Inferences based on diatom compositions improve estimates of nutrient concentrations in streams.

Nutrient concentrations in streams vary strongly with flow conditions, and routinely gathered field measurements of nutrients reflect this variability. Diatom assemblage composition has been used in previous studies to infer nutrient concentrations, and because diatoms integrate nutrient concentrations over longer periods of time, diatom inferences may be less susceptible to fluctuations in streamflow. We tested this hypothesis by leveraging differences in the flashiness of streams across a large continental data set.

Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States.

Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA.

Understanding the effects of phosphorus on diatom richness in rivers and streams using taxon-environment relationships.

Changes in phosphorus concentrations affect periphytic diatom composition in streams, yet we rarely observe strong relationships between diatom richness and phosphorus. In contrast, changes in conductivity are strongly associated with differences in both diatom composition and richness. We hypothesised that we could better understand the mechanisms that control the phosphorus-richness relationship by examining relationships between phosphorus and the occurrence of individual diatom taxa, comparing these with relationships between conductivity and taxon occurrence, and documenting how niche breadths of taxa affect richness patterns.

The application of metacommunity theory to the management of riverine ecosystems.

River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure.

Quantifying spatial and temporal relationships between diatoms and nutrients in streams strengthens evidence of nutrient effects from monitoring data.

Observational data are frequently used to better understand the effects of changes in P and N on stream biota, but nutrient gradients in streams are usually associated with gradients in other environmental factors, a phenomenon that complicates efforts to accurately estimate the effects of nutrients. Here, we propose a new approach for analyzing observational data in which we compare the effects of changes in nutrient concentrations in time within individual sites and in space among many sites. Covarying relationships between other, potentially confounding environmental factors and nutrient concentrations are unlikely to be the same in both time and space, and, therefore, estimated effects of nutrients that are similar in time and space are more likely to be accurate.

Estimating phytoplankton stoichiometry from routinely collected monitoring data.

Accurately estimating the elemental stoichiometry of phytoplankton is critical for understanding biogeochemical cycles. In laboratory experiments, stoichiometric ratios vary among species and with changes in environmental conditions. Field observations of total phosphorus (P) and total nitrogen (N) collected at regional and national scales can supplement and expand insights into factors influencing phytoplankton stoichiometry, but analyses applied to these data can introduce biases that affect interpretations of the observed patterns.

Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding.

Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km mixed land use watershed.

Continental-scale effects of phytoplankton and non-phytoplankton turbidity on macrophyte occurrence in shallow lakes.

Submerged macrophytes are key components of shallow lake biological communities, and their presence has been associated with a predominantly clear-water state. Conversely, lakes lacking macrophytes are often turbid with elevated phytoplankton abundance. One main mechanism that influences the presence or absence of submerged macrophytes is turbidity that reduces the light available to macrophytes.

Taxonomic harmonization may reveal a stronger association between diatom assemblages and total phosphorus in large datasets.

Diatom data have been collected in large-scale biological assessments in the United States, such as the U.S. Environmental Protection Agency's National Rivers and Streams Assessment (NRSA).

Modeling hypolimnetic dissolved oxygen depletion using monitoring data.

Eutrophication increases hypoxia in lakes and reservoirs, causing deleterious effects on biological communities. Quantitative models would help managers develop effective strategies to address hypoxia issues, but most existing models are limited in their applicability to lakes with temporally resolved dissolved oxygen data. We describe a hierarchical Bayesian model that predicts dissolved oxygen in lakes based on a mechanistic understanding of the factors that influence the development of hypoxia during summer stratification.

Rethinking phosphorus-chlorophyll relationships in lakes.

The empirical relationship between total phosphorus and chlorophyll has guided lake management decisions for decades, but imprecision in this relationship in individual lakes limits the utility of these models. Many environmental factors that potentially affect the total phosphorus-chlorophyll relationship have been studied, but here we hypothesize that imprecision can be reduced by considering differences in the proportions of phosphorus bound to three different "compartments" in the water column: phosphorus bound in phytoplankton, phosphorus bound to suspended sediment that is not associated with phytoplankton, and dissolved phosphorus. We specify a hierarchical Bayesian network model that estimates phosphorus associated with each compartment using field measurements of chlorophyll, total suspended solids, and total phosphorus collected from reservoirs in Missouri, USA.

A Bayesian network model for estimating stoichiometric ratios of lake seston components.

The elemental composition of seston provide insights into the functioning of lake food webs and how nutrients cycle through the environment. Here, we describe a Bayesian network model that simultaneously estimates relationships between dissolved and particulate nutrients, suspended volatile and non-volatile sediments, and algal chlorophyll. The model provides direct estimates of the phosphorus and nitrogen content of phytoplankton, suspended non-living organic matter, and suspended inorganic sediment.

