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Journal Article

NEON and STREON: opportunities and challenges for the aquatic sciences

William H. McDowell
Freshwater Science
Vol. 34, No. 1 (March 2015), pp. 386-391
DOI: 10.1086/679489
Stable URL: http://www.jstor.org/stable/10.1086/679489
Page Count: 6
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Bridges*

NEON and STREON: opportunities and challenges for the aquatic sciences

William H. McDowell1,2
1Department of Natural Resources and the Environment, University of New Hampshire, Durham, New Hampshire 03824 USA
  1. 2

    E-mail addresses:

Abstract

Creation of the National Ecological Observatory Network (NEON) provides unparalleled opportunities for continental-scale research and synthesis in aquatic sciences. Organizers of the NEON aquatic network will equip sites at 29 streams or small rivers across bioclimatic regions of the USA to measure O2 dynamics, aquatic community structure, and aquatic chemistry for up to 3 decades. Data will be collected via a suite of sensors and traditional measurements of a wide range of variables with standardized techniques. The availability of such data will usher in a new era for aquatic scientists, who can use the data to understand the influence of major drivers of stream ecosystem structure and function at regional to continental scales. This rich data stream also will present challenges for the aquatic community. These challenges include interpreting field measurements at distant sites, analysis and management of large data sets, development of appropriate tools for synthesis, and changes in the culture of aquatic science. With the advent of NEON, the most successful aquatic scientists in the coming decades will be equally versed in field measurements and sophisticated analysis of large data sets.

Key words : NEON STREON big data sensor network stream chemistry stream metabolism stream ecology culture of science

Many environmental challenges are continental to global in scale (Grand Challenges report; NRC 2001). Among these challenges are several with particular relevance for aquatic scientists, including biogeochemical cycles, climate variability, biological diversity, and ecosystem functioning. These challenges are manifested in attempts to understand and predict widely recognized problems in aquatic systems. Dead zones in coastal areas occur across the globe (Diaz and Rosenberg 2008) and are fueled primarily by nutrient and organic matter inputs from watershed sources (Rabalais et al. 2002). Hydrologic manipulations, such as dams and water abstraction, disrupt hydrologic connectivity (Pringle 1997) and cause loss of biodiversity and economically valuable fisheries. These losses are occurring globally in major rivers, such as the Nile and Mekong, and are likely to be exacerbated by the increased frequency of extreme events that are predicted for many regions and biomes in the coming decades (e.g., Hayhoe et al. 2007). Invasive species are altering aquatic systems worldwide. The resulting changes to aquatic communities have uncertain effects on the functioning of aquatic ecosystems and the ecosystem services that they provide (Strayer et al. 2006). Documenting changes in nutrient loads, hydrologic regime, and aquatic biodiversity are fundamental to understanding aquatic ecosystems.

Many of the Grand Challenges (NRC 2001) are most effectively addressed at multiple spatial and temporal scales. Spatial coverage that ranges from forest plots and stream reaches in individual watersheds, to landscapes, biomes, and continents will be necessary for continental-scale understanding and management of ecosystems (Goodman et al. 2014). Integration of remotely sensed data with site-based observations is a central challenge to developing better understanding of aquatic ecosystems in a global context. Scaling up from plot or reach to biome and scaling down from general circulation model (GCM) grid cells to regions and specific watersheds are clear challenges for all environmental sciences in the coming decades (Heffernan et al. 2014). Addressing these challenges will require new ways to address spatial variability (Hinckley et al. 2014, Soranno et al. 2014) and integration of multiple research communities in ways that cross these scales. Such integration is not yet widespread but will be central to addressing continental-scale questions effectively (Goring et al. 2014). Temporal resolution also must be expanded, particularly in studies of lotic environments, where changes in stream chemistry can occur at scales of minutes to hours and frequently are driven by hydrologic variability (Pellerin et al. 2011, Sobczak and Raymond 2014). Quantifying and understanding the drivers of temporal variability across scales of minutes, days, seasons, and decades and integrating that understanding into a coherent spatial framework will be fundamental to addressing many of the Grand Challenges that are particularly relevant for aquatic science. Without an expansion of our spatial and temporal framework, solutions for many environmental problems will remain elusive.

