Guidelines for model adaptation: a study of the transferability of a
general seagrass ecosystem DBN model
Abstract
Ecological models are extensively and increasingly used in support of
environmental policy and decision making. Dynamic Bayesian Networks
(DBN) as a tool for conservation have been demonstrated to be a valuable
tool for providing a systematic and intuitive approach to integrating
data and other critical information to help guide the decision-making
process. However, data for a new ecosystem are often sparse. In this
case, a general DBN developed for similar ecosystems could be
applicable, but this may require the adaptation of key elements of the
network. The research presented in this paper focused on a case study to
identify and implement guidelines for model adaptation. We adapted a
general DBN of a seagrass ecosystem to a new location where nodes were
similar, but the conditional probability tables varied. We focused on
two species of seagrass (Zostera noltei and Z. marina)
located in Arcachon Bay, France. Expert knowledge was used to complement
peer-reviewed literature to identify which components needed adjustment
including parameterisation and quantification of the model and desired
outcomes. We adopted both linguistic labels and scenario-based
elicitation to elicit from experts the conditional probabilities used to
quantify the DBN. Following the proposed guidelines, the model structure
of the DBN was retained, but the conditional probability tables were
adapted for nodes that characterised the growth dynamics in
Zostera spp. population located in Arcachon Bay, as well as the
seasonal variation on their reproduction. Particular attention was paid
to the light variable as it is a crucial driver of growth and physiology
for seagrasses. Our guidelines provide a way to adapt a general DBN to
specific ecosystems to maximise model reuse and minimise re-development
effort. Especially important from a transferability perspective are
guidelines for ecosystems with limited data, and how simulation and
prior predictive approaches can be used in these contexts.