Joint projects with DAF Queensland

Title: Queensland state-wide estimation of recreational fish catches

The problem:

Queenslanders are keen anglers. Each year more than 700,000 people fish for recreation, with anglers taking home around 8500 tonnes of fin fish, crabs and prawns. Queensland's fisheries resources are also important for tourism, attracting anglers from around Australia and the world.

The Queensland Government has conducted seven Statewide recreational fishing surveys  between 2007 and 2015. The surveys provide estimates of  recreational fishing harvests by species, which is a crucial input for assessing the fishing pressure on and status of fished stocks in Queensland. This information is used to safeguard marine resources for today and tomorrow.

New statistical methodologies are required to improve the accuracy of estimates and confidence intervals between surveys. This is to include regionally focused information on recreational fishing participation rates, where and how many people fish, and what they catch.

Approach:

Project statistical components include:

  • Improved estimation of state-wide recreational harvests, including resampling, bootstrap and MCMC techniques.
  • Quantify changes in survey angler avidity and recall bias between survey years and methodologies; adjust previous survey data to obtain improved estimates.
  • Evaluating sampling frames - develop methods to generate state-wide harvest estimates (and associated measures of uncertainty) from several synchronous samples taken from different sampling frames (e.g. a licence frame and a residential telephone number list).
  • Develop hierarchical and conditional mixed models for estimation of recreational fish catch and catch rates.
  • Investigate the statistical modelling of recreational survey data collected from multiple survey methods.
  • From survey to analysis: dealing with differences in the scale survey data are collected at and the scale data are analysed at.
  • Examine appropriate estimation methods for different fish species.
  • Develop statistical methods for low fish abundance or recreational species caught by ‘hard-to-reach’ fishers. 
  • Develop methods to engage and retain recreational fishers in volunteer data contribution programs.

 

Funding:

Joint project with the Department of Agriculture and Fisheries (DAF) within the Queensland Government. A PhD funding top up of $7K per year will offered.  

Supervisors/collaborators:

Clare McGrory (UQ; c.mcgrory@uq.edu.au), James Webley (DAF), Michael O’Neill (DAF), George Leigh (DAF)

Read:

https://www.daf.qld.gov.au/fisheries/monitoring-our-fisheries/statewide-and-regional-recreational-fishing-survey

Henry, G.W., and Lyle, J.M. (2003) The National Recreational and Indigenous Fishing Survey. New South Wales Fisheries Final Report Series 48, National Heritage Trust and Fisheries Research Development Corporation project 99/158. New South Wales Fisheries Final Report Series 48, National Heritage Trust and Fisheries Research Development Corporation project 99/158 No. FRDC Project 99/158.

Higgs, J. (2001) Recreational catch estimates for Queensland residents : RFISH Technical report 3 results from the 1999 diary round. Queensland Department of Primary Industries.

Higgs, J., Olyott, L., and McInnes, K. (2007) Experimental results from the third statewide Recreational Fishing Information System diary program (2002). Department of Primary Industries and Fisheries, Queensland. No. PR 07-2707.

O'Neill, M.F., and Faddy, M.J. (2003) Use of binary and truncated negative binomial modelling in the analysis of recreational catch data. Fisheries Research 60(2-3), 471-477.

Sarndal, Swensson and Wretman, 1992, Model Assisted Survey Sampling, Springer Series in Statistics

Lumley, T. 2010, Complex Surveys: A guide to analysis using R, Wiley

https://www.stat.auckland.ac.nz/showperson?firstname=Thomas&surname=Lumley.

Sharon Lohr’s book as well which is a bit simpler to follow:

http://www.cengage.com/search/productOverview.do;jsessionid=7EFFA13A60CDA3D6193215DBEFDA412E?N=16+4294922413+4294966842+4294959837&Ntk=P_EPI&Ntt=15758609321187154128517071071486767441&Ntx=mode%2Bmatchallpartial

 

Title: Fishery-dependent monitoring of Queensland’s fisheries

The problem:

The Department of Agriculture and Fisheries (DAF) within the Queensland Government monitors commercial and recreational fisheries throughout the state. The objectives of this fishery-dependent monitoring include collecting the data required to assess the status of key fish stocks and the effectiveness of current management arrangements (especially fisheries with catch / effort quotas), as well as helping develop new, effective management arrangements (https://www.daf.qld.gov.au/fisheries/monitoring-our-fisheries).