NEON and Continental-Scale Science

Network design

The fundamental premise of the National Ecological Observatory Network (NEON) is that a consistent, nationwide array of sensors, remote-sensing images, and on-site, standardized sampling across the climatic domains of the USA will provide valuable continental-scale insight into the environmental issues identified in the NRC Grand Challenges (Utz et al. 2013). NEON includes sites at 36 aquatic environments, including wadeable streams (26), small rivers (3), and lakes (7). Sites will be situated throughout the contiguous 48 states, Hawaii, Alaska, and Puerto Rico (see fig. 1 in Goodman et al. 2014). Measurements will be made for 30 y at core (permanent) sites and for a minimum of 5 to 7 y at relocatable sites. Many of the relocatable sites will be on streams with similar climatic regimes that vary as a function of a specific driver, such as land use. For example, in domain 1 (Northeast), Bigelow Brook (forested watershed) will be paired with Sawmill Brook (suburban watershed) (Goodman et al. 2014). At all sites, measurements will be made on physicochemical and biological samples (fish, invertebrates, algae, macrophytes, and microbes). Chemical analysis will include biweekly grab samples for nutrients, dissolved organic matter, major ions, dissolved gases, and selected stable isotopes, and nearly continuous monitoring by sensor arrays for dissolved O2 (DO), pH, conductivity, chlorophyll, temperature, turbidity, fluorescent organic matter, NO3, light, and discharge. Extensive quality assurance/quality control (QA/QC) is planned for all data and will be based on internationally recognized standard materials whenever feasible. Derived data products, including stream metabolism and annual nutrient export, also are planned. These high-quality data sets will be designed to serve as a platform for investigator-initiated research that answers fundamental questions in stream ecology across multiple temporal and spatial scales (Goodman et al. 2014).

In addition to the observational backbone, a major long-term nutrient addition experiment (STReam Experimental Observatory Network [STREON]) is planned at 10 stream sites, with 8 y of combined N and P addition planned downstream of the long-term stream monitoring locations. Response variables to the nutrient addition will include stream ecosystem metabolism, nutrient uptake, and response of the biotic community. An additional component of the experiment is patch-scale consumer manipulations that will be used to assess the effects of stream biota on ecosystem metabolism and nutrient uptake (see Utz et al. 2013 for details). Taken together, NEON aquatics and the STREON experiment should begin to show how ecosystem function varies in response to drivers, such as land use, climate, and biotic community structure across North America.

Using NEON in aquatic sciences—metabolism as a case study

The continental-scale design of NEON promises to enhance our understanding of the fundamental drivers that shape the structure and function of stream ecosystems. One of the clearest examples of this promise is enhanced understanding of a fundamental ecosystem process, stream metabolism. The focus on metabolism is particularly appropriate, given the maturity of the sensing technology for DO and new optical approaches (optodes) that will reduce maintenance costs and increase reliability. For decades, various types of sensors have been deployed to monitor DO continuously and to estimate productivity from DO and modeled reaeration (e.g., Kelly et al. 1983). Roberts et al. (2007) used 2 y of continuous DO measurement and frequent field measurement of reaeration to derive continuous estimates of stream metabolism. The results of these and other earlier studies showed the clear influence of season and storm events on the magnitude of gross primary productivity (GPP), ecosystem metabolism, and net ecosystem productivity in small streams. Despite the importance of this work, knowing whether the reported patterns, such as the very strong dependence of gross primary production (GPP) on light (r2 = 0.84) at the forested site studied by Roberts et al. (2007), are robust is essential. Only a continental-scale assessment could begin to address whether results from a given site are idiosyncratic, generalizable, or generalizable only within certain classes of streams.

In their study of stream ecosystem metabolism across North America, Bernot et al. (2010) described a predictive relationship between GPP and light for forested, urban, and agricultural headwater streams based on a few days of measurement at 72 sites, but the relationship was not particularly strong and explained only 25% of the variance in GPP. Thus, at the continental scale, the strong relationship between GPP and light found in earlier studies appears to break down. Is this divergence because of the snapshot nature of the metabolic rates measured by Bernot et al. (2010), or does it reflect some variation in site-specific vs continental-scale drivers of metabolic processes? The STREON experiment will help to address this question because it will allow separate assessment of the effects of nutrient levels and consumer exclusion on GPP, while light regime is held constant. The long-term nature of STREON also will allow assessment of the fundamental drivers of metabolism under the varying hydrologic conditions that are likely to occur during the 8 y of nutrient addition. Thus, a combined continental-scale observational and experimental network provides an opportunity to address the effects of 4 major drivers—light, nutrients, consumers, and stream flow—on a fundamental ecosystem process.

