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Article

Climate Variables Outstrip Deadwood Amount: Desiccation as the Main Trigger for Buxbaumia viridis Occurrence

by
Michaela Kropik
1,
Harald G. Zechmeister
2,* and
Dietmar Moser
2
1
Institute of Botany, University of Natural Resources and Life Sciences, 1180 Vienna, Austria
2
Department of Botany and Biodiversity Research, University of Vienna, 1030 Vienna, Austria
*
Author to whom correspondence should be addressed.
Plants 2021, 10(1), 61; https://doi.org/10.3390/plants10010061
Submission received: 12 December 2020 / Revised: 25 December 2020 / Accepted: 27 December 2020 / Published: 30 December 2020
(This article belongs to the Special Issue New Knowledge in Bryology)
Browse Figure
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Abstract

:
Deadwood is a biodiversity hotspot and habitat for numerous highly endangered species. Buxbaumia viridis has been assessed as a flagship species for deadwood-rich forests and is subject to monitoring under the Habitats Directive, yet we lack a solid understanding of the factors controlling its distribution. The study aimed to specify the climate and habitat preferences of Buxbaumia viridis and identify the best predictor variables. We collected presence-absence data of the species at 201 sites between 2016 and 2020. Study sites cover three biogeographic regions (Pannonian, Continental, and Alpine). They also represent a deadwood gradient ranging from managed forests to natural forest reserves and virgin forests. Our results suggest that desiccation and deadwood amount are the best predictor variables. The amount of deadwood at the colonized sites ranged from 1 m3/ha to 288 m3/ha, with a median of 70 m3/ha. The maximum desiccation, i.e., consecutive days without rain and at least 20 °C was 9.6 days at colonized sites. The results of logistic regression models suggest that desiccation limits Buxbaumia viridis occurrence on deadwood in the drier continental parts of eastern Austria. Derived details on climate and habitat requirements of Buxbaumia viridis can specify management and conservation. They clearly show how strongly the species is dependent on climate, which can counteract deadwood measures.

1. Introduction

The epixylic forest bryophyte Buxbaumia viridis (Moug. ex Lam. & DC.) Brid. ex Moug. & Nestl. is a flagship species for forests rich in deadwood. It is also subject to the monitoring obligation of the Habitats Directive. While its life cycle [1,2], substrate preferences [3,4,5,6], seasonal variations in occurrence [3,6,7,8], and distribution [9] are well documented, we lack a solid understanding of the factors that control its distribution.
In general, bryophytes react sensitively to climatic parameters [10,11,12,13]. They can even indicate past climate changes [14]. Despite their tolerance to desiccation [15,16,17], the availability of water during their life cycle is of fundamental importance: in the dispersal phase, desiccation restricts the viability of the propagation units; during establishment, it affects the carbon balance of the young plant and limits the period during which establishment is possible [17]. Sensitivity to desiccation varies widely between species [12,16,18], as does the recovery rate [17]. Most bryophytes survive short to moderate periods of desiccation, but many species adapted to a humid, shady habitat are the least tolerant and recover only slowly from long, dry periods [10]. Forest bryophytes can, therefore, be damaged by even moderate desiccation [17].
Furthermore, for the occurrence of Buxbaumia viridis, a constant high humidity, which prevents dehydration, was assumed to be one of the most important triggers [3], but no concrete figures are available. Precipitation and temperature in spring influenced the abundance of sporophytes in a local study in Sweden [19], and desiccation in summer reduced spore production [6]. Moreover, the amount of deadwood seems to be decisive for occurrence according to a local study in Italy [4]. However, numerous records in managed forests indicate a wider tolerance for the amount of deadwood [20]. To date, there is no study on a larger geographical scale that considers both climate influence and deadwood quantity, and it remains unclear whether climate or the amount of deadwood is the main trigger for the occurrence of the species. A better understanding is crucial for management decisions, as Buxbaumia viridis is an Annex II species of the Habitats Directive and an important flagship species for deadwood-rich forest ecosystems, which are a hotspot of diversity for a wide range of taxonomic groups [21,22].
In consequence, this study aims to specify the Buxbaumia viridis preferences in terms of climate and habitat, and to define the main predictors for the observed distribution. The study is based on a large dataset of field observations across the pronounced climatic gradient of Austria considering three biogeographic regions (Pannonian, Continental, and Alpine). It includes data both on climate and deadwood on a large geographical scale for the first time. In addition to standard climate variables, it takes into account several climate variables that have been adapted to the physiological needs of bryophytes.

