Introduction
Plant parasitic fungi and snow moulds may benefit from milder cold season temperatures, brought about by increased air temperature (Callaghan et al. 2011; Cooper 2014; Hoegh-Guldberg et al. 2018) and the stabilizing effect of an insulative snow layer (Wipf and Rixen 2010), as mycelial growth takes place mainly under the snow (Jung et al. 2007; Hoshino et al. 2009). Climate change is especially pronounced in high latitude and elevation regions and may therefore promote infections of plants by parasitic fungi in these cold-dominated ecosystems (Olofsson et al. 2011). For instance, increased occurrence of snow moulds in northern regions render golf course grass unsightly (Hsiang et al. 1999; McBeath 2003; Jung et al. 2007), and fungal infection reduces commercial blueberry crops in eastern North America (Brewer et al. 2014; Stewart et al. 2015), both with important economic implications. The response of native vegetation to changing warm and cold season conditions in these biomes is well documented (Wipf and Rixen 2010; Elmendorf et al 2012a, 2012b; Cooper 2014), but the role played by parasitic fungi is understudied thus far.
Long-term snow enhancement in the High Arctic with corresponding milder winter temperatures reduced the cover of dwarf shrub Cassiope tetragona (L.) D. Don. and increased that of moss (Cooper et al. 2019). Infections with parasitic fungi may have contributed to the C. tetragona decline, as infected individuals may have been more vulnerable to disturbances and thereby less likely to adapt to changing conditions. Exobasidium hypogenum Nannf. (Fig. 1) is a prominent, highly specialized biotrophic parasitic fungi only growing in C. tetragona (Nannfeldt 1981; Elvebakk and Prestrud 1996), a dominant hemi-prostrate dwarf shrub with circumpolar distribution (Walker et al. 2005). Due to C. tetragona’s susceptibility to frost damage (Semenchuk et al. 2013; Milner et al. 2016), this plant is common in areas with relatively deep snow cover (Mallik et al. 2011; Semenchuk et al. 2016a), but an increased occurrence of the easily recognized infected leaves was noted in areas of experimentally increased snow depth (own observations; Abbandonato 2014).
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Sanionia uncinata (Hedw.) Loeske is one of the dominant moss species in many Arctic ecosystems (Smith 1996; Virtanen et al. 1997), and is infected, weakened, and killed by Pythium polare Tojo, van West & Hoshino (synonym Globisporangium polare), a necrotrophic oomycete (Tojo et al. 2012; Tojo and Newshman 2012). Pythium polare is highly dependent on Sanionia uncinata in polar regions; thus, potentially affecting both Sanionia uncinata’s occurrence and its ability to respond to increased snow depth in the same way as other bryophyte species. Pythium polare infection can be easily seen by the presence of light-coloured circles of dead tissue within moss colonies (Fig. 2, Tojo and Newsham 2012). Rings of dead Sanionia uncinata observed in our study site on Svalbard indicated increased Pythium polare infection in response to experimentally increased snow (Cooper et al. 2019).
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In general, the effect of parasitic fungi on their hosts depends on their mode of nutrition; biotrophs, such as our study organism E. hypogenum, acquire nutrients from the living host plant cells, whereas necrotrophs kill the host plant and feed on the dead tissues (Ingold and Hudson 1993). As biotrophic fungi require their host to be alive to obtain nutrients, they avoid damaging the plant (Barnett and Binder 1973). They establish very specialized structures for compatibility (Mendgen and Hahn 2002; Duplessis et al. 2011), usually through coevolution with the host plant (Schulze-Lefert and Panstruga 2003), even evolving mechanisms to keep their host cells alive (Struck 2006) to reduce their impact on their host (Ingold and Hudson 1993). Over time, however, these impacts may accumulate and increase mortality over large areas, with further effects at a community level (Olofsson et al. 2011).
To investigate the effect of increased snow on these two contrasting plant parasitic fungi in the High Arctic (biotrophic E. hypogenum on C. tetragona, and necrotrophic Pythium polare on the moss Sanionia uncinata), we collected occurrence data of each species from a long-term snow fence experiment in Adventdalen, Svalbard. Snow fences were erected in 2006, i.e., 7–13 years prior to this study, and affected both abiotic and biotic conditions, including increased cold season temperatures, increased soil moisture during the early growing season, and species composition changes, among others (Semenchuk et al. 2013; Cooper et al. 2019; Mörsdorf et al. 2019). We recorded the occurrence of E. hypogenum and Pythium polare and hypothesized that each parasite’s presence in its respective host was higher with increased snow depth, a potential mechanism contributing to the documented (C. tetragona) and potential (Sanionia uncinata) decline of both hosts (Cooper et al. 2019).
