Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea: a case study of Tanaka's snailfish (Liparis tanakae)

Yunlong Chen Xiujuan Shan Dingyong Zeng Harry Gorfine Yinfeng Xu Qiang Wu Tao Yang Xianshi Jin

Yunlong Chen, Xiujuan Shan, Dingyong Zeng, Harry Gorfine, Yinfeng Xu, Qiang Wu, Tao Yang, Xianshi Jin. Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea: a case study of Tanaka's snailfish (Liparis tanakae)[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1912-1
Citation: Yunlong Chen, Xiujuan Shan, Dingyong Zeng, Harry Gorfine, Yinfeng Xu, Qiang Wu, Tao Yang, Xianshi Jin. Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea: a case study of Tanaka's snailfish (Liparis tanakae)[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1912-1

doi: 10.1007/s13131-021-1912-1

Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea: a case study of Tanaka's snailfish (Liparis tanakae)

Funds: The National Key R&D Program of China under contract No. 2018YFD0900906; the National Natural Science Foundation of China under contract No. 31872692; the Youth Talent Program Supported by Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2018-MFS-T05; the Central Public-Interest Scientific Institution Basal Research Fund, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences under contract No. 20603022019010.
More Information
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  / 
    •  
  • Figure  1.  Study area

    Figure  2.  Model performances evaluated by the area under the receiver operating characteristic curve (AUC), the Cohen’s Kappa (Kappa) and the true skill statistics (TSS) of L. tanakae. Data are expressed with mean±standard error.

    Figure  3.  Seasonal variable importance of environmental variables (BS, bottom salinity; BT, bottom temperature; BU, bottom velocity in latitude direction; BV, bottom velocity in longitude direction; CV, currents velocity; DS, distance to shore). Data are expressed with mean±standard error.

    Figure  4.  Response curves of predicted occurrence probability of Liparis tanakae against dominant environmental variable. Multiple lines represent the results of 10 evaluation repetitions.

    Figure  5.  Seasonal habitat suitability for L. tanakae predicted by random forests. Solid dots mean presence; circles, absence.

    Figure  6.  Model performances of Liparis tanakae between control (data without errors added) and degraded data (data with errors added) using RFs. Errors were added to the occurrence dataset to create false negatives. For each level of error, AUC, Kappa and TSS are expressed with mean±standard error.

