Myco- and photobiont associations in crustose lichens in the McMurdo Dry Valleys (Antarctica) reveal high differentiation along an elevational gradient

The climate conditions of the McMurdo Dry Valleys (78° S) are characterized by low temperatures and low precipitation. The annual temperatures at the valley bottoms have a mean range from −30 °C to −15 °C and decrease with elevation. Precipitation occurs mostly in form of snow (3-50 mm a−1 water equivalent) and, liquid water is rare across much of the landscape for most of the year and represents the primary limitation to biological activity. Snow delivered off the polar plateau by drainage winds, dew and humidity provided by clouds and fog are important water sources for rock inhibiting crustose lichens. In addition, the combination of the extremely low humidity and drying caused by foehn winds, confined to lower areas of the valleys, with colder and moister air at higher altitudes creates a strongly improving water availability gradient with elevation. We investigated the diversity and interaction specificity of myco-/photobiont associations of a total of 232 crustose lichen specimens, collected along an elevational gradient (171-959 m a.s.l.) within the McMurdo Dry Valleys with regard to the spatial distribution caused by climatological and geographical factors. For the identification of the mycobiont and photobiont species three markers each were amplified (nrITS, mtSSU, RPB1 and nrITS, psbJ-L, COX2, respectivley). Elevation, associated with a water availability gradient, turned out to be the key factor explaining most of the distribution patterns of the mycobionts. Pairwise comparisons showed Lecidea cancriformis and Rhizoplaca macleanii to be significantly more common at higher, and Carbonea vorticosa and Lecidea polypycnidophora at lower, elevations. Lichen photobionts were dominated by the globally distributed Trebouxia OTU, Tr_A02 which occurred at all habitats. Network specialization resulting from mycobiont-photobiont bipartite network structure varied with elevation and associated abiotic factors. Along an elevational gradient, the spatial distribution, diversity and genetic variability of the lichen symbionts appear to be mainly influenced by improved water relations at higher altitudes.


