Cite this paper:
FENG Yongjiu, CHEN Lijuan, CHEN Xinjun. The impact of spatial scale on local Moran's I clustering of annual fishing effort for Dosidicus gigas offshore Peru[J]. HaiyangYuHuZhao, 2019, 37(1): 330-343

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

FENG Yongjiu1,2,3,4, CHEN Lijuan5,6, CHEN Xinjun3,4
1 College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China;
2 Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266235, China;
3 National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;
4 Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University), Ministry of Education, Shanghai 201306, China;
5 School of Earth and Environmental Sciences, the University of Queensland, Brisbane 4072, Australia;
6 Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
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.
Key words:    Dosidicus gigas|fishing effort|high-high (HH) cluster|scale impact|local Moran's I   
Received: 2017-11-05   Revised: 2018-01-20
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References:
Anselin L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27(2):93-115.
Anselin L. 1996. The Moran scatterplot as an ESDA tool to 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 GeoDaTM:a workbook. Center for Spatially Integrated Social Science, Urbana. 61801p.
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.
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.
Cabanellas-Reboredo M, Alós J, Palmer M, Grädel R, MoralesNin B. 2011. Simulating the indirect handline jigging effects on the European squid Loligo vulgaris in captivity.Fisheries Research, 110(3):435-440.
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.
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.
Chang K T. 2015. Introduction to Geographic Information Systems. McGraw-Hill Education, New Delhi.
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.
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.
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)
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.
Cliff A D. 1981. Spatial Processes:Models & Applications.Pion, London.
Close C H, Hall G B. 2006. A GIS-based protocol for the collection and use of local knowledge in fisheries management planning. Journal of Environmental Management, 78(4):341-352.
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.
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.
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.Chinese Journal of Oceanology and Limnology, 35(4):921-935.
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 University of China, 16(3):490-500.
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.
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.
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.
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 phosphorus-application plots. Journal of Plant Nutrition and Soil Science, 174(5):785-798.
García-Charton J A, Pérez-Ruzafa Á, Sánchez-Jerez P, BayleSempere 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.
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.
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.
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.
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.
Khormi H M, Kumar L. 2015. Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS. CRC Press, Boca Raton.
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.
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.Chinese Journal of Oceanology and Limnology, 31(3):523-533.
Martin K S. 2004. GIS in Marine Fisheries Science and Decision-Making. American Fisheries Society, Bethesda, p.237-258.
Meaden G J, Aguilar-Manjarrez J. 2013. Advances in Geographic Information Systems and Remote Sensing for Fisheries and Aquaculture. FAO, Rome.
Meaden G J, Kapetsky J M. 1991. Geographical information systems and remote sensing in inland fisheries and aquaculture. FAO, Rome.
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.
Mitchell A. 2005. The Esri Guide to GIS Analysis, Volume 2:Spatial Measurements and Statistics. Esri Press, Redlands.
Ord J K, Getis A. 1995. Local spatial autocorrelation statistics:distributional issues and an application. Geographical Analysis, 27(4):286-306.
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.
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.
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
Santos A M P. 2000. Fisheries oceanography using satellite and airborne remote sensing methods:a review. Fisheries Research, 49(1):1-20.
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.
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.
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.
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.
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.
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 Sensing of Environment, 91(2):129-133.
Wang Y G, Chen X J. 2005. The Resource and Biology of Economic Oceanic Squid in the World. Ocean Press, Beijing.
Wiens J A. 1989. Spatial scaling in ecology. Functional Ecology, 3(4):385-397.
Wu J G. 2004. Effects of changing scale on landscape pattern analysis:scaling relations. Landscape Ecology, 19(2):125-138.
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)
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)
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.
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.
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.
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.
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.
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.