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

Seaton, Fiona M. ORCID: https://orcid.org/0000-0002-2022-7451; Jarvis, Susan G. ORCID: https://orcid.org/0000-0001-5382-5135; Henrys, Peter A. ORCID: https://orcid.org/0000-0003-4758-1482. 2024 Spatio-temporal data integration for species distribution modelling in R-INLA. Methods in Ecology and Evolution, 15 (7). 1221-1232. https://doi.org/10.1111/2041-210X.14356

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Official URL: https://doi.org/10.1111/2041-210X.14356

Abstract/Summary

1. 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. 2. 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. 3. 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. 4. 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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1111/2041-210X.14356
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2041-210X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	citizen science, data integration, integrated distribution models, integrated nested laplace approximation, spatio-temporal models, species distribution models, stochastic partial differential equation
NORA Subject Terms:	Ecology and Environment Data and Information
Related URLs:	Dataset Dataset Dataset Other
Date made live:	23 May 2024 10:58 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537467

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1111/2041-210X.14356

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

2041-210X

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

citizen science, data integration, integrated distribution models, integrated nested laplace approximation, spatio-temporal models, species distribution models, stochastic partial differential equation

NORA Subject Terms:

Ecology and Environment
Data and Information

Related URLs:

Date made live:

23 May 2024 10:58 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/537467