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首页> 外文期刊>International journal of remote sensing >Describing avifaunal richness with functional and structural bioindicators derived from advanced airborne remotely sensed data
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Describing avifaunal richness with functional and structural bioindicators derived from advanced airborne remotely sensed data

机译：用功能和结构生物指示剂描述航空动物的丰富度，这些指示剂来自先进的机载遥感数据

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摘要

We investigate whether the richness of distinct avian guilds (species grouped together based on similar exploitation of environmental resources) can be described using indicators of ecosystem function and tree species diversity derived from hyperspectral data and/or aspects of vegetation structure derived from lidar. Bird surveys facilitated discriminant analyses to establish which variables best differentiated between guilds. Akaike's Information Criterion (AIC) and generalized linear models (GLMs) were then utilized to develop predictive models. Bioindicators representing foliar water content and tree species diversity were the most useful hyperspectrally derived variables for differentiating between guilds (p < 0.01) and were most often selected for describing richness. Using ecosystem function bioindicators alone, the adjusted coefficient of determination (R~2_(adj)) of GLMs ranged from 0.32 (generalist) to 0.58 (forest). In contrast, mean under-, mid-, and overstorey cover and mean surface elevation were the most useful structural bioindicators for guild differentiation (p < 0.05) and were most often selected for describing richness. R~2_(adj) of GLMs built from structural bioindicators alone ranged from 0.19 (generalist) to 0.64 (forest). Overall, structural bioindicators described more variance for open country and forest guilds, whereas functional bioindicators explained more variance for generalist bird species and all guilds considered concurrently. Simultaneously considering functional and structural bioindicators accounted for the most variance in richness (59%) for open country birds; however, combining bioindicator types did not improve upon the best models for generalist and/or forest guilds.
机译：我们调查是否可以使用从高光谱数据和/或从激光雷达获得的植被结构方面的生态系统功能和树木物种多样性指标来描述不同鸟类行会（基于相似的环境资源分组而组成的物种）的丰富度。鸟类调查促进了判别分析，以确定哪些变量最能区分行会。然后利用Akaike的信息标准（AIC）和广义线性模型（GLM）来开发预测模型。代表叶面含水量和树木物种多样性的生物指标是用于区分行会的最有用的高光谱变量（p <0.01），并且经常被用来描述丰富度。仅使用生态系统功能的生物指标，对全球生物多样性的确定系数（R〜2_（adj））的调整范围就从0.32（基因学家）到0.58（森林）。相比之下，平均的地下，中层和上层覆盖以及平均表面高度是行会分化最有用的结构生物指标（p <0.05），并且最常被用来描述丰富度。仅由结构生物指标构建的GLM的R〜2_（adj）范围从0.19（一般主义者）到0.64（森林）。总体而言，结构性生物指标描述了开放国家和森林行会的更多差异，而功能性生物指标说明了通才鸟类和所有同时考虑的行会的差异。同时考虑到功能性和结构性生物指标是空旷地区家禽丰富度差异最大的部分（59％）；但是，结合生物指示剂类型并不能改善通才和/或森林协会的最佳模型。

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  • 来源
    《International journal of remote sensing》 |2013年第8期|2689-2713|共25页
  • 作者

    Trevor G. Jones; Peter Arcese; Tara Sharma; Nicholas C. Coops;

  • 作者单位

    Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada V6T 1Z4;

    Centre for Applied Conservation Research,Department of Forest Sciences, University of British Columbia, Vancouver, BC, Canada V6T 1Z4;

    Parks Canada, Gulf Islands National Park Reserve of Canada, Sidney, BC, Canada V8L 2P6;

    Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada V6T 1Z4;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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