Volume 41 Issue 1
Jan.  2022
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Lin Lei, Jintao Wang, Xinjun Chen. Influence of environmental data of different sources on marine species habitat modeling: A case study for Ommastrephes bartramii in the Northwest Pacific Ocean[J]. Acta Oceanologica Sinica, 2022, 41(1): 76-83. doi: 10.1007/s13131-021-1896-x
Citation: Lin Lei, Jintao Wang, Xinjun Chen. Influence of environmental data of different sources on marine species habitat modeling: A case study for Ommastrephes bartramii in the Northwest Pacific Ocean[J]. Acta Oceanologica Sinica, 2022, 41(1): 76-83. doi: 10.1007/s13131-021-1896-x

Influence of environmental data of different sources on marine species habitat modeling: A case study for Ommastrephes bartramii in the Northwest Pacific Ocean

doi: 10.1007/s13131-021-1896-x
Funds:  The National Key R&D Program of China under contract Nos 2019YFD0901401 and 2019YFD0901404; the National Natural Science Foundation of China under contract No. NSFC41876141; the Shanghai Science and Technology Innovation Program under contract No. 19DZ1207502; the Construction and Application of Natural Resources Satellite Remote Sensing Technology System under contract No. 202101004.
More Information
  • Corresponding author: E-mail: jtwang@shou.edu.cn
  • Received Date: 2021-02-27
  • Accepted Date: 2021-05-21
  • Available Online: 2021-09-24
  • Publish Date: 2022-01-10
  • The quality of environmental data and its possible impact on the marine species habitat modelling are often overlooked while the sources for these data are increasing. This study selected sea surface temperature (SST) from two commonly used sources, the NOAA OceanWatch and IRI/LDEO Climate Data Library, and then constructed habitat suitability index model to evaluate the influences of SST from the two sources on the outcomes of Ommastrephes bartramii habitat models for the months of July–October in the Northwest Pacific Ocean during 1996–2012. This study examined the differences in the amount of estimated unfavourable/favourable habitat area when the SST used for model building and inference were the same or different. Dynamics in suitable habitat area calculated from SST was insensitive to the two different SST products. In the fishing season of O.bartramii, the changes of magnitude and trend of monthly suitable habitat area in August and September were similar over time, whereas there were large differences for July and October. Importantly, there is a substantial lack of consistency in the O.bartramii habitat distribution based on SST of two sources. This study considered the sources of environmental data for habitat modelling and then inferred species habitat distribution whether by the same or different data source.
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  • [1]
    Bivand R. 2013. ClassInt: choose univariate class intervals R package version 0. 1–22. [2020-07-06] http://CRAN.R-project.org/package=classInt
    [2]
    Brodie S, Hobday A J, Smith J A, et al. 2017. Seasonal forecasting of dolphinfish distribution in eastern Australia to aid recreational fishers and managers. Deep-Sea Research Part II: Topical Studies in Oceanography, 140: 222–229. doi: 10.1016/j.dsr2.2017.03.004
    [3]
    Brooks R P. 1997. Improving habitat suitability index models. Wildlife Society of Bulletin, 25: 163–167
    [4]
    Chen Xinjun, Liu Bilin, Chen Yong. 2008. A review of the development of Chinese distant-water squid jigging fisheries. Fisheries Research, 89(3): 211–221. doi: 10.1016/j.fishres.2007.10.012
    [5]
    Chen Xinjun, Tian Siquan, Chen Yong, et al. 2010. A modeling approach to identify optimal habitat and suitable fishing grounds for neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean. Fishery Bulletin, 108(1): 1–14
    [6]
    Chen Xinjun, Zhao Xiaohu, Chen Yong. 2007. El Nino/La Nina influence on the western winter–spring cohort of neon flying squid (Ommastrephes bartarmii) in the Northwester Pacific Ocean. ICES Journal of Marine Science, 64: 1152–1160. doi: 10.1093/icesjms/fsm103
    [7]
    Eveson J P, Hobday A J, Hartog J R, et al. 2015. Seasonal forecasting of tuna habitat in the Great Australian Bight. Fisheries Research, 170: 39–49. doi: 10.1016/j.fishres.2015.05.008
    [8]
    Feng B, Chen X J, Xu L X. 2007. Study on distribution of Thunnuns Obesus in the Indian Ocean based on habitat suitability index. Journal of Fisheries of China, 31(6): 805–812
    [9]
    Franklin J. 2010. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge, United Kingdom: Cambridge University Press
    [10]
    Gore J A, Hamilton S W. 1996. Comparison of flow-related habitat evaluations downstream of low-head weirs on small and large fluvial ecosystems. Regulated Rivers, 12(4–5): 459–469
