A Test of the Performance of Species Predictive Models in Marine Benthic Environments Based on Predictions of Sponge Distribution on the Australian Continental Shelf  

Authors / CoAuthors

Huang, Z. | Brooke, B.P. | Li, J.

Abstract

This study tested the performance of 16 species models in predicting the distribution of sponges on the Australian continental shelf using a common set of environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distributions of sponge as a species group can be successfully predicted. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data - the new data were able to improve modelling performance for all the models both in terms of statistics (~10%) and in the predicted spatial distributions. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom water temperature and the resource variables that describe bottom water nutrient status were found to be useful surrogates for sponge distribution at the broad regional scale. This study demonstrates that predictive modelling techniques can enhance our understanding of processes that influence spatial patterns of benthic marine biodiversity. Ecological Informatics

Product Type

nonGeographicDataset

eCat Id

69569

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Keywords

• External PublicationArticle

( Theme )

• habitat

( Theme )

• mapping

( Theme )

• numerical modelling

( Theme )

• marine biodiversity

( Theme )

• marine

( Theme )

• CERF

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

Publication Date

2011-01-01T00:00:00

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Australian Government Security ClassificationSystem    

Classification - unclassified

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Creative Commons Attribution 4.0 International Licence    

Access - restricted

Use - license

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notPlanned

Topic Category

geoscientificInformation

Series Information

6:205-216

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The paper was written based on the research results of CERF Marine Biodiversity Hub.

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CERF Marine Biodivesity Hub