Project Summary: Collaborative Research: Scale, Consumers and Lotic Ecosystem Rates (SCALER):
Centimeters to Continents. Participants : W. Dodds (PI, Kansas State Univ.), K. Gido (Kansas St. Univ.),
F. Ballantyne (Univ. Kansas), W. Wollheim (Univ. New Hampshire), A. Helton (Duke Univ.), M. Whiles (Southern
Illinois Univ.), A. Rosemond (Univ. Georgia), J. Kominoski (Univ. Georgia), W. Bowden (Univ. Vermont), M.
Flinn (Murray St. Univ.), J. Jones (Univ. Alaska), T. Harms. (Univ. Alaska), W. McDowell (Univ. New Hampshire)
Intellectual Merit- The overarching question is: How can small-scale ecological experiments be applied
to understand operation of entire ecological systems? Specifically this proposal will ask how can we use
cm- and reach-scale process measurements and consumer manipulation experiments to predict
ecosystem characteristics of stream networks, and how do patterns of scaling compare across an array
of North American biomes? The SCALER experiment: a continental scale experiment encompassing five
biomes, each of which will have six sites with measurements nested at two scales (microhabitat, reach),
linked to watershed models will answer these questions. Synoptic sampling will characterize watershed
scale patterns. Rates of metabolism and nutrient uptake and responses to consumer exclusions will be
measured at micro (0.1 m) and reach (100 m) scales. Diversity and ecosystem function will be linked at a
basic level by comparing metabolism and nutrient dyamics with and without consumers larger than 0.5
cm. Experimental results will be scaled with models. Output of reach models based on microscale
measurements will be compared to reach measurements, output of watershed models based on reach
measurements will be compared to carbon and nutrient patterns observed with synoptic sampling. Finally,
the work will compare factors influencing scaling across widely divergent biomes. A stoichiometric
approach will be taken that considers interactions of source of carbon (from within or outside the system),
nitrogen and phosphorus availability and transport, and how large consumers alter ecosystem
stoichiometry. This approach is necessary because of strong upstream/downstream linkages and variations
in the relative effects of biotic and abiotic factors at different positions within watersheds.
Mechanistic explanation of how ecological measurements in streams can be scaled to watersheds will
be provided, which is needed to understand both whole-system dynamics as well as to manage human
impacts on entire watersheds. The experiments and modeling results will be relevant to ecology as a
whole because few coupled nested experimental and theoretical scaling exercises have been undertaken in
any environment. Coupling experiments and scaling exercises will characterize how plot-level
experiments relate to patterns across larger scales such as landscapes (e.g., the stream network) and help
understand the links between biodiversity and ecosystem function.
Broader Impacts- A key aim of this proposal is to allow extrapolation of typical experiments in streams
to inform people concerned with utility or protection of the environment (i.e. human management).
Management agencies and research networks (e.g., the National Ecological Observatory Network)
typically make measurements or monitor across networks. Few of these networks are arranged with the
idea of understanding how representative they are of processes occurring within watersheds, with the
assumption that enough stations can be averaged to represent system properties. A nested design, such as
employed here, is crucial for testing the ability to scale up within network measurements, and very few
monitoring networks have an explicitly nested architecture. Education and outreach will be accomplished
by providing managers results to help guide the placement of monitoring stations so that future
measurements can be scaled up and used for broad scale comparisons. Additionally, a highly
collaborative team will be created, including a mentoring plan for six graduate students, five postdoctoral
students, and three young faculty members. Several open workshops will be held and other groups will be
encouraged to collaborate and perform similar projects at additional sites.
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RESULTS FROM PRIOR RESEARCH:
LINX II Lotic Intersite Nitrogen Experiment #DEB-0111410
The LINX II project involved investigators on the current proposal (Dodds, Helton, Wollheim,
McDowell) and focused on nitrogen (N) dynamics by conducting 15NO3- (nitrate) additions to 72 streams
distributed equally among 8 biomes and 3 land use types (reference, agricultural, urbanized) across the
contiguous United States and Puerto Rico. This collaborative effort involved 19 PI’s and co-investigators
and many graduate and undergraduates at 16 different institutions. To date, this project has resulted in 42
peer reviewed publications, 3 book chapters, 10 theses or dissertations, 85 presentations at professional
meetings, and a number of additional presentations. The key paper of this project was published in Nature
(Mulholland et al. 2008) and was subject of a news article in Science.
The 15N field experiments evaluated the rates and controls on NO3- cycling in entire stream reaches.
This was the first large-scale investigation of NO3- uptake and denitrification in streams using this
powerful approach, and one of the few ecosystem-level experiments conducted the same way across so
many sites. Synoptic chemistry was sampled across approximately 50 stream and river locations within a
5th order river basin in each of the 8 study regions. A river-basin N retention model was made to scale up
N cycling from reaches to watersheds and compare expectations of the model to synoptic sampling
(Mulholland et al. 2008, Helton et al 2010).
Our major findings include, 1) Substantial NO3- uptake was verified in 69 of the 72 streams and
significant denitrification was confirmed in 49 streams for N2 production and 53 streams for N2O
production. 2) Total NO3- uptake and denitrification rates were greater in agricultural and urbanized
streams where there were greater NO3- concentrations than in reference streams. 3) NO3- uptake was
significantly related to stream discharge (+), NO3- concentration (+) and gross primary production (-), and
indirectly related to NH4+ concentration (via nitrification) as indicated by structural equation models. 4)
NO3- removal efficiency from streamwater decreased as NO3- concentrations increased. 5) In-stream
denitrification was a significant source of N2O to streamwater (accounting for a median of 15% of stream
N2O and increasing with greater streamwater NO3- concentration), but in-stream N2O production
represented generally < 1% of in-stream denitrification. 6) Fifty five of 69 streams were a source of N2O
to the atmosphere, and emission rates were significantly related to streamwater NO3- concentration at
concentrations above about 100 μg N/L. 7) Global estimates based on our flux rates double the expected
contribution of river networks to N2O in atmosphere (from about 5 to 10%). and 8) Results using our
landscape model and results from our 15N experiments showed that NO3- loading rates significantly
influence the importance of streams as landscape N-sinks. Higher NO3- loading rates stimulate NO3uptake and denitrification, but yielded a disproportionate increase in downstream NO-3 export to receiving
waters as the efficiency of NO3- removal declined. Small and large streams respond differently to
simulated increases in NO3- loading (i.e. scaling was not strictly linear and depended on network
arrangement). The simulated percentage of network NO3- load removed in small streams declined as
loading increased. Percentage removal in large streams increased with NO3- loading up to intermediate
loading rates, and then declined at higher loading.
This project illustrates the power of cross site measurements, the ability to apply a cross-scale
approach, linking empirical measurements at various scales to inform models, provides a model for the
administrative structure and mentoring philosophy of the current proposal, and provides evidence of a
highly cooperative and collaborative scientific group, several of whom are involved in the current
proposal.
BACKGROUND AND RATIONALE
Ecological processes vary across scales; capturing this variability is one of the most pressing
problems in ecology (Levin, 1992). The inability to apply results from manipulative experiments at small
spatial scales to larger scales (Thrush et al. 1997, Schindler 1998, Loreau et al. 2001, Hewitt et al. 2007,
Duffy 2009) makes it difficult to understand whole ecosystem function and hinders across ecosystem
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comparisons. Only a modest number of studies have explicitly considered scale in their experimental
design (e.g., Lowe et al. 2006, Sandel & Smith 2009). This general problem has specific implications for
a major planned and funded experimental network initiative, the STReam Experimental and Observatory
Network (STREON), part of the National Ecological Observatory Network (NEON). The scales of
experimentation for STREON (cm and stream reaches), and for that matter, most stream experimental
methods (Hauer & Lamberti 2006), have not been explicitly related to responses at watershed- and
continent-scales, despite the desire to use NEON to describe continental gradients, and the general need to
apply results of ecological experiments more broadly. The common problem of relating modestly-sized
manipulative experiments or environmental observations to scales relevant to human needs partially arises
because of the increasing complexity and variability of ecological systems as spatial scale is increased
(Thrush et al. 1997, Hewitt et al. 2007).
Stream biologists have recognized the effect of spatial scale related to unidirectional flow and
dendritic networks for at least 40 years (Hynes 1970). The emergent properties of stream ecosystem
processes and relationship to communities as a function of interconnectedness was approached decades
ago with the River Continuum Concept (Vannote et al. 1980), where it was hypothesized that some
processes could not be explained only by local conditions, but were influenced by upstream biotic and
abiotic processes. Subsequently, researchers have suggested that smaller functional areas (functional
processing zones) and their arrangement at broader spatial scales may control some aspects of stream
ecosystem function (Thorp et al 2006). Connectivity further comes into play. For example, transit time
down a drainage network controls rates of N-uptake and denitrification across the entire network
(Wollheim et al. 2008a), and hydrologic connectivity across a network maintains community structure
(Schlosser 1995, Pringle 2001). However, empirical measurements to explicitly test the effects of scale in
streams are rare. Our proposed SCALER project will combine nested empirical measurements with a
robust modeling framework to test how ecosystem processes, and effects of large consumers on those
processes, compare across scales.
