Spatio‐temporal data integration for species distribution modelling in R‐INLA

• Published in: Methods in ecology and evolution 2024-07, Vol.15 (7), p.1221-1232
• Main Authors: Seaton, Fiona M., Jarvis, Susan G., Henrys, Peter A.
• Format: Article
• Language: English
• Subjects: Aquatic insects; Biodiversity; Case studies; citizen science; Data integration; Data sources; Geographical distribution; integrated distribution models; integrated nested laplace approximation; Integration; Modelling; Population studies; Rivers; spatio‐temporal models; species distribution models; stochastic partial differential equation
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.14356

Species distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions change over both time and space. Spatio‐temporal models require large amounts of data spread over time and space, and as such are clear candidates to benefit from model‐based integration of different data sources. However, spatio‐temporal models are highly computationally intensive and integrating different data sources can make this approach even more unfeasible to ecologists. Here we demonstrate how the R‐INLA methodology can be used for model‐based data integration for spatio‐temporally explicit modelling of species distribution change. We demonstrate that this method can be applied to both point and areal data with two contrasting case studies, one using the SPDE approach for modelling spatio‐temporal change in the Gatekeeper butterfly (Pyronia tithonus) across Great Britain and the second using a spatio‐temporal areal model to describe change in caddisfly (Trichoptera) populations across the River Thames catchment. We show that in the caddisfly case study integrating together different data sources led to greater understanding of the change in abundance across the River Thames both seasonally and over 5 years of data. However, in the butterfly case study moving to a spatio‐temporal context exacerbated differences between the data sources and resulted in no greater ecological insight into change in the Gatekeeper population. Our work provides a computationally feasible framework for spatio‐temporally explicit integration of data within SDMs and demonstrates both the potential benefits and the challenges in applying this methodology to real ecological data.