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Trajectory analysis in community ecology
Author(s) -
De Cáceres Miquel,
Coll Lluís,
Legendre Pierre,
Allen Robert B.,
Wiser Susan K.,
Fortin MarieJosée,
Condit Richard,
Hubbell Stephen
Publication year - 2019
Publication title -
ecological monographs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.254
H-Index - 156
eISSN - 1557-7015
pISSN - 0012-9615
DOI - 10.1002/ecm.1350
Subject(s) - trajectory , perspective (graphical) , computer science , variation (astronomy) , variety (cybernetics) , space (punctuation) , ecology , data science , domain (mathematical analysis) , community , community structure , artificial intelligence , mathematics , habitat , physics , astronomy , biology , mathematical analysis , astrophysics , operating system
Ecologists have long been interested in how communities change over time. Addressing questions about community dynamics requires ways of representing and comparing the variety of dynamics observed across space. Until now, most analytical frameworks have been based on the comparison of synchronous observations across sites and between repeated surveys. An alternative perspective considers community dynamics as trajectories in a chosen space of community resemblance and utilizes trajectories as objects to be analyzed and compared using their geometry. While methods that take this second perspective exist, for example to test for particular trajectory shapes, there is a need for formal analytical frameworks that fully develop the potential of this approach. By adapting concepts and procedures used for the analysis of spatial trajectories, we present a framework for describing and comparing community trajectories. A key element of our contribution is the means to assess the geometric resemblance between trajectories, which allows users to describe, quantify, and analyze variation in community dynamics. We illustrate the behavior of our framework using simulated data and two spatiotemporal community data sets differing in the community properties of interest (species composition vs. size distribution of individuals). We conclude by evaluating the advantages and limitations of our community trajectory analysis framework, highlighting its broad domain of application and anticipating potential extensions.