Animal stoichiometry influences critical processes from organismal physiology to biogeochemical cycles. However, it remains uncertain whether animal stoichiometry follows predictable scaling relationships with body mass and whether adaptation to terrestrial or aquatic environments constrains elemental allocation. We tested both interspecific and intraspecific body-mass scaling relationships for nitrogen (N), phosphorus (P), and N:P content using a subset of the StoichLife database, which includes 9,933 individual animals across 1,543 species spanning 10 orders of magnitude in body mass from terrestrial, freshwater, and marine realms. Our results show that body mass predicts intraspecific stoichiometric variation, accounting for 42-45% of the variation in 27% of vertebrate and 35% of invertebrate species. However, body mass was less effective at explaining interspecific variation, with taxonomic identity emerging as a more significant factor. Differences between aquatic and terrestrial organisms were observed only in invertebrate interspecific %N, suggesting that realm has a relatively minor influence on elemental allocation. Our study, based on the most comprehensive animal stoichiometry database to date, revealed that while body mass is a good predictor of intraspecific elemental content, it is less effective for interspecific patterns. This highlights the importance of evolutionary history and taxonomic identity over general scaling laws in explaining stoichiometric variation.

Hydrologic and water quality models are often used to understand and simulate non-point source nutrient inputs to receiving waterbodies afflicted by eutrophication. The most widely used hydrologic-water quality model for estimating non-point source nutrient loads from agricultural uplands is the Soil and Water Assessment Tool (SWAT). SWAT uses the QUAL-2E 1-dimensional steady-state model to simulate in-stream processes that govern the transport of nutrients through channels and rivers. However, the instream-solute transport routine within SWAT is limited in predicting phosphorus cycling and algal dynamics. In this study, we improve the in-stream module of SWAT+, a restructured version of SWAT. We apply the modified SWAT+ to the Western Lake Erie Basin to examine how improved representation of the in-stream module influences nutrient dynamics from the edge-of-field through streams and to the watershed outlet. Our source code modifications focus on improving the representation of phosphorus exchange between the stream bed and the water column. This phosphorus exchange is governed by the equilibrium phosphorus concentration (EPC), which determines whether the stream bed is a phosphorus source or sink, and a phosphorus transformation coefficient which determines the rate of P exchange. These improvements to the in-stream routine within SWAT+ will aid decision-makers in understanding the time lags and management levers needed to achieve water quality targets for large basins.

Many waterbodies around the world are adversely impacted by harmful algal blooms (HABs). One primary driver of these blooms is often high concentrations of anthropogenic phosphorus loading. The phosphorus mitigation plans require accurate information on nutrient sources and transport, to and through water bodies, including the stream network. Diffuse sources, are particularly difficult to quantify due to the cost of in situ monitoring, and is often supplemented using various water quality models. SWAT, a comprehensive watershed-scale model, is widely used to assess and improve downstream water quality using QUAL2E equations. EPA developed QUAL2E can model phytoplankton growth but has a limited capacity to model benthic algae. Although SWAT requires a lesser number of parameters while simulating water quality outputs, unlike, HSPF, INCA, SPARROW, WASP, and MIKE-SHE, the water quality algorithm within SWAT needs modifications for simulating phosphorus legacy within the waterbodies. This study reviews the existing water quality models to improve the water quality algorithm within SWAT. Most of the water quality models can simulate processes, including the proliferation of fixed and floating algal biomass and phosphorus cycling (QUAL2E/K, WASP, HSPF). Some water quality models are better in simulating the time-dependent factors, such as light attenuation, form and concentration of nutrients, and water temperature (HSPF, INCA). There are a few water quality algorithms that can simulate both horizontal stream flow and shallow flow (SHETRAN, INCA). Both horizontal and shallow flow takes into account the anisotropy and variable biogeochemistry impacts on the turbulence of water, thus, the water quality. Some water quality models simulate the non-linear relationship between nutrient concentration and discharge timing and magnitude (SPARROW). There are some commercialized models like MIKE-SHE that simulate reasonably good results, but the water quality algorithm/equation/process is not publically available. Our review of the existing water quality models will help in identifying, modifying, and implementing the SWAT source code revisions required to improve and mitigate water quality degradation from a finer spatial scale, including small ditches and streams, to the large-scaled watershed over time.

Environmental factors influencing phytoplankton assemblage dynamics in freshwater ecosystems are an area of ongoing research in the Great Lakes, particularly Lake Erie where harmful algal blooms have impacted the system. Due to impacts on aquatic life and public health, studies worldwide have investigated environmental thresholds influencing the emergence and abundance of phytoplankton species. These thresholds are useful in the evaluation of ecosystem health and the implementation of conservation strategies. However, how these thresholds influence the phytoplankton assemblage shifts over time and space have yet to be explored in Lake Erie, USA. Our goal was to investigate the thresholds of environmental variables responsible for major phytoplankton group dynamics (cyanobacteria (CY), cryptophyta (CP), diatoms (DI), and chlorophyta (GA)) in the western basin of Lake Erie. Using phytoplankton group concentrations determined by a Fluoroprobe (bbe) and water quality data collected between spring-fall from 2015 to 2019, we explored the most significant variables driving changes in phytoplankton concentration at 4 monitoring locations. We applied a multi-method approach, starting with principal component analysis (PCA), Redundancy Analysis (RDA), Regression Trees, and ending with a change point analysis. This approach was successful in the detection of major environmental variables and their thresholds responsible for the emergence and dominance of each phytoplankton group. Results revealed that CY concentrations are primarily correlated to turbidity conditions while DI are more likely to dominate when dissolved oxygen concentrations are high. The presence of CP was mostly related to lower temperatures compared to CY. Lastly, N:SRP ratio was a strong predictor of GA. These environmental variables were relevant predictors of both seasonal and spatial dynamics. Our results reveal critical thresholds in environmental conditions that shape the predominance of each phytoplankton group in the western Lake Erie and emphasize how the spatial component of these conditions can affect phytoplankton assemblage dynamics. These findings may serve as a guide to modelers and decision-makers.