Darren Gillis

Photo of Darren Gillis


Office W361 Duff Roblin
(204) 474-9683

Research Interests

fisheries, fleet dynamics, behaviour, population ecology, mathematical and statistical ecology, Biodiversity, Ecology and Environment

Recent Publications

  • Carriere, B., Gillis, D., Halden, N., & Anderson, G. (2016). Strontium metabolism in the juvenile Lake Sturgeon,. Journal of Applied Ichthyology 32 (2), 258-266.
  • Charles, C. Gillis, D.M. Hrenchuck, L.E. Blanchfield, P.J. (2016). A method of spatial correction for acoustic positioning biotelemetry. Animal Biotelemetry 4 (5). DOI: 10.1186/s40317-016-0098-3
  • Geisler, M. E., Rennie, M. D., Gillis, D. M., & Higgins, S. N. (2016). A predictive model for water clarity following dreissenid invasion. Biological Invasions 18 (1989).
  • Kissinger, B. C., Gantner, N., Anderson, W. G., Gillis, D. M., Halden, N. M., Harwood, L. A., & Reist, J. D. (2015). Brackish-water residency and semi-anadromy in Arctic lake trout (Salvelinus namaycush) inferred from otolith. Journal of Great Lakes Research 42 (2), 267-275.
  • Colin Charles, Darren Gillis, Elmer Wade (2014). Using hidden Markov models to infer vessel activities in the snow crab (Chionoecetes opilio) fixed gear fishery and their application to catch standardization. Canadian Journal of Fisheries and Aquatic Sciences 71, 1817 - 1829.

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My current research focuses on the analysis of data gathered during the prosecution of commercial fisheries to make inferences regarding fleet dynamics (temporal and spatial variation in fishing effort) and the biology of fish. Data from commercial fisheries will always have the potential to exceed the quantity of information affordably obtained from research surveys. However, it is critical that the circumstances underlying the collection of these data are understood to avoid confusion between natural and anthropogenic phenomena. To achieve this, my research focuses on the quantification of behavioral patterns of both fishers and fish contained within catch and effort data that may produce a biased representation of the fishery.

The theoretical basis of my work can be found in the evolutionary study of behavioral ecology. I focus much of my reseach on hypotheses about the relationship between fishing success and fleet dynamics, such as vessel movements, species selection, and information exchange. Ideally models of decision processes can be developed to reflect fisher harvester's behavior. These can then be used to better interpret historical catch and effort data when estimating fish abundance.

The methods used to examine these hypotheses include conventional statistics (univariate and multivariate), computer intensive statistics, and simulation models. Past and current projects involve collaboration with provincial (Manitoba), federal (Fisheries and Oceans, Canada) and foreign (IMARES - the Dutch Institute for Marine Resources and Ecosystem Studies) agencies examining the fisheries of Lake Winnipeg, the Arctic, and the Atlantic.



At university, science students should expect to develop their skills in: 1) communication (both written and oral), 2) information use (i.e. libraries, information technologies), 3) critical thinking (including the evaluation of empirical data), 4) quantitative methods (including algebra, calculus, statistics, and computer use) and 5) laboratory and field methods specific to their subject area(s).  In contrast to a short term, vocationally oriented curriculum, a university education should prepare students to work productively both today, and in a changing future.  In short, they should learn how to learn, based upon the skills that we, as university faculty, provide to them.

To this end, I currently offer undergraduate ecological courses:

  • BIOL 3310 Foundations of population ecology
  • BIOL 4310 Applications of population ecology in fisheries and wildlife
  • BIOL 4210 Biology of fishes

and the graduate course:

  • BIOL 7360 Problems in biological statistics

These courses introduce students to the application of biological and quantitative principles in ecological analysis. Quantitative tools introduced include Microsoft Excel with the PopTools (BIOL 3310), Scilab (an accessible modeling language similar to Matlab, BIOL 4310), and the R statistical language (BIOL 7360).  These tools are used to apply theory from lectures to both actual and simulated data sets in order to develop quantitative experience among our students.  Class discussions, quizes, and workshops are used to reinforce lecture material. Independent assignments and examinations are used for the evaluation of performance.