Statistical solutions for the open challenges of integrated population models.

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In the world of animal conservation, it is important to obtain accurate estimates of features such as population size, survival rates, and fecundity. Historically, when data are collected on a species' population, a model is created to estimate these important features. However, when multiple different data types are collected on the same species, it is useful to find a way to combine these datasets to leverage the shared information. This is achieved through Integrated Population Models. However, there are still some issues with these models. For instance, choosing the best model that fits the data best becomes challenging when in the presence of multiple data types. Additionally, model-fitting algorithms that require a long running time and are difficult to implement make them less accessible for many ecologists. More efficient solutions are needed to make Integrated Population Models useful for a broader audience of ecologists. My PhD is looking at adapting and improving current model fitting algorithms, maintaining the use of closed form solutions, and thus efficiency.Research areas; Statistical Ecology, Computational StatisticsIn partnership with Olso University.