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Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States
Author(s) -
Latimer A. M.,
Banerjee S.,
Sang Jr H.,
Mosher E. S.,
Silander Jr J. A.
Publication year - 2009
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/j.1461-0248.2008.01270.x
Subject(s) - bayesian probability , generalized additive model , breeding bird survey , multivariate statistics , environmental niche modelling , data set , set (abstract data type) , species distribution , spatial analysis , ecology , regression , regression analysis , computer science , abundance (ecology) , statistics , mathematics , machine learning , habitat , biology , ecological niche , programming language
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.