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1 2016, 40 (1): Chinese Journal of Plant Ecology 1,2 1, ,2 1,2 Ali ARSHAD 1,2,5 1,2* 1, ; 2, ; 3, ; 4, ; 5 Department of Environmental Sciences, Abdul Wali Khan University, Mardan 23200, Pakistan 5 hm 2 1 cm, , ph, ; : 1),, m, 2), -, - ; -, - 3)(r = 0.12, p < ); (r = 0.16, p < ), (r = 0.13, p < ) 4) -, ; -,,,,, ; ; ; ; :,,,,, Arshad A, (2016).., 40, Geostatistical analysis of spatial variations in leaf traits of woody plants in Tiantong, Zhejiang Province XU Ming-Shan 1,2, ZHAO Yan-Tao 1,2, YANG Xiao-Dong 3, SHI Qing-Ru 4, ZHOU Liu-Li 1,2, ZHANG Qing-Qing 1,2, Ali ARSHAD 1,2,5, and YAN En-Rong 1,2* 1 School of Ecological and Environmental Sciences, East China Normal University, Shanghai , China; 2 Tiantong National Forest Ecosystem Observation and Research Station, Ningbo, Zhejiang , China; 3 Institute of Resources and Environment Science, Xinjiang University, Ürümqi , China; 4 Youth Science and Technology Guide Station of Baoshan District, Shanghai , China; and 5 Department of Environmental Sciences, Abdul Wali Khan University, Mardan 23200, Pakistan Abstract Aims Exploring spatial variations in leaf traits and their relationships with environmental properties is crucial for understanding plant adaptation strategies and community assembly. This study aimed to reveal how leaf traits varied spatially and the role of environmental factors. Methods The study was conducted in a 5-hm 2 forest plot in Tiantong, Zhejiang Province. Three leaf traits, including individual leaf area (ILA), specific leaf area (SLA), and leaf dry matter content (LDMC) were measured for individual trees with diameter at breast height (DBH) 1 cm. Soil properties measured included contents of soil total nitrogen, soil total phosphorus, soil total carbon, soil ph value, soil volumetric water content, bulk density, and humus depth. Topographic variables measured included elevation, slope and convexity. We used geostatistical analysis to reveal spatial variations of the three leaf traits. Relationships between leaf variability and Received: Accepted: * Author for correspondence ( eryan@des.ecnu.edu.cn)

2 : 49 environmental factors were analyzed using principal component analysis (PCA) and Pearson s correlation. Important findings Spatial variability followed the order of ILA > SLA > LDMC. Spatial autocorrelation of three leaf traits was weak within a distance of 5.16 m. The optimal model of the semi-variogram function was Gaussian model for ILA, and exponential model for SLA and LDMC. ILA showed the largest variability at the direction of northeast-southwest, and smallest variability at the direction of northwest-southeast. In contrast, SLA and LDMC had the highest variability at the direction of northwest-southeast and least variability at the direction of northeast-southwest. There were significantly negative relationships between ILA and topographic factors (r = 0.12, p < ), and between SLA and soil nutrients (r = 0.16, p < ). In contrast, LDMC was positively correlated with soil nutrients (r = 0.13, p < ). Relative to soil nutrients, topographic factors affected much more variations in ILA, SLA and LDMC at the direction of northeast-southwest. Distinctly, at the direction of northwest-southeast, variability of ILA was affected mainly by topographic factors, while soil nutrients resulted in the most variability of SLA and LDMC. In conclusion, leaf traits varied considerably with spatial direction in the studied forest plot. Associations between leaf traits and topographic factors and soil nutrients indirectly indicated effects of environmental filtering on community assembly. Key words leaf traits; spatial variation; soil nutrients; topographic factors; geostatistical analysis Citation: Xu MS, Zhao YT, Yang XD, Shi QR, Zhou LL, Zhang QQ, Arshad A, Yan ER (2016). Geostatistical analysis of spatial variations in leaf traits of woody plants in Tiantong, Zhejiang Province. Chinese Journal of Plant Ecology, 40, doi: /cjpe , (Reich et al., 2003; Violle et al., 2007), (Grime et al., 1997; Díaz et al., 2004;, 2007),, (Wright et al., 2004; Beaumont & Burns, 2009) (spatial variation),,,,,, (, 2011), Wright (2004) 18%; (Wang & Ni, 2005a; 2005b),,,,,, (Weiher & Keddy, 1999; Cornwell et al., 2006), (Villar & Merino, 2001; Reich et al., 2003;, 2010;, 2013) : (Liu et al., 2012),, (Hölscher et al., 2002; Craine & Lee, 2003),,,, ;,,,,,, 20 hm 2 5 hm 2, 1 cm ( ), : 1) ; 2),, ; 3)

