Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220874788> ?p ?o ?g. }
- W4220874788 endingPage "1355" @default.
- W4220874788 startingPage "1344" @default.
- W4220874788 abstract "Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species distributions and vegetation function have focused on traits associated with resource acquisition; both ignore the wider functional significance of leaf morphology.A dataset comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning) and characterise co-occurring trait syndromes (k-means clustering) and their climatic preferences.Three axes accounted for >20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature explained 8%-10% of trait variation; family 15%-32%. Microphyll or larger, mid- to dark green leaves with drip tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for example the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co-occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent and entire leaves or notophyll, waxy, dark green leaves. Synthesis. The plastic response of size, shape, colour and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function.叶片形态特征沿气候梯度呈现规律性变化。近年来植物功能生态学的研究以数量性状的分析为主,而物种分布和植被功能的数值模型则聚焦于资源获取相关的性状。这两者都忽视了叶片形态更 广泛的功能意义。中国植物性状数据库包含了来自中国92个站点的662种木本植物的22个叶片形态性状,覆盖了中国主要的生物群系。基于该数据库,本研究采用多元变量分析以确定这些性状协同变化的主要维度(对应分析)、量化气候和系统发育的贡献(典型对应分析和方差分解),并表征共同出现的性状综合征(k‐means聚类分析)及其气候偏好。多变量分析的三个主轴解释了常绿和落叶树种性状20%以上的变化。湿润指数、降水季节性和生长季平均温度解释了性状变化的8‐10%,科级阶元间差异则解释了15‐32%。在气候较湿润的地区,叶片为小型叶或更大型叶、颜色呈较绿至深绿且有滴水叶尖,而在气候较干燥的地区,叶片为微小型叶或更小型叶、叶表面覆白霜且无滴水叶尖。降水季节性变化较大地区的叶片,具有较薄、叶缘深裂、有芳香且内卷或外卷的特征,与季节性变化较小地区的厚实全缘叶形成对比。较冷的气候下的叶片厚而内卷且具毛,与较暖的气候下具有明显表面结构的薄叶形成对比。独特的性状簇与极端干燥和降水季节性强的气候紧密相关。例如在气候最干旱的地区,叶片聚类特征为鳞叶、肉质和多汁叶片;在降水季节性较强的地区则有极微小型叶、长叶、深裂、外卷或内卷和有芳香的叶片特征。某些叶片性状簇在潮湿的气候下同时出现,包括小型叶、中等厚度、平展、全缘叶的聚类特征或亚中型、蜡质、深绿色叶片的聚类特征。综合而言,叶片大小、形状、颜色和其他形态特征响应气候变化的可塑性较弱,因此它们沿气候梯度所呈现出的更替,反映了由物种替代所引起的植物在群落水平上对环境的适应。植物区系中广泛存在的叶片形态特征信息可用于加强物种分布和植被功能的预测模型。." @default.
- W4220874788 created "2022-04-03" @default.
- W4220874788 creator A5050925856 @default.
- W4220874788 creator A5053292913 @default.
- W4220874788 creator A5070757205 @default.
- W4220874788 creator A5079986902 @default.
- W4220874788 date "2022-03-30" @default.
- W4220874788 modified "2023-10-15" @default.
- W4220874788 title "Leaf morphological traits as adaptations to multiple climate gradients" @default.
- W4220874788 cites W1489326049 @default.
- W4220874788 cites W1583871471 @default.
- W4220874788 cites W1953806081 @default.
- W4220874788 cites W1977556410 @default.
- W4220874788 cites W1980853422 @default.
- W4220874788 cites W1983084194 @default.
- W4220874788 cites W1995713342 @default.
- W4220874788 cites W1998622804 @default.
- W4220874788 cites W2001046485 @default.
- W4220874788 cites W2020356777 @default.
- W4220874788 cites W2026067877 @default.
- W4220874788 cites W2030432742 @default.