2. Results

In total, we found 751 sporophytes of Buxbaumia viridis at 278 logs at 115 sites. Altitudes of populated sites ranged between 350–1650 m above sea level (a.s.l.) (median 1000 m). The most common host tree species was Picea abies (L.) H.Karst. (184 occurrences) followed by Fagus sylvatica L. (42), Abies alba Mill. (36), Larix decidua Mill. (8), and Pinus nigra J.F.Arnold (8). Buxbaumia viridis was found at all decay stages [23] between 1 and 5, with the highest frequency in decay stages 3 and 4. We found the species across all forest types, in virgin forests, natural forest reserves, and managed forests. Populated versus not populated sites differed significantly regarding altitude, climate variables, and deadwood amount (Figure 1); only for length and diameter, the difference was not significant.
The maximum number of consecutive days without precipitation and at least 20 °C (p0cont_gt_20) and the amount of deadwood (deadwood) at study site were identified as most important predictors for the occurrence of Buxbaumia viridis by a logistic regression model (Table 2). For the sake of simplicity, we refer to these two predictors as desiccation and deadwood in the following.
The effect of desiccation on the occurrence of Buxbaumia viridis was significantly negative, while deadwood had a significant positive effect. Regarding the predictive power, desiccation (variable importance 5.949) outstripped deadwood (2.053). Concrete values for desiccation at populated sites varied between 3.6–9.6 days (median 5.5 days), so there were no more than 10 consecutive warm days without rain at these sites. Values for deadwood were between 1–288 m3/ha (median 70 m3/ha). Multicollinearity was excluded due to variance inflation factors around one and thus far below the generally assumed thresholds. Based on the final model (Table 2), occurrences of Buxbaumia viridis were predicted with a sensitivity (i.e., the proportion of correctly predicted occurrences) of 84% and a specificity of 86% (i.e., the proportion of correctly predicted absences) for the test dataset, resulting in a true skill statistic value of 0.72, which can be interpreted as good agreement between training and test data.

3. Discussion

3.1. Habitat Preferences

Concerning habitat preferences, our data were consistent with previous studies. The altitudes of the populated sites were between 350–1650 m a.s.l. (median 1000 m), similar to other European countries [20]. Picea abies in decay stages 3 and 4 was the most common substrate, followed by Fagus sylvatica and Abies alba. However, we also found the species on other host trees and decay stages, which is consistent with previous studies [1,3,4,6].
In our data, there was no correlation between the volume of logs and the occurrence of Buxbaumia viridis as found for other epixylic bryophytes [24]. This is also consistent with our field observations. Under a suitable climate, the species occurred even on small broken off branches. The five most common species associated with Buxbaumia viridis were Herzogiella seligeri (Brid.) Z.Iwats., Blepharostoma trichophyllum (L.) Dumort. var. trichophyllum, Dicranum scoparium Hedw., Nowellia curvifolia (Dicks.) Mitt., and Hypnum cupressiforme Hedw. var. cupressiforme. Barely any liverworts occurred in the close vicinity of Buxbaumia viridis. The reason might be that pleurocarpous mosses provide a shadier and wetter shelter for germination and protonemal growth. Tiny liverworts on the other hand often grow in dense mats and might outcompete Buxbaumia viridis [3] which has slow protonemal growth [25].

