Ecography ECOG-01789Bässler, C., Cadotte, M. W., Beudert, B., Heibl, C., Blaschke, M., Bradtka, J. H., Langbehn, T., Werth, S. and Müller, J. 2015. Contrasting patterns of lichen functional diversity and species richness across an elevation gradient. – Ecography doi: 10.1111/ecog.01789

Supplementary material

 

1    

Appendix 1 1  

2  

Table A1. Lichen traits used for the analysis and their possible functions. 3  

Trait General life characteristics Thallus growth form/ size Thallus morphology has a strong impact on

physiological processes, water uptake and evaporation therefore determining the rate of photosynthesis activity (de Vries and Watling 2008, Sancho and Kappen 1989, Valladares 1994). This corresponds to the surface to volume ratio (e.g., Lakatos et al. 2006), respectively to the size of the thallus which is correlated with the thallus form (c.f. Johansson et al. 2007). Hence, the lichen thallus form has been attributed to basic ecological strategies (Rogers 1990) and proofed to be very sensitive to environmental changes (Ellis and Coppins 2006, Johansson et al. 2007) and was therefore often used as indicators (e.g., Giordani et al. 2012). For example, fruticose lichens were assigned to be competitors, while crustuse lichens seemed to be stress tolerant (Rogers 1990). Those crustose lichens with a cortex layer (dense glutinated hyphae) has been shown to be very less susceptible to mechanical damage but with high light absorptivity leading to effective exploitation of light under limited amounts of light (e.g., under a dense canopy, Pardow et al. 2010). Lichens act as an important habitat and diet source for animals (see Seaward 1977for an detailled overview). There is a high degree of specialization between insects or mammals and lichen species with a certain thallus growth form.  Spatial pattern in lichen epiphyte growth forms for example appears to control invertebrate community composition (André 1985). Please note that the thallus form is correlated to thallus size and therefore to biomass (resource availability) of the species (Nash 2008). Large fruticose lichens for example act as important diet source for mammals (e.g., Rominger et al. 1996). Moreover, fruticose species of the genus Bryoria, Usnea are important for nest building (Brodo et al. 2001, Sharnoff and Rosentreter 1998).

Prothallus Contributes to the hydration status of (poikilohydric) lichens with a high water absorption capacity by acting as a sponge (Lakatos et al. 2006).

Photobiont The lichen photobionts have different characteristics facilitating the lichen species to cope with specific environmental conditions (Palmqvist 2000): Green algae from the genus Trentepholia and Trebouxia (green algae) ensure photoprotection (dissipate excess excitation energy in chlorophyll into heat under light stress, Gauslaa and Solhaug 1996). Moreover, green algae are less sensitive to a low level of humidity (Honegger 1991). Lichens with

 

2    

photobionts from the genus Trentepholia for example facilitate development at higher levels of temperature (Aptroot and van Herk 2006) and allows to exist in shady habitats (Friedl and Büdel 1996). Cyanobacteria acting as photobionts on the other hand are able to fix nitrogen (N2) from the atmosphere (Palmqvist et al. 2002). They furthermore use a wide part of light spectrum using phycobilisomes as light-harvesting antennae (Rikkinen 2009) broadening the range of usable habitats. Species with cyanobacteria as a photobiont partner furthermore are able to develop after prolonged exposure to high irradiance (Giordani et al. 2014).

Secondary metabolism About 1,050 lichen substances are known and studies suggest that many of them impact biotic and abiotic interactions of lichens (reviewed in Molnar and Farkas, 2010): They may help to protect the thalli against herbivores, pathogens, competitors and external abiotic factors, such as high UV irradiation (see also Honegger 1993). Many of them exhibit multiple biological activities, such as the dibenzofuran usnic acid (e.g., antimicrobial and larvicidal effects, anticancer activities, known also for its UV-absorption). High potential for the use as pharmaceutical (see e.g., Ari et al. 2015, Mueller 2001).

Reproduction characteristics Asexual reproduction (soredia, isidia) Studies suggest that asexual reproduction increases

the ecological amplitude of the species (Bowler and Rundel 1975, Hale 1967). The production of soredia and isida seem an adaption to environmental stress (Marshall 1996, Smith, 1984). Studies furthermore suggest that establishment on resources is facilitated by asexual propagules (at the cost of dispersal – trade-off, see e.g. Ellis 2012). Species with isidia across the thallus have a higher photosynthetic rate because of the very low CO2 saturation point related to the high surface to volume ratio (Tretiach et al. 2005). Species with soredia were defined as stress tolerant species (Rogers 1990).