Materials and methods
Study site
The study took place in Adventdalen, Svalbard (78°10′N, 16°04′E), 30–80 m above sea level. Weather data from Svalbard airport, located approximately 15 km from the study site, show that average temperature for the 2010–2019 period was −2.4 °C, the coldest month being March (−10.9 °C) and the warmest July (7.4 °C) (eKlima 2020). Precipitation is low, with an average of 229 mm per year, and spread throughout the year (eight months of monthly precipitation between 19 and 28 mm), although values in the period March–June are lower (between 6 and 14 mm/month). On average (2010–2019) 173 mm falls in the period September–May, inclusive, and 56 mm in June–August, so 76% falls in the non-growing season.
The vegetation is classified as part of the middle Arctic tundra (Elvebakk 2005), dominated by C. tetragona heaths and moister meadows, with abundant Salix polaris Wahlenb. throughout. More details about soil and vegetation at the site is given in Appendix A.
Experimental set-up
Snow fences were established in Adventdalen in 2006 to study the environmental and biological response of a High Arctic ecosystem to increased snow. Four blocks were established, two in Heath vegetation and two in a moister Meadow (Morgner et al. 2010). Each block consisted of three fences (6 m long and 1.5 m high) perpendicular to the prevalent wind direction: the fence disturbs the wind flow and leads to the accumulation of snow on the lee side creating different snow depths (see Appendix A, Fig. A1). For each fence six plots were established in the unmanipulated “Ambient” regime (10–30 cm snow depth) and six at the “Deep” snow regime (1.2–1.5 m deep). In three of the plots at each regime the dominant evergreen shrub species was C. tetragona, and in the three others it was Dryas octopetala L. In 2010 further plots were marked in a third snow regime, “Medium”, farther from the fence than Deep but still influenced by it (60–100 cm snow depth). Three Medium plots were established per fence, and where possible each contained a mixture of C. tetragona and D. octopetala. Two fences were not used for this study (one collapsed and the other lacked C. tetragona behind the fence). As snow bed plants, such as bryophytes, may benefit from the decrease of shrub cover in deep snow regimes (Mark et al. 2015; Cooper et al. 2019), for the purpose of the current study only plots with C. tetragona as the dominant evergreen shrub were chosen. For Ambient and Deep regimes, this resulted in three plots studied at each fence. For the Medium regime, only plots that had C. tetragona when they were established were chosen, but some of them had to be discarded as they had been the focus of an open-top chamber (OTC) study. See Appendix A, Fig. A1 and Table A1 for an overview of the experimental design and the plots used.
The fences increased snow depth and the length of snow lie, delaying snowmelt and affecting both winter and summer temperature and soil moisture (Fig. 3). During winter 2014–2015, soils were warmer within Deep than Ambient snow regime, for approximately 77% of the time (i.e., 175 days of the 227 days of sub-zero temperatures, Mörsdorf et al. 2019). Soil in Medium was also warmer than in Ambient, but for a shorter duration. Minimum temperatures during winter were milder in Deep (−10 °C) and Medium (−17.5 °C), and they were more stable than the highly fluctuating Ambient soil temperatures (min. −23.5 °C). Soil moisture was very high directly after snowmelt, especially for the enhanced snow regimes. This gradually decreased throughout the growing season, so that by mid-July there was little difference between the regimes. This pattern of temperature and moisture was similar for all study years (e.g., Morgner et al. 2010; Cooper et al. 2011; Gillespie et al. 2016). Heaths accumulated significantly more snow than Meadows between mid-November and late May; consequently, both early and late winter surface soil temperatures were higher in Heath than Meadow for both Ambient and Deep (Morgner et al. 2010). A vegetation survey in the experiment was carried out in 2015 after nine winters of manipulated snow and reported by Cooper et al. (2019). In summary, deeper snow behind the fences resulted in shrub death (mostly C. tetragona and D. octopetala), reducing the cover of live shrubs and increasing that of bare ground and dead shrub material. Bryophyte cover also increased with deeper snow, especially in the Medium regime in Meadow and in the Deep regime in Heath.
