A Refined Methodology for Defining Plant Communities Using Postagricultural Data from the Neotropics

Abstract

How best to define and quantify plant communities was investigated using long-term plot data sampled from a recovering pasture in Puerto Rico and abandoned sugarcane and banana plantations in Ecuador. Significant positive associations between pairs of old field species were first computed and then clustered together into larger and larger species groups. I found that (1) no pasture or plantation had more than 5% of the possible significant positive associations, (2) clustering metrics showed groups of species participating in similar clusters among the five pasture/plantations over a gradient of decreasing association strength, and (3) there was evidence for repeatable communities—especially after banana cultivation—suggesting that past crops not only persist after abandonment but also form significant associations with invading plants. I then showed how the clustering hierarchy could be used to decide if any two pasture/plantation plots were in the same community, that is, to define old field communities. Finally, I suggested a similar procedure could be used for any plant community where the mechanisms and tolerances of species form the “cohesion” that produces clustering, making plant communities different than random assemblages of species.

Long-Term Data from Fields Recovering after Sugarcane, Banana, and Pasture Cultivation in Ecuador

“…e 2012 sampling will mark the sixteenth year a�er abandonment for these �elds. I have published several analyses of this dataset [8,38,55,[63][64][65].…”

A Community Detection Algorithm Based on Topology Potential and Spectral Clustering

“…Most networks show community structure [ 1 ]. The results of community detection are meaningful for forecasting the behavior and evolution trend of complex networks [ 2 ]. For example, in World Wide Web, community detection can be used to improve the performance of search engine, in social networks, community detection can be used to forecast the information propagation among users [ 3 ], in electronic commerce area, community detection can be used to select potential user for advertising; and in bioengineering area, community detection can be used to recognize functions of protein [ 4 ].…”

An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

“…Most complex networks show community structure; that is, groups of vertices that have a higher density of edges within them and a lower density of edges between groups [ 1 ]. Identifying community structure is crucial for understanding the structural and functional properties of complex networks [ 2 ]. Many works inspired by different paradigms are devoted to the development of community detection [ 3 ].…”