediosa Nagarkar & Patw. Gra sor Cru Cort GA 1 F 73 Haematomma puniceum (Sm. ex Ach.) Massal. Hae pun Haematommataceae Cru Cort GA 6 F, M, E Physciaceae 74 Heterodermia angustiloba (Müll. Arg.) D.D. Awasthi Het ang 75 Heterodermia boryi (Fée) Kr.P. Singh & S.R. Singh Het bor Fol Cort GA 3 E, F Fol Cort GA 6 E, M, C, F 76 Heterodermia comosa (Eschw.) Follman & Redon Het com Fol Cort GA 2 M, E 77 Heterodermia diademata (Taylor) D.D. Awasthi Het dia Fol Cort GA 1 F 78 Heterodermia firmula (Nyl.) Trevis. Het fir Fol Cort GA 1 E 79 Heterodermia incana (Stirt.) D.D. Awasthi Het inc Fol Cort GA 1 E 80 Heterodermia obscurata (Nyl.) Trevis. Het obs Fol Saxi GA 3 M, E 81 Heterodermia pellucida (D.D. Awasthi) D.D. Awasthi Het pel Fol Cort GA 1 E 82 Heterodermia pseudospeciosa (Kurok.) W. Culb. Het pse Fol Cort GA 1 E 83 Heterodermia rubescens (Räsänen) D.D. Awasthi Het rub Fol Cort GA 2 E, F 84 Heterodermia speciosa (Wulf.) Trevis. Het spe Fol Cort GA 3 E 85 Heterodermia togashii (Kurok.) D.D. Awasthi Het tog Fol Cort GA 6 E, M, F 86 Heterodermia tremulans (Müll. Arg.) W. Culb. Het tre Fol Cort GA 1 M 87 Heterodermia verrucifera (Kurok.) W.A. Weber Het ver Fol Cort GA 1 F 88 Hypogymnia hypotrypa (Nyl.) Rass. Hyp hyp 89 Hypogymnia vittata (Ach.) Gasil. Hyp vit Parmeliaceae Fol Cort GA 4 F, E Fol Cort GA 2 F, E 90 Hypotrachyna crenata (Kurok.) Hale Hyp cre Fol Saxi GA 1 E 91 Hypotrachyna exsecta (Taylor) Hale Hyp exs Fol Cort GA 1 E 92 Hypotrachyna infirma (kurok.) Hale Hyp inf Fol Cort GA 1 F 93 Hypotrachyna majoris (Vain.) Hale Hyp maj Fol Cort GA 1 M 94 Hypotrachyna revoluta (Flörke) Hale Hyp rev Fol Cort GA 1 M 95 Hypotrachyna scytophylla (Kurok.) Hale Hyp scy Fol Saxi GA 4 M, C, E 96 Hypotrachyna sinuosa (Sm.) Hale Hyp sin Fol Saxi GA 4 C, M, E 97 Hypotrachyna sublaevigata (Nyl.) Hale Hyp sub Fol Cort GA 1 C 98 Lasallia freyana D.D. Awasthi Las fre Umbilicariaceae Fol Saxi GA 1 M 99 Lecanora frustulosa (Dicks.) Ach. Lec fru Lecanoraceae Cru Saxi GA 1 C 100 Lecanora albella (Pers.) Ach. Lec alb Cru Cort GA 3 M, E 101 Lecanora allophana (Ach.) Nyl. Lec all Cru Cort GA 1 E 102 Lecanora campestris (Schaer.) Hue Lec cam Cru Saxi GA 2 E, F 103 Lecanora cenisia Ach. Lec cen Cru Saxi GA 4 M, F, C 104 Lecanora chlarotera Nyl. Lec chl Cru Cort GA 8 F, E ,M 105 Lecanora intricata (Ach.) Ach. Lec int Cru Saxi GA 1 M C, M 106 Lecanora polytropa (Ehrh.) Rabenh. Lec pol Cru Saxi GA 2 107 Lecanora rugosella Zahlbr. Lec rug Cru Cort GA 4 F, E 108 Lecanora saligna (Schrad.) Zahlbr. Lec sal Cru Cort GA 1 F 109 Lecanora strobilina Ach. Lec str Cru Cort GA 1 M 110 Lecanora varia (Hoffm.) Ach. Lec var Cru Cort