title: Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States.
abstract: Many critical ecological issues require the analysis of large spatial point data sets - for example, modelling species distributions, abundance and spread from survey data. But modelling spatial relationships, especially in large point data sets, presents major computational challenges. We use a novel Bayesian hierarchical statistical approach, 'spatial predictive process' modelling, to predict the distribution of a major invasive plant species, Celastrus orbiculatus, in the northeastern USA. The model runs orders of magnitude faster than traditional geostatistical models on a large data set of c. 4000 points, and performs better than generalized linear models, generalized additive models and geographically weighted regression in cross-validation. We also use this approach to model simultaneously the distributions of a set of four major invasive species in a spatially explicit multivariate model. This multispecies analysis demonstrates that some pairs of species exhibit negative residual spatial covariation, suggesting potential competitive interaction or divergent responses to unmeasured factors.
authors.affiliation: Department of Plant Sciences, University of California, Davis, CA 95616, USA. amlatimer@ucdavis.edu
authors.forename: A M
authors.initials: AM
authors.lastname: Latimer
authors.forename: S
authors.initials: S
authors.lastname: Banerjee
authors.forename: H
authors.initials: H
authors.lastname: Sang
authors.forename: E S
authors.initials: ES
authors.lastname: Mosher
authors.forename: J A
authors.initials: JA
authors.lastname: Silander
mesh_terms: D001499:Bayes Theorem; D028142:Berberis / Q000502:physiology; D031247:Celastrus / Q000502:physiology*; D029756:Euonymus / Q000502:physiology; D008962:Models, Theoretical; D009511:New England; D011156:Population Density; D011157:Population Dynamics; D027826:Rosa / Q000502:physiology