Open Accessdistantia: an open‐source toolset to quantify dissimilarity between multivariate ecological time‐series
Author(s) -
Benito Blas M.,
Birks H. John B.
Publication year - 2020
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/ecog.04895
Subject(s) - multivariate statistics , computer science , similarity (geometry) , matching (statistics) , variable (mathematics) , ecology , range (aeronautics) , series (stratigraphy) , index of dissimilarity , data mining , machine learning , artificial intelligence , statistics , mathematics , biology , mathematical analysis , paleontology , materials science , composite material , image (mathematics)
There is a large array of methods to extract knowledge and perform ecological forecasting from ecological time‐series. However, in spite of its importance for data‐mining, pattern‐matching and ecological synthesis, methods to assess their similarity are scarce. We introduce distantia (v1.0.1), an R package providing general toolset to quantify dissimilarity between ecological time‐series, independently of their regularity and number of samples. The functions in distantia provide the means to compute dissimilarity scores by time and by shape and assess their significance, evaluate the partial contribution of each variable to dissimilarity, and align or combine sequences by similarity. We evaluate the sensitivity of the dissimilarity metrics implemented in distantia , describe its structure and functionality, and showcase its applications with two examples. Particularly, we evaluate how geographic factors drive the dissimilarity between nine pollen sequences dated to the Last Interglacial, and compare the temporal dynamics of climate and enhanced vegetation index of three stands across the range of the European beech. We expect this package may enhance the capabilities of researchers from different fields to explore dissimilarity patterns between multivariate ecological time‐series, and aid in generating and testing new hypotheses on why the temporal dynamics of complex‐systems changes over space and time.