3.2. Predictors of the Occurrence of Buxbaumia viridis

Results of the model selection suggest desiccation and deadwood amount as best predictors for Buxbaumia viridis occurrence. The higher variable importance of desiccation indicates that climate determines the occurrence stronger than the amount of deadwood. A local study from the Italian Alps [4] derived a deadwood threshold of 48–61 m3/ha for Buxbaumia viridis. Even though we observed a higher number of occurrences at sites rich in deadwood, numerous occurrences were in managed forests with low amounts of deadwood. Other European studies reported numerous populations in managed forests, too [3,20]. Population dynamics for Buxbaumia viridis seem to be strongly determined by climate, as suggested by our models and as found in a local study in Sweden [7].
The dominant role of desiccation in our data is congruent with studies on the physiology of bryophytes, which suggest that changes in moisture-related climate parameters are the main drivers for mosses [10]. In particular, desiccation plays a dominant role both during the dispersal phase, as it limits the viability of the propagation units, and during establishment, as it controls the carbon balance of the young plant and limits the time during which establishment is possible [17]. Canopy species are better adapted to desiccation than understory bryophytes [26]. In general, mosses of shady, humid forests tolerate only limited desiccation [17]. Dilks and Proctor [27] reported a desiccation tolerance for selected forest mosses of four days (Rhythidiadelphus loreus (Hedw.) Warnst.) up to nine (Plagiothecium undulatum (L. ex Hedw.) Schimp.) or ten (Hylocomium splendens (Hedw.) Schimp.) days. In our data, the maximum dry days in one piece at populated sites was 9.6, which is congruent with these experimental results.
Sporophytes generally have a lower temperature optimum than gametophytes and are more susceptible to temperature stress and desiccation [28]. In contrast to other bryophytes, which generally have drought-tolerant spores [16], the spores of Buxbaumia viridis are comparatively sensitive and died after only 48 h of dry storage [29]. Our data suggest that longer desiccation events in the drier east of Austria during spring and summer limit the distribution of Buxbaumia viridis on deadwood to the Alpine and the north-western part of the Continental Biogeographic Region within Austria.
Occurrences of Buxbaumia viridis on the forest ground, like Deme et al. [30] have reported for Hungary, might be dedicated to a drier climate. The forest ground is assumed to buffer desiccation more effectively than a log, which is why bryophytes on the ground might be less subject to climatic variability than on deadwood or stumps [31]. For Austria, records of Buxbaumia viridis from the ground are rather the exceptions [32,33].
In the final model, temperature was only included in combination with precipitation, in the form of the desiccation variable. Physiological studies on bryophytes have found a greater tolerance to temperature changes than to changes in moisture-related parameters [10]. However, changes in temperature and precipitation can influence the sensitive competitive balance between vascular plants and bryophytes [34]. Moreover, higher respiration losses have been demonstrated for bryophytes to be due to increasing temperatures [35]. Previous studies also observed a decrease in the species richness of bryophytes as a result of experimental warming in winter and at the same time strongly varying reactions of the individual species [18]. This underlines the need for detailed studies on single species if the impacts of climate change are to be assessed.

3.3. Implications for Conservation

Although the current status of Buxbaumia viridis has been assessed as stable for Europe [20], the species is vulnerable. Our data suggest its occurrence is closely linked to deadwood under a suitable climate, whereby desiccation and deadwood were the key predictors. The precipitation regime could be altered by climate change [36], as could the tree-communities [37] and thus the quality of available deadwood. Although Buxbaumia viridis grew on different host tree species, it showed a preference for spruce, a tree species that will change its area across Europe fundamentally [38].
Furthermore, the quantity of deadwood is decisive. A higher amount of deadwood increases the probability for larger population sizes and in consequence the risk of getting extinct. Removal of deadwood in managed forests, therefore, is a threat for Buxbaumia viridis, as it reduces the probability of survivingon a patchy and short-lived substrate. The same is for numerous other rare epixylic bryophytes [20]. Awareness of the importance of deadwood as a diversity hotspot and refuge for rare and threatened species has increased [21,22], but the isolation of deadwood-rich forests at the landscape scale should also be considered as a severe threat for Buxbaumia viridis which was assumed to be dispersal-limited [6,8,39] like numerous other rare epixylic bryophytes [40].
Our results underline the importance of studies on both individual species and various taxonomic groups. While numerous studies have focused on the responses of vascular plants to a changing climate, e.g., [41,42], only a few have dealt with bryophytes [43] despite their importance for biogeochemistry [44]. Since bryophytes and vascular plants differ fundamentally in physiology and scale [45], results derived from vascular plants can neither be generalized to bryophytes nor can the reactions of individual bryophyte species be generalized to all bryophytes. Which parameters trigger other epixylic bryophyte species and communities needs further investigation.

4. Materials and Methods

4.1. Study Area and Field Methods

The sampling design reflects Austria in terms of climate and forest management intensity. The Austrian Federal Forestry Company (ÖBf) and the Austrian Federal Forest Research Centre (BFW) provided an extensive data set on forest habitats. Out of this, we selected 201 study sites. They spanned the maximum possible gradient in terms of mean annual precipitation sum (554.6–2117.5 mm/a) across the country. Sites depict all three biogeographic regions in which Austria has a share (Alpine, Continental, and Pannonian). They further include virgin forests, natural forest reserves (at least 20 years unmanaged), and managed forests, with markedly different deadwood amounts (1–288 m3/ha).
We obtained presence-absence data for Buxbaumia viridis between 2016 and 2020. We followed a standardized method. At each site, we randomly selectedten logs by a transect method. We chose the first log subjectively in a characteristic area of a site. Every next log was the nearest one in one of the four directions. We documented the absence or presence of Buxbaumia virids for each log, including the number of sporophytes per log. We also recorded associated bryophytes species, host tree species, length, and width of logs and decay stage in five categories [23].
Buxbaumia viridis is a small, ephemeral species, easy to be overlooked. We standardized detection probability by (1) conducting fieldwork in the optimal period for detecting sporophytes known from previous studies [46,47,48], (2) limiting all the fieldwork to the same persons, well trained over several years on the search for Buxbaumia viridis, and (3) a standardized method at all study sites, which provided not only presence but also real absence data, and therefore a comparable and representative subset of logs at each site. We used the historic geographical range [49] to assess if we covered the entire potential habitat with our observations.

