Drivers of Macrofungal Composition and Distribution in Yulong Snow Mountain, Southwest China

Drivers of Macrofungal Composition and Distribution in Yulong Snow Mountain, Southwest China

Mycosphere 7 (6):727–740 (2016) www.mycosphere.org ISSN 2077 7019 Article Doi 10.5943/mycosphere/7/6/3 Copyright © Guizhou Academy of Agricultural Sciences Drivers of macrofungal composition and distribution in Yulong Snow Mountain, southwest China Luo X1,2,3,4,6, Karunarathna SC1,2,5, Luo YH5, Xu K7, Xu JC1,2,5, Chamyuang S3 and Mortimer PE1,2,5* 1Centre for Mountain Ecosystem Studies, Kunming Institute of Botany, Chinese Academy of Sciences, 650201, Kunming, China 2 World Agroforestry Centre, East and Central Asia Office, 132 Lanhei Road, Kunming 650201, China 3School of science, Mae Fah Luang University, Chiang Rai, 57100, Thailand 4Center of Excellence in Fungal Research, and School of Science, Mae Fah Luang University, 57100, Chiang Rai, Thailand 5Key laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China 6School of Biology and Food Engineering, Chuzhou University, Chuzhou, 239000, China 7Lijiang Alpine Botanic Garden, Lijiang, 674100, China Luo X, Karunarathna SC, Luo YH, Xu K, Xu JC, Chamyuang S, Mortimer PE 2016 – Drivers of macrofungal composition and distribution in Yulong Snow Mountain, southwest China. Mycosphere 7(6), 727–740, Doi 10.5943/mycosphere/7/6/3 Abstract Although environmental factors strongly affect the distribution of macrofungi, few studies have so far addressed this issue. Therefore, to further our understanding of how macrofungi respond to changes in the environment, we investigated the diversityand community composition of fungi based on the presence of fruiting bodies in different environments along an elevation gradient in a subalpine Pine forest at Yulong Snow Mountain in southwest China. Two-hundred and twenty-eight specimens of macrofungi from twelve plots were identified using macro-morphological characteristics. We found that soil temperature had a significantly positive influence on total macrofungal and ectomycorrhizal species richness and diversity, while elevation gradients had a significantly negative influence. Furthermore, total macrofungal and ectomycorrhizal species community composition were significantly influenced by soil temperature and elevation gradients. No significant relationships were found between environment variables and the diversity and community composition of saprotrophic fungi in subalpine pine forest. Keywords – ectomycorrhizal fungi – elevation gradient – soil temperature – species diversity – species richness Introduction Macrofungi are a crucial component of biodiversity essential for decomposition, carbon cycling, and nutrient transport in forest ecosystems (Hobbie et al. 1999, Lin et al. 2015). Submitted 17 September 2016, Accepted 2 November 2016, Published online 10 November 2016 Corresponding Author: Mortimer PE – e-mail – [email protected] 727 Macrofungi include either ascomycetes or basidiomycetes; all have large, easily observed sporocarps structures that form above or below ground (Gómez-Hernández et al. 2012). Based on estimates derived using plant/macrofungi species ratios, it has been suggested that there are between 53,000 and 110,000 species of macrofungi (Mueller et al. 2007). The two main functional groups of macrofungi are ectomycorrhizal fungi (ECM), with approximately 6,000 species, and saprotrophic fungi (Johnson et al. 2005). Environmental factors such as precipitation, temperature, humidity, soil nutrient availability and plant species have been put forward as the primary factors responsible for differences in macrofungal species richness and community composition (Hillebrand 2004, Lentendu et al. 2011, Bahram et al. 2012, Singh et al. 2012, Tedersoo et al. 2014). There is some evidence that precipitation and temperature, coupled with plant diversity, are the key determinants of macrofungal distributions (Büntgen et al. 2011, Tedersoo et al. 2014). Changes in soil properties and climate parameters can directly influence the biodiversity and species composition ofassociated landscapes, and have been the focus of numerous studies looking at various organisms including fungi (Shi et al. 2014, Tello et al. 2015). However, despite these studies, many questions relating to the distribution of macrofungi and the drivers behind these distribution patterns remain unanswered (Baptista et al. 2010, Singh et al. 2012, van der Heijden et al. 2015). This paper presents an assessment of the macrofungi recorded in 12 plots across a subalpine pine forest on the slopes of Yulong Snow Mountain in southwest China. We characterized macrofungal species diversity and communities to elucidate the effect of abiotic factors on fungal distribution. The aims of the study were to determine which environmental factors drive macrofungal species diversity and community composition, and their distribution in a subalpine pine forest. We hypothesize that macrofungal diversity is influenced by environmental factors such as soil temperature and elevation. Furthermore, we predict that macrofungal