Conidia Production of conidia can be observed in many members of the ascomycetes (Carlile et al. 2001). The production of conidia might contribute to the dispersal capability but with a limited investment by the lichen. The production of conidia therefore might be a compromise between the costly sexual reproduction leading to haploid spores through meiosis with a potential for long distance dispersal (Lacey 1996) and the production of large asexual propagules (e.g. isidia) consisting of both the myco- and the phytobiont but with a limited range of dispersal (Ellis 2012).

Ascomata size (mm²) The ascomata size is probably strongly related to dispersal capability. Large ascomata areas produce more spores. If spore size is critical for dispersal and establishment (Halbwachs and Bässler 2015, Norros et al. 2014, see below), species with a large ascomata area can optimize both, the number and size of spores

 

3    

(see e.g. Bässler et al. 2014 for fungi).

Spore volume (µm³) Although fungal spores are tiny compared to plant seeds, the range in size is considerable which suggests selective forces and different ecological strategies (Halbwachs and Bässler 2015). Numbers of studies hence points towards the size of spores to have a high relevance in terms of dispersal and establishment: Generally, due to their small size, sexual spores can be trapped even on smooth surfaces (Giordani et al. 2012). Advantages of large spores:

• Large spores can contain more carbon and nutrients (Carlile et al. 2001, Deacon 2005, Hawker, and Madelin 1976, Sanders and Lucking 2002); may more easy germinate under nutrient-limited conditions.

• Show prolonged dormancy (Carlile et al. 2001, Deacon 2005).

• Might lead to a better establishment (see also asexual propagules, Sanders and Lucking 2002); to strike, e.g. tree trunks (Tulloss 2005) with a high impaction efficiency (Hawker and Madelin 1976).

Advantages of small spores:

• Increased colonization rates (Johansson et al. 2012).

• Small spores fly farther and better circumvent obstacles (Norros et al. 2014, Tulloss 2005).

• They may be more easily blown around obstacles than large spores, which is advantageous in habitats with scattered substrates or hosts (Tulloss 2005).

Spore shape Advantages of oblong spores:

• Rotate during air dispersal and thereby travel farther and improve substrate impact (Ingold 1965).

• Narrow spores in the fungal genus Amanita occur in dry and/or low-nutrient habitats and in habitats with short growing seasons, such as northern bogs and heaths (Tulloss 2005).

Advantages of globose spores:

• Globose spores expose the least possible surface to a potentially harmful environment (Carlile et al. 2001).

Spore pigmentation Pigmented, melanised spores are better protected

against UV-radiation during dispersal (Durrell 1964, Vellinga 2004), desiccation (Zhdanova et al. 1980), high and low temperatures (Rehnstrom and Free 1996), and enzymatic and microbial attacks (Kuo and Alexander 1967) than hyaline spores. In addition, melanins contribute to the mechanical stability of the spore (Cooke and Whipps 1993), allowing air-

 

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dispersed spores to survive long distances and exposure at impact site. Strongly correlated with wall thickness and hence interpreted as an important trait syndrome underpinning these possible adaptions (Halbwachs and Bässler 2015, Halbwachs et al. 2015). In contrast, hyaline spores (in combination with thin walls) germinate quickly in habitats with short growing seasons and were interpreted as an adaption for a rapid development (Sanders and Lucking 2002).

Spore septation Cell aggregates with increased chance of germination by the process of subdivision (germination insurance) (Pentecost 1981). The spore might be stabilized by the septates (Pentecost 1981) thereby optimizing spore size (see above) independent from spore wall thickness. Such a trait syndrome would improve successful establishment (large spore) and immediate germination (thin spore wall, Halbwachs et al. 2015)

4  

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southern Asia and notes on spore character variation with latitude and ecology. — 104  

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Table A2. Statistics of the environmental variables used for the analysis. Please note that 112  

maximum DBH, dead-wood amount, number of tree species (living stand) and old-growth 113  

status (not included) were subjected to a principle component analysis (for further details see 114  

methods). 115  

116  

117  

Min 25%-Quartile Median 75%-Quartile Max

Elevation (m) 287 577 810 1087 1420

Canopy cover (%) 0 61 100 123 229

DBH max (cm) 0 46 58 69 130

Dead-wood amount (m³*ha-1) 0 17 42 136 709

Tree species (n) 0 1 2 2 6

 

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Table A3. Pairwise correlation coefficients among the final predictor set. Please see methods 118  

for the exact definition of the environmental variables. Please note that succession and 119  

naturalness are the first respectively the second principle component from a PCA and hence 120  

uncorrelated. 121  

122  

123   Elevation Insolation Succession Naturalness

Elevation 1 0.43 -0.44 0.20

Insolation 0.43 1 -0.59 0.17

Succession -0.44 -0.59 1 0.00

Naturalness 0.20 0.17 0.00 1

 