    [11]
    Huang Boyin, Thorne P W, Banzon V F, et al. 2017. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. Journal of Climate, 30(20): 8179–8205. doi: 10.1175/JCLI-D-16-0836.1
    [12]
    Klemas V. 2013. Fisheries applications of remote sensing: an overview. Fisheries Research, 148: 124–136. doi: 10.1016/j.fishres.2012.02.027
    [13]
    Lee P F, Chen I C, Tzeng W N. 2005. Spatial and temporal distribution patterns of Bigeye tuna (Thunnus obesus) in the Indian Ocean. Zoological Studies, 44(2): 260–270
    [14]
    Maddock I. 1999. The importance of physical habitat assessment for evaluating river health. Freshwater Biology, 41(2): 373–391. doi: 10.1046/j.1365-2427.1999.00437.x
    [15]
    Maxwell S M, Hazen E L, Lewison R L, et al. 2015. Dynamic ocean management: defining and conceptualizing real-time management of the ocean. Marine Policy, 58: 42–50. doi: 10.1016/j.marpol.2015.03.014
    [16]
    Meng K C, Oremus K L, Gaines S D. 2016. New England Cod collapse and the climate. PLoS ONE, 11(7): e0158487. doi: 10.1371/journal.pone.0158487
    [17]
    Morris L, Ball D. 2006. Habitat suitability modelling of economically important fish species with commercial fisheries data. ICES Journal of Marine Science, 63(9): 1590–1603. doi: 10.1016/j.icesjms.2006.06.008
    [18]
    Perry A L, Low P J, Ellis J R, et al. 2005. Climate change and distribution shifts in marine fishes. Science, 308(5730): 1912–1915. doi: 10.1126/science.1111322
    [19]
    Reynolds R W, Rayner N A, Smith T M, et al. 2002. An improved in situ and satellite SST analysis for climate. Journal of Climate, 15(13): 1609–1625. doi: 10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2
    [20]
    Roper C F E, Sweeney M J, Nauen C E. 1984. FAO Species Catalogue Vol. 3. Cephalopods of the World: an Annotated and Illustrated Catalogue of Species of Interest to Fisheries. FAO Fisheries Synopsis No. 125. Rome, Italy: FAO, 277
    [21]
    Tanaka K, Chen Yong. 2015. Spatiotemporal variability of suitable habitat for American Lobster (Homarus americanus) in Long Island Sound. Journal of Shellfish Research, 34(2): 531–543. doi: 10.2983/035.034.0238
    [22]
    Tanaka K, Chen Yong. 2016. Modeling spatiotemporal variability of the bioclimate envelope of Homarus americanus in the coastal waters of Maine and New Hampshire. Fisheries Research, 177: 137–152. doi: 10.1016/j.fishres.2016.01.010
    [23]
    Tian Siquan, Chen Xinjun, Chen Yong, et al. 2009. Evaluating habitat suitability indices derived from CPUE and fishing effort data for Ommatrephes bratramii in the northwestern Pacific Ocean. Fisheries Research, 95(2–3): 181–188
    [24]
    Valavanis V D, Pierce G J, Zuur A F, et al. 2008. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia, 612(1): 5–20. doi: 10.1007/s10750-008-9493-y
    [25]
    Vinagre C, Fonseca V, Cabra H, et al. 2006. Habitat suitability index models for the juvenile soles, Solea solea and Solea senegalensis, in the Tagus Estuary: defining variables for species management. Fisheries Research, 82(1–3): 140–149
    [26]
    Wang Yaogeng, Chen Xinjun. 2005. The Resource and Biology of Economic Oceanic Squid in the World. Beijing: China Ocean Press, 79–295
    [27]
    Wang Jintao, Chen Xinjun, Tanaka K, et al. 2017. Environmental influences on commercial oceanic ommastrephid squids: a stock assessment perspective. Scientia Marina, 81(1): 37–47. doi: 10.3989/scimar.04497.25B
    [28]
    Wang Jintao, Yu Wei, Chen Xinjun, et al. 2015. Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network. International Journal of Remote Sensing, 36(13): 3317–3330. doi: 10.1080/01431161.2015.1042121
    [29]
    Welch H, Brodie S, Jacox M G, et al. 2020. Considerations for transferring an operational dynamic ocean management tool between ocean color products. Remote sensing of Environment, 242: 111753. doi: 10.1016/j.rse.2020.111753
    [30]
    Welch H, Hazen E L, Bograd S J, et al. 2019. Practical considerations for operationalizing dynamic management tools. Journal of Applied Ecology, 56(2): 459–469. doi: 10.1111/1365-2664.13281
    [31]
    Yatsu A, Midorikawa S, Shimada T, et al. 1997. Age and growth of the neon flying squid (Ommastrephes bartrami) in the North Pacific Ocean. Fisheries Research, 29(3): 257–270. doi: 10.1016/S0165-7836(96)00541-3
    [32]
    Yu Wei, Chen Xinjun, Chen Changsheng, et al. 2017. Impacts of oceanographic factors on interannual variability of the winter-spring cohort of neon flying squid abundance in the Northwest Pacific Ocean. Acta Oceanologica Sinica, 36(10): 48–59. doi: 10.1007/s13131-017-1069-0
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