Species Distribution Modeling in Community Ecology

Species distribution modelling in community ecology has evolved from single‐species habitat models into integrated frameworks that capture the joint occurrence and interactions of multiple taxa. Traditional approaches relate environmental predictors—such as climate, land cover and soil characteristics—to individual species’ presence or abundance, often neglecting biotic interactions and spatial dependency. Recent advances integrate phylogenetic relationships, species traits and co‐occurrence patterns within hierarchical and spatially explicit Bayesian models. Joint species distribution models (JSDMs) decompose community structure into shared environmental responses and residual correlation matrices, thereby revealing interactions or missing covariates. Gaussian processes and latent‐factor models allow flexible, nonlinear responses and account for spatial autocorrelation, improving interpolation and extrapolation across scales. The inclusion of biotic networks and food‐web constraints further refines predictions by embedding trophic links or competitive relationships. These multi‐species frameworks enable prediction of richness, composition and functional traits along ecological gradients, informing conservation planning, invasion risk assessments and climate-change impact forecasts at regional to global extents.

Research from Nature Portfolio

Recent studies have applied network inference algorithms to presence–absence time series in order to reconstruct trophic and nontrophic links within long‐term community data. By comparing dynamic Bayesian networks, Lasso regression and correlation methods against empirical webs, researchers have highlighted both the promise and limitations of inferring interaction structure from co‐occurrence data, emphasising caution when predicting ecological networks. In parallel, integrated experimental–distribution modelling has emerged, combining manipulative tolerance experiments with survey data to embed physiological limits and interaction effects directly into predictive maps. Such hybrid methods have demonstrated improved reliability in forecasting species’ range shifts under combined salinity and temperature change, illustrating how causal experimental knowledge can be formalised within statistical distribution frameworks.

Species Distribution Modeling in Community Ecology publication trend

The graph below shows the total number of articles in species distribution modeling in community ecology across all publications each year (not limited to Nature Index journals).

Technical terms

Species Distribution Model (SDM): A statistical or machine‐learning model relating species occurrences to environmental predictors to estimate habitat suitability or range.

Joint Species Distribution Model (JSDM): A hierarchical framework that models multiple species simultaneously, partitioning variation into shared environmental responses and residual correlations indicative of interactions or missing factors.

Spatial Autocorrelation: The tendency for observations close in space to exhibit similar values, requiring explicit modelling to avoid biased inference and improve predictive accuracy.

Gaussian Process: A flexible, nonparametric method that defines a distribution over functions, allowing smooth but complex response surfaces and spatial interpolation in a Bayesian context.

Hierarchical Bayesian Model: A multi‐level statistical approach in which parameters are assigned probability distributions, enabling partial pooling of information across species, sites or traits.

References

1. A case study on joint species distribution modelling with bird atlas data: Revealing limits to species' niches. Ecological Informatics (2023).
2. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters (2017).
3. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs (2019).
4. Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution (2014).
5. Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution (2015).
6. Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks. Ecology Letters (2017).
7. Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods in Ecology and Evolution (2016).
8. Combining food web and species distribution models for improved community projections. Ecology and Evolution (2013).
9. Computationally efficient joint species distribution modeling of big spatial data. Ecology (2019).
10. Ecological Network Inference From Long-Term Presence-Absence Data. Scientific Reports (2017).
11. Integrating experimental and distribution data to predict future species patterns. Scientific Reports (2019).