Changes in the analysis of temporal community dynamics data: a 29-year literature review

PeerJ

article

Hannah L. Buckley¹  Nicola J. Day²  Gavin Lear³  Bradley S. Case¹ 

[1] School of Science, Auckland University of Technology;School of Biological Sciences, Victoria University of Wellington;School of Biological Sciences, University of Auckland

关键词: Biological communities;    Multivariate analysis;    Spatiotemporal change;    Community ecology;    Community dynamics;    Time series;    Descriptive analysis;    Quantitative analysis;   

DOI  :  10.7717/peerj.11250

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background Understanding how biological communities change over time is of increasing importance as Earth moves into the Anthropocene. A wide variety of methods are used for multivariate community analysis and are variously applied to research that aims to characterise temporal dynamics in community composition. Understanding these methods and how they are applied is useful for determining best practice in community ecology. Methodology We reviewed the ecological literature from 1990 to 2018 that used multivariate methods to address questions of temporal community dynamics. For each paper that fulfilled our search criteria, we recorded the types of multivariate analysis used to characterise temporal community dynamics in addition to the research aim, habitat type, location, taxon and the experimental design. Results Most studies had relatively few temporal replicates; the median number was seven time points. Nearly 70% of studies applied more than one analysis method; descriptive methods such as bar graphs and ordination were the most commonly applied methods. Surprisingly, the types of analyses used were only related to the number of temporal replicates, but not to research aim or any other aspects of experimental design such as taxon, or habitat or year of study. Conclusions This review reveals that most studies interested in understanding community dynamics use relatively short time series meaning that several, more sophisticated, temporal analyses are not widely applicable. However, newer methods using multivariate dissimilarities are growing in popularity and many can be applied to time series of any length.

【 授权许可】

CC BY