  1. Challenging calibration of complex models can be approached by using prior
knowledge on the parameters. However, the natural choice of Bayesian inference
can be computationally heavy when relying on Markov Chain Monte Carlo (MCMC)
sampling. When the likelihood of the data is intractable, alternative Bayesian
methods have been proposed. Approximate Bayesian Computation (ABC) only
requires sampling from the data generative model, but may be problematic when
the dimension of the data is high.
  2. We studied alternative strategies to handle high dimensional data in ABC
applied to the calibration of a spatially explicit foraging model for
\textit{Bombus terrestris}. The first step consisted in building a set of
summary statistics carrying enough biological meaning, i.e. as much as the
original data, and then applying ABC on this set. Two ABC strategies, the use
of regression adjustment leading to the production of ABC posterior samples,
and the use of machine learning approaches to approximate ABC posterior
quantiles, were compared with respect to coverage of model estimates and true
parameter values. The comparison was made on simulated data as well as on data
from two field studies.
  3. Results from simulated data showed that some model parameters were easier
to calibrate than others. Approaches based on random forests in general
performed better on simulated data. They also performed well on field data,
even though the posterior predictive distribution exhibited a higher variance.
Nonlinear regression adjustment performed better than linear ones, and the
classical ABC rejection algorithm performed badly.
  4. ABC is an interesting and appealing approach for the calibration of
complex models in biology, such as spatially explicit foraging models. However,
while ABC methods are easy to implement, they require considerable tuning.