GA 1 M 111 Lecidea betulicola (Kullh.) H. Magn. Lec bet 112 Lecidea erythrophaea Flörke ex Sommerf Lec ery Lecideaceae Cru Cort GA 1 F Cru Cort GA 1 F 113 Lecidea fuscoatra (L.) Ach. Lec fus Cru Saxi GA 1 M 114 Lecidea vorticosa (Flörke) Körb. Lec vor Cru Saxi GA 1 M 115 Lecidella elaeochroma (Ach.) M. Choisy Lec ela Lecanoraceae Cru Cort GA 1 M 116 Lepraria crassissima (Hue) Lettau Lep cra Stereocaulaceae Cru Saxi GA 1 M 117 Lepraria ecorticata (J.R. Laundon) Kukwa Lep eco Cru Saxi GA 1 M 118 Lepraria membranacea (Dicks.) Vain. Lep mem Cru Cort GA 2 M, E 119 Leptogium askotense D.D. Awasthi Lep ask 120 Leptogium burnetiae Dodge Lep bur Collemataceae Fol Cort BGA 1 E Fol Cort BGA 3 F, E, M 121 Leptogium chloromelum (Sw.) Nyl. Lep chl Fol Cort BGA 1 F 122 Leptogium cyanescens (Rabenh.) Körb. Lep cya Fol Cort BGA 1 F 123 Leptogium pedicellatum P.M. Jørg. Lep ped Fol Cort BGA 7 E, F, M 124 Leptogium saturninum (Dicks.) Nyl. Lep sat Fol Cort BGA 1 M 125 Lethariella cladonioides (Nyl.) krog Let cla Parmeliaceae Fru Cort GA 1 M 126 Lobaria isidiosa (Müll. Arg.) Vain. Lob isi Lobariaceae Fol Cort BGA 1 F 127 Lobaria pindarensis Räsänen Lob pin Fol Cort BGA 3 F 128 Lobaria retigera (Bory) Trev. Lob ret Fol Cort BGA 5 E, F 129 Melanelia panniformis (Nyl.) Essl. Mel pan 130 Melanelia tominii (Oxner) Essl. Mel tom Parmeliaceae Fol Cort GA 1 M Fol Saxi GA 2 M 131 Menegazzia terebrata (Hoffm.) A. Massal. Men ter Fol Cort GA 6 E, F 132 Mycobilimbia hunana (Zahlbr.) D.D. Awasthi Myc hum Lecideaceae Cru Terr GA 1 C 133 Mycoblastus affinis (Schaer.) T. Schauer Myc aff Tephromelatacae Cru Cort GA 2 F 134 Myelochroa subaurulenta (Nyl.) Elix & Hale Mye sub Parmeliaceae Fol Cort GA 1 F 135 Nephroma isidiosum (Nyl.) Gyeln. Nep isi Nephromataceae 136 Nephroma nakaoi Asahina Nep nak Fol Musc BGA 1 M Fol Cort BGA 4 F, E, M 137 Nephromopsis nephromoides (Nyl.) Ahti & Randl. Nep nep Parmeliaceae Fol Cort GA 1 E 138 Ochrolechia androgyna (Hoffm.) Arnold Och and Ochrolechiaceae Cru Saxi GA 1 F 139 Ochrolechia parellula (Müll. Arg.) Zahlbr. Och par Cru Saxi GA 1 F 140 Ochrolechia rosella (Müll. Arg.) Vers. Och ros Cru Cort GA 8 E, F, M Cho ng ba ng e t a l . S.N. Name of Lichen species Short form Family 141 Parmotrema thomsonii (Stirt.) A. Crespo, Divakar & Elix Par tho Parmeliaceae 142 Parmelia squarrosa Hale Par squ Growth form Substrate Photobiont Frequency Land use group partner (Number) types Fol Cort GA 1 E Fol Cort GA 2 M 143 Parmeliella cinerata Zahlbr.) P.M. Jørg. Par cin Fol Cort BGA 1 E 144 Parmelina quercina (Willd.) Hale Par que Fol Cort GA 4 F, C, E, M 145 Parmotrema cetratum (Ach.) Hale Par