Individual-based modeling of larval fish
I use a variety of programming languages, and developmental tools (e.g. Python and Fortran) for my ecological modeling research. During the last few years I have created two individual-based models (IBM) for simulating the early life history of larval and juvenile Atlantic cod (read more on my special IBM page). The models are mechanistic in nature and can if be used for any given geographical location in the North Atlantic. They can also be used for species other than cod by changing the biological characteristics (such as e.g. metabolic rate and growth rates). The IBM model I created for my thesis was written in Fortran and contains a variety of options that you can turn on and off to simulate different scenarios that the larvae can experience. For example, the input files for this model are environmental data from the Georges Bank region, but can be replaced with any other temperature, turbulence, and prey data. The IBM was configured for Atlantic cod and has been thoroughly tested against several observational datasets in prior studies. For example, the IBM was able to reproduce growth and feeding patterns as observed in a macrocosm over the course of the first 47 days post hatching. The IBM adequately reproduced observed growth patterns when the observed environment (prey resources, temperature, turbulence) was used as input to the IBM (Kristiansen et al., 2007). The IBM has also been tested against a very detailed observational dataset on Georges Bank and proved to be able to simulate growth, feeding, behavior, and prey selection comparable to observations for two separate years 1993 and 1994 (Kristiansen et al., 2009b). The IBM consisted of 4 modules: (1) a feeding module, (2) a growth module, (3) a predator module, and (4) a behavioral module. These modules consisted of a number of functions that were estimated sequentially (Fig. 1). All processes were estimated for each time-step as responses may vary with time-of-day, the depth position in the water column, and with larval ontogeny. The mechanistic approach relied on a realistic representation of the physical and biological environment, a fundamental understanding of the biology of the prey and the larvae, and the interaction between these components. For more details, see either of these publications Kristiansen et al. 2007 (769), Kristiansen et al. 2009a (724), Kristiansen et al. 2014 (526), and Kristiansen et al. 2014 (521). Please feel free to contact me for more information on the models.
Individual-based modeling with ROMS
I also model the physics of the ocean in combination with IBM models using ROMS models (Regional Ocean Modeling System). A combination of IBM and ROMS can track the movement of organisms in a three dimensional physical-biological environment. In addition, my models can be used to analyze how differences in wind and turbulence may affect the year to year dispersal and drift of larval fish from their spawning grounds to their nursery habitats. You can read more about drift and growth of larval cod in the following publications that I have written or collaborated on Kristiansen et al. 2009c (890), Fiksen et al. 2007 (741), Vikeboe et al. 2007 (734).
Also see my publications page : http://www.trondkristiansen.com/?page_id=39
Please go to my model2roms page for more details on using that toolbox to generate forcing files required to run ROMS.