基于多元回归树的常绿阔叶林群丛数量分类——以古田山24公顷森林样地为例

常绿阔叶林植被分类一直是植被生态学研究中的难题,尤其是基本单位——群丛的分类。该文以地形因子和物种组成数据为变量的多元回归树方法,将浙江古田山24hm2森林监测样地的森林群落分为3个群丛。所得群丛既反映了群落在时间和空间上的相对间断分布,也符合植被分类基本单位的特点,为常绿阔叶林的群丛分类提供了新思路。并首次引入了指示值算法,解决了以往指示种无法量化的难题。最终以优势层优势种为主、下木层指示种为辅的命名原则,将3个群丛命名为:1)石斑木(Raphiolepis indica)+柳叶蜡梅(Chimonanthus salicifolius)-格药柃(Eurya muricata)+赤楠(Syzygium buxifolium)-木荷(Schima superba)+甜槠(Castanopsis eyrei)群丛;2)映山红(Rhododendron simsii)+满山红(Rhododendron mariesii)-短柄枹(Quercus serrata var.brevipetiolata)+灰白蜡瓣花(Corylopsis glandulifera var.hypoglauca)-马尾松(Pinus massoniana)+甜槠群丛;3)短尾越桔(Vaccinium carlesii)+毛花连蕊茶(Camellia fraterna)-杨梅叶蚊母树(Distylium myricoides)+浙江新木姜子(Neolitsea aurata)-木荷+甜槠群丛。

Numerical classification of associations in subtropical evergreen broad-leaved forest based on multivariate regression trees-a case study of 24 hm2 Gutianshan forest plot in China

Aims A 24hm2 permanent plot in Gutianshan National Nature Reserve provided a valuable case for association classification in evergreen broad-leaved forest.Our objectives were to divide the forest community into associations to provide a new classification of evergreen broad-leaved forest and introduce the algorithm of indicator value for species in associations.Indicator species previously could not be quantified.Methods We used multivariate regression trees,based on topographic factors and species composit...