4.2. Climate Data

We used a highly resolved grid dataset of daily minimum and maximum air temperature [50] and precipitation sum [51] covering Austria at a spatial resolution of 1 km2 and a temporal resolution of one day in the period 2008–2018. From this data set, we extracted climate variables (Table 1) for study sites using the programming language Python. Aside from the standard parameters mean annual temperature (temp_mean), temperature sum of days above 15 °C mean temperature (tempsum_gt_15), and the annual precipitation sum (prec sum), we adapted climate variables to the physiology of bryophytes. We calculated the number of days above 10 °C mean temperature (days_over_tmean_10), a temperature at which many forest bryophytes are on a physiological optimum [43,52,53]. We corrected the annual precipitation sum by excluding the snow (precgt0C), as the growth of bryophytes is directly related to water availability [17]. We developed climate variables approaching desiccation, which has been assumed relevant for the occurrence of Buxbaumia viridis in previous studies and due to our observations: the maximum number of consecutive days without precipitation (p0cont) and the maximum number of consecutive days without precipitation and at least 20 °C (p0cont_gt_20).

4.3. Deadwood Data

The Austrian Federal Forests Company and the Austrian Research Centre for Forests provided data on the amount of lying deadwood at study sites. For study sites where no deadwood data were available, we conducted them following the same method [54].

4.4. Statistics

We compared differences between populated and not populated sites regarding the predictors using Wilcoxon rank-sum tests. For explanatory analysis of predictors, we fitted a logistic regression model with a logistic link function to a reduced dataset of 179 entries due to missing deadwood data for 22 sites. Before analysis, we scaled all variables (Table 1) to zero mean and unit variance. To avoid highly correlated variables, we selected the best variable out of each group (Table 1) based on the Akaike information criterion (AIC). We, therefore, added each variable separately to a model and chose the variable delivering the model with the lowest AIC. We tested the resulting subset of variables by the vif-step function of the package usdm [55] and a threshold of vif 5. With the final set of variables, we ran an automated model selection based on the AIC with glmulti [56] and default settings. We calculated variance inflation factors to test the final model for multicollinearity using the vif-function [57] and assessed the predictive power of variables by the varImp-function [58]. We evaluated the final model using the true skill statistics approach (TSS) [59] by repeatedly (10 times) dividing the data set in two parts: one to calibrate the model (80%) and one to evaluate it (20%); we compared predicted occurrences of Buxbaumia viridis with observed occurrences using a confusion matrix [58]. We calculated the TSS value as the sum of specificity (i.e., the proportion of correctly predicted absences) and sensitivity (i.e., the proportion of correctly predicted occurrences) minus one. Statistical analyses and figures were conducted in R 3.6.2 [60]. Results are reported against a significance level of 0.05.

5. Conclusions

The main findings are as follows. (1) Data suggest the occurrence of Buxbaumia viridis primarily determined by climate. Desiccation, i.e., consecutive days without rain and at least 20 °C had a significant negative effect and limited the distribution of the species. The maximum duration of desiccation at populated sites was 9.6 days. (2) Data suggest that the amount and quality of deadwood were of minor relevance compared to the climate. Deadwood amount at populated sites ranged from 1 m3/ha to 288 m3/ha. (3) Climate variables that we had adapted to the physiological needs of bryophytes performed better than standard climate parameters for Buxbaumia viridis and provided consistent results like physiological experiments on forest bryophytes.

Author Contributions

Conceptualization, M.K. and H.G.Z.; methodology, M.K. and H.G.Z.; formal analysis, M.K. and D.M.; investigation, M.K. and H.G.Z.; climate data acquisition: D.M.; writing—original draft preparation, M.K.; writing—review and editing, M.K., H.G.Z., and D.M.; All authors have read and agreed to the published version of the manuscript.

Funding

The contribution of M.K. is part of her doctoral thesis sponsored by the Österreichische Bundesforste AG (ÖBf), open access funding by the University of Vienna.

Data Availability Statement

The Data are not publicly available as they are an essential part of further publications.

