Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity

Abstract

Data deficiencies among rare or cryptic species preclude assessment of community‐level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government‐funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well‐structured, design‐based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single‐species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging ‘integrated community modelling’ framework that combines both data integration and community modelling to improve inferences on species‐ and community‐level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community‐level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula‐based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community‐level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

Integrating data from different taxonomic resolutions to better estimate community alpha diversity

“…Previous studies have shown that IDMs perform better than single‐dataset models in estimating state variable parameters and prediction accuracy of new datasets (Pacifici et al 2017, Miller et al 2019, Doser et al 2022, Strebel et al 2022). Miller et al (2019) noted that IDMs present opportunities to model community dynamics and diversity from multiple datasets, and Zipkin et al (2023) presented an integrated community model to explore such opportunities. Our IDM shares information among species and provides estimates for parameters at both species and community levels (Zipkin et al 2023).…”

“…Miller et al (2019) noted that IDMs present opportunities to model community dynamics and diversity from multiple datasets, and Zipkin et al (2023) presented an integrated community model to explore such opportunities. Our IDM shares information among species and provides estimates for parameters at both species and community levels (Zipkin et al 2023). Our work provides further advancements in such integrated community models by providing an IDM that combines data from different taxonomic levels to estimate alpha diversity in a community.…”

“…Initial developments of IDMs have been developed for single species (Koshkina et al 2017, Pacifici et al 2017, Miller et al 2019). In recent years, however, much focus has been on integrated community models (Doser et al 2022, Lauret et al 2023, Zipkin et al 2023), that take advantage of data integration and hierarchical community modelling framework to combine multi‐species datasets. These integrated community models allow parameter estimation for all species (irrespective of their sample sizes; Zipkin et al 2009) by producing estimates of community‐level parameters such as variance among species (Zipkin et al 2023).…”

“…In recent years, however, much focus has been on integrated community models (Doser et al 2022, Lauret et al 2023, Zipkin et al 2023), that take advantage of data integration and hierarchical community modelling framework to combine multi‐species datasets. These integrated community models allow parameter estimation for all species (irrespective of their sample sizes; Zipkin et al 2009) by producing estimates of community‐level parameters such as variance among species (Zipkin et al 2023). The community‐level parameters are then used to make inferences about the effects of covariates and other environmental stressors in a community (Zipkin et al 2023).…”

“…These integrated community models allow parameter estimation for all species (irrespective of their sample sizes; Zipkin et al 2009) by producing estimates of community‐level parameters such as variance among species (Zipkin et al 2023). The community‐level parameters are then used to make inferences about the effects of covariates and other environmental stressors in a community (Zipkin et al 2023).…”