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The impact of spatial scale on local Moran’s I clustering of annual fishing effort for Dosidicus gigas offshore Peru

  • Aquaculture and Fisheries
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Abstract

The spatial scale (fishing grid) of fisheries research affects the observed spatial patterns of fisheries resources such as catch-per-unit-effort (CPUE) and fishing effort. We examined the scale impact of high value (HH) clusters of the annual fishing effort for Dosidicus gigas offshore Peru from 2009 to 2012. For a multi-scale analysis, the original commercial fishery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identified using the Anselin Local Moran’s I. Statistics including the number of points, mean CPUE, standard deviation (SD), skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran’s I and the number of points for HH clusters follow a power law scaling relationship from 2009 to 2012. The mean effort and its SD also follow a power law scaling relationship from 2009 to 2012. The skewness follows a linear scaling relationship in 2010 and 2011 but fluctuates with spatial scale in 2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran’s I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 – 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the fishing effort of D. gigas in the offshore Peruvian waters.

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References

  • Anselin L. 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27 (2): 93–115.

    Article  Google Scholar 

  • Anselin L. 1996. The Moran scatterplot as an ESDA toolto assess local instability in spatial association. In: Fischer M M, Scholten H J, Unwin D eds. Spatial Analytical Perspectives on GIS. Taylor & Francis, London. p.111–125.

  • Anselin L. 2004. Exploring spatial data with GeoDa TM: a workbook. Center for Spatially Integrated Social Science, Urbana. 61801p.

    Google Scholar 

  • Aswani S, Lauer M. 2006. Incorporating fishermen’s local knowledge and behavior into geographical information systems (GIS) for designing marine protected areas in Oceania. Human Organization, 65 (1): 81–102.

    Article  Google Scholar 

  • Batty M. 2005. Network geography: relations, interactions, scaling and spatial processes in GIS. In: Fisher P F, Unwin D J eds. Re–presenting GIS. John Wiley & Sons, Chichester, UK. p.149–170.

  • Bigelow K A, Hampton J, Miyabe N. 2002. Application of a habitat–based model to estimate effective longline fishing effort and relative abundance of Pacific bigeye tuna ( Thunnus obesus ). Fisheries Oceanography, 11 (3): 143–155.

    Article  Google Scholar 

  • Cabanellas–Reboredo M, Alós J, Palmer M, Grädel R, Morales–Nin B. 2011. Simulating the indirect handline jigging effects on the European squid Loligo vulgaris in captivity. Fisheries Research, 110 (3): 435–440.

    Article  Google Scholar 

  • Cao J, Chen X J, Chen Y. 2009. Influence of surface oceanographic variability on abundance of the western winter–spring cohort of neon flying squid Ommastrephes bartramii in the NW Pacific Ocean. Marine Ecology Progress Series, 381: 119–127.

    Article  Google Scholar 

  • Carocci F, Bianchi G, Eastwood P, Meaden G. 2009. Geographic Information Systems to Support the Ecosystem Approach to Fisheries: Status, Opportunities and Challenges. Food and Agriculture Organization of the United Nations, Rome.

    Google Scholar 

  • Chang K T. 2015. Introduction to Geographic Information Systems. McGraw–Hill Education, New Delhi.

    Google Scholar 

  • Chen C S, Chiu T S. 2003. Variations of life history parameters in two geographical groups of the neon flying squid, Ommastrephes bartramii, from the North Pacific. Fisheries Research, 63 (3): 349–366.

    Article  Google Scholar 

  • Chen X J, Chen Y, Tian S Q, Liu B L, Qian W G. 2008. An assessment of the west winter–spring cohort of neon flying squid ( Ommastrephes bartramii ) in the Northwest Pacific Ocean. Fisheries Research, 92 (2–3): 221–230.

