February 15-18, 2009 Washington State Convention Center Seattle ...

HOLISTIC GOODNESS-OF-FIT: COMPARING OBSERVED AQUACULTURAL
PERFORMANCE WITH THAT SIMULATED VIA ECOPHYSIOLOGICAL MODELING
William H. Neill*, Robert R. Vega, Scott J. Walker and F. Michael Speed
Department of Wildlife and Fisheries Sciences
Texas A&M University
College Station, Texas USA
w-neill@tamu.edu
It is our contention that efficient advancement of aquacultural science requires a melding of statistical analysis and systems
modeling.
Most of us who are involved in aquacultural research find it expedient if not obligatory to make our measurements on systems
that are neither rigidly controlled nor exactly replicated—i.e., the data come from groups of animals in large tanks, ponds,
net-pens, and the like. The measurements themselves connect growth, survival and other performance outputs that tend to be
far removed, in a causal sense, from inputs such as environmental factors and feed treatments. Moreover, the lack of control
over the course of a 6- or 8-week-long study leads to uncertain interaction and physiological integration of time-varying inputs,
causing outputs that are inherently in transient-state. Even differential growth and survival of the animals themselves makes
for confounded relationships among intended treatments, unintended treatments (e.g., metabolite concentrations), and animal
size and biomass.
We think it’s time to move on, from statistical testing of trivial null hypotheses and uninformed description of associations and
relationships, to more thoughtful attempts at quantitative, mechanistic explanation of observed results. This requires that we
build informed models through which the data can be processed, then statistically test for consistency between modeled and
observed outcomes. Thus, all the complexity that stands between inputs and outputs becomes reduced to the issue of goodnessof-fit,
which is simply tested—only, there is one additional twist: Goodness-of-fit needs to be holistic, in that it must extend
across the various pairs of modeled and observed measures of performance, and over all available experiments. It isn’t enough
that growth be accurately modeled, if survival is not; and, it isn’t good enough if both “fits” are adequate in one instance but
not in another. There must be concordance overall, not just good fits here and there.
Lack of holistic goodness-of-fit provides both the motivation and guidance to re-think experiments, and, in the process, to gain
new insights about mechanisms and relationships. Often, those new insights can be formalized and then evaluated with the
same dataset—without running a new experiment.
We demonstrate our thesis by using Ecophys.Fish (Neill et al. 2004. Rev. Fish. Sci. 12:233-288) to model aquacultural performance
trials in tanks and ponds.
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