50 Chinese Journal of Plant Ecology 2016, 40 (1): 48 59 1 1.1 (29.80 N, 121.78 E),,, 16.2, 1 374.7 mm,,, 1 m, 602.89 m (, 1995), 20 hm 2 (29.81 29.82 N, 121.78 121.

3 50 Chinese Journal of Plant Ecology 2016, 40 (1): (29.80 N, E),,, 16.2, mm,,, 1 m, m (, 1995), 20 hm 2 ( N, E)5 hm 2 ( m, m,, 4, )( 1),,, m, hm 2 Fig. 1 Topography map of the 5 hm 2 study plot in Tiantong, Zhejiang Province , 5 hm 2 1 cm, (, 4 ) Cornelissen (2003) :, 1(>5 m, 20 m, <5 m, 3 m ), 40,,,, 12 h 20,, (LI- COR 3100C, LI-COR, Lincoln, USA),, h, (individual leaf area, ILA), (specific leaf area, SLA), (leaf dry matter content, LDMC) 1, :, 20 m 20 m, (TP) (TN) (TC) ph (SVWC) (BD) (HD) (E) (S)(C) CTFS, (2012) 1.3, (, 2006;, 2012), (, 2000;, 2002;, 2010;, 2012),, : h, r(h) ;, ;, ;, <25%, ; 25% 75%, ; >75%, ; D

4 : hm 2 Table 1 Species list and leaf traits of woody plants in the 5 hm 2 plot Life form Evergreen trees Evergreen shrubs Species Abundance Individual leaf area (cm 2 ) Specific leaf area (cm 2 g 1 ) Leaf dry matter content (mg g 1 ) Symplocos anomala ± ± ± 0.24 Litsea coreana ± ± ± 1.06 Cyclobalanopsis gilva ± ± ± 0.67 Prunus spinulosa ± ± ± 2.70 Ilex latifolia ± ± ± 0.80 Ilex chinensis ± ± ± 1.76 Elaeocarpus decipiens Ilex buergeri ± ± ± 0.59 Photinia glabra ± ± ± 0.59 Ilex hylonoma ± ± ± 1.51 Machilus thunbergii ± ± ± 0.29 Daphniphyllum oldhamii ± ± ± 0.58 Osmanthus cooperi ± ± ± 1.03 Machilus leptophylla ± ± ± 0.33 Litsea elongata ± ± ± 0.10 Symplocos cochinchinensis ± ± ± 0.38 Castanopsis fargesii ± ± ± 0.43 Castanopsis sclerophylla ± ± ± 1.28 Castanopsis carlesii ± ± ± 0.84 Schima superba ± ± ± 0.43 Osmanthus fragrans ± ± ± 0.93 Illicium lanceolatum ± ± ± 0.45 Celtis sinensis Cyclobalanopsis glauca ± ± ± 1.92 Cephalotaxus fortunei ± ± ± 0.36 Lithocarpus glaber ± ± ± 2.17 Symplocos setchuensis ± ± ± 0.57 Ilex rotunda ± ± ± 0.91 Cyclobalanopsis gracilis ± ± ± 0.84 Cinnamomum subavenium ± ± ± 0.42 Cyclobalanopsis myrsinifolia ± ± ± 0.54 Myrica rubra ± ± ± 1.90 Distylium myricoides ± ± ± 0.17 Adinandra millettii ± ± ± 0.29 Cyclobalanopsis sessilifolia ± ± ± 0.41 Lithocarpus henryi ± ± ± 0.37 Neolitsea aurata ± ± ± 0.23 Elaeocarpus chinensis ± ± ± 4.49 Ilex kengii ± ± ± 0.86 Phoebe sheareri ± ± ± 0.97 Symplocos botryantha ± ± ± 0.83 Tarenna mollissima ± ± ± 4.44 Euonymus centidens ± ± ± 0.26 Syzygium buxifolium ± ± ± 1.04 Eurya muricata ± ± ± 0.61 Syzygium buxifolium ± ± ± 1.04 Eurya muricata ± ± ± 0.61 Symplocos lancifolia ± ± ± 0.38 Stytax suberifolius ± ± ± 7.92