    Article  Google Scholar 

  • Chen X J, Xu L X, Tian S Q. 2003. Spatial and temporal analysis of Ommastrephe bartrami resources and its fishing ground in North Pacific Ocean. Journal of Fisheries of China, 27 (4): 334–342. (in Chinese with English abstract)

    Google Scholar 

  • Chen X J, Zhao X H, Chen Y. 2007. Influence of El Niño /La Niña on the western winter–spring cohort of neon flying squid ( Ommastrephes bartramii ) in the northwestern Pacific Ocean. ICES Journal of Marine Science, 64 (6): 1 152–1 160.

    Google Scholar 

  • CliffA D. 1981. Spatial Processes: Models & Applications. Pion, London.

    Google Scholar 

  • Close C H, Hall G B. 2006. A GIS–based protocolfor the collection and use of local knowledge in fisheries management planning. Journal of Environmental Management, 78 (4): 341–352.

    Article  Google Scholar 

  • Feng Y J, Chen X J, Gao F, Liu Y. 2018. Impacts of changing scale on Getis–Ord Gi* hotspots of CPUE: a case study of the neon flying squid ( Ommastrephes bartramii ) in the northwest Pacific Ocean. Acta Oceanologica Sinica, 37 (5): 1–10.

    Article  Google Scholar 

  • Feng Y J, Chen X J, Liu Y. 2016. The effects of changing spatial scales on spatial patterns of CPUE for Ommastrephes bartramii in the northwest Pacific Ocean. Fisheries Research, 183: 1–12.

    Article  Google Scholar 

  • Feng Y J, Chen X J, Liu Y. 2017a. Detection of spatial hot spots and variation for the neon flying squid Ommastrephes bartramii resources in the northwest Pacific Ocean. Chin ese Journal of Oceanology and Limnology, 35 (4): 921–935.

    Article  Google Scholar 

  • Feng Y J, Cui L, Chen X J, Liu Y. 2017b. A comparative study of spatially clustered distribution of jumbo flying squid ( Dosidicus gigas ) offshore Peru. Journal of Ocean Univ ersity of China, 16 (3): 490–500.

    Article  Google Scholar 

  • Feng Y J, Liu Y. 2015. Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics. Ecological Indicators, 53: 18–27.

    Article  Google Scholar 

  • Fu W J, Fu Z J, Ge H L, Ji B Y, Jiang P K, Li Y F, Wu J S, Zhao K L. 2015. Spatial variation of biomass carbon density in a subtropical region of southeastern China. Forests, 6 (6): 1 966–1 981.

    Article  Google Scholar 

  • Fu W J, Jiang P K, Zhou G M, Zhao K L. 2014. Using Moran’s I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences, 11 (8): 2 401–2 409.

    Article  Google Scholar 

  • Fu W J, Zhao K L, Zhang C S, Tunney H. 2011. Using Moran’s I and geostatistics to identify spatial patterns of soil nutrients in two different long–term phosphorusapplication plots. Journal of Plant Nutrit ion and Soil Science, 174 (5): 785–798.

    Article  Google Scholar 

  • García–Charton J A, Pérez–Ruzafa Á, Sánchez–Jerez P, Bayle–Sempere J T, Reñones O, Moreno D. 2004. Multi–scale spatial heterogeneity, habitat structure, and the effect of marine reserves on Western Mediterranean rocky reef fish assemblages. Marine Biology, 144 (1): 161–182.

    Article  Google Scholar 

  • Gillis D M, Peterman R M, Tyler A V. 1993. Movement dynamics in a fishery: application of the ideal free distribution to spatial allocation of effort. Canadian Journal of Fisheries and Aquatic Sciences, 50 (2): 323–333.

    Article  Google Scholar 

  • Guidetti P, Fraschetti S, Terlizzi A, Boero F. 2003. Distribution patterns of sea urchins and barrens in shallow Mediterranean rocky reefs impacted by the illegal fishery of the rock–boring mollusc Lithophaga lithophaga. Marine Biology, 143 (6): 1 135–1 142.

    Article  Google Scholar 

  • Guinet C, Dubroca L, Lea M A, Goldsworthy S, Cherel Y, Duhamel G, Bonadonna F, Donnay J P. 2001. Spatial distribution of foraging in female Antarctic fur seals Arctocephalus gazella in relation to oceanographic variables: a scale–dependent approach using geographic information systems. Marine Ecology Progress Series, 219: 251–264.