Introduction
The McMurdo Dry Valleys (MDV) in Southern Victoria Land of Continental Antarctica are characterized by an environment that is exceptional also for Antarctica: it is extremely arid and cold, which makes it hostile for most organisms. Thus, life is rare within the valleys of this polar desert, and only few life forms can cope with these extreme conditions (e.g. Adams et al. 2006;Pointing et al. 2009). The main limiting factor for life within the MDV is water availability with fog, clouds, dew and ephemeral melting water of snow patches having important effects on the climatic conditions (Adams et al. 2006;Green et al. 2007;Pannewitz et al. 2005;Stichbury et al. 2011). Among the most diverse macro-organisms present in the MDV are lichens. Lichens represent a classic example of symbiosis, consisting of a fungus (mycobiont) and one or more photosynthetic partners (photobiont). When completely desiccated, lichens are dormant and can survive unfavorable conditions for long periods (Green 2009;Kappen and Valladares 2007). As a consequence they are able to colonize rocks and boulders above melting streams or in the vicinity of snow patches, even in such extreme environments as the MDV (e.g. Green et al. 2011b;Hertel 2007;Ruprecht et al. 2012a;Ruprecht et al. 2010;Sancho et al. 2017;Schroeter et al. 2010). The most successful species are green-algal lichens, as they do not depend on the presence of liquid water for reactivation and can be active below zero degrees, in contrast to cyanobacterial lichens that appear to be completely absent in continental Antarctica (Green et al. 2011a;Kappen 2000;Lange et al. 1986;Schlensog et al. 1997;Seppelt et al. 2010).
Several studies on mycobiont-photobiont interactions in lichens have shown that green-algal as well as cyanobacterial lichenized fungi can show considerable photobiont variability and can have more than one photobiont and even combine green alga and cyanobacteria (Fernandez-Mendoza et al. 2011;Henskens et al. 2012;Nelsen and Gargas 2009;Otalora et al. 2010;Ruprecht et al. 2014;Wornik and Grube 2010). Low photobiont specificity and a high ability to accept different photobionts might be a survival strategy and extend the ecological range of lichens (Blaha et al. 2006;Dal Grande et al. 2017;Leavitt et al. 2015;Ruprecht et al. 2012a; Wirtz et al. 2003). Furthermore, photobiont selection appears to be influenced by abiotic factors like climate (Beck et al. 2002;Fernandez-Mendoza et al. 2011;Peksa and Skaloud 2011;Yahr et al. 2006). At the local scale (for instance along elevational gradients), this may translate into habitat-specific photobiont switches (Vargas Castillo and Beck 2012). Above all, temperature has often been identified as a key factor of photobiont selection of lichens in Antarctica (Green 2009;Kappen and Valladares 2007;Ruprecht et al. 2012a). In warmer regions, myco-/photobiont interactions show increased specificity leading to one-to-one interactions in contrast to more generalist interactions in colder environments (Singh et al. 2017). Thus, it appears that symbiotic interactions in lichens can react very sensitively to environmental change although this conclusion is based on a small database, and these responses have been investigated only in a few species (Allen and Lendemer 2016;Colesie et al. 2014b;Sancho et al. 2017). In general, there is agreement that climatic changes will influence the diversity, abundance and growth of lichens (Sancho et al. 2017) and that lichens therefore represent excellent bioindicators for processes associated with global warming (Alatalo et al. 2015;Allen and Lendemer 2016;Bassler et al. 2016;Sancho et al. 2019).
Over the last decades studies on elevational gradients have re-emerged because the species composition changes remarkably with elevation suggesting a species-specific adaptation to different environmental conditions (Grytnes et al. 2006). They provide steep ecological transitions (e.g. in temperature, humidity and UV radiation) over short distances (Keller et al. 2013;Körner 2007) and several studies suggest that the structure and diversity of communities, the abundance and distribution of species and ecosystem properties and processes can change along elevational gradients (Bassler et al. 2016;Dal Grande et al. 2017;Grytnes et al. 2006;Junker and Larue-Kontic 2018;Körner 2003;Wolf 1993). For lichens, elevational gradients are reported to show large changes in species composition Leavitt et al. 2015), habitat-specific photobiont switches (Vargas Castillo and Beck 2012), and/or microclimatic partitioning of ecologically differentiated fungal and algal gene pools (Nadyeina et al. 2014).
This study focuses on saxicolous crustose lichens in continental Antarctica which are associated with green micro alga as photobionts. In general, these lichens are slow growing and restricted to microhabitats on rock surfaces (Hertel 1998), but nevertheless, due to their poikilohydric nature, they are well adapted to habitats with high insolation and with rapid fluctuations in temperature and water availability (Green et al. 2002;Lange 1997;Lange 2000;Schroeter et al. 2011). Most of the lichens analyzed here belong to the 'lecideoid' lichen group (Hertel 1984) and these species are assigned to the generic name Lecidea sensu Zahlbruckner (1925) but they do not necessarily belong to the genus in its strict sense. Due to their inconspicous growth form, distinguishable only by a few small morphological traits such as spore size and ascus-type, the identification of these lichens is difficult even under best growing conditions (Ruprecht et al. 2019). Extreme climate conditions in cold deserts result in reduced development of the thallus such as chasmolithic growth, a lack of ascomata or sparsely developed ascospores, all features that hamper identification and even the detection of the specimens in the vast landscape (Hertel 2009;Ruprecht et al. 2010). Nevertheless, these pioneers on rocks and pebbles (Hertel 1984;Hertel 1987) belong to one of the most abundant species groups in continental Antarctica (Hertel 2007;Ruprecht et al. 2012b;Ruprecht et al. 2010) and are, therefore, an excellent study system to investiagte changes in symbiotic associations along gradients. The present study covers lecideoid lichen species of the genera Carbonea Hertel, Lecanora Ach., Lecidella Körb., Rhizoplaca Zopf and the genus Lecidea Ach s.str. (Hertel 1984) plus, in addition, lichen samples of the genera Austrolecia Hertel, Buellia De Not. and Huea C.W. Dodge & G.E.
Baker which were included, because they are often have a similar appeareance.
The aim of this study was to analyze the spatial pattern and factors that might affect the distribution of both symbiotic partners within the MDV. We addressed the following objectives: 1) to confirm and extend our knowledge of the abundances of the mycoand photobiont species that have been found by previous, less extensive studies, 2) to investigate the variability of mycobiont/photobiont interactions, in particular, analyze the level of selectivity by using network statistics and 3) to study for mycobiont, photobiont and lichens the relationships with abiotic and climatic factors such as elevation and water status.

