ASSESSING THE
TOXIC EFFECTS ON POPULATION
DYNAMICS
USING INDIVIDUAL-BASED ENERGY BUDGET MODELS
Introduction
 The
research will exploit recent advances in understanding the response
of individual organisms to toxicants, as the basis for
developing models of populations potentially impacted by OCS
activities. The ecological effects of OCS activities occur
within complex ecological communities, so that the response of the
constituent populations to environmental stress is difficult to
measure. Furthermore, toxicants only affect population
dynamics indirectly via changes in the vital rates of individual
organisms. It is therefore vital to exploit the large body of
experimental information qualifying the impact of toxicants on the
individual organisms that make up populations. For this
purpose, we shall construct models of populations and communities
that are firmly based on dynamic energy budget models, developed in
part with previous MMS funding. Mathematical models, of
course, do not replace experiments, rather they are a supplement
helping us to interpret experimental results and generate testable
hypotheses in an efficient manner (see Figure 1).
The PIs have developed testable theory to
describe the effects of low, or sublethal, toxicant levels on vital
rates of individual organisms. We aimed at realism, generality
and simplicity, because we need broadly applicable models that
relate to real animals but are still mathematically tractable.
Our models of individuals were tested against a wide body of
experimental data (see next section), and has been successful in
describing MMS-funded experiments on the growth of outplanted
mussels near a produced water outfall. We plan to extend our
approach to describe reproductive effort, mortality and
fertilization success in the presence of toxicants, as these
processes are vital in describing population dynamics. Also,
our individual models have yet to be linked to the unifying approach
of Quantitative Structure-Activity Relationships (QSARs) and related
models, a powerful way of predicting the relative toxicity of
compounds.
Research on population dynamics at UCSB is
distinctive in the emphasis placed on rigorous model testing.
Our strategy for the research proposed here is to recognize that
initial testing of new concepts must exploit the most relevant data
available, whether or not it originated in the Pacific Continental
SHelf. Models, and model components that survive this initial
rigorous testing can then be applied to data from MMS-funded
research.
Figure 1. Schematic
representation of the strategy for formulating individual-based
population models are formulated, and for relating models to 'real'
world via tests and applications. Components within dashed box
have already been developed. The remaining components fall
within the scope of the present proposal.
Consistent with this philosophy, we plan to
develop population models which assume that individuals in a
population respond to toxicants in accordance with the predictions
of our existing models. We will test those population models
against experimental data obtained by Dr. Kevin Carman (Louisiana
State University) from marine microcosms involving the contaminants
associated with offshore oil production, data that were collected in
part with MMS funds. We will apply related population models
to interpret data on community response to oil seep and produced
water in the Santa Barbara Bight. Our modeling approach has
the potential to help distinguish between competing explanations of
these data.
The "deliverable", in addition to scientific
papers, will be realistic models, synthesized from a large body of
empirical data. Such models will give managers a powerful tool
for evaluating the wider ecological significance of existing MMS
data, and will be useful in the design of future experiments for
impact assessment. Also, our tests will make use of recent
"inverse methods" in which models are used to extract information on
rate processes from limited data on populations. These inverse
methods, although novel, may also prove to be valuable in
interpreting data from long-term monitoring studies by MMS and other
agencies.
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