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

Adjei, Kwaku Peprah; Carvell, Claire ORCID: https://orcid.org/0000-0002-6784-3593; Isaac, Nick J.B. ORCID: https://orcid.org/0000-0002-4869-8052; Mancini, Francesca ORCID: https://orcid.org/0000-0003-4085-4978; O'Hara, Robert B.. 2024 Integrating data from different taxonomic resolutions to better estimate community alpha diversity. Ecography, e07182. https://doi.org/10.1111/ecog.07182

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Abstract/Summary

Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species-level dataset of presence–absence records with a co-located dataset of group-level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1111/ecog.07182
UKCEH and CEH Sections/Science Areas: Biodiversity (Science Area 2017-)
ISSN: 0906-7590
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: alpha diversity, Bayesian models, Markov chain Monte Carlo methods, multispecies distribution models, UK Pollinator Monitoring Scheme
NORA Subject Terms: Ecology and Environment
Data and Information
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Date made live: 16 Feb 2024 12:01 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/536917

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