    Article  Google Scholar 

  • Harford W J, Ton C, Babcock E A. 2015. Simulated markrecovery for spatial assessment of a spiny lobster ( Panulirus argus ) fishery. Fisheries Research, 165: 42–53.

    Article  Google Scholar 

  • Khormi H M, Kumar L. 2015. Modelling Interactions Between Vector–Borne Diseases and Environment Using GIS. CRC Press, Boca Raton.

    Book  Google Scholar 

  • Levine N. 2015. CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (V 4.02). Ned Levine and Associates, Houston, Texas, and the National Institute of Justice, Washington, DC.

    Google Scholar 

  • Liu B L, Chen X J, Yi Q. 2013. A comparison of fishery biology of jumbo flying squid, Dosidicus gigas outside three Exclusive Economic Zones in the Eastern Pacific Ocean. Chin ese Journal of Oceanology and Limnology, 31 (3): 523–533.

    Article  Google Scholar 

  • Martin K S. 2004. GIS in Marine Fisheries Science and Decision–Making. American Fisheries Society, Bethesda, p.237–258.

    Google Scholar 

  • Meaden G J, Aguilar–Manjarrez J. 2013. Advances in Geographic Information Systems and Remote Sensing for Fisheries and Aquaculture. FAO, Rome.

    Google Scholar 

  • Meaden G J, Kapetsky J M. 1991. Geographical information systems and remote sensing in inland fisheries and aquaculture. FAO, Rome.

    Google Scholar 

  • Meaden G J. 2001. GIS in fisheries science: foundations for a new millenium. In: Nishida T, Kailola P J, Hollingworth C E eds. Proceedings of the First International Symposium on GIS in Fishery Science. Fishery GIS Research Group, Saitama, Japan. p.3–29.

  • Meentemeyer V, Box E O. 1987. Scale effects in landscape studies. In: Turner M G ed. Landscape Heterogeneity and Disturbance. Springer, New York. p.15–34.

    Book  Google Scholar 

  • Mitchell A. 2005. The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics. Esri Press, Redlands.

    Google Scholar 

  • Ord J K, Getis A. 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27 (4): 286–306.

    Article  Google Scholar 

  • Peeters A, Zude M, Käthner J, Ünlü M, Kanber R, Hetzroni A, Gebbers R, Ben–Gal A. 2015. Getis–Ord’s hot–and coldspot statistics as a basis for multivariate spatial clustering of orchard tree data. Computers and Electronics in Agriculture, 111: 140–150.

    Article  Google Scholar 

  • Punt A E, Walker T I, Taylor B L, Pribac F. 2000. Standardization of catch and effort data in a spatially–structured shark fishery. Fisheries Research, 45 (2): 129–145.

    Article  Google Scholar 

  • Rao K V. 1973. Distribution pattern of the major exploited marine fishery resources of India. In: Proceedings of the Symposium on Living Resources of the Seas Around India. Mandapam Camp. http://eprints.cmfri.org. in/2688/1/Article_05.pdf

    Google Scholar 

  • Santos A M P. 2000. Fisheries oceanography using satellite and airborne remote sensing methods: a review. Fisheries Research, 49 (1): 1–20.

    Article  Google Scholar 

  • Saul S E, Walter J E, Die D J, Naar D F, Donahue B T. 2013. Modeling the spatial distribution of commercially important reef fishes on the West Florida Shelf. Fisheries Research, 143: 12–20.

    Article  Google Scholar 

  • Sokal R R, Oden N L. 1978. Spatial autocorrelation in biology. 2. Some biological implications and four applications of evolutionary and ecological interest. Biological Journal of the Linnean Society, 10 (2): 229–249.

    Google Scholar 

  • Squires D. 1987. Fishing effort: its testing, specification, and internal structure in fisheries economics and management. Journal of Environmental Economics and Management, 14 (3): 268–282.

    Article  Google Scholar 

  • Tian S Q, Chen Y, Chen X J, Xu L X, Dai X J. 2009. Impacts of spatial scales of fisheries and environmental data on catch per unit effort standardisation. Marine and Freshwater Research, 60 (12): 1 273–1 284.

