Biometric Research is a consulting firm specializing in the development and application of innovative modeling and analyses for enhanced natural resource management outcomes. We help our clients design effective research and monitoring programs, and get the most out of their data to better support their research and management needs.
Our areas of expertise are, ecological research and monitoring design, likelihood estimation, Bayesian modeling, population modeling, and stochastic simulations. Based in Albany, Western Australia, we conduct research for universities, government agencies and private firms worldwide.
Hierarchical models for informing Atlantic reef fish stock assessment.
Client: National Oceanic and Atmospheric Administration, North Carolina, USA
Investigating the use of multiple sampling gears in combination with occupancy models and N-mixture models to produce indices of abundance that inform the stock assessment of marine reef fish in the mid-Atlantic. (click for more information)
Coggins, L.G., Bacheler, N.M., and D.C. Gwinn (2014). Occupancy models for monitoring marine fish: a Bayesian hierarchical approach to modeling incomplete detection with a novel gear combination. PLoS ONE 9(9):e108302. doi:10.1371/journal.pone.0108302
Gwinn, D.C. 2015. Emerging tools for population monitoring: applications to fisheries science. Invited seminar presented at NOAA’s Southeast Fisheries Science Center, Beaufort, North Carolina, USA. (link)
Gwinn, D.C. and J. Lyon. 2014. Emerging tools for population monitoring: some fishy case studies. Invited seminar presented at the Department of Environment and Primary Industries, Melbourne, Australia. (link)
Management triggers for the Barrow Island marsupial monitoring.
Contributed to the refinement of the Barrow Island faunal monitoring program. Developed preliminary population viability analysis for Golden Bandicoots to set biological meaningful triggers for management. Evaluated statistical options for approximating 80% statistical power for detecting when management triggers have been exceeded. Click here for more information.
Kuskokwim River Chinook salmon fishery stock assessment.
Client: Auburn University, Alabama, USA
Investigating under what circumstances drainage-wide Bayesian state-space run reconstruction models provide accurate and precise estimates of Kuskokwim River Chinook salmon abundance and productivity. Assessing model performance under various monitoring designs and various hypotheses about the spatial patterns and variation in sub-stock productivity.
Catalano, M.J., Staton, B.A., Farmer, T., Gwinn, D.C., and Fleischman, S. (2016)
Evaluating assessment strategies for Kuskokwim River chinook salmon. Arctic-Yukon-
Kuskokwim Sustainable Salmon Initiative, Project Final Product. DOI: 10.13140/RG.2.2.11129.06240
Large-scale monitoring of Murray cod.
Client: Department of Environment and Primary Industries, Victoria, Australia
Forster, A. 2011. Enhanced Murray cod recreational fisheries outcomes across the Murray-Darling basin through improved collaboration and alignment of management and research activities. FRDC Project Report No. 2009/060, Fisheries Research Development Corporation.
Developing novel methods for estimating patterns in abundance for Murray cod in the Murray-Darling river basin. Evaluating multiple Bayesian approaches to estimate Murray cod abundance while accounting for incomplete detection. Investigating the application of fishery independent and dependent data for future monitoring programs and various model structures to incorporate both data types in to a unified analysis.
Gwinn, D.C., C. Todd, G. Butler, A. Kitchingman, L. Coggins, P. Brown, and T. Hunt. (in prep). Monitoring a threatened fish in the third largest river basin in the world: accounting for incomplete detection under budgetary limitations.
Informing environmental flow policies to promote fish spawning.
Client: Charles Darwin University, Northern Territories, Australia and Department of Environment and Primary Industries, Victoria, Australia
Developed a multi-species Bayesian hierarchical model of fish spawning as relates to flow and non-flow environmental characteristics while accounting for imperfect observations. Applied the model to formulate predicted outcomes for competing environmental flow policies. This analysis is used as part of a larger effort to evaluate and inform the use of environmental flows for Australian river ecosystem management.
King, A.J., Z. Tonkin, J. Mahoney, D. Gwinn, and L. Beesley. 2014. Chapter: The importance of flow on spawning of riverine native fish in the Barmah-Millewa region, Murray River. In: Flow dependent ecological responses. R.T. Kingsford, R.J. Watts, J.D. Koehn, R. Thompson (eds). Pages 84-92, CSIRO, Canberra. (link)
King, A.J., D.C. Gwinn, Z. Tonkin, J. Mahoney, S. Raymond, and L. Beesley. (2015) Using abiotic drivers of fish spawning to inform environmental flow management. Journal of Applied Ecology, DOI: 10.1111/1365-2664.12542
Flow policy options for native fish recruitment.
Client: University of Western Australia, Western Australia, Australia
Developed a multi-species Bayesian hierarchical model of fish abundance as relates to wetland inundation by the Murray River while accounting for imperfect observations. This analysis was used as part of a larger effort to evaluate and inform the use of environmental flows for Australian river ecosystem management. (click here for more information, JAE blog, media coverage)