ABSTRACT
Vibrio spp. have an important role in biogeochemical cycles; some species are disease agents for aquatic animals and/or humans. Predicting population dynamics of Vibrio spp. in natural environments is crucial to predicting how the future conditions will affect the dynamics of these bacteria. The majority of existing Vibrio 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 Vibrio spp. abundance using two new and five already published longitudinal datasets on Vibrio abundance in four different habitat types. Results demonstrate that, while the models were able to predict Vibrio 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.
ABSTRACT
The need for reliable taxonomic species identification is always present. Correct identification of specimens is based on taxonomic research, scattered in the scientific literature and accessible only for a restricted group of specialists or research institutions with extensive library facilities (Zuquim et al. 2017). Therefore, identification keys that summarize the knowledge of a group of biota are very important; they provide the link between producers and users of taxonomy. Recently, printed identification keys are increasingly being replaced or supplemented by computer-aided keys.
