Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

By Laura J. Pollock, R. Tingley, W. Morris, N. Golding, R. O’Hara, Kirsten M. Parris, Peter Vesk, M. McCarthy. 2014

646 citations recorded by the source corpus.

A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co‐occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co‐occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co‐occurrence patterns into components describing shared environmental responses and residual patterns of co‐occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co‐occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co‐occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9db393d8d7b932793e659dc188c99f812f6451d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Modeling Species and Community Responses to Past, Present, and Future Episodes of Climatic and Ecological Change (2015, background): “Although other methods attempt to gain inference regarding biotic interactions (as opposed to associations, e.g., Pollock et al. 2014), the inclusion of co-occurrence patterns alone may improve the ability of ENMs to predict community responses to climate (Kissling et al. 2012, Wisz et al. 2013).”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “That models that account for associations produce better predictions could be either due to species having real ecological interactions with each other, or to unrecognized environmental covariates not included in the model (Pollock et al. 2014, Ovaskainen et al. 2017).”
Predicting distributions of invasive species (2013, background): “Further, recent progress in methods for modelling species cooccurrences (Ovaskainen et al., 2010; Pollock et al., 2014) can provide strong inference about likely interspecies effects.”
Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales (2016, background): “Another approach consists of modelling the residual co-occur- rence after accounting for the influences of measured covari- ates (Peres-Neto, Olden & Jackson 2001; Ovaskainen, Hottola & Siitonen 2010; Kissling et al. 2012; Pollock et al. 2014).”
A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, background): “In contrast, J-SDMs model a whole community of species at once, and then subsequently create predictions (Pollock et al., 2014; Warton et al., 2015).”
Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context (2017, background): “…models consider as the response variable the vector of occurrences of all species, and thus provide a model-based approach for inferring simultaneously species associations as well as species relationships to the abiotic environment (Clark et al. 2014; Pollock et al. 2014; Warton et al. 2015).”
A multispecies occupancy model for two or more interacting species (2016, methodology): “Several methods that account for dependence between two or more interacting species have been proposed (e.g. Latimer et al. 2009; Ovaskainen, Hottola & Siitonen 2010; Pollock et al. 2014) though none of *Correspondence author.”
How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, background): “…& 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 to quantify co-occurrence patterns among species (Pollock et al. 2014; Ovaskainen et al. 2016a).”
Bringing Elton and Grinnell Together: a quantitative framework to represent the biogeography of ecological interaction networks (2016, neutral): “It could arise because interactions constrain distribution (Pollock et al., 2014; Cazelles et al., 2016) or, alternatively, because of environmental requirements shared between i and j.”
Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB (2024, neutral): “…random-slopes models (Bolker et al. 2009; Asar, Bolin, Diggle, and Wallin 2020), to account for correlation between the slope coefficient(s) and intercept terms, and when using random effects to induce correlation in multivariate data (Coull and Agresti 2000; Pollock et al. 2014, for example).”
Co-occurrence is not evidence of ecological interactions. (2020, positive): “Consequently, the now socalled joint species distribution models (JSDMs; Pollock et al., 2014) were developed and predict the distribution of a set of species that are potentially interdependent based on abiotic factors using the entire incidence matrix (€ Ozesmi & € Ozesmi 1999; Latimer et al.”
Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring (2016, background): “This concern has increased interest in modelling temporal variation in species distribution via dynamic SDMs (DeVisser et al ., 2010; Merow et al ., 2011), where environmental linkages or residual spatial variation is allowed to vary over time.”
Shared resources between giant panda and sympatric wild and domestic mammals (2015, methodology): “The interactions between species, either within or between taxonomic groups, play a significant role in structuring animal communities and can range from mutually beneficial (Arnan et al., 2011; Ovaskainen et al., 2010) through neutral (Arnan et al., 2011) to mutually harmful (Pollock et al., 2014).”