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Soil moisture and temperature
During the summer of 2015, soil moisture was measured at the centre of each plot using a Theta ML 2x Probe (Delta-T Devices, Cambridge, UK). Near soil surface temperature was recorded hourly throughout the experiment using Tinytag data logger model TGP-4020 (Gemini Data Loggers, Chichester, UK), with one logger per fence and snow regime combination. Summaries of temperature data were made from the Ambient, Medium, and Deep regimes for all years 2010–2018. The date (day of year) the plots were snow free was recorded in the field and (or) found by examination of the temperature data during earlier studies. For the years where these data were missing (2014, 2016–2018), we used rounded medians across all other years, for each fence and treatment separately. The remaining summary statistics were run on block means, so snow free dates and logger temperature data were averaged within blocks. From the within-block averaged temperature data, daily averages were calculated and used thereafter. The initial freezing date was defined as the first day after 31 August (day of year 243) when the daily average temperature was less than zero. We defined the year into two seasons based on this temperature data: “warm season”, the period between snow free date and initial freezing date; and “cold season”, the period between initial freezing date and snow free date. We obtained the following values from these data: Thawing and Freezing Degree Days of the soil calculated as an accumulation of daily (positive and negative, respectively) temperature means as well as Annual Degree Day sums, together with season lengths. We present means of these data as averages across the whole experiment.
Vegetation cover
We recorded the percentage cover of plants in the plots in two field campaigns and used the 2019 C. tetragona data and 2017 bryophyte data for this study. Briefly, the percentage cover of C. tetragona, classified as alive or dead, was estimated together with that of other plants in the plot making a total plot cover of 100%, following the method outlined by Cooper et al. (2019), on 12–13 August 2019.
Detailed vegetation analysis carried out in July 2017 using the point-frame method (Jonasson 1988; Walker et al. 1996) in which we identified the bryophytes to species where possible. We later separated the bryophyte data into two groups: Sanionia uncinata, and “non-Sanionia bryophytes”, composed of Anthelia juratzkana (Limpr.) Trevis., Aulacomnium spp.,Dicranum spp. and Distichium spp.,Hylocomium splendens (Hedw.) Schimp., Polytrichum spp.,Ptilidium ciliare (L.) Hampe, Racomitrium spp., Tomentypnum nitens (Hedw.) Loeske, and unidentified bryophytes. Here we only used the “first/top-hits” giving a total number of possible hits of 100 per plot. From the 2017 field campaign we only used the bryophyte data for this study (total bryophytes, only Sanionia uncinata, non-Sanionia bryophytes, and proportion of bryophytes that are Sanionia uncinata).
Exobasidium hypogenum
Plots from all regimes (Ambient n = 30, Medium n = 14, Deep n = 29) were checked for live C. tetragona and the occurrence of E. hypogenum infection on two occasions: 7–27 August 2013, and 14–15 August 2019. Infected shoots have elongated internodes and malformed leaves, which are easily identifiable (Nannfeldt 1981, Fig. 1). Leaves grow to up to 10 × 6 mm, more than double their normal size, are triangular and the adaxial side is blood-red or whitish. No connections have been found between attacks of consecutive seasons, which suggests that each affected shoot usually develops from a new infection (Nannfeldt 1981). Having data from two different years, seven and thirteen years after establishing the fences, can give some indication of infection development through time.
Pythium spp.
During August 2014 and 2015, 1 × 1 m2 plots were established for the study of Pythium species. One Pythium plot was established at each fence (10 fences) and snow regime, totalling 30 Pythium plots. At eachPythium plot, five samples of 3–5 g (wet weight) of Sanionia uncinata were collected and later stored at 2–4 °C until use.
Four Sanionia uncinata shoots per Pythium plot and year were washed in tap water and dried with paper towels. They were later brought to a clean bench (an enclosure that provides filtered air across the work surface to protect against contamination) and buried equidistantly in a petri dish with Pythium selective NARM medium (Morita and Tojo 2007). The petri dishes were then sealed with Parafilm and brought to a cold room, where they were incubated at 4 °C for 1–2 weeks. After this period any present Pythium mycelium would have reached a size of 2–3 cm, and the number of such mycelia per petri dish was noted (usually 0 or 1, and very occasionally 2). This procedure was performed five times per plot, using four different Sanionia uncinata shoots each time, resulting in a theoretical maximum of 20 isolates per plot, 600 per year, and 1200 total.
Statistical analyses
All data were analysed in R (R Core Team 2019), applying linear mixed models.