11    

Table A4. Total species list and plot frequency 124  

125  

Species Plot frequency

Alectoria sarmentosa 2

Amandinea punctata 38

Arthonia leucopellaea 6

Arthonia radiata 23

Arthonia vinosa 4

Arthothelium ruanum 1

Bacidia globulosa 2

Bacidia rosella 2

Bacidia rubella 1

Bacidina phacodes 2

Biatora epixanthoidiza 2

Bryoria capillaris 12

Bryoria fuscescens 22

Bryoria nadvornikiana 1

Buellia griseovirens 2

Calicium glaucellum 8

Calicium salicinum 6

Calicium trabinellum 4

Calicium viride 18

Cetraria chlorophylla 19

Cetraria oakesiana 1

Cetrelia cetrarioides 2

Chaenotheca brachypoda 1

Chaenotheca brunneola 8

Chaenotheca chrysocephala 10

Chaenotheca ferruginea 10

Chaenotheca furfuracea 6

Chrysothrix candelaris 5

Cladonia bellidiflora 1

Cladonia botrytes 11

Cladonia carneola 1

Cladonia cenotea 4

Cladonia coccifera 6

Cladonia coniocraea 16

Cladonia cornuta 3

Cladonia deformis 3

 

12    

Cladonia digitata 96

Cladonia fimbriata 28

Cladonia furcata 2

Cladonia glauca 5

Cladonia gracilis 2

Cladonia macilenta 12

Cladonia polydactyla 3

Cladonia pyxidata 4

Cladonia squamosa 8

Cladonia sulphurina 4

Cladonia uncialis 1

Cyphelium inquinans 2

Cyphelium karelicum 1

Cyphelium tigillare 1

Dimerella lutea 1

Dimerella pineti 7

Evernia prunastri 6

Graphis scripta 63

Gyalecta flotowii 1

Gyalecta ulmi 1

Hypocenomyce scalaris 17

Hypogymnia austerodes 4

Hypogymnia bitteri 5

Hypogymnia farinacea 29

Hypogymnia physodes 119

Hypogymnia tubulosa 54

Hypogymnia vittata 7

Icmadophila ericetorum 4

Imshaugia aleurites 7

Lecanactis abietina 8

Lecanora albella 1

Lecanora argentata 9

Lecanora carpinea 75

Lecanora chlarotera 8

Lecanora conizaeoides 20

Lecanora expallens 17

Lecanora intumescens 6

Lecanora subintricata 1

Lecanora symmicta 1

Lecanora varia 1

 

13    

Lecidella elaeochroma 5

Lepraria incana 147

Leproloma membranaceum 3

Loxospora cismonica 3

Loxospora elatina 9

Menegazzia terebrata 1

Micarea cinerea 11

Micarea denigrata 2

Micarea prasina 4

Mycobilimbia sphaeroides 1

Mycoblastus fucatus 6

Mycoblastus sanguinarius 10

Nephroma resupinatum 1

Ochrolechia androgyna 31

Ochrolechia arborea 1

Ochrolechia pallescens 3

Ochrolechia turneri 1

Opegrapha atra 36

Opegrapha rufescens 3

Opegrapha varia 10

Opegrapha vermicellifera 4

Opegrapha viridis 10

Opegrapha vulgata 1

Parmelia acetabulum 1

Parmelia glabratula 15

Parmelia saxatilis 10

Parmelia subaurifera 1

Parmelia sulcata 19

Parmelia tiliacea 2

Parmeliopsis ambigua 76

Parmeliopsis hyperopta 38

Peltigera horizontalis 1

Peltigera praetextata 1

Pertusaria albescens 7

Pertusaria amara 10

Pertusaria coronata 1

Pertusaria hemisphaerica 1

Pertusaria leioplaca 5

Pertusaria pertusa 6

Phaeophyscia orbicularis 4

 

14    

Phlyctis agelaea 1

Phlyctis argena 67

Physcia tenella 1

Physconia perisidiosa 1

Placynthiella icmalea 1

Platismatia glauca 73

Porina aenea 8

Pseudevernia furfuracea 56

Psilolechia lucida 1

Pyrenula nitida 10

Pyrenula nitidella 2

Ramalina farinacea 6

Ramalina fastigiata 1

Ramalina fraxinea 2

Ramalina pollinaria 3

Ropalospora viridis 8

Sclerophora peronella 1

Scoliciosporum chlorococcum 1

Thelotrema lepadinum 9

Trapeliopsis flexuosa 2

Usnea filipendula 12

Usnea florida 2

Usnea hirta 7

Usnea subfloridana 2

Vulpicida pinastri 5

Xanthoria parietina 1

Xylographa parallela 7

126  

 

