Network-based exploration and visualisation of ecological data

被引:15
|
作者
Raymond, Ben [1 ]
Hosie, GFaham [1 ]
机构
[1] Australian Antarctic Div, Kingston, Tas 7050, Australia
关键词
Exploratory analyses; Data visualisation; Networks; Zooplankton; Southern Ocean; Community structure; CONTINUOUS PLANKTON RECORDER; SOUTHERN-OCEAN SOUTH; MESOZOOPLANKTON COMMUNITY STRUCTURE; FOOD-WEB; SEA-ICE; ZOOPLANKTON COMMUNITIES; SEASONAL SUCCESSION; ORDINATION; AUSTRALIA; COMPLEX;
D O I
10.1016/j.ecolmodel.2008.12.011
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Networks - structured graphs consisting of sets of nodes connected by edges - provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data. The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling. as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures. The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:673 / 683
页数:11
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