-
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。Abstract: Scientific research provides essential information for conservation of threatened species. Data deficiency due to insufficient research impedes the design of conservation plans, and research bias may mistakenly direct limited resources to low biodiversity regions or less threatened species. Here, we conducted a systematic review of published papers, grants, and graduate student training on carnivorans in China to identify species bias and research gaps. Furthermore, we collected intrinsic and extrinsic features of carnivorans, and identified features that impact research intensity using generalized linear models. We found that the amount of research on carnivorans increased markedly after 2000, but species bias existed. Bears and big cats received the greatest research attention, while most small- and medium-sized carnivorans received little attention, thus showing the 80-20 phenomenon. Species with a higher level of endemism and protection under Chinese law received more consideration. As an animal conservation icon in China, the giant panda (Ailuropoda melanoleuca) attracted more than 50% of overall carnivoran research resources. However, the giant panda also showed spillover effects, i.e., post-doctoral graduates who studied the giant panda shifted their research focus to other species after graduation, which may help improve research on other species. Thus, to improve and strengthen Carnivora research and conservation, we suggest investing greater effort in species of less concern, training of more graduate students, and reinforcing academic exchange. If such actions are not taken, many carnivoran species will continue being data deficient and threatened.
-
Key words:
- Ailuropoda melanoleuca /
- Carnivora /
- Conservation /
- Research bias /
- 80-20 phenomenon
-
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
Factor Hypothesis Data source References Body mass (kg) Larger animals tend to receive more research attention. Animal Diversity Web (https://animaldiversity.org/) Ford et al., 2017 Endemism Endemic species would be a local priority and attract more regional research attention. IUCN, 2020 Arponen, 2012 Category on IUCN Red List Species with higher extinction risk would receive more research attention. IUCN, 2020 Mace et al., 2008 Protection level in China Species that are regionally threatened attract more regional research attention. National Forestry and Grassland Administration Arponen, 2012 Evolutionary uniqueness Species 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
Group Variable Coefficient SE Relative variable importance based on ωi Including panda (Intercept) 0.190 0.172 Endemism 33.466 13.567 1.000 Evolutionary uniqueness –0.011 0.009 0.715 Chinese protection level 6.735 4.615 1.000 Excluding panda (Intercept) 0.190 0.176 Endemism 32.766 13.762 1.000 Evolutionary uniqueness –0.011 0.010 0.710 Chinese protection level 6.702 4.687 1.000
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
Group Variable K Loglik AICc ΔAICc ωi Including panda Endemism, Chinese protection level, Evolutionary uniqueness 4 –126.274 263.701 0.000 0.713 Endemism, Chinese protection level 3 –128.392 265.540 1.838 0.284 Endemism, Body mass, Chinese protection level 4 –132.683 276.520 12.818 0.001 Endemism, Body mass, IUCN category 4 –133.073 277.299 13.598 0.001 Endemism, IUCN category, Evolutionary uniqueness 4 –134.204 279.562 15.860 0.000 Excluding panda Endemism, Chinese protection level, Evolutionary uniqueness 4 –120.481 252.139 0.000 0.707 Endemism, Chinese protection level 3 –122.581 253.931 1.792 0.289 Endemism, Body mass, IUCN category 4 –126.980 265.136 12.996 0.001 Endemism, Body mass, Chinese protection level, Evolutionary uniqueness 5 –125.934 265.548 13.409 0.001 Endemism, Body mass, Chinese protection level 4 –127.533 266.243 14.104 0.001 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