Study area and sampling sites
This study was conducted as part of the New Zealand Terrestrial Antarctic Biocomplexity Survey (nzTABS, http://nztabs.ictar.aq), which was initiated during the International Polar Year 2007-2008, and drew a diverse range of international expertise to profile the biology, geochemistry, geology and climate of the MDV. The study is among the most comprehensive landscape-scale biodiversity surveys undertaken and includes nearly all trophic components found in the MDV ecosystem (Lee et al. 2019).
Sampling of soils and biological communities was carried out over two successive Austral summers (2009/10, 2010/11). The geographic area within which lichen samples were collected was the southern part of the MDV (total area: 22700 km 2 , ice-free area: 4500 km 2 ; Levy 2013; Fig. 1a, b). The landscape is a mosaic of glacially formed valleys with intervening high ground, icecovered lakes, ephemeral streams, arid rocky soils, ice-cemented soils, and surrounding glaciers along the steep scree and boulder slopes (Fig. 1b-d;Doran et al. 2002;Stichbury et al. 2011;Yung et al. 2014). There are four main valleys (Miers Valley, Garwood Valley, Hidden Valley and Marshall Valley) and some other extensive ice-free areas (Shangri-La). The topography ranges from sea level to more than 2000 m a.s.l. with granite being the dominant rock type on the ridges and hills, whilst the valley floors are covered with glacial drift. The valleys have the typical glaciated form with a U-cross-section with steep sides, often with scree slopes, which reach up to around 600 m in height. To the west the valleys are separated from the polar plateau by the Royal Society Range that has peaks over 4000 m a.s.l. in height. To the east the valleys open out onto the Ross Ice Shelf, which represents a climatically maritime influenced location within the MDV, despite the absence of ice-free sea at any time of the year (Yung et al. 2014).

