In the world of animal conservation, it is important to obtain accurate estimates of parameters such as population size, survival probability, and fecundity. Historically, when data are collected on a species’ population, a model is created to estimate these important parameters. 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, where the likelihoods of component submodels are typically multiplied together, assuming independence. However, there are still some issues with these models. For instance, model selection becomes challenging in the presence of multiple data types. Additionally, complex model-fitting algorithms that require a long running time or are difficult to implement make IPMs 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.