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Species Association in Xanthoceras Sorbifolium Bunge Communities and Selection for Agroforestry Establishment

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Species Association in Xanthoceras Sorbifolium Bunge Communities and Selection for Agroforestry Establishment Agroforest Syst https://doi.org/10.1007/s10457-018-0265-z Species association in Xanthoceras sorbifolium Bunge communities and selection for agroforestry establishment Qing Wang . Renbin Zhu . Jimin Cheng . Zhixiong Deng . Wenbin Guan . Yousry A. El-Kassaby Received: 24 August 2017 / Accepted: 15 June 2018 Ó The Author(s) 2018 Abstract We embraced the ‘‘learning from nature scaling ordination to assess community structure and and back to nature’’ paradigm to develop viable composition, and the climatic variables that most agroforestry scenarios through studying species asso- likely influenced existing species distributions. Next, ciation in 12 wild yellowhorn (Xanthoceras sorb- pairwise and multiple species associations were eval- ifolium: a Chinese endemic oil woody plants) uated using several multiple species association communities. We identified 18 species combinations indices (e.g., v2, Jaccard, Ochiai, Dice). Generally, for their suitability as agroforestry mixes where all species association indices were in agreement and positive associations were detected and thus economic were helpful in identifying several high valued benefits are anticipated. In each wild yellowhorn medicinal species that showed positive and significant community, we use nonmetric multidimensional associations with yellowhorn. Finally, we proposed several agroforestry species mixes suitable for yellowhorn. Qing Wang and Renbin Zhu have contributed equally to this work. Keywords Xanthoceras sorbifolium Á Species Electronic supplementary material The online version of association and selection Á Agroforestry mixes Á this article (https://doi.org/10.1007/s10457-018-0265-z) con- Nonmetric multidimensional scaling ordination tains supplementary material, which is available to authorized users. (NMDS) Q. Wang Á Z. Deng Á W. Guan (&) R. Zhu School of Nature Conservation, Beijing Forestry Xishuangbanna Tropical Botanical Garden, Chinese University, Beijing 100083, People’s Republic of China Academy of Sciences, Menglun 666303, Yunnan, e-mail: [email protected] People’s Republic of China Q. Wang Á Y. A. El-Kassaby (&) J. Cheng Department of Forest and Conservation Sciences, Faculty Institute of Soil and Water Conservation, Chinese of Forestry, The University of British Columbia, Academy of Sciences and Ministry of Water Resources, Vancouver, BC V6T 1Z4, Canada Yangling 712100, Shaanxi, People’s Republic of China e-mail: [email protected] R. Zhu College of Resource and Environment, North West A&F University, Yangling 712100, Shaanxi, People’s Republic of China 123 Agroforest Syst Introduction services (Schmidhuber and Tubiello 2007). Deeper understanding of species association may provide a Plant communities’ structure, composition, and envi- workable model for the development and establish- ronmental covariates represent the three fundamental ment of new agroforestry assembles through the objects that have received substantial ecological proper selection of compatible species combinations attention for more than a century (Adler and (Jose 2009; Jose et al. 2004). HilleRisLambers 2008). Recent research findings Xanthoceras sorbifolium Bunge (yellowhorn), a indicate that the on-going climate change has influ- relic oil woody plants that is endemic to China (Yang enced these ecological objects (Malanson 2017) and et al. 2005). Due to its high oil content and economic highlighted the role of species- climate variances importance, yellowhorn has received increased scien- relationship and more specifically the identification of tific and managerial attention with extensive studies which climate variances significantly affect particular covering its genetics (Bi and Guan 2014), physiology ˇ´ ´ species fitness (de Gasper et al. 2015;Sımova et al. (Zhou et al. 2012; Zhou and Liu 2012), industrial and 2015). However, plant–plant relationship has also medicinal uses (Ma et al. 2004). Knowledge on wild been proven to be effective in shaping community yellowhorn communities’ structures are very scant and composition through influencing landscape-scale pro- mainly remain unknown. Here, we studied 12 wild ductivity or species relative distribution in a more yellowhorn communities to: (1) evaluate the species substantial ways than the climate change’s short term community composition in relation to climatic vari- effects (Dohn et al. 2013; Riginos 2009). ables, (2) uncover the understory species associations Evaluating species association could help under- with special reference to woody and herbaceous standing the various relationships among species species, and (3) develop a yellowhorn agroforestry (positive or negative) as well