Potential Ministry of Fisheries – NIWA scholarship thesis topics
June 2010
The size of the projects listed below can readily be scaled for the Masters or PhD levels. In
general, a PhD is likely to also include some fieldwork to collect new data by, for example,
participating in a research survey, conducting experiments, or analysing specimens collected
during previous studies.
Note that this list is far from exhaustive and does not preclude a student coming forward with
a different suggested topic.
The following seven projects have been suggested by NIWA supervisors. Should a
student be interested in one of these projects they will be put in touch with the potential
NIWA supervisor if they successfully get to stage 2 of the application process.
1. Develop new, or improve existing, mathematical models for assessing the status of a
selected fishstock; for example,
 Basketwork eels
 One or more common rattail species
 Lookdown dory
 Stargazer
 Orange Roughy “Priceless” new area in QMA 6.
Objectives (one or more):
(i) to assemble and analyse existing biological and fisheries data for a selected fishstock;
(ii) to develop and/or use a mathematical model to determine whether the selected fishstock is
currently near or above its target biomass level;
(iii) to develop and/or use a mathematical model to estimate current exploitation rates being
applied by the fishery; and
(iv) to determine whether current catches or exploitation rates are likely to be sustainable and
whether they will move the stock towards its target biomass level.
(v) to assess uncertainty in the application of any model.
2. Develop new, or modify existing, management strategy models to evaluate the
performance of alternative approaches to managing the fisheries for a selected fishstock or
stock complex; for example,
 Trevally
 Hake
 Ling
 A multispecies inshore fishery
Objectives (one or more):
(i) to assemble and analyse existing biological and fisheries data for a selected fishstock; and
(ii) to develop and/or modify a management strategy model to compare the overall
performance of alternative management approaches in the face of uncertainty about the state
of knowledge of current and future stock size and stock dynamics.
(iii) Investigate the use of multi-criteria decision making (MCDM) in this context [MCDM is
explained by Henig & Buchanan 1998 Solving MCDM problems: Process concepts. Journal
of Multi-Criteria Decision Analysis 5: 3-21 available at
http://www3.interscience.wiley.com/journal/23929/abstract?CRETRY=1&SRETRY=0
3. Develop a rebuilding plan for a currently-depleted fishstock; for example,
 A depleted orange roughy stock
 Gemfish

Rig
Objectives (one or more):
(i) to assemble and analyse existing biological and fisheries data for a selected fishstock; and
(ii) to develop and/or use a mathematical model to determine the management actions needed
to rebuild the selected fishstock to its target biomass level.
4. Investigate and evaluate alternative stock assessment approaches for fishstocks that
have not yet been formally assessed; for example:
 Sea perch
 Spiny dogfish
 Blue moki
 Lookdown dory
Objectives (one or more)
(i) to assemble and analyse existing biological and fisheries data for one or more selected
fishstocks;
(ii) to explore alternative stock assessment methods for determining stock sizes, exploitation
rates and/or catches relative to targets or other relevant benchmarks; and
(iii) to determine priorities for future research in order to improve the state of knowledge for
the fishstocks.
5. Ageing and development of biological parameters for deepwater species, where these
parameters are deficient. This could include the estimation of growth formulae, length-weight
relationships, fishing mortality (M), maturity, length at age of maturity and the variability of
length at mean age. Deepwater species in question might include,
 basketwork eels
 Rattails (2 species)
 Slickheads (2 species)
 Robust cardinalfish
Note: This project could involve a voyage to collect samples and may be better suited to a
biology/statistics double major.
6. Investigating spatial patterns in population dynamics. Build and test models of
population dynamics that allow different spatial structures, for example for
 Oreos
 Various shellfish fisheries
 Various inshore finfish fisheries that are managed in units (Fishery
Management Areas) that are smaller than the stock area (e.g., hapuku,
tarakihi, school shark)
7. Development of appropriate error for length at-age-data (details available on request)
Note: This project is highly technical and would require a statistics student
The following six topics are proposed by Russell Millar at University of Auckland and
his
website
is
available
for
further
details
at:
http://www.stat.auckland.ac.nz/showperson?firstname=Russell&surname=Millar
or
contact Russell directly. A more modest version of the suggested PhD topics would also
be considered for an MSc thesis. His website contains the following text:
These research topics will not be suitable for students who have taken only applied undergrad
courses in statistics. For example, for students with an undergraduate degree from the
University of Auckland, these topics require successful completion of STATS 310 (or 732) or
730 with at least an A- or better. A good grade in STATS 331 or 731 is also a requirement for
those topics with a Bayesian flavour. (A PhD student could take STATS 730 and 731 in their
first year of enrolment if necessary.)
1. Title: Are there lunar patterns in fishing success for snapper?
Suitability: Graduate project, or MSc thesis
Background: Newspapers and fishing calendars publish predictions on the success of
fishing. These are usually based on the lunar calendar or Maori fishing calendar. Is
this hogwash, or can fishing success really be predicted?
Objectives:
1) Obtain suitable data on snapper catches from Northern region boat ramp surveys,
and prepare this data for analysis. [These data are available on request from NIWA.]
