How to make more out of community data? A conceptual framework and its implementation as models and software.

By Otso Ovaskainen, Gleb Tikhonov, A. Norberg, F. Guillaume Blanchet, Leo L. Duan, David B. Dunson, T. Roslin, N. Abrego. Ecology Letters, 2017

897 citations recorded by the source corpus.

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/03657b06d76a5f1b6bb31cffabfea84806e52474

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Citations in the corpus

Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions (2018, background): “In these conditions, the model must also allow for the interplay of environmental and biotic interactions (Clark et al., 2011), which has only recently started to be explored in BI-SDMs (Tikhonov, Abrego, Dunson, & Ovaskainen, 2017).”
Joint species distribution modelling with the r‐package Hmsc (2020, background): “Compared to H msc 2.0 (see Ovaskainen, Tikhonov, Norberg, et al., 2017), H msc 3.0 includes several extensions, enabling one to ask how environmental conditions influence species-to-species association matrices (Tikhonov, Abrego, Dunson, & Ovaskainen, 2017), to infer species-to-species associations…”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, methodology): “…the occurrences of all species in a community simultaneously and include a covariance structure to capture species-to-species associations, without necessarily assuming rigid species-by-species relationships (Clark et al. 2014, Pollock et al. 2014, Thorson et al. 2015, Ovaskainen et al. 2017).”
Testing species assemblage predictions from stacked and joint species distribution models (2019, background): “Future studies should evaluate these propositions across more taxa and biogeographical realms as well as for other JSDM algorithms (e.g., Harris, 2015; Clark et al., 2017; Ovaskainen et al., 2017).”
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…”
Dos and don'ts when inferring assembly rules from diversity patterns (2020, background): “…can result from model misspecification, influential but hidden abiotic variables, but could potentially also result from biotic interactions, and thus, be a signal of assembly rules (Ovaskainen et al., 2017; Tikhonov, Abrego, Dunson, & Ovaskainen, 2017; Zurell, Pollock Laura, & Thuiller, 2018).”
Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB (2024, neutral): “Early software written in R , with ecologists in mind, used Bayesian Markov chain Monte Carlo (Hui 2016; Ovaskainen et al. 2017).”