Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity

The Neuse River Estuary (North Carolina, USA) is a valuable ecosystem that has been affected by the expansion of agricultural and urban watershed activities over the last several decades. Eutrophication, as a consequence of enhanced anthropogenic nutrient loadings, has promoted high phytoplankton biomass, hypoxia, and fish kills. This study compares and contrasts three models to better understand how nutrient loading and other environmental factors control phytoplankton biomass, as chl-a, over time. The first model is purely statistical, while the second model mechanistically simulates both chl-a and nitrogen dynamics, and the third additionally simulates phosphorus. The models are calibrated to a multi-decadal dataset (1997–2018) within a Bayesian framework, which systematically incorporates prior information and accounts for uncertainties. All three models explain over one third of log-transformed chl-a variability, with the mechanistic models additionally explaining the majority of the variability in bioavailable nutrients (R2 > 0.5). By disentangling the influences of riverine nutrient concentrations, flows, and loadings on estuary productivity we find that concentration reductions, rather than total loading reductions, are the key to controlling estuary chl-a levels. The third model indicates that the estuary, even in its upstream portion, is rarely phosphorus limited, and will continue to be mostly nitrogen limited even under a 30% phosphorus reduction scenario. This model also predicts that a 10% change in nitrogen loading (flow held constant) will produce an approximate 4.3% change in estuary chl-a concentration, while the statistical model suggests a larger (10%) effect. Overall, by including a more detailed representation of environmental factors controlling algal growth, the mechanistic models generate chl-a forecasts with less uncertainty across a range of nutrient loading scenarios. Methodologically, this study advances the use of Bayesian methods for modeling the eutrophication dynamics of an estuarine system over a multi-decadal period

The Development and Application of Targeted eDNA Metabarcoding for Monitoring Freshwater and Marine AIS

Species invasions are of critical concern due to their significant impacts on ecosystems and social economies, of which aquatic invasive species (AIS) often pose significant challenges in their control and management, notably because of difficulties in early detection. Environmental DNA (eDNA) provides a promising tool in advancing the detection of newly introduced aquatic organisms because of its high sensitivity and ease of use compared to traditional capture-based methods. Although eDNA-based methods are increasingly used worldwide, especially in aquatic ecosystems, most studies focus on a limited number of target species despite a pressing need for broad taxonomic monitoring for conservation and management. In this thesis, I developed and applied an approach that capitalizes on a combination of high-sensitivity PCR primer sets and high-throughput sequencing (HTS) to detect 69 aquatic invasive species. This hybrid approach is defined as “targeted metabarcoding”. The sensitivity of the 128 primer sets ranged between 2.8 × 10–4 ng and 4.8 ng, and the inclusion of interfering plankton eDNA reduced the sensitivity by an average of approximately an order of magnitude. My targeted metabarcoding resulted in the detection of \u3e 97% of the AIS spiked into eDNA samples, and the number of HTS reads had a significantly (P \u3c 0.002) positive relationship with the amount of spiked DNA. I then applied this approach to eDNA collected at eight Canadian ports or harbors to detect potential invaders; 38.6% of anticipated species from our 69 were detected. This fast, high-sensitive, and relative cost-saving approach can be used to detect AIS at early invasion stages, which will contribute to routine aquatic invasive species detection globally

Assessing the Causes and Severity of Gulf of Mexico Hypoxia Using Geostatistical and Mechanistic Modeling.

Hypoxia, typically defined by dissolved oxygen levels below 2 mg L-1, is an environmental problem common to many coastal systems. One particularly severe example of hypoxia is the large ‘dead zone’ that forms nearly every summer on the Louisiana-Texas shelf of the northern Gulf of Mexico. While there is considerable agreement about the primary causes of hypoxia, there remains substantial uncertainty regarding its spatial and temporal variability, such that it is difficult to predict how hypoxia will respond to management actions and other environmental changes. This research focuses on improving our understanding of Gulf hypoxia through three types of quantitative modeling. First, a geostatistical regression is developed to empirically model how water column stratification (a primary driver of hypoxia) affects bottom water dissolved oxygen (BWDO) concentrations, and to also infer the importance of other primary drivers, such as nutrient loading. Second, a geostatistical spatial estimation model is developed to simulate BWDO and hypoxic layer thickness across the Gulf shelf, providing estimates of hypoxic zone area and volume for a 27-year study period. Third, a mechanistic model, driven by nutrient loading, flow, and weather conditions is developed to predict hypoxic severity, as determined from the geostatistical model. As with all environmental models, the models developed in this dissertation are approximations of reality, tuned to limited observational and experimental information, such that they contain significant uncertainty. Because of this, all models are developed within statistical frameworks that quantify uncertainty and allow results to be presented as ranges of likely values. Overall, this works suggests there has been considerable variability in the mid-summer hypoxic extent over the last few decades, and this variability is explained, in large part, by both nutrient loading and oceanographic conditions (i.e., stratification). Relatively parsimonious models that account for these two main drivers explain at least 70% of the year-to-year variability in hypoxic area and mean BWDO. Also, this work indicates that over the past few decades, the Gulf has not become increasingly susceptible to hypoxia formation (independent of the biophysical drivers considered), at least in terms of hypoxic area and mean BWDO.PHDNatural Resources and Environment and Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/100085/1/obenour_1.pd

Integrating microbial and nutrient dynamics to improve waterway management

Eutrophication is driven by high concentrations of nutrients, namely phosphorus and nitrogen, entering waterways. Wastewater pollution contributes to these nutrient loads and accounts for approximately 15% of all river inflows worldwide. However, the ecological response of a waterway to eutrophication is difficult to assess using field observations, laboratory testing, or numerical models alone. As such, interdisciplinary studies are required to integrate the physical hydrodynamics and microbial processes of a waterway, and better inform best practice management. This thesis addresses the role of nutrient additions in riverine catchments worldwide, and potential eutrophication impacts, through a multidisciplinary study that links nutrient processing, via bacterial mineralisation, with an ecosystem hydrodynamic response model. The study integrates best practice techniques in microbial ecotoxicology and genomics, biogeochemical modelling, and hydrodynamic simulations to determine the role of microbial communities in responding to, and processing nutrients from, treated effluent. Initially, a series of novel field and laboratory investigations were completed to link pressure-stressor-response relationships. This information detailed the role of microbial community interactions, shifts and functional change in response to real-world exposure from wastewater pollution. Based on these findings, computational models were derived that highlight the importance of calibrating aquatic ecosystem response models with evidence-based parameterisation and net growth rates (i.e., sum of the growth minus loss rate parameter terms) of biological functional groups. The Generalised Likelihood Uncertainty Estimation (GLUE) methodology employed to assess model performance provided new insights to understand the represented microbial processes and the sometimes-difficult trade-offs required to establish values for these parameters. The outcomes from this research were used to develop a detailed ecosystem response model containing an explicit representation of key bacterial processes, including the mineralisation of dissolved organic matter. The implementation of this model at a waterway in south-eastern Australia provided a more realistic representation of the broader aquatic ecosystem response and improved the prediction of common eutrophication indicators, such as chlorophyll-a. The outcomes of this study will assist with (i) developing data collection programs that further advance our understanding of microbial and nutrient dynamics in aquatic ecosystems, (ii) accurately representing microbial processes throughout an entire waterway, and (iii) developing integrated catchment management plans