Fishery analysis using gradient-dependent optimal interpolation

Chunling Zhang; Danyang Wang; Zhenfeng Wang

doi:10.1007/s13131-021-1895-y

Volume 41 Issue 2

Feb. 2022

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2022 > 41(2): 116-126

Chunling Zhang, Danyang Wang, Zhenfeng Wang. Fishery analysis using gradient-dependent optimal interpolation[J]. Acta Oceanologica Sinica, 2022, 41(2): 116-126. doi: 10.1007/s13131-021-1895-y

Citation:

Chunling Zhang, Danyang Wang, Zhenfeng Wang. Fishery analysis using gradient-dependent optimal interpolation[J]. Acta Oceanologica Sinica, 2022, 41(2): 116-126. doi: 10.1007/s13131-021-1895-y

Chunling Zhang, Danyang Wang, Zhenfeng Wang. Fishery analysis using gradient-dependent optimal interpolation[J]. Acta Oceanologica Sinica, 2022, 41(2): 116-126. doi: 10.1007/s13131-021-1895-y

Citation:

Chunling Zhang, Danyang Wang, Zhenfeng Wang. Fishery analysis using gradient-dependent optimal interpolation[J]. Acta Oceanologica Sinica, 2022, 41(2): 116-126. doi: 10.1007/s13131-021-1895-y

PDF( 718 KB)

Fishery analysis using gradient-dependent optimal interpolation

doi: 10.1007/s13131-021-1895-y

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
Project Management Office of China National Scientific Seafloor Observatory, Tongji University, Shanghai 200092, China

Funds: The National Natural Science Foundation of China under contract No. 4210060098; the Foundation of Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources under contract No. A1-2006-21-200201.

More Information

Corresponding author: E-mail: clzhang@shou.edu.cn; wangzhenfeng@cnsso.edu.cn
Received Date: 2021-01-13
Accepted Date: 2021-05-20

Available Online: 2021-09-10

Publish Date: 2022-02-01

Abstract

Abstract

The current lack of high-precision information on subsurface seawater is a constraint in fishery research. Based on Argo temperature and salinity profiles, this study applied the gradient-dependent optimal interpolation to reconstruct daily subsurface oceanic environmental information according to fishery dates and locations. The relationship between subsurface information and matching yellowfin tuna (YFT) in the western and central Pacific Ocean (WCPO) was examined using catch data from January 1, 2008 to August 31, 2017. The seawater temperature and salinity results showed differences of less than ±0.5°C and ±0.01 compared with the truth observations respectively. Statistical analysis revealed that the most suitable temperature for YFT fishery was 28–29°C at the near-surface. The most suitable salinity range for YFT fishery was 34.5–36.0 at depths shallower than 300 m. The suitable upper and lower bounds on the depths of the thermocline were 90–100 m and 300–350 m, respectively. The thermocline characteristics were prominent, with a mean temperature gradient exceeding 0.08°C/m. These results indicate that the profiles constructed by gradient-dependent optimal interpolation were more accurate than those of the nearest profiles adopted.
- gradient-dependent optimal interpolation,
- YFT,
- Argo profiles,
- WCPO

FullText(HTML)

References(31)

References

[1]	Bonanno R, Lacavalla M, Sperati S. 2019. A new high-resolution Meteorological Reanalysis Italian Dataset: MERIDA. Quarterly Journal of the Royal Meteorological Society, 145(721): 1756–1779. doi: 10.1002/qj.3530
[2]	Burgess T M, Webster R. 1980. Optimal interpolation and isarithmic mapping of soil properties: I. The semi-variogram and punctual kriging. European Journal of Soil Science, 31(2): 315–331. doi: 10.1111/j.1365-2389.1980.tb02084.x
[3]	Chu P C, Fan Chenwu. 2011. Maximum angle method for determining mixed layer depth from seaglider data. Journal of Oceanography, 67(2): 219–230. doi: 10.1007/s10872-011-0019-2
[4]	De Feis I, Masiello G, Cersosimo A. 2020. Optimal interpolation for infrared products from hyperspectral satellite imagers and sounders. Sensors, 20(8): 2352. doi: 10.3390/s20082352
[5]	Fiúza A F G. 1990. Applications of satellite remote sensing to fisheries. In: Operations Research and Management in Fishing. Dordrecht, The Netherlands: Springer, 257–279
[6]	Gandin L S. 1963. Objective Analysis of Meteorological Fields. Leningrad, USSR: Gidrometeoizdat, 158–210
[7]	Johnson K S, Plant J N, Riser S C, et al. 2015. Air oxygen calibration of oxygen optodes on a profiling float array. Journal of Atmospheric and Oceanic Technology, 32(11): 2160–2172. doi: 10.1175/JTECH-D-15-0101.1
[8]	Kalnay E. 2003. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge, UK: Cambridge University Press, 129
[9]	Klemas V, Yan Xiaohai. 2014. Subsurface and deeper ocean remote sensing from satellites: An overview and new results. Progress in Oceanography, 122: 1–9. doi: 10.1016/j.pocean.2013.11.010
[10]	Kluger L C, Taylor M H, Mendo J, et al. 2016. Carrying capacity simulations as a tool for ecosystem-based management of a scallop aquaculture system. Ecological Modelling, 331: 44–55. doi: 10.1016/j.ecolmodel.2015.09.002
[11]	Lan K W, Lee M A, Lu H J, et al. 2011. Ocean variations associated with fishing conditions for yellowfin tuna (Thunnus albacares) in the equatorial Atlantic Ocean. ICES Journal of Marine Science, 68(6): 1063–1071. doi: 10.1093/icesjms/fsr045
[12]	Lan K W, Shimada T, Lee M A, et al. 2017. Using remote-sensing environmental and fishery data to map potential yellowfin tuna habitats in the tropical Pacific Ocean. Remote Sensing, 9(5): 444. doi: 10.3390/rs9050444
[13]	Langley A, Briand K, Kirby D S, et al. 2009. Influence of oceanographic variability on recruitment of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences, 66(9): 1462–1477. doi: 10.1139/F09-096
[14]	Laurs R, Fiedler P. 1985. Application of satellite remote sensing to U.S. fisheries. In: OCEANS ’85-Ocean Engineering and the Environment. San Diego, CA, USA: IEEE, 320–323
[15]	Leroy B, Itano D, Nicol S. 2007. Preliminary analysis and observations on the vertical behaviour of WCPO skipjack, yellowfin and bigeye Tuna in association with anchored FADs, as indicated by acoustic and archival tagging data. Honolulu, HI, USA: Western and Central Pacific Fisheries Commission Scientific Committee
[16]	Liu Chao, Liang Xinfeng, Chambers D P, et al. 2020. Global patterns of spatial and temporal variability in salinity from multiple gridded Argo products. Journal of Climate, 33(20): 8751–8766. doi: 10.1175/JCLI-D-20-0053.1
[17]	Liu Z, Xu J, Xiu Yi, et al. 2006. The effect of reference dataset on calibration of Argo profiling float salinity data. Marine Forecasts, 23(4): 1–12
[18]	Roemmich D, Gilson J. 2009. The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82(2): 81–100. doi: 10.1016/j.pocean.2009.03.004
[19]	Schaefer K M, Fuller D W, Aldana G. 2014. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in waters surrounding the Revillagigedo Islands Archipelago Biosphere Reserve, Mexico. Fisheries Oceanography, 23(1): 65–82. doi: 10.1111/fog.12047
[20]	Schaefer K M, Fuller D W, Block B A. 2007. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the northeastern Pacific Ocean, ascertained through archival tag data. Marine Biology, 152(3): 503–525. doi: 10.1007/s00227-007-0689-x
[21]	Schaefer K M, Fuller D W, Block B A. 2009. Vertical movements and habitat utilization of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (Thunnus obesus) tunas in the equatorial eastern Pacific Ocean, ascertained through archival tag data. In: Tagging and Tracking of Marine Animals with Electronic Devices. Reviews: Methods and Technologies in Fish Biology and Fisheries. Dordrecht, The Netherlands: Springer, 9: 121–144
[22]	Schaefer K M, Fuller D W, Block B A. 2011. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the Pacific Ocean off Baja California, Mexico, determined from archival tag data analyses, including unscented Kalman filtering. Fisheries Research, 112(1−2): 22–37. doi: 10.1016/j.fishres.2011.08.006
[23]	Song L M, Wu Y P. 2011. Standardizing CPUE of yellowfin tuna (Thunnus albacares) longline fishery in the tropical waters of the northwestern Indian Ocean using a deterministic habitat-based model. Journal of Oceanography, 67(5): 541–550. doi: 10.1007/s10872-011-0055-y
[24]	Tong Mingrong, Liu Zenghong, Sun Chaohui, et al. 2003. Analysis of data quality control process of the ARGO profiling buoy. Ocean Technology (in Chinese), 22(4): 79–85
[25]	Wang Shaoqin, Xu Liuxiong, Zhu Guoping, et al. 2014. Spatial-temporal profiles of CPUE and relations to environmental factors for yellowfin tuna Thunnus albacores from purse-seine fishery in Western and Central Pacific Ocean. Journal of Dalian Ocean University, 29(3): 303–308
[26]	Weng K C, Stokesbury M J W, Boustany A M, et al. 2009. Habitat and behaviour of yellowfin tuna Thunnus albacares in the Gulf of Mexico determined using pop-up satellite archival tags. Journal of Fish Biology, 74(7): 1434–1449. doi: 10.1111/j.1095-8649.2009.02209.x
[27]	Wikle C K, Berliner L M. 2007. A Bayesian tutorial for data assimilation. Physica D: Nonlinear Phenomena, 230(1−2): 1–16. doi: 10.1016/j.physd.2006.09.017
[28]	Yang Shenglong, Zhang Bianbian, Jin Shaofei, et al. 2015. Relationship between the temporal-spatial distribution of longline fishing grounds of yellowfin tuna (Thunnus albacares) and the thermocline characteristics in the Western and Central Pacific Ocean. Haiyang Xuebao (in Chinese), 37(6): 78–87
[29]	Zagaglia C R, Lorenzzetti J A, Stech J L. 2004. Remote sensing data and longline catches of yellowfin tuna (Thunnus albacares) in the equatorial Atlantic. Remote Sensing of Environment, 93(1−2): 267–281. doi: 10.1016/j.rse.2004.07.015
[30]	Zhang Chunling, Xu Jianping, Bao Xianwen, et al. 2013. An effective method for improving the accuracy of Argo objective analysis. Acta Oceanologica Sinica, 32(7): 66–77. doi: 10.1007/s13131-013-0333-1
[31]	Zhang Chunling, Xu Jianping, Bao Xianwen. 2015. Gradient-dependent correlation scale method based on Argo. Journal of PLA University of Science and Technology (Natural Science Edition) (in Chinese), 16(5): 476–483