For the SCALER project, we define five distinct scales (while recognizing the gradations among
them): 1) Microhabitat = 0.1-3 m (amenable to experimentation with incubations and baskets), 2)
Mesohabitat (riffles, pools, large woody debris, patches of substrata) = 3-100 m (this scale is particularly
relevant for consumers because this is the scale at which they partition habitats), 3) Reach = 100-500 m
(amenable to measuring flux rates within entire stream reaches such as metabolism and nutrient uptake),
4) Watershed (the scale at which management occurs and land-use land cover change influenced by
humans is most evident), which in this proposal is specified up to 4th-5th order streams, and 5) Continent
(the scale at which biogeography becomes important). Explanation of mechanisms driving ecosystem
properties across these scales is required for comprehending how we influence the ecosystem goods and
services upon which humanity depends, as well as the intrinsic biotic integrity of ecological systems.
Streams as Sentinels of Global and Regional Change- Human impacts on Earth's ecosystems are
pervasive (Vitousek et al. 1997). Transformation of natural landscapes has benefited society (e.g., food
and energy production), but comes at a high cost. The earth is subject to more extreme and unpredictable
climate (Charlson et al. 1992, Zachos et al. 2001), depletion and degradation of freshwater (Dodds 2006,
Postel et al. 1996) and coastal marine waters (Diaz & Rosenberg, 2008), and a catastrophic loss of
biological diversity (MEA 2005, Pimm et al. 1995, Sala et al. 2000). We know much about the drivers of
ecological change (Vitousek et al. 1997), but far less is known about complex ecosystem responses to
change. Ecological forecasting requires identifying the mechanistic basis for ecosystems response to
change and internal feedbacks contributing to ecosystem resilience (MEA 2003), as well as drivers that
may drive ecosystems into unwanted alternative stable states (Scheffer & Carpenter 2003, Groffman et al.
2006, Dodds et al. 2010). The mechanisms must apply across scales, and thus cannot be associated with
the specific scales of experimentation. Feedbacks and non-linearity inherent in larger-scale processes
demand empirical research programs to uncover features that account for scale.
We can identify key local drivers of stream ecosystem processes based on local observations and
experimental results. These drivers include hydrology (flood, drying, connectivity), resource supply rate,
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and consumer effects (including direct effects such as grazing and indirect effects such as nutrient
mineralization, Fig. 1). Humans directly and indirectly influence these local processes, roughly doubling
the availability of nitrogen (N) and phosphorus (P) in river networks, which leads to eutrophication in
most water bodies (Meybeck & Helmer 1989, Kemp et al. 2005). Eutrophication has detrimental effects
on the values of ecosystem goods and services provided by freshwaters (Dodds et al. 2009). The
extinction rate of freshwater biota exceeds that for most marine or terrestrial fauna with top trophic levels
at highest risk (Allan & Flecker 1993, Ricciardi & Rasmusen 1999, Jenkins 2003), and invasive species
can simplify food webs by driving out inferior competitors or susceptible prey. Additional large-scale,
human-driven changes such as warmer global temperatures, more extreme precipitation, more variable
hydrologic events and increased loading of biologically active resources have created a novel
environment with no precedent in the history of ecological study. Results from contemporary statistical
analyses of patterns may not apply to these novel conditions, and mechanistic understanding is necessary
to extrapolate reliably to these unprecedented circumstances. As most mechanistic understanding is based
on small-scale experiments, we strive to apply mechanistic results to the larger scales relevant to whole
system behavior, and do so by testing our ability to scale ecological models and match empirical
observations.
Flood and drying
(driven by climate,
land use)
Resource supply rate
(N & P addition,
source of C)
Community structure
(numbers & types of species)
Ecosystem functions
(production, system
respiration, nutrient
retention& cycling)
Value of ecosystem
goods &services
Figure 1. Conceptual diagram linking ecological divers to values of ecosystem goods and
services provided by streams
SCALER and the STREON Experiment- The STReam Observational and Experimental Network
(STREON) is designed to parameterize the linkages in Figure 1 across US biomes over temporal scales
(years) that can be more relevant to system responses than the more typical month or few year-long
experiments (Slavik et al. 2004). STREON experimental sites are arranged across gradients of drivers that
are expected to vary at continental-scales, such as temperature, climate, geomorphology, and
biogeography. The 10 sites are not sufficient to characterize this many gradients, so the sites are chosen to
represent a wide array of biomes (e.g., tropical to arctic, mesic to xeric, different degrees of atmospheric
N loading, long versus short growing seasons) and should represent some of the extremes that could be
found related to large-scale drivers influencing processes represented in Figure 1 for streams. STREON
consists of long-term (8 years) nutrient enrichments and manipulations of consumer communities to
answer the overarching question: How will chronic nutrient inputs (N or P), higher probabilities of
extreme events (droughts and floods), and simplification of food webs (loss of large consumers) impact
the resistance and resilience of stream ecosystem function (whole ecosystem respiration, production, and
nutrient retention)? STREON will characterize nutrient effects on metabolism at the reach and
microhabitat-scale, and effects of removing large consumers (e.g., > 0.5 cm) on nutrient uptake and
metabolism at the microhabitat-scale. Hydrologic effects (e.g., responses to flooding or drying) will be
investigated opportunistically in STREON, given the probability of extreme events is very high across all
10 sites over 8 years (80 experimental years). Although the network of 10 experimental sites covers a
continental-scale, the ability of this experimental approach at any one site to be scaled regionally (to local
watersheds for example), and how the scaling will compare across the 10 sites are not as well understood.
Our proposed SCALER experiment will determine how to scale regionally, and if this scaling operates the
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same way across biomes. SCALER should apply generally to ecological systems, more directly to
dendritic systems dominated by advective transport, and to STREON specifically.
Streams are ideal study systems because they are tractable for the nested experimental designs
required to approach general ecological questions of spatial scaling. Streams are used in STREON and
SCALER because they are ideal for whole-system manipulation and measurement. Material flux within
entire reaches can be budgeted given the unidirectional advective transport. Most streams have discrete
boundaries and are not so large as to preclude experimental manipulation of an entire section of the
system while accounting for external inputs and outputs. Furthermore, many stream organisms
(particularly microbes and small animals) can respond to changes on the order of weeks, making
experimentation on proximal controls of communities and ecosystems practical.
Scale in Ecology- In a recent synthesis, Duffy (2009) highlighted the importance of scaling in studies
relating biodiversity and ecosystem function, and provided a compelling argument that most small-scale
experiments underestimate the strength of the overall relationship between biodiversity and ecosystem
function. Moreover, because management actions (conservation, water quality regulation, efforts to
maintain biotic integrity) often occur at larger scales, it is critical for ecological research to be cast in a
context that allows us to predict system response at large spatial and temporal scales (Schindler 1998,
Wiens 2002, Lowe et al. 2006). Again, mechanistic understanding must transcend the typically small
spatial scales of experiments.
Theoretical research has addressed the scaling of ecological processes and has some empirical support.
This is a huge area of research, and we only can consider a few relevant observations in this section. For
example, scale transition theory (Chesson 1978, 1996) suggests that nonlinear ecological dynamics and
environmental heterogeneity can determine if processes measured at small-scales under- or over-estimate
patterns at large spatial scales. In practice, experiments nested across spatial scales and along
environmental or physical gradients are a valuable way to explicitly evaluate scaling of ecological
processes (Hewitt et al. 2007, Sandel & Smith 2009); exactly this approach is taken in SCALER.
Scale in Stream Biology- How has scaling been approached in streams thus far? The discrepancy
between the scales of ecological experiments and flux of materials, energy, and organisms is particularly
notable in streams (e.g., Wiens 2002). Transport of matter and movement of organisms occur at
watershed-scales (e.g., Schlosser 1991, Fausch et al. 2002, Banner et al. 2009). In addition to the largescale flux of animals and materials, the hierarchical structure (Lowe et al. 2006) and characteristic
properties of dendritic ecological networks such as unidirectional flow and clear two dimensional
architecture (Campbell Grant et al. 2007) provide specific constraints and unique patterns of connectivity
that will influence scaling. These properties of streams have provided a foundation for predictions of how
ecological processes (River Continuum Concept, Vannote et al. 1980) and functional composition of
communities (Oberdorff et al.1995, Matthews 1998) vary along underlying gradients of stream size,
across habitats within stream reaches, and how habitat heterogeneity varies in space and time along the
underlying gradient. These strong environmental gradients might also allow prediction of consumer
effects on ecosystem properties throughout stream networks (Inoue & Miyayoshi 2006).
A series of metabolism measurements confirmed maximum primary production in mid-sized streams
(Bott et al. 1985). The measurements are essential to current understanding of scaling of stream
ecosystem rates as a function of stream connection and scale as predicted by the River Continuum
Concept. These ground breaking metabolism measures, explicitly arrayed across stream sizes, were made
in recirculation chambers, so this work assumed that measurements at the 0.1-m scale would apply to
reach or greater scales of interest. Subsequent research however, demonstrates the scale-dependence of
measures of basic stream ecosystem processes.