-
[1] Arponen A. 2012. Prioritizing species for conservation planning. Biodiversity and Conservation, 21(4): 875−893. doi: 10.1007/s10531-012-0242-1 [2] Barnosky AD, Matzke N, Tomiya S, Wogan GOU, Swartz B, Quental TB, et al. 2011. Has the Earth’s sixth mass extinction already arrived?. Nature, 471(7336): 51−57. doi: 10.1038/nature09678 [3] Brito D. 2008. Amphibian conservation: are we on the right track?. Biological Conservation, 141(11): 2912−2917. doi: 10.1016/j.biocon.2008.08.016 [4] Burnham KP, Anderson DR, Huyvaert KP. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65(1): 23−35. doi: 10.1007/s00265-010-1029-6 [5] Cardillo M, Purvis A, Sechrest W, Gittleman JL, Bielby J, Mace GM. 2004. Human population density and extinction risk in the world's carnivores. PLoS Biology, 2(7): e197. doi: 10.1371/journal.pbio.0020197 [6] CBD. 2010. The strategic plan for biodiversity 2011–2020 and the aichi biodiversity targets. Nagoya, Japan. [7] Christie AP, Amano T, Martin PA, Petrovan SO, Shackelford GE, Simmons BI, et al. 2021. The challenge of biased evidence in conservation. Conservation Biology, 35(1): 249−262. doi: 10.1111/cobi.13577 [8] Clark JA, May RM. 2002. Taxonomic bias in conservation research. Science, 297(5579): 191−192. [9] Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ, et al. 2011. Trophic downgrading of planet earth. Science, 333(6040): 301−306. doi: 10.1126/science.1205106 [10] Estes JA, Tinker MT, Williams TM, Doak DF. 1998. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science, 282(5388): 473−476. doi: 10.1126/science.282.5388.473 [11] Fan PF, Ma C. 2018. Extant primates and development of primatology in China: publications, student training, and funding. Zoological Research, 39(4): 249−254. doi: 10.24272/j.issn.2095-8137.2018.033 [12] Fan PF, Yang L, Liu Y, Lee TM. 2020. Build up conservation research capacity in China for biodiversity governance. Nature Ecology & Evolution, 4(9): 1162−1167. [13] Ford AT, Cooke SJ, Goheen JR, Young TP. 2017. Conserving megafauna or sacrificing biodiversity?. BioScience, 67(3): 193−196. [14] Gillespie CS. 2015. Fitting heavy tailed distributions: the powerlaw package. Journal of Statistical Software, 64(2): 1−16. [15] Gumbs R, Gray CL, Wearn OR, Owen NR. 2018. Tetrapods on the EDGE: overcoming data limitations to identify phylogenetic conservation priorities. PLoS One, 13(4): e0194680. doi: 10.1371/journal.pone.0194680 [16] Hammerschlag N, Gallagher AJ. 2017. Extinction risk and conservation of the earth's national animal symbols. BioScience, 67(8): 744−749. doi: 10.1093/biosci/bix054 [17] He L, García-Perea R, Li M, Wei FW. 2004. Distribution and conservation status of the endemic Chinese mountain cat Felis bieti. Oryx, 38(1): 55−61. [18] Horta H, Veloso FM, Grediaga R. 2010. Navel gazing: academic inbreeding and scientific productivity. Management Science, 56(3): 414−429. doi: 10.1287/mnsc.1090.1109 [19] Hu YB, Wu Q, Ma S, Ma TX, Shan L, Wang X, et al. 2017. Comparative genomics reveals convergent evolution between the bamboo-eating giant and red pandas. Proceedings of the National Academy of Sciences of the United States of America, 114(5): 1081−1086. doi: 10.1073/pnas.1613870114 [20] Hu YS, Luo ZH, Chapman CA, Pimm SL, Turvey ST, Lawes MJ, et al. 2019. Regional scientific research benefits threatened-species conservation. National Science Review, 6(6): 1076−1079. doi: 10.1093/nsr/nwz090 [21] Jiang ZG, Liu SY, Wu Y, Jiang XL, Zhou KY. 2017. Chian's mammal diversity (2nd edition). Biodiversity Science, 25(8): 886−895. (in Chinese) doi: 10.17520/biods.2017098 [22] Kruuk H. 2006. Otters: Ecology, Behaviour, and Conservation. Oxford: Oxford University Press. [23] Lau