How To Find The Most Practical Ecosystem
Management Plan
Author(s)
T. C. Haas
Abstract
A predictive understanding of how political processes produce a sequence of
ecosystem management decisions would allow environmental managers to estimate
how much social change would be needed to make an ecosystem sustainable.
To this end, a stochastic, temporal model of how political processes influence
and are influenced by ecosystem processes is being developed. This model is
realized in a set of interacting Influence Diagrams (Bayes Nets with Decision
nodes) that each represent the belief systems of political groups in countries that
affect an ecosystem. These group models also interact with a model of the affected
ecosystem.
After these models are fitted to a political-ecological data set, they are used to
find the most practical ecosystem management plan by modifying the modeled
group belief systems that were estimated from the data, until a sequence of group
actions towards the ecosystem over a future time period results in desired ecosystem
values at a designated future time point, e.g. a viable wildlife population in the
year 2058. Belief systems are minimally modified away from their data-based values
so as to produce this desired sequence of group actions towards the ecosystem.
Such a set of interacting models has been constructed for the management of
the endangered Cheetah (Acinonyx jubatus) across Kenya, Tanzania, and Uganda.
Presidential offices, wildlife protection agencies, rural residents, pastoralists, and
NGO groupswithin these countries aremodeled along with amodel of the cheetahsupporting
ecosystem. A data set has been collected that consists of political
actions by these groups along with cheetah counts by political region. The most
practical management plan for this case is computed.
Keywords: ecosystem management, environmental politics, wildlife management,
biodiversity, optimal policies.
Keywords
ecosystem management, environmental politics, wildlife management,biodiversity, optimal policies.
Related Book
Other papers in this volume
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