Similar questions can be posed about other ecosystem processes, such as controls on the export of organic matter from headwater streams (Sobczak and Raymond 2014). The promise of NEON/STREON and of continental-scale ecology, in general, is to combine high temporal resolution with broad spatial distribution to develop a truly continental-scale approach to understanding drivers of key ecosystem functions.

Opportunities and Challenges for Aquatic Scientists

Interpreting field measurements

Two fundamental strengths of the NEON approach to ecology are the consistency and high quality of the data to be collected and that they will be readily available to the broader scientific community with a fast turn-around time. However, a constant challenge in any field-based science is to interpret field observations in a broader environmental context. Despite the wide range of variables that will be measured in NEON, considerable opportunities will exist to enhance the understanding of ecosystem variables through collection of additional data at a site or across the network. For example, in the case of stream metabolism, these additional data would include inputs of leaf-litter from riparian vegetation and dissolved organic C from groundwater, which will be measured with much lower frequency than metabolism (groundwater inputs), or not at all (riparian leaf inputs). Such additional variables pose significant opportunities for work that will leverage NEON data to develop a more detailed understanding of aquatic ecosystem function at a given site or across the network.

A fundamental challenge in interpretation of data from multiple sites in any continental-scale project is that investigators may have no personal knowledge of the sites and, thus, may be unable to interpret the collected data fully. This challenge is not limited to NEON or aquatic science. Field sites in this context basically become points on a gridded raster, and investigators are able to interpret data based only on ancillary measurements made by NEON, rather than on their own field observations. This situation always has been a challenge for developing continental-scale understanding of ecological phenomena, but the issue may be more acute for NEON, in which a significant amount of resources is devoted to collecting NEON data, but not to hiring domain-based personnel, such as a Domain Chief Scientist or other individual tasked with interpretation of site observations.

Significant opportunities will exist for the environmental science community near a NEON site to take full advantage of both local and network data sets, generating what might be called the ‘locale’ effect. For example, those researchers fortunate enough to be situated near a NEON aquatic site will be able to assess site conditions easily, make additional measurements after significant events, such as floods or droughts, and train students in aquatic ecology using local data with local demonstrations of the field techniques that actually produced the data. Those who are not as fortunate will be at a considerable disadvantage, despite the large amount of metadata that will be provided on the NEON web site. Thus, physical proximity to a NEON site is a bonus for a lucky few individuals and institutions. Proximity to an active research site or field station, such as the Experimental Forest and Range (EFR) network of the US Forest Service, has always conferred such logistical advantages, but the EFR network typically has local housing and other amenities that make use of the site by visiting scientists practical. Conversely, relatively remote sites that lack strong connections to a community of environmental science researchers may be underutilized. In the absence of local expertise, data may never be fully analyzed or understood in the local context. Lack of local interest also could limit the ancillary studies that will leverage the investment made by NEON and enhance its overall effectiveness as a national research asset. Accessibility of NEON data will be equal for all, but the ability to interpret or leverage it will not be evenly distributed across the continent.

Data analysis

One of the most apt metaphors for the reality of data analysis in the age of NEON is “drinking from the fire hose.” The volume of data that will be generated for stream ecology alone is potentially overwhelming when approached with the tools that traditionally have been used for data analysis over the last few decades. By way of example, 15-min records for a single variable yield 35,040 data records for a year. Variables, such as stream flow, temperature, specific conductance, and DO, all will be collected with this temporal frequency, and thus, stream ecologists must be ready to deal with hundreds of thousands of data points per stream and per year. NEON data products probably will include intermediate data summaries that collapse these individual data points, but many opportunities will require, or benefit from, use of the full data set.

Dealing with these large data sets successfully will require use of analytical tools that are new for many aquatic ecologists (Levy et al. 2014). Some fields of environmental science (particularly remote sensing) have long dealt with very large data sets, as have life scientists studying molecular biology. The data management approaches taken in other fields will have to be more widely used by aquatic ecologists working with NEON data (e.g., Rüegg et al. 2014). Just as bioinformatics has become central to modern molecular biology, ecoinformatics is likely to become more central to ecology (Michener and Jones 2012). Most young environmental scientists appear to be unprepared for using these data sets to their full potential because they lack the needed computational skills, or at least the formal training to do so (Hernandez et al. 2012). At its simplest level, I think that a fundamental shift from spreadsheet to database as the default approach to data analysis will be one of the top priorities in the coming decade. The maximum allowable rows and, thus, the maximum number of data records in spreadsheets have increased dramatically as desktop computing power has increased, but the errors and pitfalls introduced by the cut-and-paste capabilities of spreadsheets make them less and less viable for data management and data analysis. The seductive power of visually manipulating data on a spreadsheet page is more than offset by the increased rate of errors introduced by this unlimited flexibility of spreadsheets and the difficulties in tracing data provenance through a series of analyses. Given their power, flexibility, and availability, R and broadly available structured query language (SQL) databases probably will become the new standards for routine data analysis and already are in reasonably wide use throughout the aquatic ecology community.

