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\begin{document}

\title{Integrating data from different taxonomic resolutions to better estimate
community alpha diversity}



\author[1]{Kwaku Adjei}%
\author[2]{Claire Carvell}%
\author[2]{Nick Isaac}%
\author[2]{Francesca Mancini}%
\author[1]{Robert O'Hara}%
\affil[1]{Norwegian University of Science and Technology}%
\affil[2]{UK Centre for Ecology \& Hydrology}%


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  \date{June 15, 2024}


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\begin{abstract}
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 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 IDMs to capture the true community 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.%
\end{abstract}%



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