MWN, Fellowes JR, Chan BPL. 2010. Carnivores (Mammalia: Carnivora) in South China: a status review with notes on the commercial trade. Mammal Review, 40(4): 247−292. doi: 10.1111/j.1365-2907.2010.00163.x [24] Li S, McShea WJ, Wang DJ, Gu XD, Zhang XF, Zhang L, et al. 2020. Retreat of large carnivores across the giant panda distribution range. Nature Ecology & Evolution, 4(10): 1327−1331. [25] Mace GM, Collar NJ, Gaston KJ, Hilton-Taylor C, AkÇakaya HR, Leader-Williams N, et al. 2008. Quantification of extinction risk: IUCN's system for classifying threatened species. Conservation Biology, 22(6): 1424−1442. doi: 10.1111/j.1523-1739.2008.01044.x [26] Mace GM, Gittleman JL, Purvis A. 2003. Preserving the tree of life. Science, 300(5626): 1707−1709. doi: 10.1126/science.1085510 [27] Marginson S. 2018. National/global synergy in the development of higher education and science in China since 1978. Frontiers of Education in China, 13(4): 486−512. doi: 10.1007/s11516-018-0027-8 [28] Miller B, Dugelby B, Foreman D, del Río CM, Noss R, Phillips M, et al. 2001. The importance of large carnivores to healthy ecosystems. Endangered Species Update, 18(5): 202−210. [29] Newman MEJ. 2005. Power laws, Pareto distributions and Zipf's law. Contemporary Physics, 46(5): 323−351. doi: 10.1080/00107510500052444 [30] Nie YG, Speakman JR, Wu Q, Zhang CL, Hu YB, Xia MH, et al. 2015. Exceptionally low daily energy expenditure in the bamboo-eating giant panda. Science, 349(6244): 171−174. doi: 10.1126/science.aab2413 [31] Nie YG, Wei FW, Zhou WL, Hu YB, Senior AM, Wu Q, et al. 2019. Giant pandas are macronutritional carnivores. Current Biology, 29(10): 1677−1682. doi: 10.1016/j.cub.2019.03.067 [32] Papeş M, Gaubert P. 2007. Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Diversity and Distributions, 13(6): 890−902. doi: 10.1111/j.1472-4642.2007.00392.x [33] Prugh LR, Stoner CJ, Epps CW, Bean WT, Ripple WJ, Laliberte AS, et al. 2009. The rise of the mesopredator. BioScience, 59(9): 779−791. doi: 10.1525/bio.2009.59.9.9 [34] R Core Team. 2019. R: a language and environment for statistical computing. Vienna, Austria: R for Statistical Computing. [35] Swaisgood RR, Wang DJ, Wei FW. 2018. Panda downlisted but not out of the woods. Conservation Letters, 11(1): e12355. doi: 10.1111/conl.12355 [36] Symonds MRE, Moussalli A. 2011. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology, 65(1): 13−21. doi: 10.1007/s00265-010-1037-6 [37] Tensen L. 2018. Biases in wildlife and conservation research, using felids and canids as a case study. Global Ecology and Conservation, 15: e00423. doi: 10.1016/j.gecco.2018.e00423 [38] Upham NS, Esselstyn JA, Jetz W. 2019. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biology, 17(12): e3000494. doi: 10.1371/journal.pbio.3000494 [39] Wagenmakers EJ, Farrell S. 2004. AIC model selection using Akaike weights. Psychonomic Bulletin & Review, 11(1): 192−196. [40] Wilson DE, Mittermeier RA. 2009. Handbook of the Mammals of the World. Vol. 1. Carnivores. Barcelona: Lynx. [41] Wilson KA, Auerbach NA, Sam K, Magini AG, Moss ASL, Langhans SD, et al. 2016. Conservation research is not happening where it is most needed. PLoS Biology, 14(3): e1002413. doi: 10.1371/journal.pbio.1002413 [42] Xiao LG, Zhao RQ. 2017. China's new era of ecological civilization. Science, 358(6366): 1008−1009. [43] Yu GC, Smith DK, Zhu HC, Guan Y, Lam TTY. 2017. Ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution, 8(1): 28−36. doi: 10.1111/2041-210X.12628 [44] Zhang L, Wang QY, Yang L, Li F, Chan BPL, Xiao ZS, et al. 2018. The neglected otters in China: distribution change in the past 400 years and current conservation status. Biological Conservation, 228: 259−267. doi: 10.1016/j.biocon.2018.10.028 -
下载:
点击查看大图
图(2) / 表(3)
计量
- 文章访问数: 1394
- HTML全文浏览量: 682
- PDF下载量: 195
- 被引次数: 0