Climate of the MDV
The climate of the MDV is, for several reasons, classified as that of a polar desert. First, the mountains at the west are sufficiently high to block seaward flowing ice from the East Antarctic ice sheet from reaching the Ross Sea. In addition, the Transantarctic Mountains provide a precipitation shadow, causing an extremely low humidity and lack of snow or ice cover in the MDV (Monaghan et al. 2005). Annual precipitation is < 50 mm a -1 water equivalent, with precipitation decreasing away from the coast (Fountain et al. 2010). The major source of liquid water is the seasonal melting of perennial snowbanks and glaciers (Head and Marchant 2014;Stichbury et al. 2011) but, in most cases, this water is not available for lichens that inhabit rock surfaces above the surrounding ground level. MDV climate is best known from the northern valleys, particularly Taylor (Colesie et al. 2014b;Doran et al. 2002;Ochyra et al. 2008). There is agreement that the air temperature lapse rate is close to 1 °C decline per 100 m elevation rise, as well as an increase with distance from the coast to the inland of 0.09 °C per 1 km (McKay 2015). The aspect of the valley slopes has an important impact and north facing slopes are warmer and dryer, south facing slopes are cooler and wetter (Yung et al. 2014). The wind regime is strongly topographically channeled and directed mainly up-or down-valley. During summer, easterly valley winds dominate, due to differential surface heating between the low albedo valley floors and the high albedo ice to the east (Mckendry and Lewthwaite 1990). In winter, wind direction is typically more variable. Cold air pools associated with light winds and very low minimum temperatures (-50 °C) often occupy topographic low points of the valleys during winter (Doran et al. 2002).
Almost all climate information comes from studies on the valley floors. There are, however, conditions that tend to produce a major difference in water regime between valley floors and intervening mountain ranges. First, there is a tendency at higher elevations for greater snowfall and higher humidity, as shown by the presence of clouds at higher elevations (Fig. 1d). Second, there is the regular occurrence within the valleys of what have traditionally been regarded as katabatic winds (Ayling and McGowan 2006;Mckendry and Lewthwaite 1990) but which are now suggested to be foehn winds albeit generated in a slightly different manner to the classic northern hemisphere foehns (Speirs et al. 2010). In the Taylor Valley, for example, these winds are easily recognizable by their sudden arrival, high intensity (around 15 m s -1 ), rapidly rising temperature (by around 25 °C to reach about 0 °C), and rapidly falling relative air humidity to around 20 % (Speirs et al. 2010). These foehn winds also occur in the southern valleys with an example from Miers Valley (Online Resource 2a) showing almost identical characteristics to those in the Taylor Valley. Foehn winds are extremely drying with air vapor pressure deficit rising about 50 times from 0.01 kPa (-30 °C, 80 % RH) to 0.49 kPa (0 °C, 20 % RH). They are also topographically constrained within the valleys and can apparently reach altitudes up to almost 500 m (Speirs et al. 2010). The net result of the higher elevation cold, moister air, and the extremely drying foehn winds within the valleys is that the wetness availability gradient is strongly non-linear and, for the purposes of our analyses, we defined an elevational threshold of about 600 m a.s.l. which marks the upper limit of the steeper valley sides.

Sample sources
The present study includes 232 lichen samples (lecideoid lichen species of the genera Carbonea, Lecanora, Lecidella, Please note that for most of the data evaluations, mycobionts and photobionts were treated separately. In some analyses (noted in text), only mycobiont species with n ≥ 10 (min10MycoSp) were used, whilst others included only photobiont haplotypes with n ≥ 10 (min10PhoHap).

DNA-amplification, purification and sequencing
Total DNA was extracted from the thallus and/or apothecia by using the DNeasy Plant Mini Kit (Qiagen) following the manufacturer's instructions. For all samples, we sequenced and amplified the internal transcribed spacer (ITS) region of the mycobionts' and photobionts' nuclear ribosomal DNA (nrITS). We also amplified additional markers: for the mycobionts the mitochondrial small subunit (mtSSU) and the low-copy protein coding marker RPB1 and, for the photobionts, the chloroplastencoded intergenic spacer (psbJ-L) and part of the cytochrome oxidase subunit 2 gene (COX2). This was done using newly developed specific primers and PCR-protocols in our project-framework (Ruprecht et al. 2019).
For amplifying nrITS of the mycobiont we used the primers ITS1 (White et al. 1990

Phylogenetic analysis
The sequences of the different marker regions listed above were assembled and edited using Geneious version 6.1. Phylogenetic relationships of the samples of the present study were calculated from the sequences of the marker nrITS. The other makers could not provide further intraspecific variation and were not available for every specimen; therefore they were excluded in all following analyses using sequence data.
A maximum likelihood analysis was calculated with the IQ-TREE web server (Trifinopoulos et al. 2016), using the model selection algorithm ModelFinder (Kalyaanamoorthy et al. 2017). The BIC (Bayesian information criterion) selected for the best-fit model for the mycobiont alignment TN+I+G4 and for the photobiont K2P+I. Branch supports were obtained with the implemented ultrafast bootstrap (UFBoot) (Minh et al. 2013) (number of bootstrap alignments: 1000, maximum iteration: 1000, minimum correlation coefficient: 0.99). Additionally, a SH-aLRT branch test (Guindon et al. 2010) was performed. Each branch of the resulting tree was assigned with SH-aLRT as well as UFBoot supports; the branches with SH-aLRT < 80 % and/ or UFboot < 95 % were collapsed by adding the command -minsupnew 80/95 to the script.