Vegetation cover was analysed using the function glmmTMB from the glmmTMB package (Brooks et al. 2017). This function uses beta distribution and logit link function. Beta distribution requires values between 0 and 1. Therefore, percentage values were divided by 100, live and dead covers equal to 0 were changed to 0.0001, and proportions equal to 0 and 1 were changed to 0.001 and 0.999 respectively.
In the case of C. tetragona using cover data from 2019 three models were established: live cover of C. tetragona, dead cover of C. tetragona, and proportion of dead cover (dead/(dead + live)). Here we used all permanent vegetation plots containing live C. tetragona at the start of the experiment. One plot was removed from the analysis as it was under water in 2019, and six of the Medium plots were removed from analysis as they did not contain live C. tetragona when established. For live and dead cover analyses all plots originally containing live C. tetragona were used (n = 73), although for proportion of dead cover only those plots with some C. tetragona in 2019 (dead and (or) alive) were used (n = 72). For analysis of bryophyte cover obtained in 2017 in relation to snow regime we used four models: total bryophyte cover, Sanionia uncinata cover, non-Sanionia bryophytes cover, and proportion of bryophyte cover that was Sanionia uncinata (i.e., Sanionia uncinata/total bryophyte cover). For this last analysis only plots that had bryophytes were used (n = 62).
For each of these models the explanatory variables were vegetation type and snow regime, and fence identifier was included as a random factor. A stepwise reduction approach was employed for the election of the best candidate model, which allows the retention of all important factors and avoids over-fitting. First, the response variable was modelled according to the explanatory variables (including all interactions), and then the different terms were progressively removed, and the remaining models compared (first the interaction, then the single variables). Models were compared by means of analysis of variance, obtaining Akaike’s information criterion and calculating the chi-squared test.
Infection by E. hypogenum and Pythium spp. was analysed using the function glmer from the lme4 package (Bates et al. 2015). In the case of E. hypogenum, only plots with live C. tetragona were considered in the analysis (n = 142: 75 in 2013, 67 in 2019), and a binomial error distribution was used (presence/absence data, where each plot was assigned a value of 1 if E. hypogenum was present, or 0 if it was not present). The response variable was, therefore, the presence or absence of E. hypogenum within a plot, and logit transformation was applied for modelling. In the case of Pythium a Poisson error distribution was used, where the response variable was the abundance of Pythium per fence (the number of individualPythium mycelia in the petri dishes, i.e., count data, (n = 56: 26 in 2014 and 30 in 2015)). For this analysis, all Pythium species were grouped together in a single variable, as most species were too rare for statistical analysis, and log-transformation was applied for modelling. The explanatory variables for these two models were year, vegetation type and snow regime, and fence was included as a random factor. A stepwise reduction approach was also employed, aiming at the simplest model with all significant variables.
Fungal occurrence was then modelled against the cover of the host plants. In the case of E. hypogenum the presence/absence data from 2019 was modelled against C. tetragona (live and dead cover, and proportion of dead, all in 2019) by means of glmer. We used binomial distribution and logit transformation, and fence was included as random factor. In the case of Pythium, the abundances from 2014 and 2015 were summed (and those of the two plots that were only sampled in one year were doubled), and the resulting values were modelled against bryophyte cover from 2017 (total bryophyte cover, Sanionia uncinata cover and proportion of Sanionia uncinata), using a mean bryophyte value for each fence–snow regime combination (n = 30). We used Poisson distribution and log-transformation, and fence was included as a random factor. As we used different plots within a fence–snow regime combination for Pythium sampling and bryophyte cover, we tested the Pythium–bryophyte relationship using mean bryophyte values from two different sets of plots for the bryophyte cover: (1) the plots used in the other analyses described here (i.e., Cassiope-focussed plots, n = 71), (2) all plots within these 10 fences in the whole experiment (Cassiope and Dryas focussed, without OTCs, n = 119).
We applied post-hoc analysis to all models, to get the relationships between the different levels and factors. This was done using the function emmeans from the emmeans package (Length 2020).
Results
Abiotic conditions
Deep and Medium had significantly milder cold seasons than Ambient (Table 1, Fig. 3). These enhanced snow regimes became snow free later, had shorter warm seasons and, thus, fewer days available to collect Thawing Degree Days, a cold season with higher (less negative) Freezing Degree Day values, and more stable and significantly higher soil temperatures during the cold season. These effects were stronger in Deep than in Medium. In the Ambient regime Heath was slightly colder than Meadow, in both cold and warm seasons (Appendix A, Table A2, and Figs. A2A and A2B). This pattern was reversed by snow enhancement, so that Heath was warmer than Meadow for Medium and Deep. The largest differences in temperature sums between the snow regimes was during the cold season.