Mulholland et al. (2008) measured NO3- uptake and denitrification at the reach-scale using tracers, and
then did of synoptic sampling of NO3- in nearby watersheds. Rates were combined with modeling that
scaled reach rates to watersheds, estimated whole-network nutrient retention and matched it to observed
patterns of stream nutrient contents (Helton et al in press). This research identified non-linear
relationships between nutrient input rates and fate of NO3- at the reach-scale. When results were scaled to
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entire watersheds, a non-linear response pattern to NO3- loading was predicted (Mulholland et al. 2008,
Wollheim et al. 2008a, 2008b). Under low to moderate loads, a large proportion of NO3- is removed and
retained in small headwater streams, as NO3- increase these streams become saturated and the bulk of the
retention occurs in larger streams further down in the watershed. The whole-system properties lead to a
situation where increased NO3- loading has little influence downstream up to a point, and then there is a
rapid escalation of NO3- transport into the downstream region from above. Hydrologic variability further
influences this dynamic (Wollheim et al. 2008a). Each of these studies (Helton et al. 2010, Wollheim et
al. 2008a) indicated that simple additive scaling of reach-scale experimental results to watersheds is
inappropriate. Habitat heterogeneity leading to patches of differing connectivity, residence time, reactivity
and balance of carbon (C) to N and P inputs need to be considered. A specific consideration of habitat
heterogeneity and how it differentially influences C, N and P cycling (i.e. habitat scaling effects on
stoichiometric controls of ecosystem rates), and potential effects of consumers on this stoichiometry is the
next step in these nutrient scaling approaches.
Manipulation (exclusion) of large consumers can cause significant effects on stream ecosystem
structure and function (e.g., Bertrand et al. 2009, Murdock et al. 2010) and the importance of these effects
is context dependent (e.g., Power et al. 2008, Gido et al. 2010). Empirical research on consumer effects
on stream ecosystem properties indicates significant variation across spatial scales (e.g., Taylor et al.
2002, Englund et al. 2001, Melbourne & Chesson 2006), and coordinated experiments in many
geographic locations can elucidate this scale dependence (Vanni 2010).
Experiments testing ecosystem effects of consumers are likely influenced by spatial location of
consumers and resources (e.g., McIntyre et al. 2008), which are affected by macrohabitat complexity and
network geometry (Cooper et al. 1998, Thompson & Townsend 2005, Lowe et al. 2006). For example, if
consumer effects are opposite in adjacent mesohabitats (e.g., pools versus riffles), results of an
experiment would be quite different if conducted within a single mesohabitat or at larger scales
incorporating both habitats. Understanding how longitudinal structuring and network geometry influence
the ability to scale consumer effects from small-scale experiments to entire drainage networks will help
refine models to scale these dynamics (Schneider 2001, Anderson et al. 2006). Melbourne & Chesson
(2005) suggest a systematic approach for scaling up ecological experiments that combines measures of
nonlinear dynamics with measures of spatial variation to obtain the scale transition. When applying a
stream consumer-resource relationship, Melborne & Chesson (2005) found that consumer grazing rates of
periphyton measured on individual rocks overestimated removal rates at the regional (stream network)
scale. Thus, our understanding of how consumer effects scale up from small experiments is also limited.
Scaling nutrient dynamics has also been poorly researched with respect to stoichiometry, particularly
how scaling of nutrient cycling applies to patterns of C cycling in watersheds, and how consumers might
link C, N and P cycling. Turnover of N in ecosystem compartments (consumers and producers) is
positively related to N availability as meditated by C:N ratios (Dodds et al. 2004). Thus, aquatic
ecosystems should respond differently to alterations in C influx as a function of relative rates of nutrient
influx. To further complicate matters, stream trophic state (both autotrophic and heterotrophic) is a
function of nutrient availability in addition to sources and flux of C (Dodds 2007). Finally, large
consumers influence stoichiometry of stream producers and microbial heterotrophs (e.g., Evans-White &
Lamberti 2006) which reciprocally affects the consumers (Cross et. al., 2003). Comparison across widely
varied regions is essential when testing the ability to scale up nutrient cycling, and other ecological
processes, because these stoichiometric effects are expected to vary across regions. An aim of SCALER is
to begin to account for stoichiometric effects in scaling processes from microhabitats to entire stream
networks.
QUESTIONS AND HYPOTHESES
Our overarching question is: Can small-scale ecological experiments be applied to understand
operation of entire ecological systems? Specifically this proposal will ask: How can we use cm- and
reach- scale process measurements and consumer manipulation experiments to predict ecosystem
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characteristics of stream networks, and how do patterns of scaling compare across an array of North
American biomes? To approach the questions we will perform nested experiments at two scales
(microhabitat-, reach-scale) over a range of stream size. We will measure rates of ecosystem function
(metabolism, nutrient uptake) at microhabitat- and reach-scales. We will link diversity and ecosystem
function at a basic level by comparing rates of ecosystem function with and without large consumers
(excluded at microhabitat and reach-scales). We will then apply these results to network (watershed)-scale
models and compare modeling results to patterns of N, P and C flux and fate observed at watershedscales. Finally we will ask how scaling patterns compare across widely divergent biomes. At the core of
this proposal, we suggest that if we know process rates (nutrient uptake, respiration, photosynthesis,
mineralization) for different habitat types, how trophic structure influences these rates (identity and
presence/absence of large consumers), and habitat orientation/size in the lotic landscape, we will be able
to predict dynamics for whole watersheds. This overall approach will be guided by the following
hypotheses.
Scaling from Microhabitats (0.1 m) to Reaches (100-500 m)
H1. Experimental measurements of ecosystem function rates at microhabitat-scales (i.e. baskets) can be
extrapolated to reach-scales if we can account for microhabitat distribution within the reach and
connectivity among the microhabitats. We predict that scaling from microhabitat to reach will be possible
by accounting for variation in ecosystem functions (specifically nutrient uptake, gross primary production
(GPP), respiration (R), and leaf degradation) among mesohabitats (substrate type, pools, riffles) along the
reach. We specifically predict nutrient uptake, litter degradation and primary production, processes that
are more related to the water/ benthic interface, will scale from microhabitats to reaches better than those
such as respiration that can be more influenced by hyporheic processes (i.e, connection of mesohabitats
via subsurface flow influences the ability to scale typical methods of measurement, baskets in
recirculating chambers). This hypothesis will be tested by measuring metabolism, leaf degradation and
nutrient uptake at microhabitat-scales stratified by major types of mesohabitat, using surveys of
mesohabitat relative abundance to scale to reaches, and comparing the predicted rates to measured reachscale rates. Measures of hydraulic connectivity and subsurface respiration and nutrient uptake will be
made and used to link the microhabitat measurements.
H2. Responses of ecosystem process rates to removal of large primary consumers measured at
microhabitat-scales can be extrapolated to reach-scales for direct responses (e.g., direct consumption of
materials that drive primary ecosystem process rates), but not for indirect effects that are controlled by
advective transport and downstream accumulation (such as nutrient mineralization). We expect large
primary consumers to have a larger negative effect on ecosystem process rates in microhabitat-scale
removal experiments than in reach-scale removals (contrary to the predictions of Duffy, 2009). Nutrients
that are excreted by consumers can offset the removal of biomass by stimulating microbial activity, but
dissolved nutrients are transported downstream. Experiments at the reach-scale should capture this effect,
but those at microhabitat-scales will not. Indirect effects may have a local effect or a downstream effect,
whereas direct removal of biomass or bioturbation by grazers has an immediate direct local effect. This
hypothesis will be tested by measuring metabolism and nutrient uptake at microhabitat and reach-scales in
areas where all large consumers have been excluded at microhabitat and reach-scales, and comparing
relative responses to controls at the two scales.
H3. We further hypothesize that functional consumer composition (predators versus primary consumers)
and relative biomass will determine the effects of large consumer removal on ecosystem processing rates
through direct and indirect cascading effects. We predict that in reaches where large consumers are
mostly predators, their exclusion will lead to increased activity of smaller primary consumers that will
subsequently impact nutrient uptake and metabolism. In areas where large consumers are predominantly
primary consumers (e.g., grazing minnows, detritivores) and relative biomass of these primary consumers
is high, consumers should decrease metabolism and nutrient uptake because these consumers remove
active biofilms, but increase litter decomposition because consumers physically break up leaves making
them more accessible to microbial decomposers. Distribution of large consumers varies across
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mesohabitats, so micro-scale measurements must account for mesohabitat patterns to be scaled to reaches.
This hypothesis will be tested by measuring metabolism and nutrient uptake at microhabitat- and reachscales with and without exclosure of all large consumers at each scale. Our systems have both gradients of
functional composition (e.g., detritivores vs. grazers) and food chain length (e.g., primary consumers and
secondary consumers) within watersheds and among sites, allowing us to test this hypothesis. These
gradients are stronger at some sites (e.g., Konza, Puerto Rico), than others (see below).
