...
首页> 外文期刊>Freshwater Biology >The derivation of log-transformed abundance data for the quantitative analysis of macroinvertebrate traits - an addendum to 'A macroecological perspective of trait patterns in stream communities' by Heino et al. (2013)
【24h】

The derivation of log-transformed abundance data for the quantitative analysis of macroinvertebrate traits - an addendum to 'A macroecological perspective of trait patterns in stream communities' by Heino et al. (2013)

机译:对数转换后的丰度数据的推导,用于对大型无脊椎动物特征进行定量分析-Heino等人在“河流群落特征模式的宏观生态学观点”的附录中。 (2013年)

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Heino etal. () identified the weak explanatory power of abundance data as an important limitation of macroinvertebrate trait analysis. This limitation may be an artefact of analytical design. The widespread practice of combining logarithmically transformed abundance data with trait frequencies, log(x+1)*(trait frequency), represents a nonlinear abundance weighting of trait frequencies, as opposed to an expression of trait abundances per se. Because the addition of logarithmic data is equivalent to multiplication on an arithmetic scale, summing these abundance-weighted frequencies provides an inconsistent scaling of trait abundance that may confound quantitative comparison. We provide examples of the options for estimating trait abundance from data on macroinvertebrate abundance and trait frequencies and discuss the meaning of numerical data in the context of analytical objectives. In the light of the contrasting methods that have been employed to analyse trait data for benthic macroinvertebrates, the conclusion that the explanatory power of trait abundance is inferior to that of taxonomic abundance may be premature
机译:Heino等。 ()确定了丰度数据的弱解释能力是宏观无脊椎动物性状分析的重要限制。这种限制可能是分析设计的产物。将对数变换后的丰度数据与特征频率log(x + 1)*(特征频率)相结合的普遍做法代表了特征频率的非线性丰度加权,这与特征丰度本身的表达相反。因为对数数据的添加等效于算术规模上的乘法,所以将这些丰度加权频率相加得出的性状丰度缩放比例不一致,可能会混淆定量比较。我们提供了从无脊椎动物数量和性状频率上的数据估计性状丰富度的选项示例,并讨论了分析目标中数值数据的含义。根据用于分析底栖大型无脊椎动物特征数据的对比方法,关于特征丰富度的解释能力不如生物分类丰富度的解释能力的结论可能为时过早

著录项

相似文献

  • 外文文献
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