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Although the majority of the snow melted and ran off while the soil was still frozen without the formation of obvious pools of meltwater, soil moisture levels were elevated during the first third of the growing season in Deep and Medium (Fig. 3), with Deep and Medium not differing from each other. Meadow was moister than Heath in Ambient and Medium (Appendix A, Fig. A2C) but there were no differences between the vegetation types in Deep.
Vegetation cover
The cover of live C. tetragona decreased with enhanced snow depth, was lower in Meadow than Heath, and both cover and proportion of dead C. tetragona were significantly much higher in Deep than in Ambient (Table 2, Fig. 4). Of the five plots where C. tetragona was completely dead, one was in Medium and four were in Deep (see Table 3, year 2019).
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Total bryophyte cover and cover of non-Sanionia bryophytes was higher in Meadow than Heath (Table 4, Fig. 5). Total bryophyte cover increased with snow depth in both habitats, although for both Sanionia uncinata and non-Sanionia bryophytes this occurred only in Meadow. Proportion of Sanionia uncinata did not differ between snow regimes in Heath, but was higher in Deep than Ambient in Meadow.
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Infection of Cassiope tetragona by Exobasidium hypogenum
In both Heath and Meadow, the proportion of plots with liveC. tetragona that were infected by E. hypogenum increased with snow depth, with the clearest increases in Deep (Table 3, Appendix A, Table A3). The likelihood of infection of C. tetragona by E. hypogenum did not differ between vegetation types and years, with C. tetragona in Deep being more frequently infected than in Ambient and Medium (Table 5, Fig. 6).
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The increase of infection in Deep was slightly, although insignificantly, higher in 2019 than in 2013 (Table 3 and Appendix A, Fig. A3), suggesting an increased fungal response as the experiment progressed. In four plots C. tetragona died out between 2013 and 2019, one in Medium and three in Deep, of which two (one at each snow regime) were infected by E. hypogenum (Table 3).
The fungus/host plant model showed that the presence of E. hypogenum was significantly positively related to the cover of dead C. tetragona in a plot, and had no relationship to live C. tetragona cover or proportion of C. tetragona that was dead (Table 5).
Infection of Sanionia uncinata by Pythium spp.
From the incubation trials a total of 103 isolates of Pythium spp. were obtained (Appendix A, Table A4). Two shoots gave two mycelia, the rest produced only one each. A fifth of the mycelia could not be discerned to the species level. Among the other isolates we obtained five different species of fungal mycelia; four Pythium species are new discoveries (and, therefore, we lack information about specificity). Pythium polare (synonymGlobisporangium polare), was the most abundant, accounting for 60% of the mycelial growth.
In the model reduction of the Pythium analysis the year variable was discarded, leaving snow regime, vegetation type, and their interaction as explanatory variables (Table 5). Pythium was generally more abundant in Heath than Meadow (Fig. 6). In Meadow, both Deep and Medium had significantly higher abundance of Pythium than Ambient, whereas in Heath, Deep had a higher abundance than both Ambient and Medium.
The fungus/host plant model showed that Pythium was significantly more abundant when total bryophyte cover was greater (Table 5). In addition, there was a significant inverse relationship between Pythium abundance and cover of Sanionia uncinata, and this relationship was independent of the plot selection used to calculate bryophyte cover fence–snow regime mean values.
Discussion
This study verifies our hypothesis that enhanced snow regimes increase the pathogenetic fungal occurrences of E. hypogenum and Pythium polare in their respective host organisms. The higher occurrences of both parasites may be caused by the altered abiotic microclimate under a prolonged, deeper snow cover with higher cold season soil temperatures and elevated early growing season soil moisture. Both of these factors potentially influence fungal growth and infection rates of host organisms.
Enhanced snow cover increases cold season soil temperatures, thus increasing the activity of most heterotrophic organisms (e.g., Semenchuk et al 2016a; Semenchuk et al. 2019). At this time of the year when mycelial growth is most active, higher temperatures may, therefore, promote fungal growth. This phenomenon seemed to have occurred in our Deep treatment, where the soil remained warmer than −5 °C for the first half of the cold season, and never became colder than −10 °C. Both of these temperature thresholds are commonly exceeded in most parts of all cold seasons in the unmanipulated Ambient regime, which typically reached temperatures below −15 °C for prolonged periods of time.
