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A comparison of joint species distribution models for presence–absence data
Author(s) -
Wilkinson David P.,
Golding Nick,
GuilleraArroita Gurutzeta,
Tingley Reid,
McCarthy Michael A.
Publication year - 2019
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13106
Subject(s) - notation , covariate , joint probability distribution , contrast (vision) , computer science , correlative , distribution (mathematics) , statistical model , ecology , econometrics , data mining , statistics , machine learning , mathematics , artificial intelligence , biology , mathematical analysis , linguistics , philosophy , arithmetic
Joint species distribution models ( JSDM s) account for biotic interactions and missing environmental predictors in correlative species distribution models. Several different JSDM s have been proposed in the literature, but the use of different or conflicting nomenclature and statistical notation potentially obscures similarities and differences among them. Furthermore, new JSDM implementations have been illustrated with different case studies, preventing direct comparisons of computational and statistical performance. We aim to resolve these outstanding issues by (a) highlighting similarities among seven presence–absence JSDM s using a clearly defined, singular notation; and (b) evaluating the computational and statistical performance of each JSDM using six datasets that vary widely in numbers of sites, species, and environmental covariates considered. Our singular notation shows that many of the JSDM s are very similar, and in turn parameter estimates of different JSDM s are moderate to strongly, positively correlated. In contrast, the different JSDM s clearly differ in computational efficiency and memory limitations. Our framework will allow ecologists to make educated decisions about the JSDM that best suits their objective, and enable wider uptake of JSDM methods among the ecological community.