  Analyzing ecological data often requires modeling the autocorrelation created
by spatial and temporal processes. Many of the statistical methods used to
account for autocorrelation can be viewed as regression models that include
basis functions. Understanding the concept of basis functions enables
ecologists to modify commonly used ecological models to account for
autocorrelation, which can improve inference and predictive accuracy.
Understanding the properties of basis functions is essential for evaluating the
fit of spatial or time-series models, detecting a hidden form of
multicollinearity, 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.