Arctic soil microbes have high activity in cold conditions (Mikan et al. 2002), but are dependent on the availability of liquid water in winter (Mikan et al. 2002), as frozen soil water limits microbial activity (Öquist et al. 2009). Frozen soils have films of liquid water persisting around the soil particles, especially when temperatures remain above −10 °C. Fungal mycelia can possibly access this water film, and spores might also be able to travel within it. Thus, snow accumulation enables such activity during winter.
The effect of experimentally increased snow depth on the amount of melt water locally entering the soil system may interact with the temperature effects described above. Although most of the melt water runs off prior to soil thaw and cannot enter or waterlog it (own observations; water-logging only happens when thermokarst forms), soil moisture remains slightly elevated in both of our snow manipulation treatments during the first part of the growing season with possible effects on the pathogens’ life cycle stages in that period. In fact, moisture increase has been documented to increase spore germination and infection success in various plant pathogenic fungi species (Huang et al. 1998; Manandhar 1998; Biddulph et al. 1999; Green and Bailey 2000, as cited by Pehkonen et al. 2002).
Exobasidium hypogenum
The response of E. hypogenum to increased temperature and soil water content is unknown. However, Exobasidium splendidum Nannf., a related species found in boreal and subarctic ecosystems, has been shown to have higher prevalence in forest habitats after tree clearing, where increased soil moisture due to deeper snow was suggested as causation for this pattern (Pehkonen et al. 2002). Likewise, in our study, enhanced soil moisture during the first part of the growing season may have increased the likelihood of E. hypogenum spread, thereby infecting more C. tetragona individuals.
The co-occurrence of the host plant C. tetragona’s reduced occurrence and cover supports the idea that this pathogen may play a role in the dieback of this species during climate change. To grow and survive within its host, the biotrophic fungus E. hypogenum may suppress its host plant C. tetragona’s systemic acquired resistance (Mur et al. 2015), making it more vulnerable to other possible pathogens. At the same time, biotrophs also entail an energy burden, as they take nutrients from the plant (Barnett and Binder 1973). This can have further consequences, including shoot death (Wolfe and Rissler 2000) and reduced flower production (Hildebrand et al. 2000), leading to an overall reduction of the plant’s performance (Nannfeldt 1981) as studied on another Exobasidium species in tea cultivars (Muraleedharan and Chen 1997; Premkumar et al. 2008; Ponmurugan et al. 2016). These observations imply that infected plants, even though not necessarily killed by the pathogen, are in a weakened or stressed state and may have difficulties dealing with other possible disturbances caused by our experimental treatment, such as increased respiration rates during the cold season (Cooper 2004; Semenchuk et al. 2016b), shortened growing seasons (Semenchuk et al 2016a) and, perhaps, increased soil moisture.
The high proportion of dead C. tetragona in the Deep regime may negatively impact E. hypogenum, as future fungal distribution and infection may be impacted by the reduced cover of live C. tetragona remaining. Further field surveys and analysis would be required to assess this temporal aspect, considering a more detailed quantification of the parasite together with C. tetragona cover. Experimental work specifically on this host–parasite–snow regime relationship is needed to elucidate the role of E. hypogenum in the death of C. tetragona plants under conditions of enhanced snow.
Pythium spp.
Similar to E. hypogenum, we found a higher abundance of Pythium in response to increased snow cover. This fungus is classified as a cold-tolerant species (Hoshino et al. 1999; Hoshino et al. 2002; Tojo et al. 2012; Murakami et al. 2015) as its mycelial growth and infection of its host organism occur during the cold season taking advantage of the dormant stage of the moss (Jung et al. 2007; Hoshino et al. 2009). We managed to grow Pythium at −0.5 °C (own observations), an indicator for its capability to grow at sub-zero temperatures. A longer exposure to close to zero temperatures may, thus, have been beneficial, although a more thorough study of its growth pattern at sub-zero temperatures would help understand these results. Hoshino et al. (2009) demonstrated that the mycelial growth of several snow moulds including Pythium iwayamai Ito was stopped by exposure to −20 °C for 24 h. Our experimental snow enhancement raised the minimum soil temperatures experienced during the cold season, and soil in our Deep regime was maintained above −10 °C all year, which may have enabled more Pythium mycelia to survive and grow, and therefore, have a higher abundance in this regime.
