Abstract
1) Food web models explain and predict the trophic interactions in a
food web, and they can infer missing interactions among the organisms.
The allometric diet breadth model (ADBM) is a food web model based on
the foraging theory. In the ADBM the foraging parameters are
allometrically scaled to body sizes of predators and prey. In Petchey et
al. (2008), the parameterisation of the ADBM had two limitations: (a)
the model parameters were point estimates, and (b) food web connectance
was not estimated. 2) The novelty of our current approach is: (a) we
consider multiple predictions from the ADBM by parameterising it with
approximate Bayesian computation, to estimate parameter distributions
and not point estimates. (b) Connectance emerges from the
parameterisation, by measuring model fit using the true skill statistic,
which takes into account prediction of both the presences and absences
of links. 3) We fit the ADBM using approximate Bayesian computation to
16 observed food webs from a wide variety of ecosystems. Connectance was
consistently overestimated in the new parameterisation method. In some
of the food webs, considerable variation in estimated parameter
distributions occurred, and resulted in considerable variation (i.e.
uncertainty) in predicted food web structure. 4) We conclude that the
observed food web data is likely missing some trophic links that do
actually occur, and that the ADBM likely predicts some links that do not
exist. The latter could be addressed by accounting in the ADBM for
additional traits other than body size. Further work could also address
the significance of uncertainty in parameter estimates for predicted
food web responses to environmental change.