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

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.