A comparative study of spatial interpolation methods for determining fishery resources density in the Yellow Sea

Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI) and ordinary kriging (OK). A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect. Using a paired-samples t test, no significant differences (P>0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few “bull’s-eye” patterns in some areas. However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing.

[1]  Dennis L. Corwin,et al.  Spatial interpolation quality assessment for soil sensor transect datasets , 2016, Comput. Electron. Agric..

[2]  Zekai Şen,et al.  Spatial interpolation and estimation of solar irradiation by cumulative semivariograms , 2001 .

[3]  Jenneke M. Visser,et al.  Response of estuarine phytoplankton to nutrient and spatio-temporal pattern of physico-chemical water quality parameters in Little Vermilion Bay, Louisiana , 2016, Ecol. Informatics.

[4]  K. Jerosch,et al.  Geostatistical mapping and spatial variability of surficial sediment types on the Beaufort Sea shelf based on grain size data , 2012 .

[5]  Yunfeng Xie,et al.  Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. , 2011, Chemosphere.

[6]  J. Campos,et al.  Dental caries and the nutritional status of preschool children: a spatial analysis. , 2011, Ciencia & saude coletiva.

[7]  Jin Li,et al.  Spatial interpolation methods applied in the environmental sciences: A review , 2014, Environ. Model. Softw..

[8]  T. Mueller,et al.  Map Quality for Ordinary Kriging and Inverse Distance Weighted Interpolation , 2004 .

[9]  Tao Chen,et al.  Heavy metal sources identification and sampling uncertainty analysis in a field-scale vegetable soil of Hangzhou, China. , 2009, Environmental pollution.

[10]  A. Konopka,et al.  FIELD-SCALE VARIABILITY OF SOIL PROPERTIES IN CENTRAL IOWA SOILS , 1994 .

[11]  Robert L. Miller.  Trend Surfaces: Their Application to Analysis and Description of Environments of Sedimentation , 1956, The Journal of Geology.

[12]  C. Lucas,et al.  Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study , 2013, Environ. Model. Softw..

[13]  D. Weber,et al.  Evaluation and comparison of spatial interpolators II , 1992 .

[14]  Amy J. Ruggles,et al.  An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting , 1999 .

[15]  Jin Chen,et al.  A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images , 2012 .

[16]  F. Gonzalez-Longatt,et al.  Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela , 2015 .

[17]  Ruimin Liu,et al.  Uncertainty analysis of total phosphorus spatial-temporal variations in the Yangtze River Estuary using different interpolation methods. , 2014, Marine pollution bulletin.

[18]  Q. Tang,et al.  Changes in fish species diversity and dominant species composition in the Yellow Sea , 1996 .

[19]  Tammo S. Steenhuis,et al.  Evaluation of spatial interpolation methods for groundwater level in an arid inland oasis, northwest China , 2014, Environmental Earth Sciences.

[20]  G. Pegram,et al.  Interpolation of daily rainfall networks using simulated radar fields for realistic hydrological modelling of spatial rain field ensembles , 2014 .

[21]  LI Hai-bin,et al.  Comparison of Three Spatial Interpolation Methods for Climate Variables in China , 2002 .

[22]  Yimin Ye,et al.  Rebuilding global fisheries: the World Summit Goal, costs and benefits , 2013 .

[23]  Tingting Wu,et al.  Spatial interpolation of temperature in the United States using residual kriging , 2013 .

[24]  A. Potter,et al.  Can we improve the spatial predictions of seabed sediments? A case study of spatial interpolation of mud content across the southwest Australian margin , 2011 .

[25]  Pier Paolo Gatta,et al.  state of the world fisheries and aquaculture 2010 , 2011 .

[26]  A. Stein,et al.  Spatial variogram estimation from temporally aggregated seabird count data , 2013, Environmental and Ecological Statistics.

[27]  Binduo Xu,et al.  Variations in fish community structure during winter in the southern Yellow Sea over the period 1985–2002 , 2005 .

[28]  Dennis Weber,et al.  Evaluation and comparison of spatial interpolators , 1992 .

[29]  单秀娟 Shan Xiujuan,et al.  Variations in fish community structure and diversity in the sections of the central and southern Yellow Sea , 2014 .

[30]  M. Scardi,et al.  Optimizing interpolation of shoot density data from a Posidonia oceanica seagrass bed , 2006 .

[31]  Jianbo Shen,et al.  A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China , 2014 .

[32]  X. Jin,et al.  Fish assemblage structure in the East China Sea and southern Yellow Sea during autumn and spring , 2003 .

[33]  X. Shan,et al.  Long-term variations in body length and age at maturity of the small yellow croaker Larimichthys polyactis Bleeker, 1877 in the Bohai Sea and the Yellow Sea, China , 2011 .

[34]  D. Hallema,et al.  Mapping soil hydraulic conductivity and matric potential for water management of cranberry: Characterisation and spatial interpolation methods , 2014 .

[35]  Donato Malerba,et al.  Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering , 2014, Data Mining and Knowledge Discovery.

[36]  T. Pitcher,et al.  Evaluating the status of exploited taxa in the northern South China Sea using intrinsic vulnerability and spatially explicit catch-per-unit-effort data , 2008 .

[37]  L. W. Carstensen,et al.  Performance comparison between spatial interpolation and GLM/GAM in estimating relative abundance indices through a simulation study , 2013 .

[38]  Interannual and long-term hydrographic changes in the Yellow Sea during 1977–1998 , 2010 .