New statistical approaches will be needed to deal with the interactions between space and time and with changes over time or space that are neither monotonic nor cyclical (e.g., Soranno et al. 2014). In the era of big data, ecologists need to develop more of the “Moneyball” approach to data analysis (Cohan 2012), in which patterns and relationships are extracted from very large data sets. In the era of big data, assuring the quality of large data streams and recording their provenance are particularly important. Rigorous QA/QC is planned for NEON data (Taylor and Loescher 2013) and is already in place for existing environmental data streams, such as those generated by the National Oceanic and Atmospheric Administration (NOAA) and US Geological Survey (USGS). A challenge for the aquatic community is to ensure that other large data sets produced from a wide variety of environmental sensors by individual investigators also undergo rigorous QA, and are seen as significant intellectual products that are developed, published, and cited (Hampton et al. 2013, Rüegg et al. 2014). A concerted effort to develop and adopt widely accepted conventions for data management and data analysis should be a top priority for the aquatic research community in the coming decade.

Interpreting and synthesizing

The design of NEON is dictated by placement of terrestrial sampling sites in a way that allows scaling from individual climatic domains at core sites to relocatable sites that address various environmental gradients to remotely sensed data that can then be scaled to the continent (Kao et al. 2012). The NEON aquatic and STREON sites will provide a similarly strong study plan for small streams across the continent because they will capture a broad range of climate and some landuse variation across the watersheds drained by the NEON streams. However, the positions of NEON sites across the continent represent a raster approach to environmental characterization that might be more appropriate for terrestrial processes and will not be scaled easily to entire river networks that deliver materials from the continent to the coastal zone. Only domain 8 contains a headwater and larger, downstream sites (Goodman et al. 2014), so understanding the role of headwaters in the entire drainage network will remain a key challenge for aquatic ecologists attempting to use NEON results to scale from individual sites to biomes and the continent.

An additional challenge to interpreting NEON results will be to match aquatic data to relevant watershed characteristics. In many domains, streams are near, but not part of, the terrestrial site. The core terrestrial sites are centered on the footprint of a NEON flux tower (up to a few hundred ha; Running et al. 1999), which is unlikely to be coincident with the watershed of a stream near a NEON core site. Thus, important information about soils, vegetation, and the hydrology of a stream’s watershed may be lacking or will be available only if it can be remotely sensed. Given the strong linkage between streams and their watersheds that has been evident for almost 40 y (Hynes 1975), stream ecologists may be in the curious position of having to focus primarily on terrestrial ecology to understand the wealth of data on stream ecology that will be produced for them by NEON.

NEON data products will facilitate the use of aquatic data and speed the development of synthetic approaches to characterizing stream ecosystem function. The principal data product to be developed for stream ecosystem function at NEON aquatic sites is stream metabolism, which will synthesize data on stream O2, temperature, and gas exchange into continuous measurements of GPP, ecosystem respiration, and net ecosystem productivity. A number of other data products (e.g., stream discharge derived from stage height, nutrient export, and the diversity and abundance of algae and macroinvertebrates) also will be developed. A key opportunity for the aquatic sciences will be to work with NEON and funding agencies to develop the most effective approach to using the unprecedented data sets that NEON will produce.

Maintaining a functioning scientific community

NEON is likely to have profound effects on how the aquatic science community functions. The first and most obvious effect is a likely change in funding strategies for individual researchers. The advent of freely available data at NEON sites, where much of the data needed to conduct aquatic ecology will be available at no charge to a research grant, may make justifying work at non-NEON sites increasingly difficult. Of course, leveraging of NEON data to fund work at individual sites, or multiple sites within a region, will favor those who have ready access to a NEON field site and will put at a disadvantage those who do not. However, for more synthetic work, NEON will open doors for individuals across the globe to examine, synthesize, and publish papers that relate NEON aquatic data to environmental drivers. This opportunity has the potential to broaden participation in the aquatic sciences. At the same time, the pressure to be the first to use NEON data in peer-reviewed publications will increase. For example, NEON data products will have to be released simultaneously to all potential users because a race to publish papers, such as “Continental-scale drivers of stream ecosystem metabolism”, will undoubtedly occur after release of the metabolism data product for the first year of full NEON operation. Healthy competition among researchers and research groups is an important driver of scientific progress, but the sort of competition that NEON will engender is likely to be qualitatively different from that which the field has experienced in the past, and its effects are uncertain. If NEON scientists themselves take the lead on publishing papers based on NEON samples or data products, as occurred with the recent paper by Loescher et al. (2014) on continental-scale variation in soil properties, the effects on the larger research community are even more uncertain.