Scaling from Reaches to Watersheds
H4. Scaling ecosystem processing rates from reach to watershed-scales will be more accurate than
single-element approaches when using a stoichiometric approach that accounts for advection-coupling of
serially linked reaches, and differential rates of C, N and P transformations that are influenced by stream
size gradients, habitat heterogeneity/connectivity, and dominant consumer functional groups. In
heterotrophic systems (GPP << R), determined by biome-specific degree of canopy cover, we hypothesize
that the demand for nutrients will decline as water flows downstream and C is respired, C quality
decreases, and N and P are remineralized. In autotrophic systems (GPP ≈ R), we hypothesize the demand
for N and P increases as additional C is fixed in the stream channel as we would expect in reaches with
more light (open canopy). Water column assays of C availability and N- and P-limitation will indicate the
relative degree of C, N-and P-limitation in each reach studied throughout the watershed. Experiments on
N and P uptake at the microhabitat and reach-scales will be used to assess how N and P uptake scales up
(see H1). We will use a river network model to estimate network-scale processing rates (e.g., production,
metabolism, mineralization) based on aggregating linked model subcompartments that use reach-scale
process rate measurements and measured responses to consumer influences. The model will account for
stream size related trends in drivers (temperature, light, flow, width/depth ratio) as well as habitat
heterogeneity/connectivity. Synoptic sampling of N, P and C concentrations in addition to existing data
monitoring (LTER, USGS, EPA and state agencies) will be used to test the validity of the models to
predict watershed-scale patterns.
H5. Scaling large consumer influences on ecosystem processes from reaches to watersheds will require
understanding patterns of diversity and abundance from upstream to downstream across gradients of
stream size and habitat heterogeneity. We hypothesize that variation in large consumer functional
composition will influence ecosystem properties within nested habitats and across spatial scales. We
predict that species richness will generally increase and functional diversity and redundancy will
correspondingly increase in a downstream direction (Mathews 1988). A corollary to this prediction is that
in low functional diversity headwater streams, the consumer effect will be more variable. The strength of
this gradient in species richness will be mediated by relative abundance of large consumers as it varies
locally across the network. Thus, functional composition and biomass will influence scaling of consumer
effects. We specifically expect that where large consumers are predominantly primary consumers, there
will be a larger immediate effect of their removal on ecosystem function than for predator removals (H3).
Furthermore, we predict where large consumer assemblages are dominated by detritivores, they will have
the greatest proportional effect on watershed-level C dynamics because of their role in breaking apart
wood and leaf detritus. Increasing spatial scale from reaches to watersheds will homogenize variability in
consumer effects related to mesohabitats, but at catchment-scales, changes in relative proportions of
mesohabitats and the different hydrology of lower versus higher-order streams will be the primary driver
of how reach-scale experiments scale to the watershed. We will test this hypothesis by comparing
responses to consumer manipulation and composition of the large consumer assemblage as a function of
stream order.
H6. Primary consumer (grazers and detritivores) effects on system metabolism will depend upon
consumer-resource stoichiometric feedbacks, which will vary as a function of position in a watershed and
relative resource (C, N and P) availability. We expect that when nutrient availability is high, primary
producers and microbial heterotrophs can take up resources in their preferred ratios, and the effects of
mineralization by primary consumers will be minimal. According to resource ratio theory (Sterner &
Elser 2002), when nutrient supply is limiting the less limiting resource will be over-consumed. When
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TPI 7065779
primary consumption rates are high, primary producer biomass and microbes on detritus will be low and
available nutrient concentrations will be higher with the ratio of reach-scale metabolism rates to nutrient
uptake rates matching preferred stoichiometry. In the absence of large primary consumers, primary
producer and microbial heterotrophic uptake will increase, nutrients will be drawn down to smaller
concentrations, uptake rates may deviate from preferred stoichiometry and periphyton stoichiometry will
change. We will test this hypothesis by comparing the response of primary producers and detritus (C:N:P
and biomass of periphyton and deposited detritus) to large consumer removal across a watershed as a
function of in-stream N and P concentration and C availability.
Scaling in Watersheds Compared Across Biomes
H7. Ecosystem processing rates and the ability to scale them from reaches to watersheds will differ
among biomes based on abiotic drivers such as climate, hydrology, geomorphology and terrestrial
vegetation. Terrestrial vegetative structure as well as turbidity and nutrients will alter the balance between
autotrophy and heterotrophy, which in turn will differ with biome and geology. Terrestrially-derived
resources are less variable in their quality than autochthonous resources (Cebrian and Lartigue 2004).
Thus, consumer effects in allochthonous-based food webs may be less spatially variable than in stream
networks dominated by autotrophs. In addition, the relative dominance of autochthonous vs.
allochthonous inputs may also result in more or less scalable processes. Specifically, we expect that
watersheds in which dominance shifts downstream from open to closed or closed to open riparian
canopies (with a shift in relative importance of allochthonous C inputs) will have more variable
ecosystem processing rates within the watershed compared to those with with relatively consistent open
or closed canopies along the drainage network (and therefore influenced consistently by autotrophic or
heterotrophic metabolism and N and P uptake).Results from hypotheses H1-4 (both empirical
measurements and results of scaling models) will be used to assess these hypotheses. Hypothesis tests
will be purely statistically based, as we have no way to verify results of a continental-scale model based
on aggregating our watershed models. However, the results could be the first step in creating a
continental-scale model for C dynamics, as has already been produced for N flux rates (e.g., Mulholland
et al. 2008, Wollheim et al. 2008a).
H8. Effects of consumer communities mediated by their diversity, functional composition, and relative
abundance will have influences across biomes via biogeographical patterns. The five study sites range
from simple primary consumer or predator-prey communities to complex assemblages including
numerous omnivores. Measuring responses to consumer exclosures across these different community
types will allow us to evaluate our ability to scale consumer effects across biomes. In some systems, the
entire large consumer group is dominated by predators (e.g., Alaska), whereas various large primary
consumers dominate at the remaining sites (Table 4). We predict that biomes with changing/increasing
consumer diversity/functional composition and/or relative abundance with increasing stream order will
show less predictable effects of consumers at larger spatial scales.
PROPOSED RESEARCH
The general approach of SCALER will be a nested experimental design supplemented by larger scale
observations integrated through modeling. We will make measurements and conduct manipulative
experiments at microhabitat and reach-scales at three locations of increasing stream size in each
watershed in each area of study. We will make measurements and conduct experiments at baseflow, as
this is the only condition where it is realistically possible to perform consumer exclusion experiments and
measure rates of ecosystem functions. We recognize some ecosystem process such as particulate and P
transport are dominated by rarer extreme flow events, however other central processes (e.g., mean
nutrient availability to producers) are more related to more common baseflow conditions (Banner et al.
2009). Furthermore, consumer effects are likely minimal during or immediately following floods or
droughts (Bertrand et. al., 2009, Murdock et. al., 2010). Avoidance of the disturbance by the consumers as
well as strong abiotic controls on autotrophy and microbial heterotrophy should dominate during and
immediately following extreme events. Experiments will be run at each site during times of high
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biological activity and relatively stable discharge. We will identify sites that are minimally impacted by
human activities, but realize there are likely few pristine watersheds (up to 5th order) in most areas.
The measures and manipulative experiments will be paired with broader scale synoptic sampling of N
and P, dissolved organic C, algal biomass and large consumers to determine whole watershed stream
ecological characteristics, and these data will be supplemented with existing data (e.g., USGS stations,
state monitoring data, EPA data, LTER data sets) wherever possible. The larger scale data will allow us to
test predictions made with models parameterized with measurements from microhabitat and reach-scale
manipulations arrayed along a watershed (i.e. we will compare models to phenomenological
observations). We will use data fitting with bootstrapping to test if our models explain patterns better than
random expectations within a watershed.
Once we have developed the models to integrate and scale-up the microhabitat and reach level
processes for individual watersheds, we will use these results to compare watersheds on a continentalscale. Results across our five sites will be used to create statistical models (correlation and structural
equations modeling) to identify factors most likely to explain our modeling and experimental results (i.e.,
hypotheses H1-6).
Experimental Design and Sites- Our overall experimental design at each site will consist of 6
experimental sites at in each separate region (biomes). These individual experimental sites will be in one
or two watersheds (either 3 sites in 2 watersheds or 6 sites in 1 watershed) and 2 sites each will be placed
in 1st, 2nd - 3rd, and > 3rd order streams to potentially include transitions along the stream continuum of
varied riparian influence with greater stream size. The experiments will take place over two years (a set of
1st,2nd- 3rd, and > 3rd order streams in each biome each year). Although it would be preferable to use larger
streams, the logistics and expense of the experiment would be extremely difficult in non-wadeable
streams.
Three reaches will occur at each site 1) an upstream control reach with consumers removed and
returned, 2) a smaller area with representative mesohabitats where small baskets with mechanical
consumer exclusion remove large consumers, and 3) a reach-scale consumer removal reach with an
electric curtain (sheet with electric wire along bottom to keep animals from burrowing under as they are
prone to do when upstream or downstream movement is blocked) and mesh exclosure upstream and
downstream from a reach long enough for metabolism and nutrient uptake effects to be detected (about 30
m, A. Riley personal comm.).
Our sites (Table 1) have been picked with several criteria in mind. 1) The sites occur at a wide variety
of biomes (arctic to tropical), a variety of canopy types (coniferous, deciduous and grassland vegetated
streams), and varied hydrology (spring snowmelt, tropical monsoon, spring rainy intermittent) Tables 2
and 3), 2) The sites are affiliated with a planned STREON site, and 3) the sites are all at or near LTER
sites with a substantial history of long term manipulative or observational research. The ecology of the
streams is already well characterized at most of the sites.