Acknowledgments

Many thanks for financial support to the Österreichische Bundesforste AG; for logistic support to the Bundesforschungszentrum für Wald, to K.G. Bernhardt (Institute of Botany, University of Natural Resources and Life Sciences, Vienna), and to M. Kanzian (Österreichische Bundesforste AG). Thank you also for providing deadwood data to R. Zeiner (Österreichische Bundesforste AG) and H. Steiner (Bundesforschungszentrum für Wald). The authors highly appreciate the constructive feedback of three anonymous reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wolf, T. Untersuchungen zu den Entwicklungsstadien von Buxbaumia viridis (Lam. & DC.) Moug. & Nestl. (Grünes Koboldmoos). Carolinea 2015, 73, 5–15. [Google Scholar]
  2. Callaghan, D.; Taylor, S. Classification of Sporophyte Stages in Buxbaumia viridis. Field Bryol. 2017, 117, 5–7. [Google Scholar]
  3. Holá, E.; Vrba, J.; Linhartová, R.; Novozámská, E.; Zmrhalová, M.; Plášek, V.; Kučera, J. Thirteen Years on the Hunt for Buxbaumia viridis in the Czech Republic: Still on the Tip of the Iceberg? Acta Soc. Bot. Pol. 2014, 83, 137–145. [Google Scholar] [CrossRef] [Green Version]
  4. Spitale, D.; Mair, P. Predicting the Distribution of a Rare Species of Moss: The Case of Buxbaumia viridis (Bryopsida, Buxbaumiaceae). Plant Biosyst. 2017, 151, 9–19. [Google Scholar] [CrossRef]
  5. Wiklund, K. Phosphorus Concentration and ph in Decaying Wood Affect Establishment of the Red-Listed Moss Buxbaumia viridis. Can. J. Bot. 2003, 81, 541–549. [Google Scholar] [CrossRef]
  6. Wiklund, K. Substratum Preference, Spore Output and Temporal Variation in Sporophyte Production of the Epixylic Moss Buxbaumia viridis. J. Bryol. 2002, 24, 187–195. [Google Scholar] [CrossRef]
  7. Ruete, A.; Wiklund, K.; Snäll, T. Hierarchical Bayesian Estimation of the Population Viability of an Epixylic Moss. J. Ecol. 2012, 100, 499–507. [Google Scholar] [CrossRef]
  8. Kropik, M.; Zechmeister, H.G.; Fuxjäger, C. The fate of bryophyte sporophytes—Phenology and vectors of Buxbaumia viridis in the Kalkalpen National Park, Austria. Plants 2020, 9, 1320. [Google Scholar] [CrossRef]
  9. European Environment Agency. Conservation Status and Trends of Habitats and Species. Available online: https://www.eea.europa.eu/themes/biodiversity/state-of-nature-in-the-eu/article-17-national-summary-dashboards/conservation-status-and-trends (accessed on 8 May 2020).
  10. Gignac, L.D. Invited essay—New frontiers in bryology and lichenology—Bryophytes as indicators of climate change. Bryologist 2001, 104, 410–420. [Google Scholar] [CrossRef]
  11. Pócs, T. Signs of Climate Change in the Bryoflora of Hungary. In Bryophyte Ecology and Climate Change; Tuba, Z., Slack, N.G., Stark, L.R., Eds.; Cambridge University Press: Cambridge, UK, 2011; pp. 359–370. [Google Scholar]
  12. Slack, N.G. The Ecological Value of Bryophytes as Indicators of Climate Change. In Bryophyte Ecology and Climate Change; Tuba, Z., Slack, N.G., Stark, L.R., Eds.; Cambridge University Press: Cambridge, UK, 2011; pp. 3–12. [Google Scholar]
  13. Scarpitta, A.B.; Bardat, J.; Lalanne, A.; Vellend, M. Long-term community change: Bryophytes are more responsive than vascular plants to nitrogen deposition and warming. J. Veg. Sci. 2017, 28, 1220–1229. [Google Scholar] [CrossRef]
  14. Zechmeister, H.G.; Kropik, M.; Popovtschak, M.; Scharrer-Liska, G. Bryophytes in a latrine as indicators of climate change in the 17th century. Veg. Hist. Archaeobot. 2019, 28, 575–581. [Google Scholar] [CrossRef] [Green Version]
  15. Proctor, M.C.F. Climatic Responses and Limits of Bryophytes: Comparisons and Contrasts with Vascular Plants. In Bryophyte Ecology and Climate Change; Tuba, Z., Slack, N.G., Stark, L.R., Eds.; Cambridge University Press: Cambridge, UK, 2011; pp. 35–54. [Google Scholar]
  16. Proctor, M.C.F.; Tuba, Z. Poikilohydry and homoihydry: Antithesis or spectrum of possibilities? New Phytol. 2002, 156, 327–349. [Google Scholar] [CrossRef] [Green Version]
