New statistical models for smartphone app data on recreational fishing

CASE Award with Cefas (MATECHOU_K19ARIES)

New statistical models for smartphone app data on recreational fishing

CASE Award with Cefas (MATECHOU_K19ARIES)

Project Description

Supervisors

Dr Eleni Matechou (University of Kent)

Dr Maria Kalli (University of Kent)

David Maxwell (Cefas)

Dr Kieran Hyder (Cefas)

Dr Christian Skov (Technical University of Denmark)

Dr Paul Venturelli (Ball State University, USA)

Scientific background

Marine recreational fishing (MRF) is a high-participation activity with significant socio-economic benefits, contributing to global food security that can have significant fish stock impact. However, data-collection for this seasonally and spatially-variable activity is limited due to challenges with surveying long coastlines and diverse fishers. Smartphone-apps provide an alternative source for collecting MRF data. For example, Fishbrain (www.fishbrain.org) has millions of users worldwide logging thousands of catches each week. Nevertheless, there are considerable issues with data consistency, quality and representativeness.

Research methodology

The student will develop novel statistical approaches for analysing data collected by Fishbrain to advance our understanding of MRF. The research will involve:

Y1-2: Building an integrated modelling framework for the joint analysis of the large opportunistic citizen-science app-derived data and smaller high-quality traditional survey data.

Y2-3: Extending and applying statistical methods for probabilistic clustering to identify spatial and temporal angling behaviours.

Y4: The results will inform aspects of MRF including: numbers of anglers, angling methods, catch rates, and species caught, and contribute to monitoring and management of MRF.

Training (constituent skills given in parentheses)

The student will lead the development of state-of-the-art statistical methods on the timely topic of inference from large citizen-science data collected using new technologies (field leader). They will develop high-level, highly transferable statistical, programming and data skills, working with large app-derived data-sets and designed surveys using tools such as R, Python and Stan (skilled with data). The work will be presented and communicated to statisticians, fisheries-experts, and policy-makers (effective communicator).

Through supervision and time visiting Cefas (www.cefas.co.uk), the student will experience working in a multi-disciplinary science organisation and learn how their research fits into the wider policy context (broad vision). Their work will be part of an MRF research programme at Cefas, Ball State and Danish Technical Universities, and broader fisheries advice through ICES (http://www.ices.dk/) (innovator).

Person specification

Applicants should have a good degree in statistics, mathematics, computer science, or related subjects with a strong numerical component. They will be comfortable working with data and learning new methods, determined, and interested in engaging with the practical applications of their research.

 

References

  • Venturelli PA, Hyder K, Skov C (2017) Angler apps as a source of recreational fisheries data: opportunities, challenges and proposed standards. Fish and Fisheries, 18: 578-595.
  • Kelling S et al (2018) Finding a Signal in the Noise of Citizen Science Observations. doi: http://dx.doi.org/10.1101/326314
  • Matechou E et al. (2013) Integrated Analysis of Capture–Recapture–Resighting Data and Counts of Unmarked Birds at Stop-Over Sites. Journal of Agricultural, Biological, and Environmental Statistics 18: 120-135.
  • Matechou E et al. (2016). Biclustering models for two-mode ordinal data. Psychometrika, 81: 611-624.
  • Hyder K et al. (2018) Recreational sea fishing in Europe in a global context - Participation rates, fishing effort, expenditure, and implications for monitoring and assessment, Fish and Fisheries, 19: 225-243.

Open for applications

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