Statzner Bernhard, Bonada Núria, Dolédec Sylvain
CNRS-Ecologie des Hydrosystèmes Fluviaux, Université Lyon 1, 69622 Villeurbanne Cedex, France.
Oecologia. 2008 May;156(1):65-73. doi: 10.1007/s00442-008-0972-7. Epub 2008 Feb 13.
Is there a relationship between the abundance of organisms and particular biological attributes? To assess this old, yet still acutely debated key question of ecology, we have used large databases on 312 stream macroinvertebrate genera (from 27 orders) that describe (1) invertebrate abundance at 527 least human-impacted European stream sites, (2) 11 biological traits (size, life-history, food, among others) described in 61 biological trait categories (BTCs; e.g. small, intermediate or large size) and (3) 14 attributes indicating specialization (AISs; e.g. species richness, size and food diversity). We applied interactive procedures to obtain models (for BTCs, AISs and a mixture of both descriptions) explaining as much as possible of the abundance variability of the genera with the lowest number of significant and ecologically meaningful attributes and assessed the predictive power of these models (in crosswise validations) by comparing predicted and observed abundances. Mean European invertebrate abundance increased with BTC affinities favouring viability in stream systems (e.g. attachment to the stream bottom to resist the flow, aquatic passive dispersal with the flow, exploitation of abundant food sources) and decreased with BTC affinities disfavouring this viability (e.g. drag force increase associated with larger body size, flow exposure associated with aerial respiration). Abundance consistently decreased with specialization of the genera (e.g. low species richness, oddity of their overall BTC profile from an "average" European genus). The model including a mixture of a few BTCs and AISs had the greatest predictive power: it predicted 35% of the observed abundance (ln-transformed) variability of the genera; these predictions were marginally affected by taxonomy (using orders as categorical variables). We conclude that a better appreciation of the influence of the examined taxonomic diversity, number and type of biological attributes, environmental system and spatial scale could enable abundance predictions using different sets of biological attributes for different taxonomic groups and systems.
生物的丰富度与特定生物学属性之间是否存在关联？为了评估这一古老但仍备受激烈争论的生态学关键问题，我们使用了关于312个溪流大型无脊椎动物属（来自27个目）的大型数据库，这些数据库描述了：（1）在至少受人类影响最小的527个欧洲溪流站点的无脊椎动物丰富度；（2）在61个生物学性状类别（BTCs，例如小、中或大尺寸）中描述的11个生物学性状（大小、生活史、食物等）；以及（3）14个表明专业化程度的属性（AISs，例如物种丰富度、大小和食物多样性）。我们应用交互式程序来获得模型（针对BTCs、AISs以及两者描述的混合），这些模型用最少数量的显著且具有生态意义的属性尽可能多地解释属的丰富度变异性，并通过比较预测丰度和观测丰度来评估这些模型的预测能力（在交叉验证中）。欧洲无脊椎动物的平均丰富度随着有利于在溪流系统中生存的BTC亲和力增加（例如附着在溪流底部以抵抗水流、随水流进行水生被动扩散、利用丰富的食物来源）而增加，随着不利于这种生存能力的BTC亲和力降低（例如与较大体型相关的阻力增加、与空气呼吸相关的水流暴露）而降低。丰富度随着属的专业化程度持续降低（例如低物种丰富度、其整体BTC概况与“普通”欧洲属相比的奇特之处）。包含一些BTCs和AISs混合的模型具有最大的预测能力：它预测了属的观测丰度（对数转换后）变异性的35%；这些预测受分类学（将目用作分类变量）的影响很小。我们得出结论，更好地理解所研究的分类多样性、生物学属性的数量和类型、环境系统以及空间尺度的影响，能够使用针对不同分类群和系统的不同生物学属性集进行丰富度预测。