  17. Proctor, M.C.F.; Oliver, M.J.; Wood, A.J.; Alpert, P.; Stark, L.R.; Cleavitt, N.L.; Mishler, B.D. Desiccation-tolerance in bryophytes: A review. Bryologist 2007, 110, 595–621. [Google Scholar] [CrossRef]
  18. Bates, J.W.; Thompson, K.; Grime, J.P. Effects of simulated long-term climatic change on the bryophytes of a limestone grassland community. Glob. Chang. Biol. 2005, 11, 757–769. [Google Scholar] [CrossRef]
  19. Ruete, A.; Yang, W.; Barring, L.; Stenseth, N.C.; Snäll, T. Disentangling Effects of Uncertainties on Population Projections: Climate Change Impact on an Epixylic Bryophyte. R. Soc. Lond. B Biol. Sci. 2012, 279, 3098–3105. [Google Scholar] [CrossRef] [PubMed]
  20. Hodgetts, N.; Blockeel, T.; Konstantinova, N.; Lönnell, N.; Papp, B.; Schnyder, N. Buxbaumia viridis; The IUCN Red List of Threatened Species 2019: E.T84329324A87793246; Available online: https://www.iucnredlist.org/species/84329324/87793246 (accessed on 5 May 2020).
  21. Lassauce, A.; Paillet, Y.; Jactel, H.; Bouget, C. Deadwood as a surrogate for forest biodiversity: Meta-analysis of correlations between deadwood volume and species richness of saproxylic organisms. Ecol. Indic. 2011, 11, 1027–1039. [Google Scholar] [CrossRef]
  22. Paillet, Y.; Berges, L.; Hjalten, J.; Odor, P.; Avon, C.; Bernhardt-Romermann, M.; Bijlsma, R.J.; De Bruyn, L.; Fuhr, M.; Grandin, U.; et al. Biodiversity Differences between Managed and Unmanaged Forests: Meta-Analysis of Species Richness in Europe. Conserv. Biol. 2010, 24, 101–112. [Google Scholar] [CrossRef]
  23. Düggelin, C.; Kellner, M. Schweizerisches Forstinventar. Feldaufnahme-Anleitung 2017. Available online: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:16234 (accessed on 13 May 2020).
  24. Odor, P.; Van Hees, A.F.M. Preferences of Dead Wood Inhabiting Bryophytes for Decay Stage, Log Size and Habitat Types in Hungarian Beech Forests. J. Bryol. 2004, 26, 79–95. [Google Scholar] [CrossRef]
  25. Duckett, J.G.; Burch, J.; Fletcher, P.W.; Matcham, H.W.; Read, D.J.; Russell, A.J.; Pressel, S. In vitro cultivation of bryophytes: A review of practicalities, problems, progress and promise. J. Bryol. 2004, 26, 3–20. [Google Scholar] [CrossRef]
  26. Pardow, A.; Lakatos, M. Desiccation Tolerance and Global Change: Implications for Tropical Bryophytes in Lowland Forests. Biotropica 2013, 45, 27–36. [Google Scholar] [CrossRef]
  27. Dilks, T.J.K.; Proctor, M.C.F. Pattern of Recovery of Bryophytes after Desiccation. J. Bryol. 1974, 8, 97–115. [Google Scholar] [CrossRef]
  28. Tuba, Z. Bryophyte Physiological Processes in a Changing Climate: An Overview. In Bryophyte Ecology and Climate Change; Tuba, Z., Slack, N.G., Stark, L.R., Eds.; Cambridge University Press: Cambridge, UK, 2011; pp. 13–32. [Google Scholar]
  29. Wiklund, K.; Rydin, H. Ecophysiological Constraints on Spore Establishment in Bryophytes. Funct. Ecol. 2004, 18, 907–913. [Google Scholar] [CrossRef]
  30. Deme, J.; Erzberger, P.; Kovacs, D.; Toth, I.Z.; Csiky, J. Buxbaumia viridis (Moug. ex Lam. & DC.) Brid. ex Moug. & Nestl. in Hungary predominantly terricolous and found in managed forests. Cryptogam. Bryol. 2020, 41, 89–103. [Google Scholar] [CrossRef]
  31. Spitale, D. The interaction between elevational gradient and substratum reveals how bryophytes respond to the climate. J. Veg. Sci. 2016, 27, 844–853. [Google Scholar] [CrossRef]
  32. Grims, F.; Köckinger, H.; Krisai, R.; Schriebl, A.; Suanjak, M.; Zechmeister, H.; Ehrendorfer, F. Die Laubmoose Österreichs. Catalogus Florae Austriae, II. Teil, Bryophyten (Moose), Heft 1, Musci (Laubmoose); Österreichische Akademie der Wissenschaften: Wien, Austria, 1999. [Google Scholar]
  33. Juratzka, J. Die Laubmoosflora von Oesterreich-Ungarn; Zoologisch-Botanische Gesellschaft: Wien, Austria, 1882. [Google Scholar]
