#system ("cp /home/refs/collection.bib collections.bib")
system ("cp collections.bib /home/refs/collection.bib ")Applied ecology involves informing policy makers through providing predictive links between management actions and ecosystem responses. Understanding and predicting the feeding behaviour of organisms can be a key element of this process.
Bayesian inference treats model parameters as random variables whereas frequentist inference considers them to be estimates of true fixed values. Data coming from many different studies have to be integrated in order to assess the empirical evidence for a new theory, and Bayesian statistics lends itself very well to this.
The results demonstrate the importance of making full use of subsidiary data and contextual information regarding the process being studies when fitting models. This is particularly important when observational data are used to parameterise models. Measures of the goodness of fit of a model based on purely statistical considerations may often be misleading. Rather than evaluating the fit of a model to the data or the fit of data to a model it may often be preferable to consider the fit of the model to its purpose. Proponents of likelihood based model comparison suggest that data can be used to arbitrate between competing models (Hilborn and Mangel 1997). This can be an appropriate approach in some circumstances. However it assumes that all the relevant information required for model arbitration is included in the data and used in the inference process through the likelihood function.
Seen from the standpoint of the philopsophy of science the model fitting process used in this paper could be regarded as following the approach of Polanyi (1967) who advocated the use of tacit, contextual understanding, plausibility and concensus when evaluating models and theories. In an applied context determining the parameters of a functional response representing feeding behaviour can only lead to predictions regarding population dynamics through the use of more complex models. So model fitting occurs within the context of a chain of inferential steps which include expert knowedge derived from field observations, consensual agreement among experts concerning the relative importance of observable and/or unobservable factors involved in animal behaviour and their relative importance in determining energy budgets. Constraining inference within plausible limits defined by the limited available evidence is the best that can often be achieved.
Hilborn, R. and Mangel, M., 1997. The ecological detective: Confronting models with data. Princeton University Press, Princeton.
Polanyi, M., 1967. The tacit dimension. Garden City, NY: Doubleday.