5 52 Chinese Journal of Plant Ecology 2016, 40 (1): ( ) Table 1 (continued) Life form Evergreen shrubs Deciduous trees Deciduous shrubs Species Abundance Individual leaf area (cm 2 ) Specific leaf area (cm 2 g 1 ) Leaf dry matter content (mg g 1 ) Camellia japonica Ternstroemia gymnanthera ± ± ± 1.26 Loropetalum chinensis ± ± ± 1.17 Symplocos stellaris ± ± ± 0.63 Camellia cuspidata ± ± ± 1.22 Rhododendron ovatum ± ± ± 0.39 Vaccinium mandarinorum ± ± ± 0.66 Nandina domestica Symplocos sumuntia ± ± ± 0.28 Rhaphiolepis indica ± ± ± 2.36 Eurya loquaiana ± ± ± 0.07 Prunus phaeosticta ± ± ± 0.35 Eurya rubiginosa ± ± ± 0.39 Cornus controversa ± ± ± 2.89 Meliosma oldhamii Juglans cathayensis ± ± ± 1.99 Diospyros oleifera ± ± ± 2.20 Schoepfia jasminodora ± ± ± 1.51 Albizia kalkora ± ± ± 0.98 Prunus discoidea ± ± ± 5.95 Diospyros glaucifolia ± ± ± 1.92 Aphananthe aspera ± ± ± 1.50 Sassafras tzumu ± ± ± 0.69 Euodia fargesii Liquidambar formosana ± ± ± 1.02 Ulmus changii ± ± ± 0.63 Ehretia acuminata ± ± ± 2.67 Platycarya strobilacea ± ± ± 0.77 Dalbergia hupeana ± ± ± 1.62 Fraxinus insularis ± ± ± 0.98 Carpinus viminea ± ± ± 0.76 Alangium kurzii ± ± ± 1.70 Acer pubinerve ± ± ± 1.31 Choerospondias axillaris ± ± ± 0.31 Alniphyllum fortunei ± ± ± 0.69 Cyclocarya paliurus ± ± ± 1.39 Acer acutum ± ± ± 2.08 Randia cochinchinensis ± ± ± 0.93 Litsea cubeba ± ± ± 1.37 Idesia polycarpa ± ± ± 3.89 Sapindus saponaria ± ± ± 1.12 Celtis vandervoetiana ± ± ± 1.76 Ilex micrococca ± ± ± 3.07 Toxicodendron succedaneum ± ± ± 1.47 Vernicia fordii ± ± ± 0.94 Celtis biondii ± ± ± 1.60 Premna microphylla ± ± ± 1.87 Lindera glauca ± ± ± 2.40 Vaccinium bracteatum ± ± ± 0.89 Machilus apeltus ± ± ±