  34. Turetsky, M.R.; Bond-Lamberty, B.; Euskirchen, E.; Talbot, J.; Frolking, S.; McGuire, A.D.; Tuittila, E.S. The resilience and functional role of moss in boreal and arctic ecosystems. New Phytol. 2012, 196, 49–67. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Nakatsubo, T. Predicting the impact of climatic warming on the carbon balance of the moss Sanionia uncinata on a maritime Antarctic island. J. Plant Res. 2002, 115, 99–106. [Google Scholar] [CrossRef]
  36. IPCC. Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R.K., Meyer, L.A., Eds.; IPCC: Geneva, Switzerland, 2014; p. 151. [Google Scholar]
  37. Lexer, M.J.; Honninger, K.; Scheifinger, H.; Matulla, C.; Groll, N.; Kromp-Kolb, H.; Schadauer, K.; Starlinger, F.; Englisch, M. The sensitivity of Austrian forests to scenarios of climatic change: A large-scale risk assessment based on a modified gap model and forest inventory data. For. Ecol. Manag. 2002, 162, 53–72. [Google Scholar] [CrossRef]
  38. Lindner, M.; Maroschek, M.; Netherer, S.; Kremer, A.; Barbati, A.; Garcia-Gonzalo, J.; Seidl, R.; Delzon, S.; Corona, P.; Kolstrom, M.; et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For. Ecol. Manag. 2010, 259, 698–709. [Google Scholar] [CrossRef]
  39. Zechmeister, H.G.; Kropik, M.; Schröck, C. Erfassung der Moose im Anhang II Der FFH-Richtlinie im Nationalpark Kalkalpen. 2017. Available online: https://www.kalkalpen.at/de/Erfassung_der_FFH_Moose_im_Nationalpark_Kalkalpen (accessed on 4 September 2020).
  40. Köckinger, H. Die Horn- und Lebermoose Österreichs (Anthocerotophyta und Marchantiophyta). In Catalogus Florae Austriae, II, Teil; Österreichische Akademie der Wissenschaften: Wien, Austria, 2017. [Google Scholar]
  41. Pauli, H.; Gottfried, M.; Dullinger, S.; Abdaladze, O.; Akhalkatsi, M.; Alonso, J.L.B.; Coldea, G.; Dick, J.; Erschbamer, B.; Calzado, R.F.; et al. Recent Plant Diversity Changes on Europe’s Mountain Summits. Science 2012, 336, 353–355. [Google Scholar] [CrossRef] [Green Version]
  42. Steinbauer, M.J.; Grytnes, J.A.; Jurasinski, G.; Kulonen, A.; Lenoir, J.; Pauli, H.; Rixen, C.; Winkler, M.; Bardy-Durchhalter, M.; Barni, E.; et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 2018, 556, 231–234. [Google Scholar] [CrossRef]
  43. He, X.L.; He, K.S.; Hyvonen, J. Will bryophytes survive in a warming world? Perspect. Plant Ecol. Evol. Syst. 2016, 19, 49–60. [Google Scholar] [CrossRef]
  44. Cornelissen, J.H.C.; Lang, S.I.; Soudzilovskaia, N.A.; During, H.J. Comparative cryptogam ecology: A review of bryophyte and lichen traits that drive biogeochemistry. Ann. Bot. 2007, 99, 987–1001. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Proctor, M.C.F. Mosses and alternative adaptation to life on land. New Phytol. 2000, 148, 1–3. [Google Scholar] [CrossRef]
  46. Köckinger, H.; Suanjak, M.; Schriebl, A.; Schröck, C. Die Moose Kärntens; Verlag des Naturwissenschaftlichen Vereins für Kärnten: Klagenfurt, Austria, 2008; Volume 4. [Google Scholar]
  47. Zechmeister, H.G.; Hagel, H.; Gendo, A.; Osvaldik, V.; Patek, M.; Prinz, M.; Schröck, C.; Köckinger, H. Die Rote Liste der Moose Niederösterreichs. Wiss. Wiss. Mitt. Niederösterreichischen Landesmus. 2013, 24, 7–126. [Google Scholar]
  48. Schröck, C.; Köckinger, H.; Schlüsselmayr, G. Katalog und Rote Liste der Moose Oberösterreichs. Stapfia 2014, 100, 1–247. [Google Scholar]
  49. Schröck, C.; Amann, G.; Köckinger, H.; Krisai, R.; Schlüsselmayr, G.; Zechmeister, H. Stellungnahme zum Abdeckungsgrad der österreichischen N2000-Gebiete gemäß FFH-Richtlinie im Bereich der Organismengruppe der Moose. Available online: https://www.researchgate.net/publication/310467667_Stellungnahme_zum_Abdeckungsgrad_der_osterreichischen_N2000-Gebiete_gemass_FFH-Richtlinie_im_Bereich_der_Organismengruppe_der_Moose (accessed on 10 May 2020).
  50. Hiebl, J.; Frei, C. Daily temperature grids for Austria since 1961—Concept, creation and applicability. Theor. Appl. Climatol. 2016, 124, 161–178. [Google Scholar] [CrossRef]