Turning data into advice about the status of Queensland’s fish stocks or the sustainability of Queensland’s fisheries is the challenge and occurs in a number of different ways. The most regular assessments now occur annually for key species, as outlined in the “Framework for Defining Stock Status” (https://www.daf.qld.gov.au/__data/assets/pdf_file/0003/63930/PMS-Framework.pdf) and the national framework (https://www.daf.qld.gov.au/fisheries/monitoring-our-fisheries/data-reports/sustainability-reporting/stock-status-assessments).The main activity in defining stock status each year is a workshop, which involves assessing all the available fish length and age data. Formal stock assessments are carried out less frequently (https://www.daf.qld.gov.au/fisheries/monitoring-our-fisheries/data-reports/sustainability-reporting/stock-assessment-reports) using mathematical modelling to reconstruct the history of species-specific fisheries from all available data.

New statistical methodologies are required to: 1) evaluate to the effective sampling of fish length and age data, and 2) quantify rates of fish mortality and reference points.

Approach:

Review and evaluate efficient sampling programs: Is the right amount of sampling occurring for each species? Are there any significant biases in the sampling programs for each species?

Assess whether routine analyses are being carried out correctly and to develop new analyses for fisheries management.

Project components include:

  • Develop quantitative analyses to optimise fishery-dependent sampling across multiple species and regions.
    • Includes developing routine methods for assessing precision of current sampling of fish length and age.
  • Develop new methods for turning fish length and age data into advice (indicators) about fishing pressure and the status of fish stocks.
  • Develop a corresponding harvest strategy and reference points for judging the performance of the indicators.

Funding:

Joint project with the Department of Agriculture and Fisheries (DAF) within the Queensland Government. A PhD funding top up of $7K per year will offered.  

Supervisors/collaborators:

(UQ), Johnathan Stauton-Smith (DAF), Michael O’Neill (DAF), George Leigh (DAF) and Alex Campbell (DAF)

Read:

Sloan, S., Smith, T., Gardner, C., Crosthwaite, K., Triantafillos, L., Jeffriess, B., and Kimber, N. 2014. National guidelines to develop fishery harvest strategies. FRDC report - project 2010/061. Primary Industries and Regions, South Australia, Adelaide, March. CC BY 3.0. http://frdc.com.au/research/Documents/Final_reports/2010-061-DLD.pdf(last accessed 27th October 2015).

Aanes, S., and Volstad, J. H. 2015. Efficient statistical estimators and sampling strategies for estimating the age composition of fish. Canadian Journal of Fisheries and Aquatic Sciences, 72: 938-953.

Zhou, S., Pascoe, S., Dowling, N., Haddon, M., Klaer, N., Larcombe, J., Smith, A. D. M., et al. 2013. Quantitatively defining biological and economic reference points in data poor fisheries. Final report on FRDC project 2010/044. Canberra, Australia. 306 pp.

Smith, M. W., Then, A. Y., Wor, C., Ralph, G., Pollock, K. H., and Hoenig, J. M. 2012. Recommendations for catch-curve analysis. North American Journal of Fisheries Management, 32: 956-967.

Millar, R. B. 2015. A better estimator of mortality rate from age-frequency data. Canadian Journal of Fisheries and Aquatic Sciences: 1-12.

Francis, R., and Campana, S. E. 2004. Inferring age from otolith measurements: a review and a new approach. Canadian Journal of Fisheries and Aquatic Sciences, 61: 1269-1284.

Mapstone, B. D., Little, L. R., Punt, A. E., Davies, C. R., Smith, A. D. M., Pantus, F., McDonald, A. D., et al. 2008. Management strategy evaluation for line fishing in the Great Barrier Reef: Balancing conservation and multi-sector fishery objectives. Fisheries Research, 94: 315-329.

 

CARM is offering 4 PhDs, all with a high likelihood of receiving a top-up of $7K per year and $5K for operating expenses.

PhD Topics – Richardson Lab (UQ):

Project 1. Impacts of climate change on regional and global biodiversity

Methods:Developing gradient forest and compositional modelling in marine systems

This century, climate change is likely to become the major threat to marine biodiversity. This project will use massive Australian, regional and global datasets of marine biodiversity (with 10s of millions of records) in combination with environmental layers to model the current distribution of global biodiversity using compositional modelling (gradient forest and generalized dissimilarity modelling). Outputs from Global Climate Models will then be used to map the potential changes in community ranges. Using these derived data, the adequacy and representativeness of the existing global Marine Protected Area (MPA) network will be assessed for time slices in the future. This project will also develop optimal configurations of the MPA network that might facilitate the climate-driven movement and ultimate protection of marine biodiversity.

