From individual to joint species distribution models: a comparison of model complexity and predictive performance
Caradima B, Schuwirth N & Reichert P
Caradima, B., Schuwirth, N., & Reichert, P. (2019). From individual to joint species distribution models: a comparison of model complexity and predictive performance. Journal of Biogeography, 46(10), 2260-2274. https://doi.org/10.1111/jbi.13668
Persistent URL https://www.dora.lib4ri.ch/eawag/islandora/object/eawag:19054
Aim: Species distribution models (SDMs) are widely used to study geographic distributions of taxa in response to natural and anthropogenic environmental conditions. For a community, common approaches include fitting individual SDMs (iSDMs) to all taxa or directly modelling community properties such as richness. However, the parameters of iSDMs are difficult to identify for rare taxa, and community properties do not reveal taxon‐specific responses. Individual models can be combined into a hierarchical multispecies distribution model (mSDM) that constrains taxon‐specific parameters according to overarching community parameters, or a joint model (jSDM) in which interdependencies between taxa are jointly inferred. We compare how individual, hierarchical multispecies and joint SDMs differ in quality of fit, explanatory power and predictive performance, and analyse how these properties depend on the prevalence of taxa.
Taxa: Presence–absence observations of 245 benthic macroinvertebrate taxa identified at a mixed taxonomic resolution.
Location: Four hundred and ninety‐two sites in rivers throughout Switzerland.
Methods: Individual, hierarchical and joint hierarchical generalized linear models (GLM) were developed for all taxa. Parameters were estimated using maximum likelihood estimation or Bayesian inference with Hamiltonian Markov chain Monte Carlo simulations. Predictive performance was assessed with cross‐validation. In addition, the predicted family and species richness of the models was compared with a GLM for richness.
Results: Individual models show a slightly higher quality of fit largely due to overfitting for rare taxa. The mSDM achieves a similar quality of fit and explanatory power, mitigates overfitting for rare taxa and considerably improves predictive performance over the whole community. The joint models further improve the quality of fit, but decrease predictive performance and increase predictive uncertainty.
Main conclusions: We show that even a relatively simple mSDM combines many of the analytical capabilities of iSDMs and improves predictive performance. Increasingly complex mSDMs and jSDMs provide additional analytical possibilities, but depending on the data and research questions, different levels of complexity may be appropriate.
Caradima, Bogdan (Systems Analysis, Integrated Assessment and Modelling SIAM)
Schuwirth, Nele (Systems Analysis, Integrated Assessment and Modelling SIAM)
Reichert, Peter (Systems Analysis, Integrated Assessment and Modelling SIAM)
Journal of Biogeography
benthic macroinvertebrates
hierarchical multispecies models
joint models
predictive modelling
species distribution models
stacked individual models
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