Table 1. Experimental sites, identifier codes, and location of related STREON sites
Name of location
Code
Property owner
Latitude
Longitude
Primary property
access point
Arctic
ARC
BLM
68.646049
-149.407309
Road, Helicopter
Caribou Poker
Creek
Coweeta
CPC
University of Alaska
65.160000
-147.500000
Road, ATV trails
CWT
35.058633
-83.445144
Road
Konza
KNZ
UDSA Forest
Service
Nature Conservancy
39.103807
-96.595539
Road
Rio Guilarte
GPR
USFS
18.188333
-66.767154
Road
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TPI 7065779
Table 2. Characteristics of STREON sites linked to experimental watersheds, representative of conditions
in the proposed experimental watersheds. A = watershed area, Q = discharge, T= mean air temp, regime =
hydrologic regime (sensu Poff 1996). Nutrient concentrations= (µg/L, TN = total nitrogen, TP = total
phosphorus, DIN = dissolved inorganic N, SRP = soluble reactive P, LIM = limiting nutrient.
Site
code
A
(ha)
Q
(L/s)
ARC
1400
2000
CPC
1000
50
CWT
2185
5
KNZ
1070
10
GPR
540
150
Biome
-10
Ann
preci
p
(mm)
300
Snowmelt
200
Taiga
-4
400
Snowmelt
Eastern
decid. forest
Tallgrass
prairie
Subtrop.wet
forest
13
2000
13
835
23
2500
Perennial
runoff
Intermit.
flashy
Perennial
runoff
Tundra
T
(oC)
Regime
TN
TP
DIN
SRP
LIM
6
30
2.5
P
1000
10
600
7
P
105
15
60
2
N+P
700
30
30
<1
N+P
730
12
700
12
N+P
Table 3. Dominant large-bodied organisms at representative sites within the proposed experimental
watersheds. Note the relative increase in consumer assemblage/functional diversity with increasing stream
order. Also note that large consumers have much greater functional diversity at some sites than others
1st-order
2nd - 3rd-order
Insectivores (Arctic
grayling, sculpin [Cottus])
Insectivores (Arctic
grayling)
Insectivores (Arctic grayling,
sculpin [Cottus])
Insectivores (Arctic grayling)
Insectivores (Arctic
grayling)
Insectivores (Arctic
grayling)
CWT
Insectivores (salamanders
[Desmognathas, Eurycea])
Large predatory fish
community (Omnivores,
Insectivores, Grazers)
High
KNZ
Grazers (Campostoma)
Omnivores (crayfish
[Orconectes spp.])
Omnivores (crayfish
[Cambarus bartonii], trout
[Salvelinus fontinalis])
Insectivores (salamanders,
trout, sculpin )
Grazers (Campostoma)
Omnivores (crayfish)
Moderate
-high
GPR
Omnivores (shrimps,
mountain mullet,
[Agonostomus monticola],
crab [Epilobicera])
Grazers (Campostoma)
Insectivores (minnows)
Secondary Predators
(Micropterus)
Omnivores
(native shrimps, fishes)
Site
code
ARC
CPC
Omnivores
(exotic tilapia [Tilapia
melanopleura], native
shrimps, fishes)
4th - 5th-order
Scaledriven
diversity
gradient
Low
Low
Moderate
-high
Experimental Measurements-Wherever possible, the methods for SCALER will mirror those developed
for STREON and generally follow standard methods (Eaton & Franson 2005) or those specific for
streams (Hauer & Lamberti 2006). STREON methods were initially proposed by 50 stream scientists. The
NEON project then consulted with professional scientists to choose the suite of methods, sponsored a
workshop with over 20 experts to work on methods and experimental design, and eventually hired a
postdoctoral aquatic researcher to select the final suite of measurements. This suite was then subjected
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TPI 7065779
another round of internal and external peer review. The methods selected represent consensus on the most
scientifically defensible methods currently available balanced against a reasonable cost for repeated
deployment over long time periods. Measures and methods we will duplicate include using baskets in
recirculation chambers for microscale metabolism and nutrient uptake. And baskets for algal biomass, and
macroinvertebrate community structure. At the reach-scale we will adopt the approaches taken by
STREON for metabolism (two station oxygen method with direct measure of aeration with gas releases
and inert solute releases). Stream chemistry will also be determined with standard methods. Only methods
that are unique to this proposal are discussed in detail here; methods not unique to the proposal that will
follow standard methods include water chemistry (Total N and P, dissolved inorganic nutrients, dissolved
organic C), benthic chlorophyll concentrations, periphyton and leaf litter stoichiometry, inert solute
releases, N and P uptake length measurements, and quantitative sampling of macroinvertebrate and fishes.
Sampling for percentage cover of various substrata (mesohabitats) will be accomplished by taking point
measurements and replicated transects within experimental reaches, standard pebble counts, and noting
areal cover of woody debris or macrophytes. At least 10 transects at each experimental reach will be taken
across the stream with 10 equally spaced point measures at each transect.
Consumer Exclusion and Microscale Substrata Incubation- Microscale exclusion will be accomplished
with 0.5-cm mesh cages, all with upstream closed, and half with downstream closed (exclusion), and half
with downstream open (control with equivalent hydrologic alteration, Murdock et al. 2010). Each
exclosure or control will contain five baskets with native substrata, 4 at the surface, that will be used to
estimate metabolism, N and P uptake, algal biomass, and small invertebrate community structure, and one
basket below the top layer of baskets (for hyporheic respiration). Each exclosure or control will also
contain three litter decomposition leaf packs. Exclosures will be triplicated in each of 2-3 major
mesohabitat types (e.g., riffle or pool, fine sediments vs. cobble) at each site, and will be run for 30 days.
Reach-scale exclusion will be accomplished by blocking the top and bottom of each reach with 0.5 cm
mesh exclosures; a electrified wires run along the bottom will be used to stop organisms from burrowing
under. Prior experience shows that microscale exclosures are not as subject to this activity as mesh
exclosures that stop animal movement across the entire stream, hence electrical exclosures only in reachscale treatments. Large consumers will be removed from control and treatment reaches by electro fishing
with large organisms returned to the control reach (to control for physical effects of consumer removals).
Consumer removals will occur every 10 days in the 30 day experiments. Passes will be made until few
organisms are caught. All organisms will be measured and identified and mesohabitat use will be
recorded to estimate biomass and functional composition in each mesohabitat and reach.
Carbon and Nutrient Cycling, and Hydraulic Connectivity-Processing rates of terrestrial organic matter
from dominant riparian vegetation will be measured at the microhabitat and reach-scales at each site.
Freshly senesced litter (leaves and/or needles) of local dominant vegetation will be collected and air-dried
prior to use. Litter will be weighed (equal mass will be used in all packs), and individual species of litter
will be either 1) bound (non-mesh enclosed) to assess direct effects of large consumers, or 2) placed in
coarse- (5 mm) or 3) fine-mesh (0.48 mm) litter cylinders to measure indirect effects of invertebrates +
microbes and microbes only. Each cylinder (24.5 cm long × 6 cm diameter) will be fitted with mesh (0.48
mm) at both the upstream and downstream ends, sufficient to allow the free movement of water through
the cylinders while preventing the loss of the smallest litter (i.e. conifer needles). In half of the cylinders,
holes (5.0-mm diameter) will be drilled on the upstream sides to allow access by microbes as well as
invertebrates (Kominoski et al., in press). Bound litter and litter cylinders will be deployed inside the
microhabitat- and reach-scale consumer exclusions and controls and will be harvested at the end of the 30
days.
Stoichiometric assessment of N, P and dissolved organic C (DOC) availability via the water column
will be done at each experimental site. The biological availability of DOC will be determined by loss of
DOC measured over 42 day incubations to establish labile and relatively refractory fractions (McDowell
et al. 2006). This procedure uses natural microbial communities at each site to gauge relative carbon
availability. Nutrient limitation, to gauge relative intensity of limitation of autotrophic and heterotrophic
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process as supplied by nutrients in the water column, will be measured using nutrient diffusing substrata
including organic (wood veneer) and inorganic (sintered glass) substrata and analysis for GPP and R as in
Johnson et al (2009).
Nutrient (simultaneous addition of NH4NO3 and KH2PO4) and metabolism at the microscale will be
measured on baskets removed from exclosures and incubated in recirculating chambers as in Murdock et
al (2010), with chambers designs following those recommended in Dodds & Brock (1998). Nutrient
(simultaneous addition of NH4NO3 and KH2PO4) uptake rate at the reach-scale will be measured at the
end of each reach-scale experiment in consumer exclusion and control reaches with N and P releases at
several concentrations above moderately above ambient (Dodds et al. 2002) to allow extrapolation to
ambient rates. Nutrient uptake will be used as an independent measure of reach-scale metabolism
(Webster et al. 2003).