2) Obtain lunar phases and Maori calendar predictions for the relevant time periods.
3) Analyse catch-rate data, corrected for appropriate covariates, for any association
with lunar cycle of Maori calendar predictions.
4) If an MSc thesis: This work will be of immense general interest, and a manuscript
will be prepared for submission to an international ecology or marine journal.
2. Title: Update of software for modeling the size-selectivity of fishing gear.
Suitability: Project, or MSc thesis
Background: This software is written in R, but is now getting old. It has been
provided openly as a collection of functions, but has never been provided in the form
of a formal R package. Currently, a new version of this software is a work in
progress.
Objectives:
1) Complete the new version, primarily by adding flexibility to handle a greater
variety of selection curves.
2) Produce a fully documented R package and submit to CRAN.
3) If an MSc thesis: The newly developed code will include functionality to include
random effects (such as between-deployment) variation.
3. Title: Comparison of ADMB and WinBUGS software for Bayesian analyses.
Suitability: Project, or MSc thesis
Background: These are now both freeware. How do they compare for Bayesian
analyses?
Objectives:
1) Download, install, and gain familiarity with both ADMB and WinBUGS.
2) Compare their performance with a variety of models, e.g., implement some
documented ADMB examples in WinBUGS, and vice-versa. Make recommendations
as to when to use WinBUGs, and when to use ADMB.
3) If an MSc thesis: Delve more intensely into the comparison, perhaps based on
differences between the Gibbs sampler (WinBUGS) and Metropolis-Hastings
algorithm (ADMB). Prepare a short draft for submission to an international
software/computing journal.
4. Title: Modeling the size-selectivity of fishing gear.
Suitability: PhD
Background: Size-selectivity data provide all kinds of interesting data-analytic
challenges. This is especially due to the presence of random effects, but also from
overdispersion and subsampling. In particular, due to sub-sampling of the catches it is
the case that conditional (i.e., subject specific) inference is the most rigorous, yet it
can be argued that marginal (i.e., population specific) inference is most relevant.
Some work has been done in this area, but more needs to be done. As yet, very little
has been done via the Bayesian paradigm.
Objectives:
1) The main objective will be to extensively explore the Bayesian paradigm for
modelling of size-selectivity data. This will present many interesting questions and
challenges.
2) Masses of selectivity data from all over the world, all having different nuances,
will be analysed.
3) Consider the fundamental research question (i.e., what is it that the fisheries
managers need to know about the fishing gears?) and the best statistical paradigm for
answering this question.
5. Title: Merging prior information in Bayesian models.
Suitability: PhD
Background: A standard Bayesian model requires priors on model parameters.
However, in many situations, it will be more natural to provide prior information on
some function of the model parameters, or on predictive distributions of new
observations, say. One example is animal population models, where initial historical
biomass is a model parameter. However, the modeller may have little prior
knowledge on historical biomass, but good knowledge about current biomass. How
can this information be incorporated in a logically cohesive fashion?
Objectives:
1) This is a wide area, often known as Bayesian synthesis, and an extensive literature
review will be required.
2) The Bayesian melding approach (Poole & Raftery, 2000, JASA p. 1244-1255)
looks to be very promising in this area. However, it is computationally cumbersome.
Can this approach be modified to ease implementation, or can clever algorithms be
devised?
3) The pros and cons of Bayesian synthesis will be evaluated under a wide variety of
models and priors, and recommendations made.
6. Title: Investigation of Integrated Likelihood
Suitability: PhD
Background: Integrated likelihood (Berger et al., 1991, Stat. Sci. p. 1-28) is a hybrid
of likelihodist-Bayesian methodology in which nuisance parameters are eliminated by
integrated, but all other parameters are treated within a traditional maximum
likelihood framework.
Objectives:
1) Gain familiarity with the notion of integrated likelihood and general methods of
implementation. Integrated likelihood is supported under the ADMB software. Can it
be implemented in R or R2WinBUGs?
2) Evaluate the performance and properties of integrated likelihood under a wide
range of models.
3) Provide an assessment of integrated likelihood. E.g, should it be part of the
statisticians toolbox, or is it just a pacifier for statisticians who aren't yet ready to
adopt Bayesian methodology. This assessment will use knowledge gained in part 2),
and will also include research into the fundamental statistical philosophies underlying
the competing paradigms.
7. Title: Analysis of catches in 100mm and 130mm diamond, and 90mm square mesh.
Suitability: Graduate project or MSc thesis
Background: These data were obtained from a trawl experiment where a 100m mesh
codend was paired with 130mm diamond and 90mm square mesh codends. I have
already analysed the 100mm vs 130mm data, to obtain an estimate of the size
selectivity of these two meshes.
Objectives:
1) Become familiar with size-selectivity modeling.
2) Extend the analysis to estimate the size-selectivity of the square mesh. This
analysis will use knowledge about selectivity of the 100mm mesh, probably in the
form of an "integrated" analysis. This may be easiest using the Bayesian approach.
3) If an MSc thesis, explore additional approaches. E.g., a maximum likelihood
analysis, or additional applications of this approach.