A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes

A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad-scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes >= 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a (Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.

Automated summary

A new geographic classification approach was developed for lakes in the conterminous U.S., based on lake archetypes representing extreme points along gradients of multiple geographic features. Seven lake archetypes were identified, and individual lakes were assigned weights for each archetype. This approach improved understanding and prediction of lake responses to environmental drivers, and outperformed global models and ecoregion classifications.