15    

Table A5. Tree of lichen species in Newick format 127  

((Arthonia_vinosa:0.1286,Arthothelium_ruanum:0.1286,Chrysothrix_candelaris:0.1286,(Opegrapha_atra:0.0857128  1630139,Arthonia_radiata:0.08571630139)100:0.04286297068,(((Opegrapha_viridis:0.03212053191,(Opegraph129  a_varia:0.008806314479,Arthonia_leucopellaea:0.008806314479)19:0.02331421743)47:0.03217994276,(Opegr130  apha_vermicellifera:0.03213155954,Opegrapha_vulgata:0.03213155954)100:0.03216891513)30:0.03211742804131  ,(Opegrapha_rufescens:0.03616071935,Lecanactis_abietina:0.03616071935)82:0.06025718337)19:0.032161369132  36)98:0.1214203676,((Pyrenula_nitida:0.1666666545,Pyrenula_nitidella:0.1666666545,((Ropalospora_viridis:0.133  0833,Sclerophora_peronella:0.083,(Chaenotheca_brachypoda:0.04166666661,Chaenotheca_furfuracea:0.041666134  66661)76:0.04166611039,(Chaenotheca_chrysocephala:0.04159016653,(Chaenotheca_ferruginea:0.0134673618135  8,Chaenotheca_brunneola:0.01346736188)38:0.02812280465)67:0.04174261047)98:0.04166527717,(((((Scolici136  osporum_chlorococcum:0.003968955996,(Psilolechia_lucida:0.002540691554,Xanthoria_parietina:0.00254069137  1554)43:0.001428264443)8:0.01191363671,(Mycoblastus_sanguinarius:0.009182607238,((((Lecanora_expallen138  s:0.002297433384,Lecanora_symmicta:0.002297433384)22:0.004651442455,((Lecanora_subintricata:0.002141139  09559,(Lecanora_conizaeoides:0.0006780901396,Lecanora_varia:0.0006780901396)83:0.001463005451)75:0.0140  03933194629,((Lecanora_albella:0.005293879952,Lecidella_elaeochroma:0.005293879952)24:0.000281185116141  7,(Lecanora_chlarotera:0.001780903273,Lecanora_carpinea:0.001780903273)25:0.003794161796)36:0.000499142  2251509)29:0.0008745856192)4:0.001144647238,Lecanora_intumescens:0.008093523077)6:0.0009229022707,143  ((((Ramalina_farinacea:0.007361175787,((Ramalina_pollinaria:0.002817084008,(Ramalina_fraxinea:0.0025475144  04068,(Bacidia_globulosa:0.0020043372,(((Calicium_trabinellum:0.0004183709765,Calicium_glaucellum:0.00145  04183709765)100:0.0009502350415,Cyphelium_karelicum:0.001368606018)53:0.0004651146894,Bacidina_ph146  acodes:0.001833720707)4:0.0001706164929)8:0.0005431668677)6:0.0002695799404)8:0.002571188367,Rama147  lina_fastigiata:0.005388272376)8:0.001972903412)8:0.0006912526777,(Bacidia_rosella:0.002810960873,Bacid148  ia_rubella:0.002810960873,Biatora_epixanthoidiza:0.002810960873)100:0.005241467591)8:0.0004555538205,149  ((((((Cladonia_carneola:0.001093491488,Cladonia_botrytes:0.001093491488)70:0.000463321249,((Cladonia_b150  ellidiflora:0.0004841383105,Cladonia_digitata:0.0004841383105)6:0.000170507983,Cladonia_sulphurina:0.000151  6546462935)2:0.0009021664438)2:0.0005149124818,(Cladonia_cornuta:0.001431514001,(((Cladonia_pyxidata152  :0.0007984883922,(((Cladonia_cenotea:0.000330573802,Cladonia_squamosa:0.000330573802)84:0.000191645153  4668,((Cladonia_macilenta:0.0002936091789,(Cladonia_polydactyla:0.0002551775714,Cladonia_glauca:0.0002154  551775714)8:3.843160751e-155  05)16:0.0001621217635,(Cladonia_coccifera:0.0001890223302,Cladonia_deformis:0.0001890223302)27:0.000156  2667086122)3:6.648832648e-157  05)1:0.000133373787,Cladonia_furcata:0.0006555930558)3:0.0001428953364)3:0.0001787563563,(Cladonia_f158  imbriata:0.0001737277499,Cladonia_coniocraea:0.0001737277499)11:0.0008035169987)1:0.0004339409298,C159  ladonia_gracilis:0.001411185678)0:2.032832229e-160  05)0:0.0006402112186)19:0.0003340602965,Cladonia_uncialis:0.002405785516)90:0.001080931348,(Micarea161  _denigrata:0.001237464974,Micarea_cinerea:0.001237464974)74:0.002249251889)23:0.002556223455,(Leprar162  ia_incana:0.0006078847306,Leproloma_membranaceum:0.0006078847306)100:0.005435055588)9:0.00246504163  1967)2:0.0002607412937,((((((Usnea_hirta:0.002042608713,(Usnea_filipendula:0.001780177293,(Usnea_florid164  a:0.001724855211,Usnea_subfloridana:0.001724855211)97:5.532208217e-165  05)93:0.0002624314206)95:0.0005656492885,((Parmeliopsis_ambigua:0.0008424533716,Parmeliopsis_hypero166  pta:0.0008424533716)91:0.001313536272,Cetrelia_cetrarioides:0.002155989644)56:0.0004522683583)7:0.000167  7532560146,((Mycoblastus_fucatus:0.002637162911,(Parmelia_saxatilis:0.00231351978,Parmelia_sulcata:0.00168  231351978)45:0.0003236431307)10:0.0003653996797,Parmelia_tiliacea:0.00300256259,Parmelia_acetabulum:169  0.00300256259)2:0.0003589514263)2:0.0006144399466,((Parmelia_subaurifera:0.002118705645,Parmelia_gla170  bratula:0.002118705645)87:0.0006967963269,(Cetraria_chlorophylla:0.001752063463,Cetraria_oakesiana:0.00171  1752063463)93:0.001063438509)44:0.001160451992)7:0.0009668090799,(Hypogymnia_austerodes:0.0013465172  80286,Hypogymnia_bitteri:0.001346580286,(Hypogymnia_farinacea:0.0007359595131,Hypogymnia_tubulosa:173  0.0007359595131)63:0.0006106207726)37:0.003596182757)5:0.002500487022,((Alectoria_sarmentosa:0.0015174  05290291,(Platismatia_glauca:0.001313050964,Imshaugia_aleurites:0.001313050964)20:0.000192239326)26:0.175  001832061713,(((Hypogymnia_vittata:0.0003713269839,Hypogymnia_physodes:0.0003713269839)48:0.00098176  2098299,Vulpicida_pinastri:0.001353425283)6:0.001826053295,((Pseudevernia_furfuracea:0.002552614351,(E177  vernia_prunastri:0.002113654232,((Bryoria_fuscescens:1.684529722e-07,Bryoria_capillaris:1.684529722e-178  07)100:0.001141837516,Bryoria_nadvornikiana:0.001142005969)95:0.0009716482636)31:0.000438960119)17:179  0.0004927661742,Menegazzia_terebrata:0.003045380526)1:0.0001340980526)1:0.0001578734255)5:0.004105180  898061)29:0.001325473514)3:0.000247701769)5:0.00016618189)9:0.006699985472)11:0.007913577982,((((C181  alicium_salicinum:0.002003871319,Calicium_viride:0.002003871319)99:0.002421230952,Cyphelium_tigillare:182  0.004425102271)95:0.002096362206,(Amandinea_punctata:0.006025914987,(Buellia_griseovirens:0.00578702183  1384,(Cyphelium_inquinans:0.005593707986,((Physconia_perisidiosa:0.003019838853,Physcia_tenella:0.0030184  19838853)100:0.00225872704,Phaeophyscia_orbicularis:0.005278565894)65:0.0003151420927)29:0.00019331185  33974)15:0.0002388936028)24:0.0004955494902)47:0.007886073569,(Nephroma_resupinatum:0.00556820096186  