species richness, diversity, and community composition, decline with increasing elevation. Materials & methods Study site The study area is located on the slopes of Yulong Snow Mountain, which is located in Lijiang County, southwest China, and which reaches 5,596 m above sea level (m.a.s.l.) at its highest point (Chang et al. 2014). The climate in this region experiences a summer monsoon wet season, lasting from May to October, and is dominated by the Qinghai-Tibetan Plateau circulation and westerly winds in the dry season, which lasts from November to April (Du et al. 2013). Mean annual temperature is around 10.3 °C, while the mean annual precipitation is 786.5 mm. The vegetation type for the study area is classified as subalpine coniferous forestand is dominated by coniferous species such as Pinus armandii and Pinus yunnanensis (Table 1), and some broad leaf trees such as Quercus senescens and Rhododendron decorum. The coniferous forest covering the study sites is about 45 years old, with a low level of disturbance. The study sites were established in the Lijiang subalpine Botanical Garden (27°00′ N, 100°11′ E; 2830 m.a.s.l.) at four elevation points, ranging from 2,700 to 3,400 m.a.s.l. and consisting of 12 plots, three replicate plots at least 50 m apart were established at each elevation (Table 1). The sites were located in a continuous band of mixed Pinus forest. The main plants were identified by Yahuang Luo of the Kunming Institute of Botany, China. Replicate plots were established for each elevation point along the gradient. Soil temperature and humidity (10 cm depth) 728 Table 1 Description of the sites selected in the Lijiang Botanical Gardens, on the slopes of Yulong Snow Mountain, Lijiang County, China. Sites were covered in a continuous band of Pine forest, and were selected along an elevation gradient ranging from 2700 m.a.s.l to 3400 m.a.s.l. The climatic conditions were measured throughout the collecting season at each of the sites. Replicate Elevation (m.a.s.l.) Coordinate Dominant vegetation MAT MAW ST 1 2718 27º0’0.01”N, 100º11’0.92”E P. yunnanensis and P. armandii 15.13(℃) 98.59(%) 16.23(℃) 2 2752 26º59’0.99”N, 100º11’0.83”E P. yunnanensis and P. armandii 3 2785 26º59’0.98”N, 100º11’0.74”E P. yunnanensis and P. armandii 1 2917 27º0’0.18”N, 100º11’0.58”E P. yunnanensis and P. armandii 13.85 97.10 14.62 2 2964 27º0’0.21”N, 100º11’0.55”E P. yunnanensis and P. armandii 3 2987 27º0’0.24”N, 100º11’0.56”E P. yunnanensis and P. armandii 1 3185 28º0’0.33”N, 100º10’0.95”E P. yunnanensis and P. armandii 12.41 89.94 16.37 2 3224 27º0’0.13”N, 100º10’0.99”E P. yunnanensis and P. armandii 3 3257 27º0’0.33”N, 100º11’0.17”E P. yunnanensis and P. armandii 1 3346 27º0’0.02”N, 100º10’0.69”E P. yunnanensis and P. armandii 12.24 97.05 13.38 2 3376 27º0’0.21”N, 100º10’0.73”E P. yunnanensis and P. armandii 3 3343 27º0’0.21”N, 100º10’0.76”E P. yunnanensis and P. armandii were recorded using HOBO Tidbit RG3-Mduration of the season (Luo et al. 2016). Air temperature and humidity were recorded in the plots duration of the rainy season, using a Vaisala MAWS 300. Canopy cover was estimated according to the method described by Sysouphanthong et al. (2010). Macrofungal sampling Macrofungal sporocarp sampling was conducted from July to September 2014. Every plot was surveyed once per week for a total of 14 visits across all plots. All aboveground macrofungal fruiting bodies in the 12 plots were collected and wrapped in aluminum foil, before being taken to the field station where macro-morphological characteristics were recorded and sporocarp dried in a food drier at 35 ℃. The dried macrofungal samples were sealed in Ziplock plastic bags for further morphological characterization. Based on the macro-characteristics, and with the aid of mushroom guide books, papers and online resources (http://www.indexfungorum.org/, http://mushroomexpert.com/, Lincoff 2000, Kendrick 2000, Hall et al. 2003), the collected specimens were identified to species. The taxonomic classification of species was based on the Index Fungorum (2016), which was used as the nomenclatural source. In addition, all macrofungi were grouped as either ectomycorrhizal, parasitic or saprotrophic fungi, based on mode of nutrition. The collected specimens were registered and deposited in the herbarium of the Kunming Institute of Botany, Chinese Academy of Sciences. 729 Statistical analyses All statistical analyses were performed in the vegan and MASS packages in R (R 3.1.2) (Dixon 2003). Species richness within each plot was quantified as the total community. Alpha-diversity was expressed as species diversity at each elevation gradient using the Shannon diversity index (H) (Shannon & Weaver 2015). The beta-diversity index was used to compare the difference in macrofungal species diversity between two locations using the Bray-Curtis and the Sorenson pair-wise dissimilarity matrix in Betapart package; multivariate analysis of variance of between elevations was used to examine the significancebased on beta diversity using the Arrhenius model.

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