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构建特征数据集:方法选择对入侵研究的影响。

Building trait datasets: effect of methodological choice on a study of invasion.

机构信息

School of Ecosystem and Forest Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia.

Department of Geography, King's College London, 30 Aldwych, London, WC2B 4BG, UK.

出版信息

Oecologia. 2022 Aug;199(4):919-935. doi: 10.1007/s00442-022-05230-8. Epub 2022 Aug 17.

Abstract

Trait-based approaches are commonly used to understand ecological phenomena and processes. Trait data are typically gathered by measuring local specimens, retrieving published records, or a combination of the two. Implications of methodological choices in trait-based ecological studies-including source of data, imputation technique, and species selection criteria-are poorly understood. We ask: do different approaches for dataset-building lead to meaningful differences in trait datasets? If so, do these differences influence findings of a trait-based examination of plant invasiveness, measured as abundance and spread rate? We collected on-site (Victoria, Australia) and off-site (TRY database) height and specific leaf area records for as many species as possible out of 157 exotic herbaceous plants. For each trait, we built six datasets of species-level means using records collected on-site, off-site, on-site and off-site combined, and off-site supplemented via imputation based on phylogeny and/or trait correlations. For both traits, the six datasets were weakly correlated (ρ = 0.31-0.95 for height; ρ = 0.14-0.88 for SLA), reflecting differences in species' trait values from the various estimations. Inconsistencies in species' trait means across datasets did not translate into large differences in trait-invasion relationships. Although we did not find that methodological choices for building trait datasets greatly affected ecological inference about local invasion processes, we nevertheless recommend: (1) using on-site records to answer local-scale ecological questions whenever possible, and (2) transparency around methodological decisions related to selection of study species and estimation of missing trait values.

摘要

基于特征的方法通常用于理解生态现象和过程。特征数据通常通过测量当地标本、检索已发表的记录或两者的组合来收集。基于特征的生态研究中方法选择的影响——包括数据来源、插补技术和物种选择标准——理解得很差。我们问:数据集构建的不同方法是否会导致特征数据集有意义的差异?如果是这样,这些差异是否会影响基于特征的植物入侵性研究的发现,以丰度和传播率来衡量?我们在现场(澳大利亚维多利亚州)和场外(TRY 数据库)收集了尽可能多的 157 种外来草本植物的高度和比叶面积记录。对于每个特征,我们使用现场收集、场外收集、现场和场外组合收集以及场外通过基于系统发育和/或特征相关性的插补补充的记录,构建了六个物种水平平均值数据集。对于这两个特征,六个数据集相关性较弱(高度的 ρ=0.31-0.95;比叶面积的 ρ=0.14-0.88),反映了来自各种估计的物种特征值的差异。跨数据集的物种特征均值不一致并没有导致特征-入侵关系的巨大差异。尽管我们没有发现构建特征数据集的方法选择对当地入侵过程的生态推断有很大影响,但我们仍然建议:(1)尽可能使用现场记录来回答局部尺度的生态问题,以及(2)在选择研究物种和估计缺失特征值的方法决策方面保持透明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380a/9464113/4539a2c260df/442_2022_5230_Fig1_HTML.jpg

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