Supervisors:Anthony J. Richardson (UQ; a.richardson@maths.uq.edu.au), David S. Schoeman (University of the Sunshine Coast), Hugh Possingham (UQ)

Collaborators:Nick Ellis (CSIRO), Roland Pitcher (CSIRO), Simon Ferrier (CSIRO)

Methods:Generalized dissimilarity modelling; gradient forest modelling

Read:Ellis et al. (2012) Ecology, Ferrier et al. (2007) Diversity and Distributions, Richardson (report SE Aus)

 

Project 2. Using climate velocity to develop marine protected areas

Methods:Building individual based models and species distribution models using velocity

Marine biodiversity will be impacted by climate change. Expectations for the rates of species’ range shifts and changes in the timing of biological events can be generated by combining spatial and seasonal temperature gradients with predictions of temperature under global warming. This project will use the new concepts of climate velocity and seasonal shift and their trajectories to investigate factors that might drive the reshuffling of biodiversity at a community level in time and space. The focus will be on applying the concepts of velocity, seasonal shift and trajectories in developing conservation strategies.

Read: Burrows et al. (2011) Science, Burrows et al. (2014) Nature, Molinos et al. (2015) Nature Climate Change

Supervisors:Anthony J. Richardson (UQ; a.richardson@maths.uq.edu.au), David Schoeman (University of the Sunshine Coast), Hugh Possingham (UQ)

Collaborators:Chris Brown (UQ), Mike Burrows (Scottish Association of Marine Science), Elvira Poloczanska (CSIRO)

 

Project 3. Using zooplankton size distributions to improve ecosystem models

Methods:Developing ecosystem models used for water quality assessment and fisheries that better represent lower trophic levels

It is currently challenging to represent zooplankton in biogeochemical and ecosystem models because of the complexity in the zooplankton community, with at least a dozen common phyla. As respiration, growth, mobility, abundance, and mortality are all related to organism size, describing the zooplankton community in terms of size of individuals and incorporating this into models provides a natural way to move forward.

In this project, the PhD student will use a global dataset of zooplankton size spectra to develop methods of incorporating size into existing coastal and global ecosystem models, and investigate the impact of these formulations on model predictions.

This PhD position is part of a larger Australian Research Council Discovery project that is analyzing a global database of zooplankton size spectra to investigate how zooplankton control the transfer of energy from phytoplankton to fish.  The PhD student will have access to an unprecedented national and international database of zooplankton size spectra, and extensive collaborations with statisticians, biologists and ecosystem modellers.

Supervisors:Assoc. Prof. Anthony Richardson (UQ; a.richardson@maths.uq.edu.au), Prof. Iain Suthers (UNSW), Dr Jason Everett (UNSW), Dr Mark Baird (Hobart)

 

Project 4. Linking fish population and movement models: Spangled emperor in Ningaloo

Methods:Fisheries population models; state-spaced movement models

Effective spatial management of marine species requires informed planning, as well as ongoing assessment. For mobile species such as fish, knowledge of the scale and variation in movement is central to key planning decisions, such as the size and shape of marine reserves as well as to predicting the response of protected populations. For example, populations of species that require large areas of habitat may not show increases in abundance inside small reserves. Calculating optimal reserve size is complicated not only by species level properties but also by individual variations in behaviour. Individual fish movements can now be tracked using acoustic tagging technology, but in order for this information to be accurately applied to prediction of population level responses that inform marine reserve planning and assessment it needs to be incorporated in demographic models. Ningaloo Reef is a newly declared World Heritage area, protected by a large multiple use zoned Marine Park that includes 34% no take zoning.  Despite this the populations of Spangled emperor have declined over the past 10 years, suggesting that the design of no-take zones needs to be better understood in .relation to the habitat use of spangled emperor and current levels of fishing pressure.   In this project the goal would be to incorporate acoustic tracking data on habitat use and behaviour of the spangled emperor (Lethrinus nebulosus) at Ningaloo, into spatially explicit individual based model of spangled emperor populations.  While this approach has not previously been applied in Australia, extensive acoustic tracking data is available from ongoing projects at Ningaloo (Ningaloo Reef Ecosystem Tracking Array – NRETA) and as a fisheries indicator species basic demographic parameters for spangled emperor are available through the literature. The challenge for this project will be to construct a simulation model that will apply this information in a spatial context, including habitat type and management zoning, that will have the potential to assist marine park managers in prioritising potential management actions. 

Supervisors:Anthony J. Richardson (UQ; a.richardson@maths.uq.edu.au), Russ Babcock (CSIRO),  Eva Plagañyi-Lloyd, (CSIRO)