Combining national and state data improves predictions of microcystin concentration.

Data collected from lakes at national (regional) scales and state (local) scales can provide different insights regarding relationships between environmental factors and biological responses, and combining these two types of data can potentially yield more precise and accurate understanding of ecological phenomena. National data can include many measures, cover large spatial areas, and span broad environmental gradients. Because of these characteristics, analyses of these data can yield accurate estimates of relationships among different lake characteristics.

The challenges that spatial context present for synthesizing community ecology across scales.

Accurately characterizing spatial patterns on landscapes is necessary to understand the processes that generate biodiversity, a problem that has applications in ecological theory, conservation planning, ecosystem restoration, and ecosystem management. However, the measurement of biodiversity patterns and the ecological and evolutionary processes that underlie those patterns is highly dependent on the study unit size, boundary placement, and number of observations. These issues, together known as the modifiable areal unit problem, are well known in geography.

Changes in the relationship between zooplankton and phytoplankton biomasses across a eutrophication gradient.

The relationship between zooplankton biomass and phytoplankton biomass can provide insight into the structure and function of lake biological communities. We used a Bayesian network model to analyze a continental-scale data dataset to estimate changes in the relationship between zooplankton (Z) and phytoplankton (P) biomasses along a eutrophication gradient. The Bayesian network model allowed us to combine two different measurements of phytoplankton biomass (chlorophyll concentration and directly observed biovolume) to improve the precision of estimates of true biomass within each sample.

Using National-Scale Data To Develop Nutrient-Microcystin Relationships That Guide Management Decisions.

Quantitative models that predict cyanotoxin concentrations in lakes and reservoirs from nutrient concentrations would facilitate management of these resources for recreation and as sources of drinking water. Development of these models from field data has been hampered by the high proportion of samples in which cyanotoxin concentrations are below detection limits and by the high variability of cyanotoxin concentrations within individual lakes. Here, we describe a national-scale hierarchical Bayesian model that addresses these issues and that predicts microcystin concentrations from summer mean total nitrogen and total phosphorus concentrations.

Modeled hydrologic metrics show links between hydrology and the functional composition of stream assemblages.

Flow alteration is widespread in streams, but current understanding of the effects of differences in flow characteristics on stream biological communities is incomplete. We tested hypotheses about the effect of variation in hydrology on stream communities by using generalized additive models to relate watershed information to the values of different flow metrics at gauged sites. Flow models accounted for 54-80% of the spatial variation in flow metric values among gauged sites.

Classifying lakes to quantify relationships between epilimnetic chlorophyll a and hypoxia.

Excess nutrient loading increases algal abundance which can cause hypoxia in many lakes and reservoirs. We used a divisive partitioning approach to analyze dissolved oxygen profile data collected across the continental United States to increase the precision of estimated relationships between chlorophyll a (chl a) concentrations and the extent of hypoxia in the water column. Chl a concentrations predicted the extent of hypoxia most accurately in lakes that were stratified at the time of sampling with a maximum temperature gradient of at least 1.

Estimating the effects of excess nutrients on stream invertebrates from observational data.

Increased nutrient concentrations in streams and rivers have altered biological structure and function. Manipulative studies have provided insights into different mechanisms by which changes in nutrient concentrations influence aquatic biota, but these studies are limited in spatial scope and in their quantification of nutrient effects on aggregate measures of the invertebrate assemblage. Observational data provide a complementary source of information to manipulative studies, but these data must be analyzed such that the potential effects of spurious correlations are minimized.

Using propensity scores to estimate the effects of insecticides on stream invertebrates from observational data.

Analyses of observational data can provide insights into relationships between environmental conditions and biological responses across a broader range of natural conditions than experimental studies, potentially complementing insights gained from experiments. However, observational data must be analyzed carefully to minimize the likelihood that confounding variables bias observed relationships. Propensity scores provide a robust approach for controlling for the effects of measured confounding variables when analyzing observational data.

Assessing the relative severity of stressors at a watershed scale.

Water quality monitoring data are usually used independently to report on the condition of streams and watersheds. For example, watersheds are often rated as good, fair, or poor with regard to a single stressor or with regard to an index of biotic integrity. The utility of monitoring data may be enhanced by integrating stressor-response information with the observed stressor data, and reporting stressor levels in terms of their relative effects upon valued ecological resources.

Using spatial interpolation to estimate stressor levels in unsampled streams.

Accurate estimates of stressor levels in unsampled streams would provide valuable information for managing these resources over large regions. Spatial interpolation of stream characteristics have rarely been attempted, partly because defining separation distances between distinct stream samples is not straightforward. That is, conventional Eulerian definitions of separation distance may not apply to stream networks where information flows along distinct paths.