Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models

1. Pollination is a key ecosystem service for global agriculture but
evidence of pollinator population declines is growing. Reliable spatial
modelling of pollinator abundance is essential if we are to identify areas
at risk of pollination service deficit and effectively target resources to
support pollinator populations. Many models exist which predict pollinator
abundance but few have been calibrated against observational data from
multiple habitats to ensure their predictions are accurate. 2. We selected
the most advanced process-based pollinator abundance model available and
calibrated it for bumblebees and solitary bees using survey data collected
at 239 sites across Great Britain. We compared three versions of the
model: one parameterised using estimates based on expert opinion, one
where the parameters are calibrated using a purely data-driven approach
and one where we allow the expert opinion estimates to inform the
calibration process. 3. All three model versions showed significant
agreement with the survey data, demonstrating this model's potential
to reliably map pollinator abundance. However, there were significant
differences between the nesting/floral attractiveness scores obtained by
the two calibration methods and from the original expert opinion scores.
4. Our results highlight a key universal challenge of calibrating
spatially-explicit, process-based ecological models. Notably, the desire
to reliably represent complex ecological processes in finely mapped
landscapes necessarily generates a large number of parameters, which are
challenging to calibrate with ecological and geographical data that is
often noisy, biased, asynchronous and sometimes inaccurate. Purely
data-driven calibration can therefore result in unrealistic parameter
values, despite appearing to improve model-data agreement over initial
expert opinion estimates. We therefore advocate a combined approach where
data-driven calibration and expert opinion are integrated into an
iterative Delphi-like process, which simultaneously combines model
calibration and credibility assessment. This may provide the best
opportunity to obtain realistic parameter estimates and reliable model
predictions for ecological systems with expert knowledge gaps and patchy
ecological data.