Improving spatial modelling of seabed sediments for biodiversity prediction: a case study from southwest Australian margin  

Authors / CoAuthors

Li, J. | Heap, A.D. | Potter, A. | Huang, Z. | Daniell, J.J.

Abstract

Robust methods for generating spatially continuous data from point locations of physical seabed properties are essential for accurate biodiversity prediction. For many national-scale applications, spatially continuous seabed sediment data are typically derived from sparsely and unevenly distributed point locations, particularly in the deep ocean due to the expense and practical limitations of acquiring samples. Methods for deriving spatially continuous data are usually data- and variable-specific making it difficult to select an appropriate method for any given physical seabed property. To improve the spatial modelling of physical seabed properties, this study compared the results of a variety of methods for deriving spatially continuous mud content data for the southwest margin of Australia (523,400 km2) based on 177 sparsely and unevenly distributed point samples. For some methods, secondary variables were also used in the analysis, including: bathymetry, distance-to-coast, seabed slope, and geomorphic province (i.e., shelf, slope, etc.). Effects of sample density were also investigated. The predictive performance of the methods was assessed using a 10-fold cross validation and visual examination. A combined method (random forest and ordinary kriging: RFrf) proved the most accurate method, with an RMAE up to 17% less than the control. No threshold sample density was detected; as sample density increased so did the accuracy of the method. The RMAE of the most accurate method is about 30% lower than that of the best methods in previous publications, further highlighting the robustness of the method developed in this study. The results of this study show that significant improvements in the accuracy of the spatially continuous seabed properties can be achieved through the application of an appropriate interpolation method. The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity prediction.

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nonGeographicDataset

eCat Id

70270

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Keywords

• External Publication

( Theme )

• GIS

( Theme )

• environmental

( Theme )

• model

( Theme )

• numerical modelling

( Theme )

• data

( Theme )

• marine

( Theme )

• CERF

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

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2010-01-01T00:00:00

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Australian Government Security ClassificationSystem    

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Creative Commons Attribution 4.0 International Licence    

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geoscientificInformation

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