Haplotype analysis
We determined the haplotypes (h) of the different mycobiont species and photobiont OTUs by using the function haplotype() of the R package pegas (Paradis 2010) (note: the function only takes into account transversions and transitions but ignores insertions and deletions). For min10MycoSp species and photobiont OTUs with h ≥ 2 and at least two haplotypes with n ≥ 3 (Lecidea cancriformis, Lecidella greenii, Rhizoplaca macleanii and photobiont OTU Tr_A02), haplotype networks were computed, using the function haploNet() of the R package pegas (Paradis 2010). The frequencies were clustered in 10% ranges, for example the circles of all haplotypes making up between 20-30 % have the same size.

Analysis of spatial distribution
To analyze how the distribution of the lichen specimens correlated with abiotic factors, the sampling sites of the different lichen species or haplotypes in the investigated areas were compared with respect to their environmental specifics. For this we tested the only relevant variable which was elevation. All other variables such as latitude, longitude, and the BIOCLIM variables generated by Wagner et al. (2017) providing a spatial resolution of 1 km, were not suitable for the relatively small area (data not shown). To assure a minimum group size of 10 sample points, the tests only included the min10MycoSp species and min10PhoHap haplotypes.
In addition, the elevation of the sample sites of the two most dominant photobiont OTUs (Tr_A02 and Tr_S15) were compared by conducting a nonparametric t-test, using the R function npar.t.test() of the package nparcomp (Konietschke et al. 2015). We used nonparametric multiple comparisons for relative effects (mctp-test; function mctp() of the R package nparcomp (Konietschke et al.

2015)
, which conducts pairwise comparisons of all possible combinations.

Analysis of mycobiont -photobiont associations
The associations between mycobiont and photobiont haplotypes were analyzed by computing bipartite networks, using the R function plotweb() of the package bipartite (Dormann et al. 2008). For the bipartite network including mycobiont species and photobiont haplotypes the indices H2' and d' (Blüthgen et al. 2006) were calculated. Both indices are derived from Shannon entropy. H2' characterizes the degree of complementary specialization or partitioning among the two parties of the entire bipartite network, while d' describes the degree of complementary specialization at species or haplotype level. They both range from 0 for the most generalized to 1 for the most specialized case and were computed using the R functions H2fun() and dfun() of the package bipartite (Dormann et al. 2008).
Phylogenetic species diversity of the interaction partners was quantified by calculating a number of further metrices listed in Table   2, including the indices NRI (Net relatedness index), PSV (Phylogenetic species variability) and PSR (Phylogenetic species richness).

Analysis of DNA polymorphism
For each identified mycobiont and photobiont species with more than one sample, we calculated the haplotype as well as the nucleotide diversity using p-distances with DnaSP v5 (Librado and Rozas 2009). Gaps and missing data were excluded. We focused on h / N (number of haplotypes, h, divided by number of samples, N, per species), Hd (haplotype diversity, the probability that two randomly chosen haplotypes are different; Nei 1987) and π (nucleotide diversity, average number of nucleotide differences per site between two randomly chosen DNA sequences; Nei and Li 1979).
In order to analyze the dependence of haplotype and nucleotide diversity values on elevation, we used the defined threshold of 600 m a.s.l. The h / N, Hd, d', and PSV of those min10MycoSp species with mean values above this threshold were grouped together and then compared to those species with mean values below 600 m a.s.l., using the R function nonpartest() of the package npmv (Ellis et al. 2017), which performs nonparametric comparisons of multivariate samples. (Note: π, NRI, and PSR were excluded because of high correlations (r ≥ 0.85) with h / N (π), Hd (π), d' (π) and PSV (NRI and PSR).

Phylogenetic analysis
The molecular phylogenies for the mycobiont (Online Resource 2b) and the photobiont (Online Resource 2c) are based on the marker nrITS, because the additional markers (mycobiont: mtSSU, RPB1; photobiont: psbJ-L, COX2) showed little sequence variation in this area. Both analyses include only accessions from the study sites (Online Resource 1a, b) to present the various species-and diversity levels. therefore renamed.

