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

A multispecies hierarchical model to integrate count and distance-sampling data

Integrated community models-an emerging framework in which multiple data sources for multiple species are analyzed simultaneously-offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.

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

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.