An important insight brought by the AmP project is the classification of animals energetics in a family of related DEB models that is structured on the basis of the mode of metabolic acceleration, with close links to the development of larval stages. We discuss the evolution of metabolism in this context among animals in general and ray-finned fish, molluscs and crustaceans in particular.
New DEBtool code for estimating DEB parameters from data has been written, and AmPtool code for analyzing patterns in parameter values. A new web-interface supports multiple ways to visualize data, parameters en implied properties from the entire collection as well as on an entry by entry basis.
The DEB models proved to fit data well, the median relative error is only 0.07, for the 1016 animal species at 2018/01/24, including some extinct ones, from all large phyla and all chordate orders, spanning a range of body masses of 16 orders of magnitude.
This study is a first step to include evolutionary aspects into parameter estimation, allowing to infer properties of species for which very little is known.
Apart from goodness-of-fit as estimation criterion, relations with parameter values of other species are important, since DEB parameters have a clear physiological interpretation and a good fit for the wrong reasons is always a risk to consider.
We developed and optimized methods for this type of parameter estimation-in-context and organized the results of over 1000 animal species in the open-access Add-my-Pet (AmP) data base, to which 125 authors contributed so far. We also developed software package AmPtool to compare parameter values in the collection, that builds on DEBtool to assist applications of DEB theory.
A family of related DEB models, structured with respect to the modes of metabolic acceleration, captures biodiversity, including various life stages. We discuss some features of the family structure of DEB models in an evolutionary context.
The AmP collection has a great potential for research on the role of biodiversity in ecosystem structure and functioning, which will grow with the size of the data base.