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Species bias and spillover effects in scientific research on Carnivora in China

Zhi-Ning Wang Li Yang Peng-Fei Fan Lu Zhang

Zhi-Ning Wang, Li Yang, Peng-Fei Fan, Lu Zhang. Species bias and spillover effects in scientific research on Carnivora in China. Zoological Research, 2021, 42(3): 354-361. doi: 10.24272/j.issn.2095-8137.2021.033
Citation: Zhi-Ning Wang, Li Yang, Peng-Fei Fan, Lu Zhang. Species bias and spillover effects in scientific research on Carnivora in China. Zoological Research, 2021, 42(3): 354-361. doi: 10.24272/j.issn.2095-8137.2021.033

中国食肉目研究中的物种偏倚与溢出效应

doi: 10.24272/j.issn.2095-8137.2021.033

Species bias and spillover effects in scientific research on Carnivora in China

Funds: This study was supported by the National Natural Science Foundation of China (31900372, 31822049)
More Information
  • 摘要: 科学研究可以为濒危物种的保护提供必要的信息。然而,研究不足造成的数据缺乏会阻碍保护计划的制定,而研究偏倚则可能会导致有限的研究资源被不恰当地分配至生物多样性较低的地区或受威胁程度较低的物种。为了确定中国食肉目研究中的物种偏倚与研究空缺,该文对中国食肉目动物相关的论文发表、基金资助与人才培养进行了系统梳理。同时,我们收集了食肉目动物的生物学和生态学特征,使用广义线性模型来确定影响对其研究强度的因素。我们发现中国的食肉目研究在2000年后有大幅度增长,然而物种偏倚一直存在。熊科和大型猫科动物获得了较多的研究,而中小型食肉目动物相关研究很少,研究数量的分布符合80/20法则(二八现象)。模型显示分布区在中国境内比例较大或在中国的保护等级较高的物种获得了更多的研究。作为中国物种保护的标志,大熊猫获得的研究资源占整个食肉目研究资源的一半。同时,大熊猫研究存在溢出效应,即部分在博士期间进行大熊猫研究的人员在毕业后转而研究其他物种,这种溢出可能有益于其他物种的研究。为了提升与加强食肉目动物的研究与保护,我们建议增加对受忽视物种的投入,培养更多的学生,并加强学术交流。如果没有这些行动,很多食肉目动物将持续面临数据缺乏的困境并且受到威胁。
  • Figure  1.  General patterns and trends of carnivoran research in China up to 2019

    A: Number of Chinese and English papers; B: Proportion of international cooperation in Chinese and English papers; C: Research fields of papers; D: Number of grants; E: Amount of grants; F: Research fields of grants; G: Number of Master’s and PhD graduates; H: Number of academic institutions that have graduates on carnivorans research; I: Research fields of graduate theses. Panels in left and middle columns show average annual figures. EC: Ecology and conservation; GE: Genetics; VS: Veterinary science; PH: Physiology; CM: Captive management; MI: Microbiome; BE: Behavior; AM: Anatomy and morphology, QU: Quarantine; CT: Computer technology.

    Figure  2.  Research bias among species and families on a phylogenetic tree

    Black circles on end of lines show research score (RS) of species. Shadows on clades show actual RS minus expected RS for each family.

    Table  1.   Potential variables resulting in species bias in carnivoran research in China

    FactorHypothesisData sourceReferences
    Body mass (kg)Larger animals tend to receive more research attention.Animal Diversity Web (https://animaldiversity.org/)Ford et al., 2017
    EndemismEndemic species would be a local priority and attract more regional research attention.IUCN, 2020Arponen, 2012
    Category on IUCN Red ListSpecies with higher extinction risk would receive more research attention.IUCN, 2020Mace et al., 2008
    Protection level in ChinaSpecies that are regionally threatened attract more regional research attention.National Forestry and Grassland AdministrationArponen, 2012
    Evolutionary uniquenessSpecies with higher evolutionary uniqueness would attract more research attention because extinction of these species will cause more genetic diversity loss.Gumbs et al., 2018; EDGE of Existence Programme (https://www.edgeofexi stence.org/)Mace et al., 2003
    下载: 导出CSV

    Table  3.   Model-averaged coefficients (+SE) and relative importance based on AICc weight (ωi) for each variable that affected research scores (RSs) of carnivorans in China

    GroupVariableCoefficientSERelative variable importance based on ωi
    Including panda(Intercept)0.1900.172
    Endemism33.46613.5671.000
    Evolutionary uniqueness–0.0110.0090.715
    Chinese protection level6.7354.6151.000
    Excluding panda(Intercept)0.1900.176
    Endemism32.76613.7621.000
    Evolutionary uniqueness–0.0110.0100.710
    Chinese protection level6.7024.6871.000
    下载: 导出CSV

    Table  2.   Top five GLMs ranked by second-order Akaike information criterion (AICc) predicting research scores (RSs) of carnivorans in China

    GroupVariableKLoglikAICcΔAICcωi
    Including pandaEndemism, Chinese protection level, Evolutionary uniqueness4–126.274263.7010.0000.713
    Endemism, Chinese protection level3–128.392265.5401.8380.284
    Endemism, Body mass, Chinese protection level4–132.683276.52012.8180.001
    Endemism, Body mass, IUCN category4–133.073277.29913.5980.001
    Endemism, IUCN category, Evolutionary uniqueness4–134.204279.56215.8600.000
    Excluding pandaEndemism, Chinese protection level, Evolutionary uniqueness4–120.481252.1390.0000.707
    Endemism, Chinese protection level3–122.581253.9311.7920.289
    Endemism, Body mass, IUCN category4–126.980265.13612.9960.001
    Endemism, Body mass, Chinese protection level, Evolutionary uniqueness5–125.934265.54813.4090.001
    Endemism, Body mass, Chinese protection level4–127.533266.24314.1040.001
    K: Number of parameters; Loglik: Log-likelihood; ΔAICc: Difference in AICc values between each model and best model; ωi: AICc weight.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-01
  • 录用日期:  2021-04-13
  • 网络出版日期:  2021-05-08
  • 刊出日期:  2021-05-18

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