as providing insights plantation mixture resembling those present in wild on community’s structure and dynamic under climate communities. change. Species association is usually shaped by the differences in community habitat affecting species’ distribution (i.e., the spatial arrangement of a biolog- Materials and methods ical taxon) (Greig-Smith 1983). Positive species association may exists when one species relies on Study area another or when both species are affected by the same bioclimatic or no-bioclimatic factors, while negative The present study was carried out between July 2014 association is triggered by competition over space, and June 2015 and covered 12 yellowhorn communi- nutrition, allele-chemical interaction, or demand of ties in northern China, representing 12 counties in 6 different environment (shade or light preference). The provinces (Shaanxi, Shanxi, Ningxia, Gansu, Qinghai, majority of research on species association has focus and Hebei) (Table 1). The ecology of this region is on community structure (Masaki et al. 1992; Tokeshi characterized as arid to semi-arid with 300–600 mm 1993; Wilson et al. 1995); however, the literature rainfall with 90% occurring between July and Septem- lacked its possible role on modern agroforestry ber (Kimura et al. 2007; Xin et al. 2011). July and establishment (management system that combines January mean temperatures are 17 °C and - 5 °C, trees and crops for the creation of diverse, sustainable, respectively (Maher 2016). and ecologically sound land use). Modern agroforestry should be designed to minimize interspecific compe- Data collection tition and maximize benefits, so it can provide ecosystem services and economic commodities. The The 12 yellowhorn communities were investigated by Millennium Ecosystem Assessment (Assessment line transects. In each location, a randomly located 2005) and the International Assessment of Agricul- plot of 3000 m 9 10 m was studied (36 ha in total). tural Science and Technology for Development The minimum distance separating any two sampling (IAASTD) (Kiers et al. 2008) recognized the benefits plots was 11.5 km. All woody and herbaceous species of modern agroforestry while considering the tradeoff within each plot were recorded, and included 52 between landowners/farmers and environmental woody and 97 herbaceous species; however, 100 123 Agroforest Syst Table 1 Summary of the twelve locations along with their locations (Lat., Long., Elev.), canopy coverage, soil texture, and slope Location Latitude Longitude Elevation Canopy Soil Slope (°N) (°E) (m) coverage (%) texture (°) 1 Xifeng District, Qingyang City, Gansu 35°40035.3900 107°29037.9600 1120 5 Typical 40 Province loess 2 Heshui County, Qingyang City, Gansu 36°04056.5400 108°19042.2200 1360 20 Typical 20 Province loess 3 Yu County, Zhangjiakou City, Hebei 40°05041.6900 115°03024.5700 1186 10 Typical 15 Province loess 4 Pingluo County, Shizuishan City, 38°53024.7500 106°07015.1100 1461 5 Sierozem 35 Ningxia Province soil 5 Xunhua County, Haidong City, Qinghai 35°49047.8700 102°41034.3900 1961 10 Typical 30 Province loess 6 Fangshan County, Luliang City, Shanxi 37°52044.0300 111°14046.0000 1219 20 Typical 25 Province loess 7 Ji County, Lingfen City, Shanxi 36°09031.2600 110°41014.5300 1086 40 Typical 40 Province loess 8 Shilou County, Luliang City, Shanxi 37°01016.9600 110°44051.4000 1085 10 Typical 15 Province loess 9 Tianlong Mountain, Taiyuan City, 37°42033.8300 112°23038.6600 973 50 Clayey 25 Shanxi Province loess 10 Fanzhi County, Yizhou City, Shanxi 39°13024.8300 113°17013.2900 1026 5 Sandy 15 Province loess 11 Ganquan County, Yan’an City, Shaanxi 36°14051.4500 109°20049.6500 1060 20 Typical 20 Province loess 12 Feng County, Baoji City, Shaanxi 34°03044.9400 106°41039.7000 1123 60 Clayey 25 Province loess species were removed from the analysis (see below) differences (seasonal effects) did not play a role in the (Table S1). For each plot, a total of 188 climatic observed species. variables representing the prevailing climatic condi- tions present during the surveys were generated by Ordination analysis between species and climate estimating climate norms from geographic coordi- variance nates using the software package Climate AP (Wang et al. 2016). The Nonmetric Multidimensional Scaling (NMDS) technique which is known to be effective with Data analysis ordination when compared to other multivariate techniques in handling ecological data (Bettinetti For species association analyses, 100 of the 149 et al. 2000; Kenkel and Orlo´ci 1986) was used and species occurred only in a single plot (i.e., singletons), implemented in R ‘‘Vegan’’ package to interpret the and were excluded, thus the subsequent analyses were relationship between species (dependent variables) based on the remaining 49 species (5 annual or and climatic variables (Oksanen et al. 2013). biennial forb, 25 perennial forb, and 19 woody species, Table
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