- W4220874788 cites W2030533609 @default.
- W4220874788 cites W2032802402 @default.
- W4220874788 cites W2033738683 @default.
- W4220874788 cites W2037840294 @default.
- W4220874788 cites W2041837153 @default.
- W4220874788 cites W2048350538 @default.
- W4220874788 cites W2066885363 @default.
- W4220874788 cites W2097601813 @default.
- W4220874788 cites W2105196038 @default.
- W4220874788 cites W2108872139 @default.
- W4220874788 cites W2115952566 @default.
- W4220874788 cites W2117236901 @default.
- W4220874788 cites W2117329063 @default.
- W4220874788 cites W2118295263 @default.
- W4220874788 cites W2120863043 @default.
- W4220874788 cites W2123175155 @default.
- W4220874788 cites W2131042289 @default.
- W4220874788 cites W2138240373 @default.
- W4220874788 cites W2140821838 @default.
- W4220874788 cites W2144704825 @default.
- W4220874788 cites W2151678090 @default.
- W4220874788 cites W2153478495 @default.
- W4220874788 cites W2164450756 @default.
- W4220874788 cites W2167110740 @default.
- W4220874788 cites W2169487497 @default.
- W4220874788 cites W2170408123 @default.
- W4220874788 cites W2174465708 @default.
- W4220874788 cites W2176083171 @default.
- W4220874788 cites W2176831400 @default.
- W4220874788 cites W2183181448 @default.
- W4220874788 cites W2480287155 @default.
- W4220874788 cites W2497656902 @default.
- W4220874788 cites W249990119 @default.
- W4220874788 cites W2507320813 @default.
- W4220874788 cites W2511944800 @default.
- W4220874788 cites W2546424806 @default.
- W4220874788 cites W2738802217 @default.
- W4220874788 cites W2750842042 @default.
- W4220874788 cites W2767915279 @default.
- W4220874788 cites W2769948578 @default.
- W4220874788 cites W2884466881 @default.
- W4220874788 cites W2892087403 @default.
- W4220874788 cites W2899138366 @default.
- W4220874788 cites W2908319623 @default.
- W4220874788 cites W2913380212 @default.
- W4220874788 cites W2922116679 @default.
- W4220874788 cites W2943787661 @default.
- W4220874788 cites W2949218221 @default.
- W4220874788 cites W3011591149 @default.
- W4220874788 cites W3043877689 @default.
- W4220874788 cites W3046275761 @default.
- W4220874788 cites W3088624612 @default.
- W4220874788 cites W3104375919 @default.
- W4220874788 cites W3121020597 @default.
- W4220874788 cites W3128923589 @default.
- W4220874788 cites W3134669466 @default.
- W4220874788 cites W3166704773 @default.
- W4220874788 cites W3186059513 @default.
- W4220874788 cites W3188305913 @default.
- W4220874788 cites W4205785327 @default.
- W4220874788 cites W968329797 @default.
- W4220874788 doi "https://doi.org/10.1111/1365-2745.13873" @default.
- W4220874788 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35915621" @default.
- W4220874788 hasPublicationYear "2022" @default.
- W4220874788 type Work @default.
- W4220874788 citedByCount "11" @default.
- W4220874788 countsByYear W42208747882022 @default.
- W4220874788 countsByYear W42208747882023 @default.
- W4220874788 crossrefType "journal-article" @default.
- W4220874788 hasAuthorship W4220874788A5050925856 @default.
- W4220874788 hasAuthorship W4220874788A5053292913 @default.
- W4220874788 hasAuthorship W4220874788A5070757205 @default.
- W4220874788 hasAuthorship W4220874788A5079986902 @default.
- W4220874788 hasBestOaLocation W42208747881 @default.
- W4220874788 hasConcept C106934330 @default.
- W4220874788 hasConcept C110872660 @default.
- W4220874788 hasConcept C125403950 @default.