cet Fol Cort GA 2 E, F 146 Parmotrema latissimum (Fée) Hale Par lat Fol Cort GA 1 F 147 Parmotrema nilgherrense (Nyl.) Hale Par nil Fol Cort GA 5 E, F, M 148 Parmotrema praesorediosum (Nyl.) Hale Par pra Fol Cort GA 1 M 149 Parmotrema pseudocrinitum (Abbayes) Hale Par pse Fol Cort GA 1 M 150 Parmotrema pseudonilgherrense (Asahina) Hale Par pse Fol Cort GA 8 E, M, F 151 Parmotrema reticulatum (Taylor) M. Choisy Par ret Fol Saxi GA 2 C, E 152 Parmotrema saccatilobum (Taylor) Hale Par sac Fol Cort GA 1 E 153 Parmotrema sancti-angelii (Lynge) Hale Par san Fol Saxi GA 1 M 154 Parmotrema subarnoldii (Abbayes) Hale Par sub Fol Saxi GA 2 C, M 155 Parmotrema tinctorum (Despr. ex Nyl.) Hale Par tin Fol Cort GA 1 E 156 Parmotrema ultralucens (Krog) Hale Par ult Fol Saxi GA 1 M 157 Peltigera didactyla (With.) J.R. Laundon Pel did 158 Peltigera dolichorrhiza (Nyl.) Nyl. Pel dol Peltigeraceae Fol Musc BGA 1 E Fol Cort BGA 3 F, E 159 Peltigera dolichospora (D.A. Lu) Vitik. Pel dol Fol Cort BGA 2 E, F 160 Peltigera malacea (Ach.) Funck Pel mal Fol Musc BGA 1 F 161 Peltigera membranacea (Ach.) Nyl. Pel mem Fol Musc BGA 2 F 162 Peltigera polydactylon (Neck.) Hoffm. Pel pol Fol Musc BGA 3 E, F 163 Peltigera praetextata (Flörke) Zopf Pel pra 164 Pertusaria albescens (Huds.) M. Choisy & Wern. Per alb Pertusariaceae Fol Musc BGA 1 C Cru Cort GA 1 E 165 Pertusaria amara (Ach.) Nyl. Per ama Cru Cort GA 1 F 166 Pertusaria amarescens Nyl. Per ama Cru Saxi GA 2 M, E 167 Pertusaria commutata Müll. Arg. Per com Cru Saxi GA 1 E 168 Pertusaria composita Zahlbr. Per com Cru Cort GA 1 M 169 Pertusaria hemisphaerica (Flörke) Erichsen Per hem Cru Cort GA 1 F 170 Pertusaria krogiae A.W. Archer, Elix, Eb. Fisch., Killmann & Sérus Per kro Cru Cort GA 1 E 171 Pertusaria lactea (L.) Arnold Per lac Cru Cort GA 1 F 172 Pertusaria ophthalmiza (Nyl) Nyl. Per oph Cru Cort GA 1 F 173 Pertusaria pertusa (Weigel) Tuck. Per per Cru Cort GA 2 E, F 174 Pertusaria psoromica A.W. Archer & Elix Per pso Cru Cort GA 2 M, F 175 Pertusaria umbricola A.W.Archer & Elix Per umb Cru Cort GA 2 E 176 Pertusaria velata (Turner) Nyl. Per vel Cru Cort GA 1 F 177 Pertusaria xanthoplaca Müll. Arg. Per xan 178 Phaeophyscia ciliata (Hoffm.) Moberg Pha cil Physciaceae Cru Cort GA 1 E Fol Cort GA 4 C, E 179 Phaeophyscia endococcina (Körb.) Moberg Pha end Fol Saxi GA 2 M, C 180 Phaeophyscia hispidula (Ach.) Moberg Pha his Fol Cort GA 1 M 181 Phaeophyscia hispidula var. exornatula (Zahlbr.) Moberg Pha his Fol Cort GA 2 F 182 Phaeophyscia primaria (Poelt) Trass Pha pri Fol Saxi GA 1 C 183 Phaeographis extrusa (Stirt.) Zahlbr. Phe ext Graphidaceae Cru