International Institute of Fisheries Economics and Trade
Abstract
The motivation of this paper is to detail the application of Markov chains in
simulating fleet dynamics in Australia's Northern Prawn Fishery (NPF).
The Markov chains are enhanced through the use of the multinomial logit
(MNL) and Seemingly Unrelated Regressions (SUR) models to explain
transition probabilities. The terms MNL Markov and SUR Markov are
coined, therefore. The MNL Markov and the SUR Markov are novel, as
they describe, capture and forecast time-variant (time-inhomogeneous) and
time-invariant (time-homogeneous) fleet dynamics within any defined
spatial fishery structure. The models yield reliable forecasts, when applied
to data-rich fisheries and have a potential of yielding similar forecasts
when customised for use in data-poor fisheries. In the paper, the theoretical
structure of the MNL Markov and SUR Markov is shown. Results using
data from the NPF are provided, and forecasts of reliability are presented.
These forecasts can form the pillar of any fishery management strategy
evaluation. The two models are novel, and offer a lot of possibilities for
answering marine resource use questions with respect to the allocation of
fisheries resources. The models represent a unique, simple, effective and
novel approach to fishery management, and particularly for understanding
the key drivers of effort allocation in fisheries. The original structure of
these models is described in detail in Ngwenya (1997), and in an
unpublished PhD thesis (Ngwenya, 2001). The MNL Markov and SUR
Markov provide a practical way of integrating multiple fisheries objectives,
and using economic drivers of fleet dynamics to manage outcomes of a
commercial fishery.Keywords: Fisheries Economics, Fisheries Modeling, Modeling and Economic Theor