 

16    

8,Mycobilimbia_sphaeroides:0.005568200968,(Peltigera_praetextata:0.001521946459,Peltigera_horizontalis:0.0187  01521946459)91:0.004046254509)76:0.008839337078)12:0.009388632645)23:0.01588475438,(Xylographa_pa188  rallela:0.0297,((Placynthiella_icmalea:0.005688872072,Trapeliopsis_flexuosa:0.005688872072)96:0.007369806189  282,(((Graphis_scripta:0.001904461663,Thelotrema_lepadinum:0.001904461663,Dimerella_lutea:0.001904461190  663,Dimerella_pineti:0.001904461663)92:0.002316598322,((Phlyctis_argena:0.0004641961055,Phlyctis_agelae191  a:0.0004641961055)100:0.003292703891,((Gyalecta_ulmi:0.002251375891,Gyalecta_flotowii:0.002251375891192  )98:0.001007001243,Porina_aenea:0.003258377134)52:0.0004985228625)29:0.0004641599884)88:0.00336072193  3364,(Pertusaria_leioplaca:0.003952497158,(Pertusaria_pertusa:0.001587922114,Pertusaria_coronata:0.001587194  922114)100:0.002364575044)100:0.003629286191)19:0.005476895005)31:0.01663388527,(Icmadophila_ericet195  orum:0.01845749213,(((Ochrolechia_androgyna:0.001439833615,Ochrolechia_turneri:0.001439833615)92:0.00196  811739586,Ochrolechia_pallescens:0.009557229475,Ochrolechia_arborea:0.009557229475)99:0.004170909704197  ,(Pertusaria_hemisphaerica:0.008392552738,(Pertusaria_amara:0.00322512896,Pertusaria_albescens:0.0032251198  2896)100:0.005167423778)25:0.005335586441)55:0.004729352947)60:0.0112350715)40:0.009988361452)12:199  0.00793903895,((Loxospora_elatina:0.01343135807,Loxospora_cismonica:0.01343135807)78:0.004747553595,200  Hypocenomyce_scalaris:0.01817891166)42:0.02944105236)9:0.07737809014)3:0.04166860033)0:0.041667788201  57,(Micarea_prasina:0.01425187968,Lecanora_argentata:0.01425187968)48:0.1940825634)6:0.04166519659)2202  1 203  