Haplotype analysis
The following analyses were based on the nrITS sequences of myco-and photobionts. The number of haplotypes differed significantly between myco-and photobionts. We identified 48 different mycobiont but only 17 different photobiont haplotypes.
The most frequent mycobiont haplotype was Lecidella greenii_h01 with 28 samples, the most frequent photobiont haplotype was     Fig. 1b & 2). Roman numerals at the center of the pie charts refer to the haplotype IDs, italic numbers next to the pie charts to the total number of samples per haplotype. The circle sizes reflect relative frequency within the species/OTU; in doing so, frequencies were clustered in ten, so that for example the circles of all haplotypes making up between 20-30 % have the same size.
Three different mycobiont species (Lecidea cancriformis, Lecidella greenii and Rhizoplaca macleanii) and the most common photobiont OTU (Tr_A02) met the required criteria defined above for the construction of haplotype networks (h ≥ 2 and at least two haplotypes with n ≥ 3). In Fig. 4 the respective haplotype networks show the spatial location within the four areas. As shown in Fig. 2 for mycobiont species/photobiont OTU, the distribution again turned out to be rather uniform, with most of the haplotypes found in all of the four areas.

Analysis of spatial distribution
For 12 of the 28 pairwise comparisons for the mycobionts species (min10MycoSp) and photobiont haplotypes (min10PhoHap)

Analysis of mycobiont-photobiont associations
The bipartite network was calculated for all associations between the mycobiont species (min10MycoSp; lower level) and the respective photobiont haplotypes (higher level; Fig. 6 Table 3.

Species richness of mycobiont vs photobiont
The number of different symbiotic partners at haplotype level (SR) as a function of the number of mycobiont haplotypes (h in Table 3) is illustrated in Fig. 7 for the min10MycoSp species. The two variables show a correlation of r = 0.701; thus, highly variable mycobionts tend to be associated with a higher number of photobiont haplotypes.

Analysis of DNA polymorphism and nonparametric comparisons of multivariate samples
Analyses of DNA polymorphism and nonparametric comparisons of multivariate samples were achieved for min10MycoSp and min10PhoHap including the parameters h / N (number of haplotypes, h, divided by number of samples, N, per species), Hd (haplotype diversity), and π (nucleotide diversity), d' (specialization index), NRI (net relatedness index), PSV (phylogenetic species variability), SR (species richness) and PSR (phylogenetic species richness; Table 3).

