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Applying generalized allometric regressions to predict live body mass of tropical and temperate arthropods

机译:应用广义异度回归预测热带和温带节肢动物的活体质量

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The ecological implications of body size extend from the biology of individual organisms to ecosystem‐level processes. Measuring body mass for high numbers of invertebrates can be logistically challenging, making length–mass regressions useful for predicting body mass with minimal effort. However, standardized sets of scaling relationships covering a large range in body length, taxonomic groups, and multiple geographical regions are scarce. We collected 6,212 arthropods from 19 higher‐level taxa in both temperate and tropical locations to compile a comprehensive set of linear models relating live body mass to a range of predictor variables. We measured live weight (hereafter, body mass), body length and width of each individual and conducted linear regressions to predict body mass using body length, body width, taxonomic group, and geographic region. Additionally, we quantified prediction discrepancy when using parameters from arthropods of a different geographic region. Incorporating body width into taxon‐ and region‐specific length–mass regressions yielded the highest prediction accuracy for body mass. Using regression parameters from a different geographic region increased prediction discrepancy, causing over‐ or underestimation of body mass depending on geographical origin and whether body width was included. We present a comprehensive range of parameters for predicting arthropod body mass and provide guidance for selecting optimal scaling relationships. Given the importance of body mass for functional invertebrate ecology and the paucity of adequate regressions to predict arthropod body mass from different geographical regions, our study provides a long‐needed resource for quantifying live body mass in invertebrate ecology research.
机译:人体大小的生态影响从单个生物的生物学延伸到生态系统级过程。测量大量无​​脊椎动物的体重在逻辑上可能具有挑战性,这使得长度-质量回归对于以最小的努力来预测体重非常有用。但是,缺乏覆盖人体长度,生物分类群和多个地理区域的标准化缩放关系集。我们在温带和热带地区从19个较高级别的分类单元中收集了6,212个节肢动物,以建立一套将活体质量与一系列预测变量相关联的线性模型。我们测量了每个人的活体重(以下称体重),体重和身长,并进行了线性回归,使用体重,身高,分类群和地理区域来预测体重。此外,当使用来自不同地理区域节肢动物的参数时,我们量化了预测差异。将身体宽度纳入分类单元和特定区域的长度-质量回归中,可以得出最高的体重预测准确性。使用来自不同地理区域的回归参数会增加预测差异,导致对体重的估计过高或过低,具体取决于地理来源以及是否包括身体宽度。我们提供了用于预测节肢动物体重的全面参数,并为选择最佳比例关系提供了指导。鉴于体重对于功能性无脊椎动物生态学的重要性以及缺乏足够的回归来预测不同地理区域的节肢动物的体重,我们的研究为量化无脊椎动物生态学研究中的活体体重提供了长期需要的资源。

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