Our group if focused on developing more “efficient” theories in ecology, and for the application of such theories to understand the phenomenology of complex ecological systems from local to the biosphere scale, their response to human driven changes (i.e. climate change) and to guide efforts to set-up of experiments and to design large-scale monitoring programs. Since more efficient theories tend to make fewer, simpler, and more fundamental assumptions based upon first principles, and to generate a greater number of testable predictions per free parameter than do less efficient theories, they lend themselves to easier integration. We represent several different perspectives in theoretical ecology, who have come together inspired by the progress that each perspective has made, with the goal of pursuing linkages and integration between our perspectives that can accelerate progress towards a general theory of biodiversity.
The efficient theories we have identified (Metabolic Theory, Neutral Theory, Maximum Entropy Theory and Network Theory) usually interact in non-trivial ways. For example, The maximum entropy theory of ecology (METE) is built upon a state variable description of ecosystems (Harte 2011). The state variables used in the original formulation of METE are the area of an ecosystem, A0, the total number of species, S0 that are found in that area, the total number of individuals, N0, and the total metabolic rate of all those individuals, E0. Knowledge of numerical values of these state variables provides the constraints that are the input to the maximum information entropy (MaxEnt) inference calculation. The outputs of the theory are predictions of many of the metrics of macroecology: the functional forms describing patterns in spatial distribution, abundance and energetics within and across species. Interestingly this theory inputs (state variables) are the predictions of Metabolic Theory (West et al 1997, Brown et al 2004), which is based upon the constraints imposed by the architecture of energy and material delivery networks within organisms. Further, METE as well as the Neutral Theory of Biodiversity (Hubbell 2001), which is based upon and Master equation approach, make prediction of the same patterns (e.g. Species abundance distributions) meaning that in principle there could be a deep connection between them. In fact John Harte is currently making MaxEnt dynamic using a Master equation approach.