Cort GA 1 F 184 Phlyctis argena (Ach.) Flot. Phl arg Phlyctidaceae Cru Cort GA 1 F 185 Physcia caesia (Hoffm.) Fürnr. Phy cae Physciaceae 186 Physcia dilatata Nyl. Phy dil Fol Saxi GA 3 M, C Fol Cort GA 1 E 187 Physcia semipinnata (Gmelin) Moberg Phy sem Fol Cort GA 1 E 188 Physcia tenella (Scop.) DC. Phy ten Fol Saxi GA 1 M E, F, M 189 Platismatia erosa W. Culb. & C. Culb. Pla ero Parmeliaceae Fol Cort GA 3 190 Polychidium stipitatum Vězda & W.A. Weber Pol sti Massalongiaceae Fru Cort BGA 1 F 191 Porina chlorotica (Ach.) Müll.Arg. Por chl Porinaceae Cru Saxi GA 1 F 192 Porpidia albocoerulescens (Wulfen) Hertel & Knoph Por alb Lecideaceae Cru Saxi GA 1 F 193 Pyxine berteriana (Fée) Imsh. Pyx ber Caliciaceae Fol Cort GA 2 F, E 194 Ramalina conduplicans Vain. Ram con Ramalinaceae 195 Ramalina hossei Vain. Ram hos Fru Cort GA 8 F, E, M Fru Cort GA 4 E, F, M 196 Ramalina roesleri (Hochst) Hue Ram roe Fru Cort GA 3 M, F 197 Ramalina sinensis Jatta Ram sin Fru Cort GA 1 F 198 Rhizocarpon badioatrum (Flörke ex Spreng.) Th. Fr. Rhi bad 199 Rhizocarpon obscuratum (Ach.) A. Massal. Rhi obs Rhizocarpaceae Cru Saxi GA 2 E, M Cru Saxi GA 1 M 200 Rhizoplaca chrysoleuca (Sm.) Zopf Rhi chr Lecanoraceae Fol Saxi GA 2 M 201 Rinodina efflorescens Malme Rin eff Physciaceae Cru Cort GA 1 M 202 Rinodina instrusa (Krempelh. in Nyl.) Mamle Rin ins Cru Cort GA 2 M, E 203 Rinodina lecideina H. Mayrhofer & Poelt Rin lec Cru Saxi GA 1 C 204 Rinodina sophodes (Ach.) A. Massal. Rin spo 205 Sclerophora amabilis (Tibell) Tibell Scl ama Coniocybaceae 206 Stereocaulon paradoxum I.M. Lamb Ste par Stereocaulaceae 207 Stereocaulon piluliferum Th.Fr. Ste pil 208 Sticta nylanderiana Zahlbr. Sti nyl 209 Sticta praetextata (Räsänen) D.D. Awasthi Sti pra 210 Sticta weigelii (Ach.) Vain. Sti wei 211 Sulcaria sulcata (Lév.) Bystr. ex Brodo & D. Hawksw. Sul sul Lobariaceae Parmeliaceae Cru Saxi GA 1 M Fru Cort GA 1 F Fru Saxi GA 7 M, E, C Fru Saxi GA 2 C, E Fru Cort GA 2 F Fru Cort GA 2 F, E Fru Cort BGA 1 F Fru Cort GA 2 F, E 212 Nephromopsis ahtii (Randl. & Saag) Randl. & Saag Nep aht Fol Cort GA 7 E, F 213 Nephromopsis laureri (Kremp.) Kurok. Nep lau Fol Cort GA 5 F, E, M 57 58 R esearch S.N. Name of Lichen species Short form Family Growth form Substrate Photobiont Frequency Land use group partner (Number) types 214 Umbilicaria badia Frey Umb bad Umbilicariaceae Fol Saxi GA 4 C, E, 215 Umbilicaria indica var. indica Frey Umb ind Fol Saxi GA 8 F, M, E 216 Umbilicaria vellea (L.) Ach. em. Frey Umb vel 217 Usnea bailey (Stirt.) Zahlbr. Usn bai Parmeliaceae Fol Saxi GA 3 M, F Fru Cort GA 1 E 218 Usnea cirrosa Motyka Usn cir