首页> 外文会议>International Symposium on Aquatic Weeds >Predicting interactions between wetland vegetation and the soil-water and surface-water environment using diversity, abundance and attribute values
【24h】

Predicting interactions between wetland vegetation and the soil-water and surface-water environment using diversity, abundance and attribute values

机译:使用多样性,丰度和属性值预测湿地植被与土壤水域和表面水环境之间的相互作用

获取原文

摘要

This study investigated the response of freshwater wetland vegetation to hydrological driving factors by assessing collective vegetation variables, traits of dominant plant populations and hydrological and hyd-rochemical variables, repeat-sampled within wetland sites across Scotland and northern England. Sampling was conducted at 55 permanent sample stations located along 11 independent transects. Eco-hydrological interactions were investigated using a regression-based modelling approach. Facets of the water-table dynamic (e.g., level of drawdown, level of fluctuation), along with vegetation abundance (e.g., biomass, stem density) and diversity (e.g., species richness) values, were used to build predictive models. Of the models predicting vegetationcharacteristics, the greatest predictive power was R~2 = 0.67 (p < 0.001) for a model predicting stem density (m~(-2)). Conversely, vegetation variables proved useful for predicting characteristics of the water-table environment. In this instance, the greatest predictive power was R~2 = 0.79 (p < 0.001) for a model predicting minimum water table level (i.e. maximum level of drawdown). The models were tested using data collected during 2000 from repeat sites and independent sites. This approach might be successfully applied for the purposes of integrated eco-hydrological management and monitoring of freshwater wetland vegetation.
机译:本研究通过评估了集体植被变量,占苏格兰北部湿地景点的集体植被变量,主导植物种群的特性,在湿地景点中重复抽样,调查了淡水湿地植被对水文驱动因素的反应。采样是在沿着11个独立横断面的55个永久性样品站进行的。使用基于回归的建模方法研究生态水文相互作用。水表动态的刻面(例如,缩减水平,波动水平)以及植被丰度(例如,生物质,茎密度)和多样性(例如,物种丰富的)值,用于构建预测模型。对于预测植被特征的模型,最大的预测力是预测茎密度的型号的R〜2 = 0.67(P <0.001)(m〜(-2))。相反,植被变量证明有助于预测水表环境的特征。在这种情况下,最大的预测功率是R〜2 = 0.79(p <0.001),用于预测最小水位水平的模型(即最大绘制水平)。使用2000年期间的数据从重复站点和独立站点进行测试。这种方法可以成功地应用于综合生态水文管理和淡水湿地植被监测的目的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号