  51. Hiebl, J.; Frei, C. Daily precipitation grids for Austria since 1961—Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theor. Appl. Climatol. 2018, 132, 327–345. [Google Scholar] [CrossRef]
  52. Proctor, M.C.F. Physiological Ecology: Water Relations, Light and Temperature Responses, Carbon Balance. In Bryophyte Ecology; Smith, A.J.E., Ed.; Chapman and Hall: London, UK, 1982; pp. 333–381. [Google Scholar]
  53. Vanderpoorten, A.; Goffinet, B. Introduction to Bryophytes; Cambridge University Press: New York, NY, USA, 2009; ISBN 978-0-521-70073-3. [Google Scholar]
  54. Steiner, H.; Oettel, J.; Langmaier, M.; Lipp, S.; Frank, G. Anleitung zur Wiederholungsaufnahme in Naturwaldreservaten. Bundesforschungszentrum Wald BFW-Dok. 2018, 26, 62. [Google Scholar]
  55. Naimi, B.; Hamm, N.; Groen, T.A.; Skidmore, A.K.; Toxopeus, A.G. Where is positional uncertainty a problem for species distribution modelling? Ecography 2014, 37, 191–203. [Google Scholar] [CrossRef]
  56. Calcagno, V. Model Selection and Multimodel Inference Made Easy. R Package Version 1.0.8. Available online: https://CRAN.R-project.org/package=glmulti (accessed on 2 November 2020).
  57. Fox, J.; Weisberg, S. An R Companion to Applied Regression; Sage: Thousand Oaks, CA, USA, 2019. [Google Scholar]
  58. Kuhn, M. Building Predictive Models in R Using the caret Package. J. Stat. Softw. 2008, 28, 1–26. [Google Scholar] [CrossRef] [Green Version]
  59. Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]
  60. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
Plants 10 00061 g001 550
Figure 1. Boxplots for not populated (=0) and populated sites (=1) regarding the climate variables (for abbreviations see Table 1); * and ** give information on the p-value’s significance (* = p < 0.05, ** = p < 0.01).
Figure 1. Boxplots for not populated (=0) and populated sites (=1) regarding the climate variables (for abbreviations see Table 1); * and ** give information on the p-value’s significance (* = p < 0.05, ** = p < 0.01).
Plants 10 00061 g001
Table
Table 1. List of climatic variables used in the analysis.
Table 1. List of climatic variables used in the analysis.
TemperatureTemp_MeanMean Annual Temperature
tempsum_gt_15temperature sum of days with more than 15 °C mean temperature
days_over_tmean_10number of days above 10 °C mean temperature
Precipitationprec_sumannual precipitation sum
precgt0Cannual precipitation sum of days with a mean temperature above 0 °C
Desiccationp0contmaximum number of consecutive days without precipitation
p0cont_gt_20maximum number of consecutive days without precipitation and at least 20 °C
Table
Table 2. Final model to explain the occurrence of Buxbaumia viridis across Austrian; AIC = Akaike information criterion; for abbreviations of climate variables see Table 1.
Table 2. Final model to explain the occurrence of Buxbaumia viridis across Austrian; AIC = Akaike information criterion; for abbreviations of climate variables see Table 1.
EstimateStd.Errorz-Valuep-Value
AIC = 173.27; R2 = 0.312
p0cont_gt_20−0.9340.157−5.9490.000
deadwood0.0090.0042.0530.040
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Kropik, M.; Zechmeister, H.G.; Moser, D. Climate Variables Outstrip Deadwood Amount: Desiccation as the Main Trigger for Buxbaumia viridis Occurrence. Plants 2021, 10, 61. https://doi.org/10.3390/plants10010061

AMA Style

Kropik M, Zechmeister HG, Moser D. Climate Variables Outstrip Deadwood Amount: Desiccation as the Main Trigger for Buxbaumia viridis Occurrence. Plants. 2021; 10(1):61. https://doi.org/10.3390/plants10010061

Chicago/Turabian Style

Kropik, Michaela, Harald G. Zechmeister, and Dietmar Moser. 2021. "Climate Variables Outstrip Deadwood Amount: Desiccation as the Main Trigger for Buxbaumia viridis Occurrence" Plants 10, no. 1: 61. https://doi.org/10.3390/plants10010061

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