Reach-scale metabolism will be measured using the two station, whole stream diel O2 method
(Marzolf et al. 1994, 1998, Mulholland et al. 2001) including direct measurement of aeration with SF6 and
inert solute releases. Detailed sampling during the inert solute releases will be used to estimate degree of
hydraulic exchange among various microhabitats (e.g., subsurface flow, exchange with backwaters in
pools) using the measurement and modeling methods described in Briggs et al. (2009, 2010).
Consumers- In the micro-scale experiments, one basket from each treatment will be gently removed at
the end of the treatment and preserved for identification of small (< 0.5 cm) animals. At the reach-scale,
small animals will be sampled with appropriate methods (e.g., Surbur sample in flow, core sampler in
pools) with 6 replicate samples taken from each mesohabitat type in control and treatment reaches at the
end of the experiment. Samples analysis for invertebrates and organic matter will occur at Southern
Illinois University.
Synoptic Sampling-In the experimental watersheds, we will sample 50 sites at base flow once each
experimental year for NH4+ NO3-, PO43-, DOC, benthic algal chlorophyll (6 rocks), and standing stocks of
detritus (20 random quadrats). At each of these sites we will also characterize discharge, light inputs to
stream surface, light to bottom, water temperature, stream widths, depths, velocity (averaged over a
reach). This information will be used to test or parameterize various aspects of the network model. The
scale of synoptic sampling will be slightly larger than the scale of experimental manipulation, to link to
the slightly larger scale required for modeling (see the next section). Twenty sites will be characterized
for large consumer community composition (electroshocking). Sample sites will be chosen to broadly
represent spatial variability across the watershed such as variations in slope, vegetation and geology.
Some locations may be chosen based on accessibility.
Synthesis and Modeling-We will implement a multi-scale modeling approach to test our hypotheses. We
will assess how processes translate from one scale to another using both empirical results (measurements
and experiments) and models. Testing whether microhabitat information can be scaled to reaches can be
straightforward using empirical results alone (e.g., Findlay et al. 2010). However, we will also use models
to test the mechanisms, set forth in our hypotheses, by which scaling from microhabitats to reaches
succeeds or fails. In contrast, it is very difficult to directly test if reach information can be scaled to
watersheds using empirical measurements alone, as whole-watershed manipulations of 4th to 5th order
systems and whole-network rate measurements are not practical. Models are needed to understand
network-scale dynamics that influence downstream fluxes, or to quantify whole-network rates of GPP or
R, and the influence of consumers. Earlier efforts at scaling results from reach to network suggest that
scaling based on short term empirical findings at reach-scales requires improved ability to characterize
land to water fluxes, hydrologic exchange among multiple aquatic habitats, and representation of
interacting element cycles (Helton et al. in press). Our research effort is designed to account for these
characteristics in order improve understanding of dynamics across spatial scales.
We will develop reach-scale and network-scale process models that predict the aquatic transformations
of C, N, and P. At each scale, we will take into account the variety of microhabitats with varying degrees
of connectivity, multiple interacting constituents, including C, N, P, and their major forms, consumer
influences, and abiotic drivers (discharge, water temperature, and light). The models will be
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TPI 7065779
parameterized using the empirical findings newly derived from this project, and supplemented by the
literature. Model development will be initiated early in the project using literature values to conduct
sensitivity analyses that can inform field planning (e.g., choice of specific study reaches assessment if
replication levels are adequate). At each scale up to watersheds, we will parameterize our models using
rate measurements made at the finer scale and compare predicted outcomes with those measured at the
scale under consideration. Perturbations introduced experimentally (large consumer removals) will be
used to further test the mechanisms. We will test the resulting models with information collected at
broader scales, both part of this project (Synoptic Sampling section above) and using existing independent
data sets (e.g., state agencies, USGS).
Reach-scale modeling – A reach-scale model will be specified for each study stream reach. At the
reach-scale, we will model metabolism and N and P transformations, and their response to experimental
perturbations. Upstream boundary conditions (discharge and water temperature) and local drivers (e.g.,
light) will be specified from direct measurements. The reach will be partitioned into segments based on
mesohabitats (e.g., pools and riffles), each represented by microhabitat measurements. In each biome,
reach-scale model development will occur in stages adding complexity as needed to identify which level
of complexity is needed to recreate the rates measured at reach-scales. The simplest model is areal scaling
of rates measured in microhabitats (e.g., Findlay et al. 2010). The next level of complexity will account
for transformations that occurred as water travels through sequential segments of the stream reach (e.g.,
Wollheim et al. 1999), which may or may not be dominated by different mesohabitats. The final level will
also account for varying degrees of connectivity among the microhabitats within pools and riffles and
hyporheic flow (e.g., Mulholland & DeAngelis 2000). The basic model will account for GPP, autotroph
R, nitrification , denitrification, heterotroph R, and mineralization through grazers (measured here or
derived from published values). Field sampling will set the boundary and initial conditions (upstream
influx, biomass) in each microhabitat, as well as connectivity among microhabitats (Briggs et al. 2009,
2010). Stoichiometry of autotrophs, detritus and consumers will drive the relative demand for N and P,
and mineralization rates (Dodds et al. 2004), similar to the approach described in previous models (Small
et al. 2009, Ballantyne 2008). Parameters governing process rates will be based on results of microhabitat
measurements (see above).
Network-scale modeling – For each biome, we will apply a river network model that places the
intensively studied reaches in their network context (e.g., Helton et al. in press, Wollheim et al. 2008a,
Fig. 2). The modeled network-scale will be slightly larger than the watershed of the largest study reach,
but synoptic sampling will be taken up to this scale. River network topology for each study network will
be at 15 arc second resolution (~500m, see Data Management Plan), which is sufficient for explicit
representation of the vast majority of headwater streams in a network (Leopold et al. 1995). The
distribution of major drivers of discharge, water temperature, light, loading of carbon and nutrients from
land will be modeled for baseflow conditions, our targeted time period, as described in the Data
Management Plan. A two transient storage zone model that includes a representation of connectivity with
the main channel flow (Stewart, personal com.), will be adapted for this analysis. This model offers better
representation of fast and slow exchange pools in rivers and streams (e.g., backwaters of pools vs.
hyporheic water), and our detailed hydrologic measurements will provide parameters for this approach.
The elemental unit used in the reach scaling model will be embedded in an existing gridded river
network modeling system (FrAMES, Wollheim et al 2008a,b, Wisser et al. 2009, Stewart et al. 2010).
The rates will be parameterized based on the reach-scale empirical measurements. Reach-scale control of
the various processes will be determined, potentially as function of concentration, stream size, light, water
temperature, hyporheic zone size, etc, (e.g., as denitrification was dependant on concentration in
Mulholland et al. 2008). FrAMES will then be used to estimate the distribution and network-aggregated
quantities of major aquatic ecosystem processes including GPP, R, uptake/mineralization, and fluxes of
ammonium, nitrate, phosphate, and DOC (e.g., Wollheim et al. 2008a). Because model parameters will
also be influenced by consumers, we will be able to explore their potential role in determining networkscale processes through modeling experiments that include or exclude consumers in different stream
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TPI 7065779
orders. Model parameters will be based on reach-scale empirical results that span river size (e.g., Briggs
et al. 2010), and heterogeneity incorporated based on variability in these relationships (Stewart 2009,
Stewart et al. in review). The river network implemented within FrAMES defines upstream-downstream
connectivity, heterogeneity caused by river network junctions, and serial processing along flow paths that
is critical for evaluating hypotheses H4-H6 and providing predictions for hypotheses H7 and 8.
Microhabitat connectivity within reaches will be parameterized should the reach-scale modeling identify
this is an important scaling component. Synoptic measurements along longitudinal transects and in
independent major subbasins will be used to evaluate model realism (see Data Management Plan)
Figure 2. Example of elemental model unit that can be embedded in a river network context within
FrAMES (Stewart 2009). The reach model has transient storage (TS) models for in the stream
channel (Surface) and subsurface (Hyporheic). Similar microhabitat models will be aggregated to
form the reach model. We will modify this general approach to account for major ecosystem
processes as described in the main text. This example dissolved inorganic N (DIN) flux through a
river network. Figure at right highlights streams of different stream order. Parameter values can be
varied as a function of stream size, concentration, surveyed canopy cover, habitat connectivity and
other values described in the text.
Testing the hypotheses with models: To test H1-H3, we will compare aggregated mesohabitat models,
with and without consumers, to reach-scale measurements of metabolism. We will determine the extent to
which microhabitat-scale models can be used to predict metabolism at the reach-scale. To test H4, we will
create models of increasing complexity and determine which version better reflects observed fluxes and
synoptically measured process rates throughout the network. For example, we can specify processes as
constant (independent of stream size), as a function of stream size only (gradients), gradients with
heterogeneity, heterogeneity independent of stream size, nutrient controls dependant and independent of
stoichiometry, and consumer dependant rates. Preliminary analyses of this sort have tested the sensitivity
of network-scale processes (N removal) to different transient storage zone parameterization schemes
(Stewart 2009, Stewart et al. 2010). To test H5 and H6, we will explore sensitivity of the network model
results (C, N, P fluxes) with and without large consumers that vary predominate as a function of stream
size (Table 3), accounting for their influence on processing rates based on the exclusion experiments
across watersheds. We will test H6 based on natural differences in nutrients in the watersheds coupled
with modeled responses to consumers. We will test H7 and H8 by evaluating how model results compare
across biomes.
