Geology topics

Killian Davis

Publications and source records attributed to Killian Davis.

3 recordsLinked to original sources

USGS2025

Re-oligotrophy in the Upper Mississippi River, USA, occurred in just a few years

Ecological systems can undergo large changes and regime shifts that are either catastrophic, neutral, or desirable. Rivers worldwide have recently undergone desirable regime shifts related to re-oligotrophy, which is a notable and ongoing reduction in concentrations of total suspended solids (TSS), total N, total P, or phytoplankton. For example, the Upper Mississippi River, USA, has experienced major water-quality changes in multiple river reaches in recent decades. In this study, we sought to understand the timing and magnitude of re-oligotrophy in the Mississippi River over a 20-y period. We used 2 topological data analysis algorithms to address hypotheses related to the following questions: What were the order and timing of water-quality changes? What was the time period over which the major changes occurred? What was the magnitude of water-quality change before and after change points (i.e., specific years when water-quality conditions transitioned abruptly to new states)? We examined 6 water-quality state variables that defined the ecological regime for the Upper Mississippi River. In one river reach, we found that strong reductions in phytoplankton/chlorophyll a had occurred first (2008), followed by total P (2013), and last in TSS (2014). In a downriver reach, we found notable reductions for chlorophyll a (2007) but substantial increases in TSS (2013). In both reaches, the water-quality changes trended over ≥15 y, but the largest changes and a likely regime shift occurred in just 6 y. The timing (2007–2014) and range (~6 y) of water-quality changes were similar between the 2 river reaches, but the directionality of the regime shift indicated re-oligotrophy for the upstream reach and water-quality degradation for the downstream reach. Topological methods applied to long-term datasets can aid our understanding of re-oligotrophication and degradation processes and may help resource managers restore desirable regimes.

Freshwater Science↗

USGS2023

Quantifying ecosystem states and state transitions of the Upper Mississippi River System using topological data analysis

Aquatic systems worldwide can exist in multiple ecosystem states (i.e., a recurring collection of biological and chemical attributes), and effectively characterizing multidimensionality will aid protection of desirable states and guide rehabilitation. The Upper Mississippi River System is composed of a large floodplain river system spanning 2200 km and multiple federal, state, tribal and local governmental units. Multiple ecosystem states may occur within the system, and characterization of the variables that define these ecosystem states could guide river rehabilitation. We coupled a long-term (30-year) highly dimensional water quality monitoring dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years in the river to guide conservation. Across the entire system, TDA identified five ecosystem states. State 1 was characterized by exceptionally clear, clean, and cold-water conditions typical of winter (i.e., a clear-water state); State 2 had the greatest range of environmental conditions and contained most the data (i.e., a status-quo state); and States 3, 4, and 5 had extremely high concentrations of suspended solids (i.e., turbid states, with State 5 as the most turbid). The TDA mapped clear patterns of the ecosystem states across several riverine navigation reaches and seasons that furthered ecological understanding. State variables were identified as suspended solids, chlorophyll a , and total phosphorus, which are also state variables of shallow lakes worldwide. The TDA change detection function showed short-term state transitions based on seasonality and episodic events, and provided evidence of gradual, long-term changes due to water quality improvements over three decades. These results can inform decision making and guide actions for regulatory and restoration agencies by assessing the status and trends of this important river and provide quantitative targets for state variables. The TDA change detection function may serve as a new tool for predicting the vulnerability to undesirable state transitions in this system and other ecosystems with sufficient data. Coupling ecosystem state concepts and TDA tools can be transferred to any ecosystem with large data to help classify states and understand their vulnerability to state transitions.

PLOS Computational Biology↗

USGS2023

Reconstructing missing data by comparing interpolation techniques: Applications for long-term water quality data

Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP) −1 , TN had a MAE of 0.39 mg (L-TN) −1 , and water velocity had a MAE of 0.10 m s −1 . The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