Furthermore, Pythium increases zoospore production after the addition of melt water suggesting that this species utilizes melt water to spread its zoospores (Lipps 1980), thereby increasing the likelihood of infecting its host. The increased soil moisture in our enhanced snow regimes may thereby promote the spread of zoospores to new host individuals.
Bryophytes generally react positively to enhanced snow in our study site, either due to dieback of competing vascular plants such as C. tetragona (Cooper et al. 2019 and this study) or due to the slightly increased soil moisture or both, with Sanionia uncinata even reacting disproportionally strongly in Meadows. The inverse relationship between Pythium abundance and cover of Sanionia uncinata should be interpreted with caution as a causal relationship can only be confirmed by further experimental studies, including inoculation of Pythium into different moss species and subsequent measurements of the host’s survival. However, this relationship suggests that increased Pythium weakens and kills more of its host, leading to a decline in Sanionia uncinata cover.
Conclusions
This study showed that enhanced snow considerably reduced the severity of winter soil surface temperatures and increased the infection of C. tetragona by E. hypogenum and Sanionia uncinata by Pythium polare and other Pythium species. These fungal interactions contribute to changes in vegetation composition, and so have consequences at the community level.
We recommend that sub-zero temperature responses of Pythium should be investigated, and fungal inoculation experiments carried out to better understand the dynamics of these relationships. Furthermore, the temporal aspect of fungal infection is not well understood and should be the focus of future studies.
Author contributions
The project was initiated and designed by E.J.C. Field work was carried out by all authors, M.T and T.Y. carried out the mycelial growth lab study, P.R.S summarised soil temperature data and M.M.A. carried out statistical analyses. M.M.A. and E.J.C. wrote the manuscript with contributions from the other authors.
Acknowledgements
Funding for this project came from the Norwegian Research Council (“SnoEco” project, number 230970), FRAM Centre Terrestrial Framework (project: “Summer’s End”), and the Norwegian Centre for International Cooperation in Education (SIU) High North Programme (“JANATEX” project, number HNP2013/10092), all to E.J.C., and from the Japan Society for the Promotion of Science grant-in-aid for scientific research (project number 19K12421) to M.T. We thank The University Centre in Svalbard (UNIS) for providing safety training for field work, and our field assistants Fumino Maruo, Yuko Kusama, Anna Katharina Pilsbacher, Kathrin Bender, Katariina Vuorinen, Karolina Paquin, and Masashi Kemmotsu.
Appendix A
Site Description: Soil, vegetation, and herbivory
Calcareous sand, silt, and shale originating during the Mesozoic era compose the main parent material (Tolgensbakk et al. 2000). Soils are poorly developed and moderately acidic (5–6.5), with a thin top part (usually 2 cm) consisting of lightly decomposed organic material and live plant roots (O horizon). Below that is a dark-brown A horizon of a similar thickness (2 cm, 1–5 cm), and a B/C horizon of grey silt (Strebel et al. 2010). The concentration of soil organic carbon and total nitrogen are highest at the top and decrease through the soil profile.
The most common species are the deciduous shrub Salix polaris Wahlenb. and evergreen shrubs Dryas octopetala and Cassiope tetragona, the graminoids Luzula confusa Lindeb., Alopecurus borealis Trin., andCarex rupestris All., and the herbs Bistorta vivipara L., Pedicularis hirsuta L., and Stellaria longipes Goldie. Common mosses in the area are Aulacomnium spp., Distichium spp.,Dicranum spp., Hylocomium splendens,Polytrichum spp., Racomitrium spp., Sanionia uncinata,Tomentypnum nitens, and liverworts includingAnthelia juratzkana and Ptilidium ciliare were also present. Stereocaulon and Rhizocarpon geographicum L. are the most common lichens, together with Thamnolia vermicularis Sw. andCetrariella delisei Bory ex Schaer. Lichen abundance is low in this valley due to continual grazing pressure from Svalbard reindeer (Rangifer tarandus subsp. platyrhynchus (Vrolik, 1829)). All treatments were equally available for herbivory.[Omitted: See PDF]
Extra background concerning microbes especially fungi
Arctic soil microbes have high activity in cold conditions (Mikan et al. 2002), with bacteria more susceptible to freeze–thaw cycles than fungi (Skogland et al. 1998; Sharma et al. 2006). Soil microorganisms are involved in decomposition of organic material and most of the microbial growth in our system occurs during the snow-covered months (Jonasson et al. 1999; Schimel and Bennett 2004; Brooks et al. 2011). Microbial processes such as soil respiration have been shown to happen faster in higher sub-zero temperatures (Morgner et al. 2010; Semenchuk et al. 2016). Deeper snow also increases the fungal richness of ectomycorrhizae and reduces that of saprotrophs (Mundra et al. 2016).