Fru Cort GA 8 E, F, M 219 Usnea compressa Taylor Usn com Fru Cort GA 5 F, M, E 220 Usnea cornuta Körb. Usn cor Fru Cort GA 4 E, M, F 221 Usnea himalayana Bab. Usn him Fru Cort GA 2 E, F 222 Usnea longissima Ach. Usn lon Fru Cort GA 3 F, E 223 Usnea pygmoidea (Asahina) Y. Ohmura Usn pyg Fru Cort GA 1 M 224 Usnea sp1 Dill. ex Adans. Usn sp1 Fru Cort GA 3 M, E 225 Usnea sp2 Dill. ex Adans. Usn sp2 Fru Cort GA 2 F, M 226 Verrucaria nigrescens Pers. Ver nig Verrucariaceae Cru Saxi GA 1 C 227 Xanthoparmelia tinctina (Maheu & A. Gillet) Hale Xan tin Parmeliaceae Fol Cort GA 1 M 228 Xanthoria fallax (Hepp) Arnold Xan fal Teloschistaceae 229 Xanthoria parietina (L.) Th. Fr. Xan par Appendix 2 – TukeyHSD test for multiple comparisons of mean species richness of lichens between land-use types and b. Biplot CCA scores. a) TukeyHSD test for multiple comparisons of mean species richness of lichens between land-use types. Variables Difference Lower Upper p adjusted Fol Cort GA 2 M, C Fol Cort GA 1 F b) Pearson correlations between environmental variables and CCA axes. Variables CCA1 CCA2 0.964 −0.184 Elevation Exploited forest −0.005 −0.178 Natural forest −0.187 −0.573 Exploited-Cultivated 14.46 3.93 24.99 0.00 Meadow 0.161 0.355 Natural-Cultivated 12.33 2.05 22.60 0.01 Canopy openness 0.193 0.755 9.90 −0.62 20.43 0.07 Natural-Exploited −2.13 −12.09 7.82 0.94 Meadow-Exploited −4.56 −14.77 5.66 0.63 Meadow-Natural −2.42 −12.38 7.53 0.91 Meadow-Cultivated Appendix 3 – Regression analysis results modelled for lichen species richness, growth forms, substrate types and photobiont types as response variables and canopy openness and elevation as predictor variables. The Quasi-Poisson family error fitted in GLM (Generalized Linear Model). p-values refer to linear (linear model) or quadratic (linear & quadratic model) coefficient. p-value codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1. ns (non-significant) for p>|0.1| which means marginal significance. Predictor variables Response variables Canopy openness Total lichen richness Crustose species richness Fruticose species richness Corticolous species richness Saxicolous species richness Cyanolichen species richness Green algal lichen species richness Elevation Total species richness Crustose species richness Model Degrees of Residual freedom deviance Deviance explained ΔD2 p(>|t value|) Intercept 35 222.23 0 Linear 34 199.19 23.04 0.104 *** * Linear & quadratic 33 192.35 6.85 0.134 ns *** Intercept 35 65.36 0 Linear 34 58.56 6.80 0.104 . Linear & quadratic 33 57.96 0.60 0.113 ns *** Intercept 35 111.10 0 Linear 34 99.39 11.71 0.105 Linear & quadratic 33 90.94 8.45 * 0.18 0.09 *** Intercept 35 289.78 0 Linear 34 218.15 71.63 0.247 ** Linear & quadratic 33 204.48 13.67 0.294 ns 0 *** Intercept 35 104.63 Linear 34 79.87 24.76 0.23 ** Linear & quadratic 33 75.72 4.15 0.27 ns Intercept 35 68.57 0 0.37 Linear 34 55.61 12.96 0.189 * Linear & quadratic 33 54.72 0.89 0.202 ns *** Intercept 35 189.80 0 Linear 34 174.93 14.87 0.078 . Linear & quadratic 33 168.55 6.38 0.112 ns 0 *** Intercept 35 222.23 Linear 34 163.35 53.88 0.26 ** Linear & quadratic 33 160.42 2.93 0.28 ns 0 *** Intercept 35 65.36 Linear 34 56.24 9.12 0.14 * Linear & quadratic 33 55.97 0.27 0.14 ns Cho ng ba ng e t a l . Predictor variables Response variables Elevation Foliose species richness Fruticose species richness Corticolous species richness Cyanolichen species richness Green algal lichen species richness Model Degrees of Residual freedom deviance Deviance explained ΔD2 p(>|t value|) Intercept 35 137.8 Linear 34 104.81 32.99 0.24 0 *** ** Linear & quadratic 33 102.2 2.61 0.26 ns *** Intercept 35 111.10 0 Linear 34 92.02 19.08 0.172 * Linear & quadratic 33 91.65 0.37 0.152 ns *** Intercept 35 289.78 0 Linear 34 235.08 54.7 0.189 ** Linear & quadratic 33 227.09 7.99 0.216 ns 0.23 Intercept 35 68.57 0 Linear 34 59.13 9.44 0.138 * Linear & quadratic 33 58.38 0.75 0.149 ns Intercept 35 189.80 0 Linear 34 140.67 49.13 0.259 *** ** Linear & quadratic 33 138.19 2.48 0.272 ns Appendix 4 – Representative hemispherical photographs chosen from the analysed images characterizing transects in Ghunsa Valley, Kanchenjunga. (1 = 2 000 m, 2 = 2 600 m, 3 = 3 000 m, 4 = 3 400 m and 5 = 3 800 m; E = eastern slope, W = western slope; c = cultivated land, e = exploited forest, m = meadows and f = natural forest). 1Ecb 1Emb 1Eea 1Efb openness = 70.5 % openness = 62.4 % openness = 55.75 % openness = 9.98 % 1Wcb 1Wmb 1Web 1Wfb2 openness = 84.76 % openness = 82.81 % openness = 37.05 % openness = 38.46 % 2Ecb1 2Efb 2Wcb 2Wmb openness = 62.35 % openness = 31.48 % openness = 65.46 % openness = 58.33 % 2Web 2Wfb 3Emc 3Eeb openness = 20.42 % openness = 16.48 % openness = 55.92 % openness = 24.23 % 59 60 R esearch 3Efb 3Wcb 3Wmb 3Web openness = 14.06 % openness = 69.39 % openness = 61.24 % openness = 36.53 % 3Wfb 4Ecb 4Emb 4Eeb openness = 17.19 % openness = 55.98 % openness = 7.16 % openness = 50.71 % 4Efb 4Wcb 4Wmb 4Wee openness = 21.51 % openness = 61.36 % openness = 61.04 % openness = 18.34 % 4Wfb1 5Emb 5Eeb 5Efb openness = 17.51 % openness = 47.72 % openness = 21.05 % openness = 19.78 % 5Wcb 5Wmb 5Wec 5Wfc openness = 47.85 % openness = 62.88 % openness = 35.16 % openness = 31.24 %