6 : 53 1 ( ) Table 1 (continued) Life form Deciduous shrubs Species Abundance Individual leaf area (cm 2 ) Specific leaf area (cm 2 g 1 ) Leaf dry matter content (mg g 1 ) Vaccinium trichocladum ± ± ± 1.37 Clerodendrum cyrtophyllum ± ± ± 1.01 Mussaenda shikokiana ± ± ± 3.80 Rhododendron simsii ± ± ± 3.22 Lindera rubronervia ± ± ± 0.85 Styrax confusus ± ± ± 0.48 Trema cannabina ± ± ± 1.30 Ficus erecta ± ± ± 2.03 Photinia parvifolia ± ± ± 5.21 Viburnum erosum ± ± ± 1.60 Hovenia acerba ± ± ± 9.21, 2,, ; 2, 2r(h) = h (4 2D) r(h) h,,, 5 hm 2, ArcGIS Geostatistical analyst,, (RMSS) 1, ArcGIS Geostatistical analyst- ---4,,, Kriging5 hm 2, ArcGIS, 5 hm 2,, Pearson,, -() -( ) - -, Pearson ArcGIS 10.0 Geostatistical Analyst, PearsonR 3.0.2, Origin , ( 2)( (R 2 ) = 0.95, =1.41), (R 2 = 0.88, = 0.90)(R 2 = 0.96, =1.29) ( ), ( ), ( ) ,,,, 75%,

7 54 Chinese Journal of Plant Ecology 2016, 40 (1): 48 59, 4.80 m 5.16 m 4.92 m ,,, 4 6 m 8 m( 2), -,,,, 2 Table 2 Semi-variogram model of plant leaf traits Leaf trait Individual leaf area Specific leaf area Leaf dry matter content Model Nugget Still Structure ratio Range (m) Determination coefficient Root mean square standardized Spherical model Exponential model Gaussian model Spherical model Exponential model Gaussian model Spherical model Exponential model Gaussian model Fig. 2 Anisotropy function of plant leaf traits. 3 Table 3 Anisotropy parameters of plant leaf traits Leaf traits - East-West - Northeast-Southwest - South-North - Northwest-Southeast D R 2 D R 2 D R 2 D R 2 Individual leaf area Specific leaf area Leaf dry matter content D, ; R 2, D, fractal dimension; R 2, determination coefficient.

8 : 55 (D = 1.93) 2, (R 2 = 0.97);, -,,, (D = 1.98; D = 1.98 ) 2, (R 2 = 0.70; R 2 = 0.67),, , 70.0%,, (PC1),, (0.35)(0.43) (0.29), ph ( 0.36) ( 0.40)(PC2), (0.32) (0.42)(0.32) PC1 () PC2 () 5PC2 (r = 0.12, p < ); PC1 (r = 0.16, p < ), PC1 (r = 0.13, p < ) - -, PC1 PC26-, PC2, PC2 (r = 0.07, p = 0.003); PC2 (r = 0.08, p = 0.001); PC2 (r = 0.11, p = 0.001) -, PC2 (r = 0.13, p < ); PC1 (r = 0.25, p < ); PC1 (r = 0.22, p < ),, - -,, -, -, 3 3.1, ;,,,, (2013),,, cm 2, %; cm 2 g 1, 39.67%; mg g 1, 18.50%,,, (Loropetalum chinensis) (Syzygium buxifolium), (Juglans cathayensis)(sapindus mukorossi) ( 1),, 4.8 m 5.2 m 4.9 m : 5 m( ),,,,,, 108,,,,, 3.2,,,, ;,,,

9 56 Chinese Journal of Plant Ecology 2016, 40 (1):

10 : 57,,,,,,, ( 2), (Wright et al., 2004), (Milla & Reich, 2007) (Cornelissen et al., 2003), (, 2008), (r = 0.49, p < 0.01),,,,,,,, 3.3,, (, 2001),,, (, 2009; Aiba et al., 2012) (Wright et al., 2004),,,, (Ordoñez et al., 2009) Liu (2012), ( 7), ph (p < 0.05), (p < 0.05); ph (p < 0.05),, (p < 0.05);, (p < 0.05), ph (p < 0.05),,, Liu (2012),, PC1, PC2 PC1 PC2,, - -, -,, ; -,, - -,,,, ; (Liu et al., 2012),

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壹、前言

壹、前言行政院農業委員會林務局委託研究計畫系列 99-00-03-01 台灣中部中高海拔地區防火樹種之篩選及葉部分解速率之研究 The selection of fire-resistant tree species and its leaf decomposition rate in middle and high elevation