More subtle effects from NEON on the aquatic research community also are probable. For example, the academic promotion and tenure process may be affected (Goring et al. 2014). Faculty who work primarily with data collected by others may become more common in the coming years, and departments will have to develop a culture that expects this strategy and rewards those who do this effectively if they have not done so already. Geospatial scientists already have been grappling with this issue in their professional careers as members of Natural Resources or Environmental Science departments. The nature and frequency of interdisciplinary collaborations also may change as NEON data become more broadly available because available data sets and analytical tools affect the formation of effective collaborations (Pennington 2008). For example, when stream nutrient-chemistry data are freely available at NEON sites, aquatic biologists may find it more advantageous to collaborate with statisticians who analyze trends than the biogeochemists who previously collected the field data. Alternately, freely available high-quality biogeochemical data may enhance interdisciplinary collaborations that previously were limited by the costs of sample collection and analysis. Predicting how NEON will affect academic careers and the nature of interdisciplinary collaborations is difficult, but change can be expected, and broadening of the nature and type of interdisciplinary collaborations is a likely outcome (Goring et al. 2014).

The final opportunity and challenge that NEON poses to the aquatic science community is student recruitment. Few students currently in the aquatic sciences were drawn to the field by the ability to use massive data sets and publish insightful papers without ever going outside. However, we may need to attract such students in the future as our traditional field sites are replaced by virtual ones and the need for strong quantitative skills becomes even more pressing (Hernandez et al. 2012). More and more citizens are comfortable living in a digital, virtual, and highly controlled world with little connection to the life-support systems provided by the natural world, so recruiting these students may not be such a difficult task. Instead, the real challenge ahead may be to inspire students to engage actively with the complexities, uncertainties, and wonder of the natural world. More broadly, lack of exposure to the natural world has spawned a movement to enhance citizen involvement in environmental issues, i.e., the “No Child Left Inside” movement championed by the Chesapeake Bay Foundation. Much would be lost from our field without place-based, field experiences to inform aquatic science and inspire students. However, to thrive as a discipline, we must be able to attract and educate a new generation of students who will be equally comfortable in R and hip boots.

Acknowledgements

Experience gained through work on grants funded by the National Science Foundation (DEB-0620919, EPS-1101245, and EF-1065286) was central to developing the ideas presented here, as were discussions with all the participants in the Lotic Intersite Nitrogen Experiment (LINX) II over the past decade. I thank Allison Hunt Roy and 2 anonymous referees, whose comments greatly improved the manuscript.

  1. *

    BRIDGES is a recurring feature of FWS intended to provide a forum for the interchange of ideas and information relevant to FWS readers, but beyond the usual scope of a scientific paper. Articles in this series will bridge from aquatic ecology to other disciplines, e.g., political science, economics, education, chemistry, or other biological sciences. Papers may be complementary or take alternative viewpoints. Authors with ideas for topics should contact BRIDGES Co-Editors, Ashley Moerke () and Allison Roy ().

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Acknowledgements

Experience gained through work on grants funded by the National Science Foundation (DEB-0620919, EPS-1101245, and EF-1065286) was central to developing the ideas presented here, as were discussions with all the participants in the Lotic Intersite Nitrogen Experiment (LINX) II over the past decade. I thank Allison Hunt Roy and 2 anonymous referees, whose comments greatly improved the manuscript.

  1. *

    BRIDGES is a recurring feature of FWS intended to provide a forum for the interchange of ideas and information relevant to FWS readers, but beyond the usual scope of a scientific paper. Articles in this series will bridge from aquatic ecology to other disciplines, e.g., political science, economics, education, chemistry, or other biological sciences. Papers may be complementary or take alternative viewpoints. Authors with ideas for topics should contact BRIDGES Co-Editors, Ashley Moerke () and Allison Roy ().

Literature Cited

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