Smut fungi are parasites characterized by highly specialized plant–parasite interactions (Begerow et al. 1997). In the order Exobasidiales, these close interactions have evolved into species that can survive well within the host, without the need for resting spores (Begerow et al. 2002). As a result, these fungi are extremely species-specific, and their phylogeny can reflect the diversification of their hosts (Jackson 2004; Begerow and Kemler 2018). Although not much is known ecologically about E. hypogenum, the genusExobasidium generally parasitizes plants the Ericaceae family including cultivated blueberry and cranberry crops (Brewer et al. 2014; Stewart et al. 2015). Exobasidium infections can manifest in the stem, flower, shoot, and buds of their hosts, although exact deformities may vary per fungal species and host plant (Nannfeldt 1981; Brewer et al. 2014; Stewart et al. 2015).
Pythium is a genus of oomycetes within the commonly known snow moulds: psychrophilic or psychrotrophic fungal pathogens that actively attack dormant plants under the snow cover (Hsiang et al. 1999; Hoshino et al. 2009). The systematics of this genus is in a transitional period, as a phylogenetic study in 2010 suggested its division into five different genera (Uzuhashi et al. 2010) and Pythium polare may be renamed as Globisporangium polare. However, the term Pythium for the genus is still currently more accepted and is used throughout this paper. Pythium has a world-wide distribution (Lévesque and De Cock 2004; Kirk et al. 2008), and some of the species have been found to infect and weaken mosses in both the Arctic and Antarctic (Hoshino et al. 1999; Hoshino et al. 2002; Bridge et al. 2008).[Omitted: See PDF][Omitted: See PDF][Omitted: See PDF][Omitted: See PDF][Omitted: See PDF][Omitted: See PDF]
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Mikel Moriana-Armendariz
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT- The Arctic University of Norway, N-9037 Tromsø, Norway.
Holly Abbandonato
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT- The Arctic University of Norway, N-9037 Tromsø, Norway.
Department of Geography and Environment, Mount Allison University, Sackville, NB, E4L 1A7, Canada.
Takahiro Yamaguchi
Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Sakai, Japan.
Nara Plant Protection Center, Sakurai, Nara, Japan.
Martin A. Mörsdorf
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT- The Arctic University of Norway, N-9037 Tromsø, Norway.
Faculty of Biology – Geobotany, University of Freiburg, Schaenzlestr. 1, D-79104 Freiburg, Germany.
Karoline H. Aares
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT- The Arctic University of Norway, N-9037 Tromsø, Norway.
Philipp R. Semenchuk
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT- The Arctic University of Norway, N-9037 Tromsø, Norway.
Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030 Vienna, Austria.
Motoaki Tojo
Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Sakai, Japan.
Elisabeth J. Cooper [email protected]
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT- The Arctic University of Norway, N-9037 Tromsø, Norway.
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Abstract
In the Arctic, fungal mycelial growth takes place mainly during the cold season and beginning of growing season. Climate change induced increases of cold season temperatures may, hence, benefit fungal growth and increase their abundance. This is of particular importance for parasitic fungi, which may significantly shape Arctic vegetation composition. Here, we studied two contrasting plant parasitic fungi’s occurrences (biotrophic Exobasidium hypogenum Nannf. on the vascular plant Cassiope tetragona (L.) D. Don., and necrotrophic Pythium polare Tojo, van West & Hoshino on the moss Sanionia uncinata (Hedw.) Loeske) in response to increased snow depth, a method primarily used to increase cold season temperatures, after 7–13 years of snow manipulation in Adventdalen, Svalbard. We show that enhanced snow depth increased occurrences of both fungi tested here and indicate that increased fungal infections of host plants were at least partly responsible for decreases of host occurrences. Although bryophyte growth, in general, may be influenced by increased soil moisture and reduced competition from vascular plants, Pythium polare is likely enhanced by the combination of milder winter temperatures and moister environment provided by the snow. The relationships between host plants and fungal infection indicate ongoing processes involved in the dynamics of compositional adjustment to changing climate.