    Article  Google Scholar 

  • Turner M G, O'Neill R V, Gardner R H, Milne B T. 1989. Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecology, 3 (3–4): 153–162.

    Article  Google Scholar 

  • Waluda C M, Yamashiro C, Elvidge C D, Hobson V R, Rodhouse P G. 2004. Quantifying light–fishing for Dosidicus gigas in the eastern Pacific using satellite remote sensing. Remote Sens ing of Environ ment, 91 (2): 129–133.

    Article  Google Scholar 

  • Wang Y G, Chen X J. 2005. The Resource and Biology of Economic Oceanic Squid in the World. Ocean Press, Beijing.

    Google Scholar 

  • Wiens J A. 1989. Spatial scaling in ecology. Functional Ecology, 3 (4): 385–397.

    Article  Google Scholar 

  • Wu J G. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology, 19 (2): 125–138.

    Article  Google Scholar 

  • Xu B, Chen XJ, Qian W G, Tian S Q. 2011. Spatial and temporal distribution of fishing ground for Dosidicus gigas in the offshore waters of Peru. Periodical of Ocean University of China, 41 (11): 43–47. (in Chinese with English abstract)

    Google Scholar 

  • Yang M X, Chen X J, Feng Y J, Guan W J. 2013. Spatial variability of small and medium scales’ resource abundance of Ommastrephes bartramii in Northwest Pacific. Acta Ecologica Sinica, 33 (20): 6 427–6 435. (in Chinese with English abstract)

    Article  Google Scholar 

  • Yu W, Chen X J, Chen Y, Yi Q, Zhang Y. 2015. Effects of environmental variations on the abundance of western winter–spring cohort of neon flying squid ( Ommastrephes bartramii ) in the Northwest Pacific Ocean. Acta Oceanologica Sinica, 34 (8): 43–51.

    Article  Google Scholar 

  • Yu W, Chen X J, Yi Q, Chen Y. 2016. Spatio–temporal distributions and habitat hotspots of the winter–spring cohort of neon flying squid Ommastrephes bartramii in relation to oceanographic conditions in the Northwest Pacific Ocean. Fisheries Research, 175: 103–115.

    Article  Google Scholar 

  • Yuan Y M, Cave M, Zhang C S. 2018. Using Local Moran’s I to identify contamination hotspots of rare earth elements in urban soils of London. Applied Geochemistry, 88: 167–178.

    Article  Google Scholar 

  • Zainuddin M, Saitoh S I, Saitoh K. 2004. Detection of potential fishing ground for albacore tuna using synoptic measurements of ocean color and thermal remote sensing in the northwestern North Pacific. Geophysical Research Letters, 31 (20): L20311.

    Book  Google Scholar 

  • Zhang C S, Luo L, Xu W L, Ledwith V. 2008. Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of the Total Environment, 398 (1–3): 212–221.

    Article  Google Scholar 

  • Zhao K L, Fu W J, Liu X M, Huang D L, Zhang C S, Ye Z Q, Xu J M. 2014. Spatial variations of concentrations of copper and its speciation in the soil–rice system in Wenling of southeastern China. Environmental Science and Pollution Research, 21 (11): 7 165–7 176.

    Article  Google Scholar 

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Acknowledgement

We would like to thank anonymous reviewers for their helpful comments and feedback.

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Correspondence to Yongjiu Feng or Xinjun Chen.

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Supported by the National Natural Science Foundation of China (No. 41406146), the Laboratory for Marine Fisheries Science and Food Production Processes at Qingdao National Laboratory for Marine Science and Technology of China (No. 2017-1A02), and the Shanghai Universities First-class Disciplines Project-Fisheries (A)

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Feng, Y., Chen, L. & Chen, X. The impact of spatial scale on local Moran’s I clustering of annual fishing effort for Dosidicus gigas offshore Peru. J. Ocean. Limnol. 37, 330–343 (2019). https://doi.org/10.1007/s00343-019-7316-9

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