  • [1] Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: Prevalence, Kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6): 1223–1232. doi: 10.1111/j.1365-2664.2006.01214.x
    [2] Barbet-Massin M, Jiguet F, Albert C H, et al. 2012. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3(2): 327–338,
    [3] Basher Z, Bowden D A, Costello M J. 2018. Global Marine Environment Datasets (GMED). Version 2.0 (Rev. 02.2018). http://gmed.auckland.ac.nz [2018-07-09/2020-09-19]
    [4] Becker L R, Bartholomä A, Singer A, et al. 2020. Small-scale distribution modeling of benthic species in a protected natural hard ground area in the German North Sea (Helgoländer Steingrund). Geo-Marine Letters, 40(2): 167–181. doi: 10.1007/s00367-019-00598-8
    [5] Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32. doi: 10.1023/A:1010933404324
    [6] Brotons L, Thuiller W, Araújo M B, et al. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27(4): 437–448. doi: 10.1111/j.0906-7590.2004.03764.x
    [7] Chen Dagang. 1991. Fishery Ecology of the Bohai Sea and the Yellow Sea (in Chinese). Beijing: China Ocean Press: 383–386
    [8] Chen Yunlong, Shan Xiujuan, Jin Xianshi, et al. 2018. Changes in fish diversity and community structure in the central and southern Yellow Sea from 2003 to 2015. Journal of Oceanology and Limnology, 36(3): 805–817. doi: 10.1007/s00343-018-6287-6
    [9] Chen Yunlong, Shan Xiujuan, Ovando D, et al. 2021. Predicting current and future global distribution of black rockfish (Sebastes schlegelii) under changing climate. Ecological Indicators, 128: 107799. doi: 10.1016/j.ecolind.2021.107799
    [10] Chen Yunlong, Shan Xiujuan, Zhou Zhipeng, et al. 2013. Interannual variation in the population dynamics of snailfish Liparis tanakae in the Yellow Sea. Acta Ecologica Sinica, 33(19): 6227–6235. doi: 10.5846/stxb201304170731
    [11] Chernova N V, Stein D L, Andriashev A P. 2004. Family Liparidae Scopoli 1777—snailfishes. Annotated Check lists of Fishes No.31. In San Francisco: California Academy of Sciences, 1–72
    [12] Cohen J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1): 37–46. doi: 10.1177/001316446002000104
    [13] Comte L, Grenouillet G. 2013. Species distribution modelling and imperfect detection: comparing occupancy versus consensus methods. Diversity and Distributions, 19(8): 996–1007. doi: 10.1111/ddi.12078
    [14] Cutler D R, Edwards Jr T C J, Beard K H, et al. 2007. Random forests for classification in ecology. Ecology, 88(11): 2783–2792. doi: 10.1890/07-0539.1
    [15] Elith J, Leathwick J R, Hastie T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77(4): 802–813. doi: 10.1111/j.1365-2656.2008.01390.x
    [16] Fernandes R F, Scherrer D, Guisan A. 2019. Effects of simulated observation errors on the performance of species distribution models. Diversity and Distributions, 25(3): 400–413. doi: 10.1111/ddi.12868
    [17] Fu Caihong, Olsen N, Taylor N, et al. 2017. Spatial and temporal dynamics of predator-prey species interactions off western Canada. ICES Journal of Marine Science, 74(8): 2107–2119. doi: 10.1093/icesjms/fsx056
    [18] Gibson L, Barrett B, Burbidge A. 2007. Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Diversity and Distributions, 13(6): 704–713. doi: 10.1111/j.1472-4642.2007.00365.x
    [19] Guo Yanning, Xu Zhen, Zhang Luping, et al. 2014. Occurrence of Hysterothylacium and Anisakis nematodes (Ascaridida: Ascaridoidea) in the tanaka’s snailfish Liparis tanakae (Gilbert & Burke) (Scorpaeniformes: Liparidae). Parasitology Research, 113(4): 1289–1300. doi: 10.1007/s00436-014-3767-2
    [20] Hanley J A, McNeil B J. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1): 29–36. doi: 10.1148/radiology.143.1.7063747
    [21] Hao Tianxiao, Elith J, Guillera-Arroita G, et al. 2019. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Diversity and Distributions, 25(5): 839–852. doi: 10.1111/ddi.12892
    [22] Jin Xianshi, Tang Qisheng. 1996. Changes in fish species diversity and dominant species composition in the Yellow Sea. Fisheries Research, 26(3–4): 337–352. doi: 10.1016/0165-7836(95)00422-X
    [23] Jin Xianshi, Xu Binduo, Tang Q isheng. 2003. Fish assemblage structure in the East China Sea and southern Yellow Sea during autumn and spring. Journal of Fish Biology, 62(5): 1194–1205. doi: 10.1046/j.1095-8649.2003.00116.x
    [24] Jin Xianshi, Zhang Bo, Xue Ying. 2010. The response of the diets of four carnivorous fishes to variations in the Yellow Sea ecosystem. Deep-Sea Research Part II: Topical Studies in Oceanography, 57(11–12): 996–1000. doi: 10.1016/j.dsr2.2010.02.001
    [25] Lauria V, Gristina M, Attrill M J, et al. 2015. Predictive habitat suitability models to aid conservation of elasmobranch diversity in the central Mediterranean Sea. Scientific Reports, 5: 13245. doi: 10.1038/srep13245
    [26] Lobo J M, Jiménez-Valverde A, Hortal J. 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography, 33: 103–114. doi: 10.1111/j.1600-0587.2009.06039.x
    [27] Manceur A M, Kühn I. 2014. Inferring model-based probability of occurrence from preferentially sampled data with uncertain absences using expert knowledge. Methods in Ecology and Evolution, 5(8): 739–750. doi: 10.1111/2041-210x.12224