Discussion
The In our study, we found that the different mycobiont species and photobiont OTUs within the MDV appear to be relatively evenly distributed across all four primary sample sites (Fig. 2), which is in basic agreement to the previous study. However, our results show distinct patterns for distribution, genetic diversity and specificity. These results contrast with Pérez-Ortega et al. (2012) where the distribution of mycobionts and photobionts was independent of elevation and other abiotic factors. A clear trend has now emerged showing that the distribution of species/OTUs is significantly related to elevation, using 600 m a.s.l. as a defined threshold dividing higher and lower sites. The mycobiont species Carbonea vorticosa, Lecidea polypycnidophora and Lecidella greenii were found almost exclusively below and Lecidea cancriformis and Rhizoplaca macleanii above the threshold (Fig. 5), which was supported by mctp-tests for pairwise comparisons (Online Resource 1c).
In contrast, the dominant photobiont OTU Tr_A02 is distributed everywhere whilst the remaining and distantly related OTUs (Tr_S02, Tr_ S15 and Tr_ S18) are mostly restricted to the higher elevation habitats (cold and humid;  Yahr et al. 2006), and this has been interpreted as evidence for ecological specialization (Muggia et al. 2014;Ruprecht et al. 2012a).
The mycobiont species min10MycoSp not only show clear spatial differentiation with respect to elevation for species and OTUs but also for variables expressing the genetic diversity and specialization towards both symbiotic partners. A higher elevation correlates with a higher number of haplotypes (Hd) and an increased nucleotide diversity (π) which leads to a greater intraspecific diversity within the mycobionts (Table 3). These differences are also partially reflected by a higher d', PSV, PSR and a low NRI which show a low relatedness to the co-occurring photobiont, associated with the rarely occurring and highly differentiated other OTUs Tr_S02, Tr_S15 and Tr_S18. Consequently, mycobionts with a high genetic diversity have a higher number of interacting partners. These findings are partially supported by the study of Singh et al. (2017), who reported climate as a selective pressure in terms of increased specificity of myco-/photobiont interactions.
Our study has also shown that highly variable mycobionts are associated with a larger number of photobiont haplotypes (Fig. 7).
If we focus on the species which are significantly distributed either below or above the threshold of 600 m a.s.l. three main scenarios emerged: (1) mycobionts with low genetic diversity (Carbonea vorticosa, Lecidea polypycnidophora and Lecidella greenii) are associated with one photobiont OTU Tr_A02, and were found in only the lower area; (2) a mycobiont with a high genetic diversity (Rhizoplaca macleanii) is still solely associated to one photobiont OTU (Tr_A02) and is only located at the high elevated areas and (3) the mycobiont with the highest genetic diversity (Lecidea cancriformis) is associated with highest number of photobiont OTUs, in the high elevation sites. These findings are in agreement with the known distribution of L. greenii and Tr_A02 (Trebouxia URa2), which, so far, have only been reported for sites in the more northern parts of the Ross Sea region and have never been found at the most extreme southern environments like the Darwin area (Ruprecht et al. 2012a;Ruprecht et al. 2012b). In contrast, L. cancriformis is one of the most widespread lichens, being distributed all over Continental Antarctica and is associated with all known photobiont species (Castello 2003;Ruprecht et al. 2012a;Ruprecht et al. 2010).
The above results suggest that the mycobionts are dependent on the availability of climatically adapted photobionts. However, the mycobionts seem to have also their unique climate specific preferences because they do not make use of the whole climate niche of the associated photobionts. These findings are only partially in line with previous studies (e.g. Romeike et al. 2002;Wirtz et al. 2003) that suggest that in extreme environments like the Antarctic continent there might be a selection pressure against photobiont specificity so that more versatile mycobionts are favored. Flexibility concerning the partner choice has been the MDV is temperature, which is accepted to be inversely correlated to elevation (McKay 2015). However, much less is known about the conditions for wetness and humidity. The available wetness index for the MDV quantifies the expected wetness of a unit within the watershed by calculating the amount of possible water flowing into that unit from estimated snow fall (Stichbury et al. 2011). For rock associated lichens this is not relevant, because they are not connected to this source of water. They are, therefore, dependent on moisture provided by the very low precipitation, infrequent melting snow (Head and Marchant 2014) and humidity provided by incoming fog and clouds from the sea. Additionally, it is now clear if the occasional foehn wind events cause severe drying within the valleys at altitudes up to 500 m. At higher elevations there is cold and moister air and this establishes a strong moisture availability gradient with elevation (Speirs et al. 2010; Fig. 1d). Our results suggest that our defined elevation threshold of about 600 m a.s.l. is a reasonable level which marks the shift from lower, dryer to higher, more humid conditions.
Habitat aspect is also known to be important. Yung et al. (2014) described large differences with respect to just aspect for their chasmoendolithic microbial communities at Miers Valley. Similar results were also reported for the more maritime site, Botany Bay (Seppelt et al. 2010). We did not find any impact of other topographical features such as distance to coast, slope, aspect and substrate. The collection sites were mainly N-facing or on plateaus, our transects were narrow and consistently only five to ten km inland plus the underlying rock in the whole area is granite and the investigated lichens are restricted to siliceous rock (Ruprecht et al. 2012b;Ruprecht et al. 2010). However, our sampling was equally distributed below and above the threshold of 600 m a.s.l., so the differences found for species distribution, genetic diversity and specificity appears to be due to the changing climate conditions, particularly moisture, along the elevational gradient.