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The basis function approach for modeling autocorrelation in ecological data
Author(s) -
Hefley Trevor J.,
Broms Kristin M.,
Brost Brian M.,
Buderman Frances E.,
Kay Shan L.,
Scharf Henry R.,
Tipton John R.,
Williams Perry J.,
Hooten Mevin B.
Publication year - 2017
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1002/ecy.1674
Subject(s) - autocorrelation , spatial analysis , collinearity , basis (linear algebra) , computer science , autocorrelation technique , ecology , inference , range (aeronautics) , data mining , artificial intelligence , mathematics , statistics , biology , materials science , geometry , composite material
Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time‐series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.