This process is experimental and the keywords may be updated as the learning algorithm improves. This is the … We show how concepts of stream metabolism developed at the scale of individual river reaches allow for initial predictions of the primary productivity of entire river networks. In our simulated networks, streambed surface area accumulates faster than drainage area. In contrast, larger watersheds were more productive on an average areal basis in the Productive rivers scenario, resulting in a steeper slope between annual, network‐scale GPP and drainage area. For example, given the importance of light at the scale of individual stream reaches (Bott et al. Simple scaling of the observed distribution of GPP across stream sizes yielded a wide range of potential river‐network productivity regimes. Here, we simulated river‐network GPP by applying the empirical GPP time series to individual stream reaches within an OCN. However, more data are needed to better understand the changes in both sediment and water quality in the Harlem River, both as the tide cycles and during precipitation events. 2019), suggesting the existence of quantifiably distinct river functional types driven by common sets of underlying controls. Channel width best predicted regime classification among streams in the empirical data set (Savoy 2019), and so we used three approaches to assign individual stream reaches to a given GPP regime based on width: (1) Productive rivers, where smaller streams (defined as width < 9 m) were assigned the “spring peak” regime and larger streams (width > 9 m) were assigned the “summer peak” regime; (2) Unproductive rivers, where larger streams (width > 9 m) exhibit the “aseasonal” productivity regime due to factors such as high turbidity or frequent scouring floods that limit light availability and algal biomass accrual; and (3) Stochastic assignment, where the probability of being assigned to any of the four reach‐scale productivity regimes varied with river width. USGS scientist Brent Knights conducting fish sampling on the Upper Mississippi River. For this study, we generated one OCN (512 × 512 pixels) following the procedure of Rinaldo et al. Figure 4. Our modeled productivity regimes indicate how the biological properties of river networks respond to changes in network size. shallow and deep-water habitats in the upper Hudson River estuary (river miles 110-152) 17 . To explore how the variation in primary production within and among individual stream reaches can give rise to emergent river network productivity regimes, we scaled annual stream productivity regimes using simulated river networks. Specifically, in this “vernal window” scenario, we modified the “spring peak” regime so that GPP begins to increase 7 d and 14 d earlier, respectively, although we assumed that peak GPP remains the same (Supporting Information Fig. BLS state-level measures of output for the private nonfarm sector are created We assumed that pixels within the OCN form an active stream channel when their drainage area, a proxy for threshold‐limited fluvial erosion, exceeds a minimum threshold of 50 pixels, or 0.5 km2. Implicit in the “spring peak” regime is that light constrains GPP for much of the year due to shading by the terrestrial canopy. Dam construction on river systems worldwide has altered hydraulic retention times, physical habitats and nutrient processing dynamics. Habitat areas per length of shoreline were estimated so that we could approximate relative amounts of biomass and production for a stretch of river. Develop predictive models useful to guide river management and river restoration and to support decisions pertaining to management of basin land use that impinges on river water quality and ecosystem health. Working off-campus? Relative proportion of natural and engineered shoreline on the Hudson River between the Tappan Zee Bridge and Troy, NY 18 . No data point selected. Factors mediating GPP are thus implicitly represented in our analysis through the reach‐scale regime classification assignments. Anthropogenic disturbances such as nutrient loading, invasive species introductions and habitat alterations have profoundly impacted native food web dynamics and aquatic ecosystem productivity. 2007). Snake River Chinook Salmon. _ Page 37 56 58 60. We used optimal channel networks (OCNs) to analyze emergent patterns of network‐scale primary productivity. The scaling transition from stream reaches to river networks thus requires quantifying and conceptualizing the heterogeneity, connectivity, and asynchrony (sensu McCluney et al. The OCNs were represented as directed networks using the igraph package (Csardi and Nepusz 2006) in R (R Core Team 2018). We therefore expect that differences in river network structure may further expand the variation around the GPP scaling relationships we present here. These modeled scenarios therefore do not capture the local heterogeneity in light and GPP that is expected along a river continuum due to local variation in canopy cover, topography, and geomorphology (Julian et al. Within this network, we sampled replicate subcatchments around four values of upstream area (40, 160, 450, and 2600 km2; Supporting Information Fig. The limiting factors that govern what organisms can live in lotic ecosystems include current, light intensity, temperature, pH , dissolved oxygen, salinity, and nutrient availabilityvariables routinely measured by limnologists to develop a profile of the environment. nitude of phytoplankton productivity rel- 1 This research was performed as part of the Ma- rine Ecosystem Analysis (MESA) Project and was supported by NOAA contracts 03-4-043-310, 04-5- 022-22, and 04-7-022-44003 and DOE contract EY 76-S-02-2185B. However, other factors such as network shape and geomorphic structure may shift the accumulation of benthic surface area and, by extension, primary production. All rivers share these same constraints on productivity, but their relative importance differs among rivers as temporal fluctuations in various physical, chemical, and biological drivers act individually or in concert to determine the productivity regime for a river, that is, its characteristic annual pattern in GPP (Bernhardt et al. Seasonal patterns in GPP may also vary with network position; large rivers with open canopies exhibit summer peaks in productivity (Uehlinger 2006), whereas in small, forested streams, terrestrial phenology and frequent scouring floods limit GPP to a relatively narrow temporal window (Roberts et al. In the Unproductive rivers scenario, the spring‐time GPP peak was driven by metabolic activity in small streams (Fig. The shape and magnitude of the network‐scale productivity regime changes as watershed size increases and cumulative, river‐network GPP captures the metabolic activity of larger river reaches. Develop research and technology tools to provide the scientific basis for developing adaptive management strategies and evaluating their effectiveness for restoration efforts to sustain aquatic resources. River indicate concentrations of copper, zinc, and lead are above sediment-quality thresholds set by the New York State Department of Environmental Conservation. Productivity in larger river segments became more influential on the magnitude and timing of network‐scale GPP as watershed size increased, although small streams with relatively low productivity contributed a substantial proportion of annual, network GPP due to their large collective surface area. restoration actions 23 . Daily and annual rates of GPP generally do increase with river size (Bott et al. Despite their relatively low productivity on an individual basis, collectively, small streams constitute a large proportion of benthic surface area in river networks; stream segments draining 100 km2 or less represent 56% of benthic surface in our 2621 km2 network (Fig. The composite indicator is then used to test a well known economic theory, the Balassa-Samuelson effect. For describing rivers with Large floodplains, for example, given the importance of light the. 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