The basis function approach for modeling autocorrelation in ecological data. Issue 3 (1st February 2017)

• Record Type: Journal Article
• Authors: Hefley, Trevor J.; Broms, Kristin M.; Brost, Brian M.; Buderman, Frances E.; Kay, Shannon L.; Scharf, Henry R.; Tipton, John R.; Williams, Perry J.; Hooten, Mevin B.
• Is Part Of: Ecology. Volume 98:Issue 3(2017)
• Page Start: 632
• Page End: 646
• Publication Date: 2017-02-01
• Subjects: autocorrelation -- Bayesian model -- collinearity -- dimension reduction -- semiparametric regression -- spatial statistics -- time series; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecy.1674
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract: 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.