Rethinking discretization to advance limnology amid the ongoing information explosion

Abstract

Limnologists often adhere to a discretized view of waterbodies-they classify them, divide them into zones, promote discrete management targets, and use research tools, experimental designs, and statistical analyses focused on discretization. By offering useful shortcuts, this approach to limnology has profoundly benefited the way we understand, manage, and communicate about waterbodies. But the research questions and the research tools in limnology are changing rapidly in the era of big data, with consequences for the relevance of our current discretization schemes. Here, I examine how and why we discretize and argue that selectively rethinking the extent to which we must discretize gives us an exceptional chance to advance limnology in new ways. To help us decide when to discretize, I offer a framework (discretization evaluation framework) that can be used to compare the usefulness of various discretization approaches to an alternative which relies less on discretization. This framework, together with a keen awareness of discretization's advantages and disadvantages, may help limnologists benefit from the ongoing information explosion.

Keywords: Big data; Classification; Computing; Discretization evaluation framework; Management; Statistics; Trophic state; Zonation.