Investigating the Ability of Growth Models to Predict In Situ spp. Abundances.

spp. have an important role in biogeochemical cycles; some species are disease agents for aquatic animals and/or humans. Predicting population dynamics of spp. in natural environments is crucial to predicting how the future conditions will affect the dynamics of these bacteria. The majority of existing spp. population growth models were developed in controlled environments, and their applicability to natural environments is unknown. We collected all available functional models from the literature, and distilled them into 28 variants using unified nomenclature. Next, we assessed their ability to predict spp. abundance using two new and five already published longitudinal datasets on abundance in four different habitat types. Results demonstrate that, while the models were able to predict spp. abundance to an extent, the predictions were not reliable. Models often underperformed, especially in environments under significant anthropogenic influence such as aquaculture and urban coastal habitats. We discuss implications and limitations of our analysis, and suggest research priorities; in particular, we advocate for measuring and modeling organic matter.