Generalized joint attribute modeling for biodiversity analysis: median‐zero, multivariate, multifarious data
2016/11/16 by James S. Clark, Diana R. Nemergut, Diana Nemergut +4 · 290 citations
Engineering · Environmental Science · Mathematics · #Artificial intelligence #Biology #Computer science #Ecology #Ecology and Vegetation Dynamics Studies #Econometrics #Engineering #Habitat #Imputation (statistics) #Inference #Joint probability distribution #Mathematics #Missing data #Multivariate statistics #Probabilistic logic #Range (aeronautics) #Species Distribution and Climate Change #Species distribution #Statistics #Wildlife Ecology and Conservation
paper · doi:10.1002/ecm.1241
published in Ecological Monographs 87(1), 34-56 (Wiley)
openalex publication_date 2016/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract
Abstract Probabilistic forecasts of species distribution and abundance require models that accommodate the range of ecological data, including a joint distribution of multiple species based on combinations of continuous and discrete observations, mostly zeros. We develop a generalized joint attribute model ( GJAM ), a probabilistic framework that readily applies to data that are combinations of presence‐absence, ordinal, continuous, discrete, composition, zero‐inflated, and censored. It does so as a joint distribution over all species providing inference on sensitivity to input variables, correlations between species on the data scale, prediction, sensitivity analysis, definition of community structure, and missing data imputation. GJAM applications illustrate flexibility to the range of species‐abundance data. Applications to forest inventories demonstrate species relationships responding as a community to environmental variables. It shows that the environment can be inverse predicted from the joint distribution of species. Application to microbiome data demonstrates how inverse prediction in the GJAM framework accelerates variable selection, by isolating effects of each input variable's influence across all species.
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