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INTELLECTUAL MERIT
We will provide specific information on how ecological measurements in streams can be scaled to
watersheds; information that is needed to understand both whole-system dynamics as well as how to
manage human impacts on entire watersheds. These experiments will assess how small-scale experiments
relate to whole system dynamics, and if such experiments scale the same way across disparate biomes.
Our experiments will be relevant to ecology as a whole, because few such scaling exercises have taken
place in any environment. Our experiments are particularly relevant given the recent emphasis on
biodiversity and ecosystem function (e.g., Cardinale et al. 2006) and could help understand how plot-level
experiments relate to patterns across larger scales such as landscapes (e.g., the stream network is the
landscape-scale in streams). In a more specific sense, SCALER will explore how we can make results
from NEON (in general) and STREON (in specific) relevant to regional-scales, and if the ability to do so
will vary across biomes.
BROADER IMPACTS
A key aim of this proposal is to allow extrapolation of typical experiments in streams to scales more
relevant for people concerned with utility or protection of the environment (i.e. human management).
National and state management agencies typically make measurements or monitor hydrology, water
quality and biotic integrity across networks. Few of these networks were planned ad nested within
watersheds, with the assumption being made that enough stations can be averaged to represent system
properties (e.g., Banner et al. 2009). Our results will allow improved interpretations of these networks and
planning of networks, and we will invite managers and researchers to open-access workshops on our
results every year in association with the North American Benthological Society meetings (as did the
LINX projects). SCALER participants will give presentations at local and regional meetings that are
attended by state and regional federal agency personnel as well. Communication of results is described in
supplementary materials (Project management section). Our results will also benefit the NEON and
STREON efforts by explicitly addressing how well results from those initiatives can be scaled regionally
and across the continent.
We will mentor six graduate students, five postdoctoral associates, and three junior faculty members.
We will create a cross-university discussion group that meets once a month via teleconference. Details of
mentoring and project structure are given in the required supplementary materials. Finally, all sites will
have undergraduates directly involved in side projects related to this research. All projects will have
access to LTER REU students, site REU students or will apply for REU support associated with this
grant. We also have included support for undergraduate hourly help, and these students will be exposed to
the realities of field and laboratory research.
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TPI 7065779
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advection-dominated systems. American Naturalist 168:358-372.
Ballantyne, F., D. Menge, A. Ostling and P. Hosseini. 2008. Nutrient recycling affects autotroph and
ecosystem stoichiometry. The American Naturalist 171(4):511-523.
Banner, E., A. Stahl and W. Dodds. 2009. Stream discharge and riparian land use influence in-stream
concentrations and loads of phosphorus from Central Plains watersheds. Environmental
Management 44:552-565.
Bertrand K, K. Gido, W. Dodds, J. Murdock and M. Whiles. 2009. Disturbance frequency and functional
identity mediate ecosystem processes in prairie streams. Oikos 118:917.
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Postdoctoral Researcher Mentoring Plan
We plan to have at least five postdoctoral researchers, some fully supported by the project, others
receiving partial support or just travel to collaborate with the group. One will be sited at Kansas State
University, but will coordinate joint experimental materials and travel to each site to help them set the
experiment to ensure that methods are equivalent across all sites. This postdoc will be mentored by
Dodds. The second postdoc, John Kominoski, will be at Coweeta. He will run the experimental
manipulations there and will be involved in synthesis of C dynamics. He will be mentored on-site by
Amy Rosemond. John has already been involved with Wil Wollheim in creating a group to synthesize C
dynamics in rivers and streams across the continent. Wollheim and Doolittle will serve as mentors for
Kominoski on the modeling/ synthesis side of things. The third postdoc will be hired on the modeling/
synthesis group and will be mentored by Wil Wollheim. The fourth postdoc, Ashley Helton, will be
mentored by the senior members of the Synthesis/ Modeling team. Considering the leadership role and
intellectual developments she led in the LINX II project, Dr. Helton probably needs only a modest
amount of mentoring at this point, and the project leaders will probably learn as much from her as she
does from us. The fifth postdoc, Tamara Harms, will be local coordinator at the Caribou Poker Creek site
and will be mentored by Jay Jones.
All postdocs and graduate students will take part in the initial planning workshop, the mid project
planning meetings, and the final synthesis workshop. They will also participate in monthly project
videoconferences. Kominoski, Harms and Helton have already been preparation of this proposal, and all
postdocs will be encouraged to collaborate on proposals with senior faculty members.
We will also encourage the postdoctoral researchers and junior faculty members to become
involved in the STREON planning and advisory capacities. This is important because STREON is a 10
year experiment and the aquatic portion of NEON is planned for 30 years. The junior faculty members
and the postdoctoral and graduate students will be the scientists who benefit from these initiatives, and
give them their true scientific relevance.
We will train future scientists to work in interdisciplinary teams, because that is the foundation
for this project. We will use the LINX model, where graduate students and postdocs are welcome to
participate in the intellectual development of the project from the first meetings through the synthesis
meetings. We will allow students and postdocs to author key synthesis papers, as part of this current
group did in the LINX I and LINX II projects (i.e. a key intellectual component to the existing proposal is
a paper by Helton et al (in press)).
The LINX I and LINX II models were successful based on outcomes; they were central to the
research careers of many currently successful scientists who were graduate students or postdoctoral
students involved in the projects and who are now at the Assistant Professor level or above, including but
not limited to Jen Tank (Professor, Notre Dame), Bob Hall (Professor, University of Wyoming), Steve
Thomas (Associate Professor, University of Nebraska), Maury Valette (Professor, Montana State
University), Melody Bernot (Assistant Professor, Ball State University), Michelle Evans-White (Assistant
Professor, University of Arkansas), Wil Wollheim (Assistant Professor, University of New Hampshire),
Amy Burgin (Assistant Professor, Wright State University). Many of the additional junior project
participants are employed by agencies such as the EPA and the USGS, and are successful, publishing
scientists.
TPI 7065779
Project Management
Each experimental site and the synthesis/ modeling group will have a senior PI and a junior faculty PI or
postdoctoral researcher, to allow for mentoring and training. Table 1 lists participants, project structure,
site affiliations and research expertise. The project will be divided into three conceptual groups (as it was
for creation of this proposal): 1) Carbon/ nutrient flux, 2) Consumers, and 3) Synthesis/ modeling. These
are rough delineations, participants will be involved in more than one group. The Synthesis modeling
group will work to ensure cross-disciplinary communication and coordination of intellectual product. All
participants will be involved in production of final synthesis products (papers).
Table 1. Project participants and respective roles. Those researchers indicated with an asterisk* are at a
rank of Assistant Professor or below and will be mentored by the senior mentor at the site or leader of the
research team they are part of. Research teams are Carbon/Nutrient Dynamics (CARB), Consumers
(CONS) and Synthesis and Modeling (SYNTH)
Site/ group
Researcher
Role
Research
team
CARB
CONS
Expertise
Core/Konza
W. Dodds
K. Gido
Postdoc *
Synthesis modeling
W. Wollheim
M. Whiles
Lead PI
CO- PI CONS leader
Cross-site
coordinator
CO-PI SYNTH
leader
Modeling
collaborator
Modeler
CO-PI-Senior
collaborator
CO-PI
CARB/
SYNTH
SYNTH
Nutrient modeling
SYNTH
SYNTH/
CONS
CONS
Ecological modeling
Stoichiometry in ecosystem
A. Rosemond*
J. Kominoski*
W. Bowden
CO-PI
CO-PI
CO-PI
CONS
CARB
CARB
M. Flinn
J. Jones
CO-PI
PI CARB leader
CONS
CARB
T. Harms*
W. McDowell
CO-PI
CO-PI
CARB
CARB
Nutrient dynamics
Consumer carbon links
Nutrient dynamics and
Hydrodynamics
Invertebrate Ecology
Hydrodynamics and
Nutrients
Nutrient dynamics
Nutrient dynamics
A. Helton*
Postdoc*
F. Ballantyne*
Macroinvert.
ID
Coweta
Arctic
Caribou
Poker Creek
Rio Guilarte
Nutrient dynamics
Fish ecology
CONS or CARB
Nutrient modeling
Invertebrate ecology
The project data management/ intellectual development will be modeled on the LINX I and LINXII
approaches. These two projects were extremely successful and scientifically productive endeavors, and
our group has some shared members with these two groups as well as a similar collaborative nature. The
approach is one of consensus decision making based on a 2-day workshop to establish initial methods,
yearly day-long meetings before or after the North American Benthological Society meeting in May or
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June, and a 2-day workshop at the end of the project for synthesis of data, with follow-up meetings at the
North American Benthological Society. Graduate students and postdoctoral researchers are included in
these meetings as fully contributing members. We will hold regular video conferences (each month)
between all project participants will be made to coordinate logistics, deal with specific problems that
arise, and discuss relevant scientific issues (e.g., new published papers with relevance to our experiment).
We will also create an email listserv to facilitate communication among all the project participants.
