Emily K. Read

Creating and maintaining high‐performing collaborative research teams: the importance of diversity and interpersonal skills

Collaborative research teams are a necessary and desirable component of most scientific endeavors. Effective collaborative teams exhibit important research outcomes, far beyond what could be accomplished by individuals working independently. These teams are made up of researchers who are committed to a common purpose, approach, and performance goals for which they hold themselves mutually accountable. We call such collaborations “high-performing collaborative research teams”. Here, we share lessons learned from our collective experience working with a wide range of collaborative teams and structure those lessons within a framework developed from literature in business, education, and a relatively new discipline, “science of team science”. We propose that high-performing collaborative research teams are created and maintained when team diversity (broadly defined) is effectively fostered and interpersonal skills are taught and practiced. Finally, we provide some strategies to foster team functioning and make recommendations for improving the collaborative culture in ecology.

Phosphorus speciation in a eutrophic lake by 31P NMR spectroscopy

For eutrophic lakes, patterns of phosphorus (P) measured by standard methods are well documented but provide little information about the components comprising standard operational definitions. Dissolved P (DP) and particulate P (PP) represents important but rarely characterized nutrient pools. Samples from Lake Mendota, Wisconsin, USA were characterized using 31-phosphorus nuclear magnetic resonance spectroscopy ((31)P NMR) during the open water season of 2011 in this unmatched temporal study of aquatic P dynamics. A suite of organic and inorganic P forms was detected in both dissolved and particulate fractions: orthophosphate, orthophosphate monoesters, orthophosphate diesters, pyrophosphate, polyphosphate, and phosphonates. Through time, phytoplankton biomass, temperature, dissolved oxygen, and water clarity were correlated with changes in the relative proportion of P fractions. Particulate P can be used as a proxy for phytoplankton-bound P, and in this study, a high proportion of polyphosphate within particulate samples suggested P should not be a limiting factor for the dominant primary producers, cyanobacteria. Hypolimnetic particulate P samples were more variable in composition than surface samples, potentially due to varying production and transport of sinking particles. Surface dissolved samples contained less P than particulate samples, and were typically dominated by orthophosphate, but also contained monoester, diester, polyphosphate, pyrophosphate, and phosphonate. Hydrologic inflows to the lake contained more orthophosphate and orthophosphate monoesters than in-lake samples, indicating transformation of P from inflowing waters. This time series explores trends of a highly regulated nutrient in the context of other water quality metrics (chlorophyll, mixing regime, and clarity), and gives insight on the variability of the structure and occurrence of P-containing compounds in light of the phosphorus-limited paradigm.

The importance of lake-specific characteristics for water quality across the continental United States.

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agencys 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

Microbial Contributions to Phosphorus Cycling in Eutrophic Lakes and Wastewater

Phosphorus is a key element controlling the productivity of freshwater ecosystems, and microbes drive most of its relevant biogeochemistry. Eutrophic lakes are generally dominated by cyanobacteria that compete fiercely with algae and heterotrophs for the element. In wastewater treatment, engineers select for specialized bacteria capable of sequestering phosphorus from the water, to protect surface waters from further loading. The intracellular storage molecule polyphosphate plays an important role in both systems, allowing key taxa to control phosphorus availability. The importance of dissolved organic phosphorus in eutrophic lakes and mineralization mechanisms is still underappreciated and understudied. The need for functional redundancy through biological diversity in wastewater treatment plants is also clear. In both systems, a holistic ecosystems biology approach is needed to understand the molecular mechanisms controlling phosphorus metabolism and the ecological interactions and factors controlling ecosystem-level process rates.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Water quality data for national‐scale aquatic research: The Water Quality Portal

Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.

An Analysis of Water Data Systems to Inform the Open Water Data Initiative

Improving access to data and fostering open exchange of water information is foundational to solving water resources issues. In this vein, the Department of the Interiors Assistant Secretary for Water and Science put forward the charge to undertake an Open Water Data Initiative (OWDI) that would prioritize and accelerate work toward better water data infrastructure. The goal of the OWDI is to build out the Open Water Web (OWW). We therefore considered the OWW in terms of four conceptual functions: water data cataloging, water data as a service, enriching water data, and community for water data. To describe the current state of the OWW and identify areas needing improvement, we conducted an analysis of existing systems using a standard model for describing distributed systems and their business requirements. Our analysis considered three OWDI-focused use cases—flooding, drought, and contaminant transport—and then examined the landscape of other existing applications that support the Open Water Web. The analysis, which includes a discussion of observed successful practices of cataloging, serving, enriching, and building community around water resources data, demonstrates that we have made significant progress toward the needed infrastructure, although challenges remain. The further development of the OWW can be greatly informed by the interpretation and findings of our analysis.

Generating community-built tools for data sharing and analysis in environmental networks

Rapid data growth in many environmental sectors has necessitated tools to manage and analyze these data. The development of tools often lags behind the proliferation of data, however, which may slow exploratory opportunities and scientific progress. The Global Lake Ecological Observatory Network (GLEON) collaborative model supports an efficient and comprehensive data–analysis–insight life cycle, including implementations of data quality control checks, statistical calculations/derivations, models, and data visualizations. These tools are community-built and openly shared. We discuss the network structure that enables tool development and a culture of sharing, leading to optimized output from limited resources. Specifically, data sharing and a flat collaborative structure encourage the development of tools that enable scientific insights from these data. Here we provide a cross-section of scientific advances derived from global-scale analyses in GLEON. We document enhancements to science capabilities made possible by the development of analytical tools and highlight opportunities to expand this framework to benefit other environmental networks.