A framework for evaluating complex environmental monitoring data and application to state-wide, long-term nongame fish and algae monitoring in Kansas

Abstract

Environmental monitoring data provide resource managers with broad snapshots of environmental quality and/or species distributions and are essential to understand the current state and trends in ecosystems and natural resources they provide. However, the very nature of environmental monitoring data, with their breadth in time and space, makes them notoriously difficult to statistically analyze. In this thesis, two environmental monitoring datasets from the State of Kansas are considered: algae and broader water quality in Kansas’ reservoirs and nongame fish and habitat in Kansas streams. The aim of this research is to introduce and apply a framework for systematically selecting, exploring, analyzing, and testing environmental datasets such as these. More specifically, this research uses long-term, state-wide datasets documenting water quality conditions, fish presence and absence, and fish habitat data in Kansas streams, lakes, and reservoirs to characterize the current conditions and relationships between water quality and cyanobacterial abundance in reservoirs and as well as fish presence with stream habitat characteristics. Ultimately, this thesis will contribute to understanding of the conditions that drive cyanobacterial blooms and subsequent effects on reservoir ecosystems. In addition to this, it will provide an understanding of the habitat variables that are associated with fish presences compared to absences. The goal of this data analysis was not to come to a single significant conclusion, but more so an approach to process and review variables in state-wide databases to gain a better understanding of what the monitoring data can provide. Then, using this information, provide a systematic framework for a comprehensive, responsible, and a useful approach to monitoring data.