dc.contributor.advisor |
Millar, Russell |
|
dc.contributor.advisor |
Tuck, Ian |
|
dc.contributor.advisor |
Fewster, Rachel |
|
dc.contributor.author |
Nottingham, Christopher David |
|
dc.date.accessioned |
2022-04-11T01:41:51Z |
|
dc.date.available |
2022-04-11T01:41:51Z |
|
dc.date.issued |
2021 |
en |
dc.identifier.uri |
https://hdl.handle.net/2292/58651 |
|
dc.description.abstract |
This thesis comprises two bodies of work that are presented in the form of unpublished
articles. These lay the groundwork and begin the process of proactively
exploring different management options for New Zealand’s surfclam stocks. The first
body work presents an R package named spatialSim, which was developed to carry
out simulation studies for comparing the performance of different fisheries management
strategies. Explicitly, the package provides users with an R interface to a
C++ based multi-species spatiotemporal size-structured operating model for sedentary
species. Four key features of the model include (1) the efficient simulation
of systems at high spatial resolution, (2) an efficient harvest algorithm that supports
the simulation of realistic commercial fisher dynamics and catch data through
the specification of site selection constraints (e.g., economic) and different levels of
preferential targeting and fishing intensity across space, (3) support for simulating
scientific abundance surveys with any user specified design (e.g., a stratified survey
of the biomass or number of individuals at size), and (4) support for specifying different
levels of density dependence in recruitment. Overall, the spatialSim operating
model supports more complexity and realism than a lot of the currently available
alternatives, yet retains enough simplicity to allow the model and its parameters to be easily understood by users.
The second body of work describes two bespoke geostatistical models that were developed
for application to New Zealand’s surfclam stocks. Additionally, it reports
the results of a spatialSim based simulation study that assessed the geostatistical
model’s ability to estimate surfclam biomass using simulated scenarios representing
New Zealand’s Kapiti Coast S. aequilatera and M. murchisoni surfclam fishery. This
study compared the relative performance of the geostatistical models to the random
stratified survey estimator that is currently used for assessing these stocks. Overall,
the geostatistical models performed similarly, with both demonstrating potential to
produce less biased and markedly more accurate estimates of each stock’s biomass
than the survey-estimator. Moreover, they were able to achieve these superior levels
of performance while using less data. The results demonstrated that the newly
presented models could be very effective tools for supporting the sustainable management
of New Zealand’s surfclam stocks, and they highlighted different aspects of
each model that could benefit from further improvement. |
|
dc.publisher |
ResearchSpace@Auckland |
en |
dc.relation.ispartof |
PhD Thesis - University of Auckland |
en |
dc.relation.isreferencedby |
UoA |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ |
|
dc.title |
Geostatistical tools supporting improved management practices for the New Zealand surfclam fishery |
|
dc.type |
Thesis |
en |
thesis.degree.discipline |
Statistics |
|
thesis.degree.grantor |
The University of Auckland |
en |
thesis.degree.level |
Doctoral |
en |
thesis.degree.name |
PhD |
en |
dc.date.updated |
2022-03-30T01:12:49Z |
|
dc.rights.holder |
Copyright: The author |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |