Myrtaceae floristic survey and vegetation distribution in a central portion of Chapada Diamantina, Brazil
Myrtaceae floristic survey and vegetation distribution in a central portion of Chapada Diamantina, Brazil
复制标题
巴西 Chapada Diamantina 中部桃金娘科植物区系调查和植被分布
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
N. Roque
中科院分区：
文献类型：
--
作者：
Luísa Lucresia;A. Stadnik;L. Campos;N. Roque
桃金娘科是新热带地区的一个重要科，因其与多种植被的相关性而受到重视，包括在埃斯皮尼亚索山脉 (EMR) 内发现的植被。目前工作的主要目标是分析巴伊亚州 Chapada Diamantina 穆库格市的桃金娘科植物区系组成和植被分布。在植物标本室对标本进行了分析，进行了两次实地考察，并通过遥感进行了土地覆盖分类。编译的数据集提供了 438 条具有有效分类学鉴定的记录，作者在植物标本馆中看到了其中 374 条记录。发现了桃金娘科 9 属 66 种，代表了 Chapada Diamantina 迄今为止记录的最大桃金娘科多样性。在土地覆盖分类中检测到四个植被类别（campo rupestre、cerrado sensu lato、caatinga sensu lato 和常绿森林），包括 12 种经现场验证的植物面貌。通过重叠植被分类和桃金娘科记录，对不同植被类别内的物种多样性进行了比较，还强调了定向抽样工作，即受到农业扩张威胁的区域，既进行了二次抽样又知之甚少。
Myrtaceae is an important family in the neotropics, being highlighted for its relevance in a wide range of vegetations, including those found within the Espinhaço Mountain Range (EMR). The main goal of the present work was to analyze Myrtaceae floristic composition and vegetation distribution in the municipality of Mucugê, Chapada Diamantina, Bahia. Specimens were analyzed in herbaria, two field expeditions were carried out and a landcover classification was performed through remote sensing. The compiled dataset presented 438 records with valid taxonomical identification, from which 374 records were seen by the authors in herbaria. Nine genera and 66 species of Myrtaceae were found, representing the largest Myrtaceae diversity recorded in Chapada Diamantina so far. Four vegetation classes were detected in the landcover classification (campo rupestre, cerrado sensu lato, caatinga sensu lato, and evergreen forest), encompassing 12 phytophysiognomies validated in situ. Comparisons on species diversity within different classes of vegetation were made by overlapping the vegetation classification and Myrtaceae records, also highlighting a directional sampling effort, being the areas threatened by the agriculture expansion both subsampled and poorly known.