Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales

By Otso Ovaskainen, N. Abrego, P. Halme, David B. Dunson. 2016

199 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/4713984e04df1e4d6c2e43b1b8de018f7fbb88d2

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In Ecology, Evolution, Behavior and Systematics

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Joint species distribution modelling with the r‐package Hmsc (2020, background): “…influence of missing environmental covariates are captured by residual species-to-species association matrices, which may be estimated at multiple spatio-temporal scales (Ovaskainen, Abrego, Halme, & Dunson, 2016; Ovaskainen, Roy, Fox, & Anderson, 2016; Ovaskainen, Tikhonov, Dunson, et al., 2017).”
Measuring and predicting the influence of traits on the assembly processes of wood‐inhabiting fungi (2017, methodology): “…we identified species pairs that were linked in the sense that their residual covariance (after accounting for the species’ responses to measured environmental covariates) was positive or negative with at least 95% posterior probability, using the same approach as in Ovaskainen et al. (2016a).”
How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, methodology): “…to seek community-level patterns in how species respond to their environment (e.g. Ovaskainen & Soininen 2011; Wang et al. 2012; Hui et al. 2015; Ovaskainen et al. 2016b), to relate such patterns to species traits and phylogenies (Pollock et al. 2012; Brown et al. 2014; Abrego et al. 2017a) and…”
Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions (2018, background): “…of covariates, which can be counteracted by including as predictors only the most abundant species (Le Roux, Pel-lissier, Wisz, & Luoto, 2014), or only those species that are part of the immediate food web of the focal species (Ovaskainen, Abrego, Halme, & Dunson, 2016; Pellissier et al., 2013).”
Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, background): “Newly proposed “latent variable models” provide a flexible way to incorporate correlation in analyses of many species (Warton et al. 2015, Ovaskainen et al. 2016a, Ovaskainen et al. 2016b).”
Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context (2017, methodology): “…of species associations are more representative of true interactions than raw co-occurrence indices (Stone & Roberts 1990; Gotelli 2000), especially if inference is derived from partial correlations (Ovaskainen et al. 2016a) or from time-series data (Ives et al. 2003; Thorson et al. 2016).”
Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes (2017, background): “, between P (Yij = 1) and P (Yij′ = 1) (Ovaskainen et al., 2015).”
Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring (2016, methodology): “Latent variable models are seeing increased use in community ecology as a sparse method for analysing correlations among multiple species (Ovaskainen et al ., 2015; Thor-son et al ., 2015a; Warton et al ., 2015).”
Co-occurrence is not evidence of ecological interactions. (2020, background): “…species that are potentially interdependent based on abiotic factors using the entire incidence matrix (€Ozesmi & €Ozesmi 1999; Latimer et al., 2009; Ovaskainen et al., 2010, 2016, 2017; Clark et al., 2014; Kaldhusdal et al., 2015; Warton et al., 2015; Hui, 2016; Clark et al., 2017; Staniczenko…”