 

17    

Table A6. Co-variance (spearman’s correlation) among all trait pairs. 204  

Thallus form Prothallus Photobiont

Secondary

metabolism

Asexual

reproduction Conidia

Spore

pigmentation

Spore

septation

Ascomata

size

Spore

volume

Spore

shape

Thallus form 1 -0.24 0.31 0.23 0.45 0.30 -0.33 -0.38 0.65 -0.03 -0.14

Prothallus -0.24 1 0.10 0.09 0.01 0.19 -0.02 0.11 -0.27 0.19 0.16

Photobiont 0.31 0.10 1 0.62 0.42 0.09 -0.21 -0.43 0.22 -0.08 -0.31

Secondary

metabolism 0.23 0.09 0.62 1 0.22 0.23 -0.18 -0.42 0.21 -0.02 -0.36

Asexual

reproduction 0.45 0.01 0.42 0.22 1 0.06 -0.29 -0.24 0.30 -0.02 0.00

Conidia 0.30 0.19 0.09 0.23 0.06 1 -0.09 0.02 0.18 0.19 0.08

Spore

pigmentation -0.33 -0.02 -0.21 -0.18 -0.29 -0.09 1 0.32 -0.42 0.17 -0.09

Spore

septation -0.38 0.11 -0.43 -0.42 -0.24 0.02 0.32 1 -0.38 0.27 0.52

Ascomata

size 0.65 -0.27 0.22 0.21 0.30 0.18 -0.42 -0.38 1 0.09 -0.27

Spore

volume -0.03 0.19 -0.08 -0.02 -0.02 0.19 0.17 0.27 0.09 1 -0.01

Spore

shape -0.14 0.16 -0.31 -0.36 0.00 0.08 -0.09 0.52 -0.27 -0.01 1

205  

 

18    

Table A7. Results of linear regression analyses of effects of predictor variables (t-value and significance, *<0.05, **<0.01, ***<0.001) on the 206  

functional composition of lichen assemblages fitted by the linear model function. The response variables were used as relative frequency values 207  

(percentage of species with a specific trait characteristic). 208  

Elevation

(m a.s.l.)

Insolation

(Canopy cover % *-1)

Succession

[PCA 1 (DBH)]

Naturalness

[PCA 2 (dead-wood)]

Growth characteristics leprose -7.54*** 2.67** 1.87 0.77

immerse -5.58*** 1.12 1.22 2.22*

emerse 1.55 -1.35 3.08** -0.03

foliose 11.38*** 3.42*** 1.10 1.34

fruticose 7.92*** 3.54*** -0.82 2.67**

Prothallus -1.08 -1.81 2.35* 0.13

Photobiont partner trentepohlioid -7.63*** -0.70 1.79 0.90

trebouxioid 8.62*** 0.33 -1.52 -0.18

Defense Secondary metabolism 8.33*** 0.14 -1.55 -0.14

Establishment Asexual reproduction 3.98*** 2.28* -1.22 -0.85

Reproduction Conidia 5.31*** -2.86** -0.62 -0.76

Spore pigmentation -5.92*** 2.59* 2.07* 1.52

Spore septation -6.48*** 1.42 2.80** 1.31

209  

 

19    

210  

Figure A1. Relationship between elevation and (a) mean annual temperature and (b) mean 211  

total precipitation. (c) Principle component analysis (PCA) based on mean annual 212  

temperature, mean total precipitation, and various related extreme variables. (d) Relationship 213  

between the first principle component of the PCA and elevation. (For details on the variables 214  

and climate model, see Bässler et al. 2010, Müller et al. 2008). 215  

216  

References 217  

Bässler, C. et al. 2010. Effects of resource availability and climate on the diversity of wood-218  

decaying fungi. — J. Ecol. 98: 822-832. 219  

Müller, J. et al. 2008. Molluscs and climate warming in forests of a low mountain range, 220  

submitted. — Malacologia 51: 133-153. 221  

 