    [28] Marx M, Quillfeldt P. 2018. Species distribution models of European Turtle Doves in Germany are more reliable with presence only rather than presence absence data. Scientific Reports, 8(1): 16898. doi: 10.1038/s41598-018-35318-2
    [29] Melnychuk M C, Peterson E, Elliott M, et al. 2017. Fisheries management impacts on target species status. Proceedings of the National Academy of Sciences of the United States of America, 114(1): 178–183. doi: 10.1073/pnas.1609915114
    [30] Molloy S W, Davis R A, Dunlop J A, et al. 2017. Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation, 18: 27–46. doi: 10.3897/natureconservation.18.12235
    [31] Park J M, Kwak S N, Huh S H, et al. 2017. Diets and niche overlap among nine co-occurring demersal fishes in the southern continental shelf of East/Japan Sea, Korea. Deep-Sea Research Part II: Topical Studies in Oceanography, 143: 100–109. doi: 10.1016/j.dsr2.2017.06.002
    [32] Phillips N D, Reid N, Thys T, et al. 2017. Applying species distribution modelling to a data poor, pelagic fish complex: the ocean sunfishes. Journal of Biogeography, 44(10): 2176–2187. doi: 10.1111/jbi.13033
    [33] Pons M, Melnychuk M C, Hilborn R. 2018. Management effectiveness of large pelagic fisheries in the high seas. Fish and Fisheries, 19(2): 260–270. doi: 10.1111/faf.12253
    [34] Record S, Strecker A, Tuanmu M N, et al. 2018. Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions. PLoS One, 13(4): e0194650. doi: 10.1371/journal.pone.0194650
    [35] Rubio I, Ganzedo U, Hobday A J, et al. 2020. Southward re-distribution of tropical tuna fisheries activity can be explained by technological and management change. Fish and Fisheries, 21(3): 511–521,
    [36] Sarquis J A, Cristaldi M A, Arzamendia V, et al. 2018. Species distribution models and empirical test: comparing predictions with well-understood geographical distribution of Bothrops alternatus in Argentina. Ecology and Evolution, 8(21): 10497–10509. doi: 10.1002/ece3.4517
    [37] Schickele A, Leroy B, Beaugrand G, et al. 2020. Modelling European small pelagic fish distribution: Methodological insights. Ecological Modelling, 416: 108902. doi: 10.1016/j.ecolmodel.2019.108902
    [38] Soberón J, Nakamura M. 2009. Niches and distributional areas: concepts, methods, and assumptions. Proceedings of the National Academy of Sciences of the United States of America, 106(S2): 19644–19650. doi: 10.1073/pnas.0901637106
    [39] Tanaka K R, Torre M P, Saba V S, et al. 2020. An ensemble high-resolution projection of changes in the future habitat of American lobster and sea scallop in the Northeast US continental shelf. Diversity and Distributions, 26(7): 987–1001. doi: 10.1111/ddi.13069
    [40] Thuiller W, Georges D, Gueguen M, et al. 2016. Biomod2: Ensemble platform for species distribution modeling. https://cran.r-project.org/package=biomod2 [2021-6-11/2021-7-22]
    [41] Tomiyama T, Uehara S, Kurita Y. 2013a. Feeding relationships among fishes in shallow sandy areas in relation to stocking of Japanese flounder. Marine Ecology Progress Series, 479: 163–175. doi: 10.3354/meps10191
    [42] Tomiyama T, Yamada M, Yoshida T. 2013b. Seasonal migration of the snailfish Liparis tanakae and their habitat overlap with 0-year-old Japanese flounder Paralichthys olivaceus. Journal of the Marine Biological Association of the United Kingdom, 93(7): 1981–1987. doi: 10.1017/S0025315413000544
    [43] van Hecke T. 2012. Power study of anova versus Kruskal-Wallis test. Journal of Statistics and Management Systems, 15(2–3): 241–247. doi: 10.1080/09720510.2012.10701623
    [44] Wan Ruijing, Jiang Yanwei. 2000. The species and biological characteristics of the eggs and larvae of osteichthyes in the Bohai Sea and Yellow Sea. Journal of Shanghai Fisheries University, 9(4): 290–297
    [45] Wang Fan, Liu Chuanyu. 2009. An N-shape thermal front in the western South Yellow Sea in winter. Chinese Journal of Oceanology and Limnology, 27(4): 898. doi: 10.1007/s00343-009-9045-y
    [46] Wisz M S, Broennimann O, Grønkjær P, et al. 2015. Arctic warming will promote Atlantic-Pacific fish interchange. Nature Climate Change, 5(3): 261–265. doi: 10.1038/nclimate2500
    [47] Zhang Bo, Jin Xianshi, Dai Fangqun. 2011. Feeding habits and their variation of seasnail (Liparis tanakae) in the central and southern Yellow Sea. Journal of Fisheries of China, 35(8): 1199–1207
    [48] Zhong Mingyu, Wu Huifeng, Mi Wenying, et al. 2018. Occurrences and distribution characteristics of organophosphate ester flame retardants and plasticizers in the sediments of the Bohai and Yellow Seas, China. Science of The Total Environment, 615: 1305–1311. doi: 10.1016/j.scitotenv.2017.09.272
    [49] Zhou Zhipeng, Jin Xianshi, Shan Xiujuan, et al. 2012. Seasonal variations in distribution and biological characteristics of snailfish Liparis tanakae in the central and southern Yellow Sea. Acta Ecologica Sinica, 32(17): 5550–5561. doi: 10.5846/stxb201108061152
  • 加载中
计量
  • 文章访问数:  17
  • HTML全文浏览量:  4
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-09
  • 录用日期:  2021-08-03
  • 网络出版日期:  2022-05-10

目录

    /

    返回文章
    返回