We will produce a synthetic overview paper, and focus groups will be organized to publish more
in-depth analyses of specific aspects of the project. Data are not shared outside of the group until the
group has a chance to publish their results (one year after completion of the last experiment). We will use
an organizational approach where some samples will be shipped to central analytical facilities to allow for
similar analyses methods to be applied to all samples. This approach worked well in the LINXII project.
The timing of data products and other activities is indicated in the time line.
We will produce a web site with information on the project, including the proposal and summary
of the results, available to the general public. A password-protected area of the site will be used to
exchange data and manuscripts, papers for discussion on our monthly video conferences, or links to sites
to do so. Doodle will be used to schedule meetings and a shared Google calendar will be used to schedule
all meetings, transfer of experimental equipment, travel of the experimental postdoctoral student to the
sites, and individual site experimental dates. This scheduling approach will allow for an archived record
of all project activities to be stored. Time management production of products will be based on the
following time line.
Time Line: SP= Spring, FA = Fall, SU = Summer, WI = Winter, EXP = experiments, ARC = Arctic,
CPC = Bonanza/Caribou Poker Creek, KNZ = Konza, GPR = Rio Guilarte, Puerto Rico, CWT = Coweta
Task
Planning workshop
Mid project workshop
Monthly video conference
Hire personnel
Create/ update webpage
Exploratory Modeling
Exp ARC
EXP CPC
EXP CWT
EXP KNZ
EXP GPR
Analyze samples
Model based on
experimental results
Write site based papers
Final synthesis workshop
Write Synthesis Papers
S
P
2011
S F W S
U A I P
2012
S F W S
U A I P
2013
S F W S
U A I P
2014
S F W
U A I
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Budget lines that specifically support these management and coordination activities include travel
in the core budget and the collaborative budgets for travel to workshops each year. We also include travel
to the annual participants meeting. We include funds for a data manager at the core site who will facilitate
information transfer. The lead PI will coordinate overall project logistics and is supported for one month
per year for this effort. Individual site PI’s with mentoring responsibilities also have some salary support.
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Data Management and Access- We will hire a half time data manager who will attend all meetings to
develop our protocols. This will ensure proper metadata and full integration of data management with
project development. We will use current standards for data formatting and metadata Ecological Metadata
Language (EML) (http://knb.ecoinformatics.org/software/eml/) unless NEON implements a different
system.
Data will go through a process of submission by each site PI, entered into the system, and re-checked
by the PI for final distribution among the group. This process will be overseen by the data manager at the
core site. We will ultimately use NEON to archive and make our data accessible; the NEON web site
states “NEON, Inc. will endeavor to archive and distribute data generated by individual investigators at
NEON sites, provided that data and meta-data are produced in accordance with NEON formats”.
Preliminary data will be stored by individual site PI’s or archived by synthesis modeling teams,
intermediate data (i.e. data for storage before NEON infrastructure is set to handle our data set) will be
housed on the Konza LTER computers (with appropriate backup procedures). We will also accomplish
document sharing and version control (procedure manuals, group meeting presentations, synthesis
manuscripts) online on a password protected site. We will use the data sharing policies generated by the
LINX group, essentially each site has 6 months after the last experiment is completed to publish their own
data, and then it is permissible for the group to publish the data in cross-site synthesis analysis and
publication. One year following the completion of the last experiment (at the termination of the project)
will be the deadline to make the data available to the entire scientific community.
Model Description and VerificationReach scale modeling- Metabolism and nutrient transformations at the mesohabitat scale will be modeled
using stoichiometrically explicit, mass balance models (sensu Sterner 1990, Elser and Urabe 1999,
Ballantyne et al. 2008). Nutrient fluxes (arrows for C, N, P) in Figure DM1 will be constrained by C:N:P
requirements organisms of different
trophic status. We will use empirical
values of C:N:P stoichiometry (Sterner
and Elser 2002, Falkowski and Raven
2007, Cleveland and Liptzin 2007) for
periphyton, consumers and microbes
combined with observed measurements
(blue text in Figure DM1) to estimate
nutrient fluxes and metabolism in the
presence and absence of consumers of
different types using maximum likelihood
(Mangel and Clark 1997, Bolker 2008).
Parameter estimates will be obtained for
the different mesohabitats using basket
measurements (microhabitat scale).
These microhabitat scale measurements
will then be aggregated to generate
reach scale predictions for metabolism and compared with diel oxygen measurements, allowing us to
directly test how accurately we can scale up microhabitat models to reaches.
Network scale modeling- Reach scale models, the result of aggregating microhabitat models for multiple
mesohabitats, will be used as a basis for scaling metabolism, nutrient dynamics and consumers effects
Figure DM1. Metabolism and nutrient transformations in
mesohabitat scale models. Blue text indicates experimental
measurements that will be used to estimate parameters.
Red text indicates experimental manipulations.
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from reaches to entire river networks. The Framework for Aquatic Modeling of the Earth System
(FrAMES) is a system for modeling horizontal fluxes of point and nonpoint source pollution and water
quality conditions throughout gridded river, lake and reservoir networks, applied over a wide array of
scales from local to continental (Stewart 2009, Stewart et al., 2010, Stewart et al. In Review, Wollheim et
al. 2008a, Wollheim et al. 2008b). The network scale model with habitat connectivity is based on the
Mulholland and DeAngelis (2000) hyporheic process model adapted for two storage zones plus a main
channel (Stewart 2009, Stewart et al. In Review; Figure 2, project desription). The conceptual reach scale
model of Figure 2 is the elemental unit implemented within a river network context. The size of the
element (reach length) is dependent on the river network resolution (see Data Sources). Our existing
model (Stewart et al. in review) focuses on a single constituent, and a single process (denitrification). The
same general approach will be applied for multiple constituents and for multiple processes.
The network model accounts for important drivers of discharge, light, and water temperature. We will
focus on processes occurring at baseflow, so runoff will be specified from USGS gage observations
during the study period (e.g., using the techniques of Fekete et al. (2002)). Discharge heterogeneity will
be determined by the junctions of tributaries and increasing stream order. River width and depth is
calculated based on typical downstream and at-a-site relationships as described in Wisser et al. (2009).
The role of heterogeneity in these parameters can be readily implemented into FrAMES.
Light reaching the stream bottom in FrAMES is based on the model of Julian et al. (Julian et al. 2008)
as a function of incoming solar radiation (based on seasonality), cloud cover, canopy height/width, stream
width, leaf area index , surface water reflectance, and decay coefficient (a function of turbidity and DOC),
and water depth. These controlling factors will be measured during the study in both our intensively
studied sites and synoptic surveys to characterize how they change through the network.
Water temperature is a function of runoff temperature and re-equilibration during routing (Dingman
1974). FrAMES includes a full hydrology/water temperature loading routine that will not be necessary in
this application because of our focus on baseflow conditions. Because groundwater sources dominate
baseflow in these relatively prisitine basins, we will assume runoff has the mean annual water temperature
of the source area. FrAMES uses a temperature exchange model based on Dingman (1974) to determine
water temperature changes during routing. Temperature change is a function of solar radiation, wind
speed, stream length, and stream width. Exchange parameters are empirically derived (Dingman 1974).
The water temperature model has been shown to recreate temporal time series well in a global analysis,
and recreates longitudinal trends along basin mainstems (Stewart et al. 2010).
Loading of C, N and P will be based on runoff and typical concentrations measured synoptically
during baseflow in headwater streams as part of this study. Sites located as far upstream as possible will
be sampled, in order to minimize any aquatic processing of the terrestrial signal. A similar approach was
taken in previous studies (Helton et al. 2010, Wollheim et al. 2008a). Nutrient concentration vs. land use
attributes based on the synoptic surveys will be explored and implemented in the network model if
appropriate (e.g., DOC vs. wetland relationships).
These approaches should provide a reasonably close approximation to spatially distributed conditions
within our relatively unimpacted study systems (i.e. low human dominated influence), and will account
for major differences among the biomes. Major drivers of reach scale process controls can thereby be
implemented throughout the river networks.
Model results will be validated against observed fluxes of C, N and P distributed throughout the
network. Comparison of predicted and observed values will be made for different stream orders (to insure
independence), which will allow us to also evaluate at which scale the model fails, and when our
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knowledge of scaling is inadequate. Observed and predicted C, N, and P concentrations and fluxes will be
compared and the distribution of bias for each will be assessed. Nash Sutcliffe measure of model
efficiency (NSE; Nash and Sutcliffe 1970) and Root Mean Square Errors (RMSE) will be calculated
(Legates and McCabe 1999). To quantify model uncertainty, we will conduct a Monte Carlo analysis that
draws on the parameter distributions we measured in the field. This will allow us to put error bars on our
predictions, accounting for uncertainties in our parameter measurements (Stewart 2009; Stewart et al. in
review).
Data Sources
Existing documented datasets will serve as inputs to the FrAMES network modeling environment (e.g.,
Hydrosheds Gridded River Network for latitudes < 60 degrees (Lehner et al. 2008)). Where published
data layers are not available for gridded hydrologic networks, topographic data and remote imagery will
be used to create the gridded network. Empirical relationships on gradients and heterogeneity of
discharge, stream width, stream depth, riparian vegetation, turbidity, and DOC (influencing light) will be
obtained from the synoptic surveys.
References for Data management plan are found in References section.
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