20    

222  

Figure A2. Species accumulation curves (method coleman) and extrapolated species richness 223  

applying different estimates (chao, first- and second-order jack-knife, bootstrapping) for all 224  

sampled lichen species. 225  

 

21    

226  

Figure A3. Phylogenetic tree based on eight genetic markers (genes encoding the largest and 227  

second largest RNA polymerase II subunits, translation elongation factor EF-1α, beta-tubulin, 228  

and genes encoding 28S, 18S, 12S, and 5.8S rRNA; for details, see Appendix 2) used in the 229  

analysis. Thallus growth form is represented in color: black = leprose, green = emerse, red = 230  

immerse, blue = foliose, and light blue = fruticose. 231  

 

22    

232  

Figure A4. Comparison of the phylogenetic diversity and environmental variables based on 233  

two different principles of creating a phylogenetic hypothesis. (a–d) Phylogeny from topology 234  

(see below for further information). (e–h) Phylogeny from sequence data as described in the 235  

main body of the paper. Note that the two approaches produced almost similar results. 236  

237  

Remarks on the methods 238  

We first constructed an approximate phylogenetic tree for all sampled species (n = 143) using 239  

published DNA-based trees (e.g. Schoch et al. 2009). The tree constructed with this procedure 240  

had altogether 70 nodes. Since the dating of fungal divergences is still a challenge (Berbee 241  

and Taylor 2010), we tried to overcome the uncertainty of our tree by using two approaches to 242  

estimate the branch length. In the first, more conservative approach, we set the branch length 243  

simply to 1. In the second approach, we used recently published ages of fungal divergences 244  

from the literature to calibrate the tree (Prieto and Wedin 2013), and we were able to date 245  

five nodes across the tree at different levels. Two calibration nodes were related to the class 246  

level, one to the subclass level, and two to the order level. Based on these calibration nodes, 247  

the other nodes were estimated using the bladj function within the program pyhlocom (Webb 248  

 

23    

et al. 2008). This is a simple utility that takes a phylogeny, fixes the root node at a specified 249  

age, and fixes other nodes where age estimates are available. It then sets all other branch 250  

lengths by placing the nodes evenly between dated nodes, and between dated nodes and 251  

terminals (beginning with the longest ‘chains’). This has the effect of minimizing variance in 252  

branch length within the constraints of dated nodes and thus produces a pseudo-chronogram. 253  

Even with only a few nodes dated, the resulting phylogenetic distances can be a marked 254  

improvement over simply using the number of intervening nodes as a phylogenetic distance 255  

(Webb et al. 2008). 256  

257  

References 258  

Berbee, M. L. and Taylor, J. W. 2010. Dating the molecular clock in fungi - how close are 259  

we? — Fungal Biology Reviews 24: 1-16. 260  

Prieto, M. and Wedin, M. 2013. Dating the Diversification of the Major Lineages of 261  

Ascomycota (Fungi). — PLoS ONE 8: 262  

Schoch, C. L. et al. 2009. The Ascomycota Tree of Life: A Phylum-wide Phylogeny Clarifies 263  

the Origin and Evolution of Fundamental Reproductive and Ecological Traits. — 264  

Systematic Biol. 58: 224-239. 265  

Webb, C. O. et al. 2008. Phylocom: software for the analysis of phylogenetic community 266  

structure and trait evolution. — Bioinformatics 24: 2098-2100. 267  

 

24    

268  

Figure A5. Functional dispersion according to Laliberté and Legendre (2010) based on lichen 269  

traits along the environmental gradients. 270  

271  

References 272  

Laliberté, E. and Legendre, P. 2010. A distance-based framework for measuring functional 273  

diversity from multiple traits. — Ecology 91: 299-305. 274  

 

25    

275  

276  

Figure A6. Results of linear regression analyses of effects of predictor variables on epiphytic 277  

species diversity, functional diversity and the functional composition of lichen assemblages 278  

fitted by the linear model function. Single traits are the number of species having a specific 279  

character or mean values (black dots), and effect sizes of diversity were measured as 280  

dispersion in null models with 999 randomizations (gray dots). Shaded areas indicate range of 281  

non-significant values (t-values: ± 2.0). Note that the scale of the x-axes differs among 282  

predictors, and that we did not calculate models for species characterized by a cyanobacterial 283  

photobiont partner since only two of the sampled species were related this kind of photobiont. 284  

 

26    

285  

Figure A7. Spline (cross)-correlograms of the linear model residuals (diversity models). The 286  

non-centered correlograms provide estimates of the spatial correlation for discrete distance 287  

classes and is based on Moran’s I. 288  

 

