Abstract
Network modularity is a well-studied large-scale connectivity pattern in networks. The detection of modules in real networks constitutes a crucial step towards a description of the network building blocks and their evolutionary dynamics. The performance of modularity detection algorithms is commonly quantified using simulated networks data. However, a comparison of the modularity algorithms utility for real biological data is scarce. Here we investigate the utility of network modularity algorithms for the classification of ecological plant communities. Plant community classification by the traditional approaches requires prior knowledge about the characteristic and differential species, which are derived from a manual inspection of vegetation tables. Using the raw species abundance data we constructed six different networks that vary in their edge definitions. Four network modularity algorithms were examined for their ability to detect the traditionally recognized plant communities. The use of more restrictive edge definitions significantly increased the accuracy of community detection, that is, the correspondence between network-based and traditional community classification. Random-walk based modularity methods yielded slightly better results than approaches based on the modularity function. For the whole network, the average agreement between the manual classification and the network-based modules is 76% with varying congruence levels for different communities ranging between 11% and 100%. The network-based approach recovered the known ecological gradient from riverside – sand and gravel bank vegetation – to dryer habitats like semidry grassland on dykes. Our results show that networks modularity algorithms offer new avenues of pursuit for the computational analysis of species communities.
Highlights
Network modularity is a wellstudied large-scale connectivity pattern in networks [1,2], with several detection algorithms described in the literature
We further investigated the effects of several parameters affecting network based classification and determine to what extent this approach, entailing minimal a priori information, can approximate the results of manual classification for the same data
modularity maximization (ModMax) yielded a low-resolution modularity structure where the P-CHE plots are grouped together with R-AL and RO-A plots into one module. Various statistical methods such as principal component analysis (PCA) and multidimensional scaling (MDS) have been used in the past to analyze and identify species communities
Summary
Network modularity (community structure) is a wellstudied large-scale connectivity pattern in networks [1,2], with several detection algorithms described in the literature (for a review, see [2]). We test the applicability of network modularity algorithms as a method to classify plant species communities. A potentially more critical limitation is that the method demands an a priori knowledge about the respective diagnostic species whose identity can be a matter of debate Diagnostic species include those particular species whose occurrence in the relevés may serve as an important telltale for the plant community classification. We investigated four different modularity functions that do not require a pre-determined number of expected modules These four algorithms are based on two main approaches. The utility of the four modularity algorithms for plant communities classification was tested using data surveyed in the Lower Rhine floodplain vegetation plots. We further investigated the effects of several parameters affecting network based classification and determine to what extent this approach, entailing minimal a priori information, can approximate the results of manual classification for the same data
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