Patterns of nitrate loading and removal in stream networks: An interbiome comparison

A.M. Helton¹, G.C. Poole^1,2, J.L. Meyer¹, C. Arango³, L.R. Ashkenas⁴, C.N. Dahm⁵, W.K. Dodds⁶, S.V. Gregory⁴, N.B. Grimm⁷, R.O. Hall⁸, S.H. Hamilton⁹, S.L. Johnson¹⁰, W.H. McDowell¹¹, P.J. Mulholland¹², B.J. Peterson¹³, J.L. Tank³, H.M. Valett¹⁴, and J.R. Webster¹⁴.¹Institute of Ecology, University of Georgia, Athens GA 30602, ²Eco-Metrics, Inc., Tucker, GA 30084, ³Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, ⁴Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97331, ⁵Department of Biology, University of New Mexico, Albuquerque, NM 87131, ⁶Divison of Biology, Kansas State University, Manhattan, KS 66506, ⁷School of Life Sciences, Arizona State University, Tempe, AZ 85287, ⁸Department of Zoology and Physiology, University of Wyoming, Laramie, WY 82071, ⁹Department of Zoology, Michigan State University, Hickory Corners, MI 49060, ¹⁰United States Forest Service Pacific Northwest Research Station, Corvallis, OR 97331, ¹¹Department of Natural Resources, University of New Hampshire, Durham, NH 03824, ¹²Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, ¹³Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA 02543, ¹⁴Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061

Patterns of nitrogen delivery to and uptake within stream networks are important determinants of the fate and transport of nitrogen. As part of the second Lotic Intersite Nitrogen eXperiment (LINX-II), we developed a stream network model of in-stream nitrate dynamics to facilitate inter-site comparisons across seven biomes in the United States and Puerto Rico. The model simulates expected patterns of nitrate concentration ([NO₃]) within stream networks based on catchment land cover, in-stream uptake, network structure, and stream channel morphology. In six of the seven networks, observed [NO₃] variation within the network is substantially higher than modeled variation. Although land-cover patterns account for some variation in loading rates, additional unexplained spatial heterogeneity in loading rates is a likely explanation for the unexpectedly high variation. By comparing modeled and observed patterns of [NO₃] within each network, we can characterize the distribution of reach-scale loading rates for each network and make comparisons across biomes. Although other researchers have found correlations between cover type and annual load, our results suggest that these correlations do not generally exist at finer spatial and temporal scales. Thus, the conceptual model of non-point source loading as ‘diffuse and consistent, varying primarily with cover type’ may be overly simplistic.