27    

289  

Figure A8. Spline (cross)-correlograms of the linear model residuals (mean assemblage trait 290  

models). The non-centered correlograms provide estimates of the spatial correlation for 291  

discrete distance classes and is based on Moran’s I. 292  

1    

Appendix 2 1  

2  

Detailed description of the construction of the fungal phylogeny modeled using a data-3  

assembly pipeline in R language. 4  

5  

A phylogenetically balanced sampling yields an adequate phylogenetic hypothesis. We 6  

therefore extended our study group of 143 sampled species of dicaryotic fungi by adding 7  

another 962 “scaffold” species covering the whole phylogenetic breadth of Dicaryota. Five 8  

species of Glomeromycota, the putative sister lineage of Dicaryota (Tehler et al. 2003), were 9  

chosen as outgroup. We downloaded all available nuclear and mitochondrial sequences from 10  

the nucleotide repository of GenBank using E-utilities (Sayers 2009 onwards). We used a 11  

heuristic algorithm to filter out dubious sequence records that constantly arise owing to 12  

misidentification of specimens or errors during DNA sequencing and data handling, including 13  

during the upload to NCBI repositories. Using a literature-based guide tree (e.g., Hibbett et al. 14  

2007 and references therein), we successively aligned the remaining sequences of each 15  

genetic marker with the L-INS-i algorithm (MAFFT; Katoh et al. 2005). Non-alignable blocks 16  

of sequences were those having a pairwise genetic distance ≥ 0.5. This threshold is arbitrary, 17  

but in a series of trials always led to the same arrangement of homologous alignment blocks 18  

that manual correction would have generated. After removing ambiguously aligned nucleotide 19  

positions (Gblocks 0.91b; Castresana 2000), alignment blocks of all markers and genomes 20  

were concatenated into a single supermatrix (730 taxa × 9,096 bp). Tree topology and branch 21  

lengths were modeled in a maximum-likelihood framework (RAxML v7.6.6; Stamatakis 22  

2006) as implemented on Xsede and accessed through the Cyberinfrastructure for 23  

Phylogenetic Research (CIPRES) science gateway (Miller et al. 2010). Sequence evolution 24  

was modeled with the GTRCAT approximation for each marker separately (Stamatakis 2006) 25  

on a common topology. The tree topology confidence was assessed via 1,000 non-parametric 26  

2    

bootstrap replicates (Stamatakis et al. 2008). Branch lengths were converted from substitution 27  

per site to relative time, i.e., the tree was made ultrametric, using the R package ape with a 28  

penalized likelihood approach using a relaxed clock model of rate evolution and the 29  

smoothing parameter lambda = 1.0 (Paradis 2013, Sanderson 2002). Finally, all scaffold taxa 30  

not belonging to the study group of sampled species were pruned from the topology. 31  

32  

References 33  

Castresana, J. 2000. Selection of conserved blocks from multiple alignments for their use in 34  

phylogenetic analysis. — Mol. Biol. Evol. 17: 540-552. 35  

Hibbett, D. S. et al. 2007 A higher-level phylogenetic classification of the Fungi. — Mycol. 36  

Res. 111 509-541. 37  

Katoh, K. et al. 2005. MAFFT version 5: improvement in accuracy of multiple sequence 38  

alignment. — Nucleic Acids Res. 33: 511-518. 39  

Miller, M. A. et al. 2010. Creating the CIPRES Science Gateway for inference of large 40  

phylogenetic trees. — Proceedings of the Gateway Computing Environments 41  

Workshop (GCE), 14 Nov. 2010, New Orleans, LA pp 1-8. 42  

Paradis, E. 2013. Molecular dating of phylogenies by likelihood methods: A comparison of 43  

models and a new information criterion. — Mol. Phylogenet. Evol. 67: 436-444. 44  

Sanderson, M. J. 2002. Estimating absolute rates of molecular evolution and divergence 45  

times: a penalized likelihood approach. — Mol. Biol. Evol. 19: 101-109. 46  

Sayers, E. 2009 The E-utilities In-Depth: Parameters, Syntax and More. 2009 May 29 47  

[Updated 2013 Aug 9]. In: Entrez Programming Utilities Help [Internet]. Bethesda 48  

(MD): National Center for Biotechnology Information (US); 2010-. Available from: 49  

http://www.ncbi.nlm.nih.gov/books/NBK25499/. 50  

3    

Stamatakis, A. 2006. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses 51  

with thousands of taxa and mixed models. — Bioinformatics 22: 2688-2690. 52  

Stamatakis, A. et al. 2008. A rapid bootstrap algorithm for the RAxML web-servers. — 53  

Systematic Biol. 75: 758-771. 54  

Tehler, A. et al. 2003. The full-length phylogenetic tree from 1551 ribosomal sequences of 55  

chitinous fungi, Fungi. — Mycological Res. 107: 901-916. 56