Replicated Spatial Point Pattern Analyses for Ecological Inference: A Tutorial Using the RSPPlme4 Package in R

• Main Authors: Bagchi, R. (Author), Dalui, D. (Author), LaScaleia, M.C (Author), Milici, V.R (Author)
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: ecological statistics; K-function; mixed-effects model (MEM); R; replication; spatial structure
• Online Access: View Fulltext in Publisher

 The analysis of spatial point patterns has greatly advanced our understanding of ecological processes. However, the methods currently available for analyzing replicated spatial point patterns (RSPPs) are rarely used by ecologists. One barrier to the use of RSPP analyses is a lack of software to implement the approaches that have been developed in the statistical literature. Here, we provide a practical guide to RSPP analysis and introduce the RSPPlme4 R package that implements the approaches we discuss. The methods we outline use a linear modeling framework to link variation in the spatial structure of point patterns to discrete and continuous explanatory covariates. We describe methods for linear models and mixed-effects models of RSPPs, including approaches to estimating confidence intervals via semi-parametric bootstrapping. The syntax for model fitting is similar to that used in linear and linear mixed-effects modeling packages in R. The RSPPlme4 package also allows users to easily plot the results of model fits. We hope that this tutorial will make methods for RSPP analysis accessible to a wide range of ecologists and open new avenues for gaining insight into ecological processes from spatial data. Copyright © 2022 Bagchi, LaScaleia, Milici and Dalui.