The basis function approach for modeling autocorrelation in ecological data

TL;DR

This paper explains how basis functions can be used to model autocorrelation in ecological data, enhancing model accuracy and interpretability by providing ecologists with practical tools and insights.

Contribution

It introduces key concepts and properties of basis functions, demonstrating their application in ecological models to improve autocorrelation handling.

Findings

Basis functions help improve model fit for spatial and temporal data.

Understanding basis functions aids in detecting multicollinearity.

Tools for modeling autocorrelation are illustrated with ecological data examples.

Abstract

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.