IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v206y2007i1p1-17.html
   My bibliography  Save this article

Indirect effects and distributed control in ecosystems: Comparative network environ analysis of a seven-compartment model of nitrogen flow in the Neuse River estuary, USA—Time series analysis

Author

Listed:
  • Whipple, Stuart J.
  • Borrett, Stuart R.
  • Patten, Bernard C.
  • Gattie, David K.
  • Schramski, John R.
  • Bata, Seth A.

Abstract

Network environ analysis is motivated by a desire to investigate ecosystems from a holistic perspective. It provides a quantitative measure of the integral (direct plus indirect) relationship between compartments and their within-system environments. In this analysis, each compartment within a system has an incoming interactive network that brings matter to it from the system's boundary inputs, and an outgoing network that takes matter from it to boundary outputs. These are, respectively, input and output environs. Methods described herein are used to compare environs from a time series of ecological networks for which the flow structure remains constant while flow quantities change. Observed differences in environs are analyzed with respect to: (a) differences between the steady-state seasonal ecosystem networks, and (b) which compartment receives the ‘analytical input’ in output-environ analysis. The Neuse River estuary is an ideal subject for this because a time series of 16 seasonal steady-state networks of nitrogen (N) storage and flow were constructed for the period spring 1985 through winter 1989 by Christian and colleagues. We explore two levels of analysis. The first is macro-level analysis of whole environs; total environ throughflow, an index of whole-environ activity, is computed and compared. The second is micro-level analysis which involves the individual intercompartmental flow and boundary output elements of output environs for two selected focal compartments, phytoplankton (x1-PN-Phyto) and nitrate/nitrite (x5-NOx). Our findings indicate that most of the observed variation in environs is being driven by differences in the seasonal networks analyzed. The macro-scale patterns observed for the environs show the same seasonal patterns evident in the whole steady-state network time series. These results support and extend the constancy of temporal indirect effects results reported by Borrett et al. [Borrett, S.R., Whipple, S.J., Patten, B.C., 2006. Indirect effects and distributed control in ecosystems: temporal variation of indirect effects in a seven-compartment model of nitrogen flow in the Neuse River estuary, USA—time series analysis. Ecol. Model. 194, 178–188]. When comparing different environs within the same season, the macro-scale patterns show that the environs are quantitatively very similar; however, when micro-scale patterns are observed, it is found that the environs are indeed unique. Macro-scale similarities are thought to be driven by high cycling indices and high network homogenization. A conclusion from the comparative analysis of the Neuse estuary networks is that these environs display a weak autonomy. We hypothesize that the individuality of the environs is suppressed by two characteristics of the Neuse estuary networks: they are strongly connected graphs, and they contain many linked autocatalytic cycles. Two aspects of our results provide a means to link ecological measures important to the Neuse River estuary and environ analysis. Environ throughflow (TE¯T) is driven by boundary inputs, which is equivalent to the ecological measure of N loading, with dominance of total TE¯T by DON (x4-DON), nitrate–nitrite (x5-NOx), and ammonium (x6-NH4), which receive the largest boundary inputs. Previous network analysis demonstrated that, on average, one-half of the nitrogen needs of phytoplankton are met by nitrogen that once resided in the sediments. Phytoplankton (x1-PN-Phyto) and sediment (x3-Sed), and ammonium (x6-NH4) had distinctly different TE¯T pattern than the steady-state TST pattern. The key ecological driver of the sediment source for N needs of phytoplankton is the release of N from sediments in the warmer months as ammonium which is taken up by phytoplankton; low TE¯T during winter for phytoplankton (x1-PN-Phyto), sediment (x3-Sed), and ammonium (x6-NH4) reflects the low levels of ammonium release and low growth rates; high TE¯T during summer corresponds to greater release of ammonium (x6-NH4) from the sediments (x3-Sed) and greater uptake of ammonium (x6-NH4) by phytoplankton (x1-PN-Phyto).

Suggested Citation

  • Whipple, Stuart J. & Borrett, Stuart R. & Patten, Bernard C. & Gattie, David K. & Schramski, John R. & Bata, Seth A., 2007. "Indirect effects and distributed control in ecosystems: Comparative network environ analysis of a seven-compartment model of nitrogen flow in the Neuse River estuary, USA—Time series analysis," Ecological Modelling, Elsevier, vol. 206(1), pages 1-17.
  • Handle: RePEc:eee:ecomod:v:206:y:2007:i:1:p:1-17
    DOI: 10.1016/j.ecolmodel.2007.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380007001202
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2007.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Schramski, J.R. & Gattie, D.K. & Patten, B.C. & Borrett, S.R. & Fath, B.D. & Whipple, S.J., 2007. "Indirect effects and distributed control in ecosystems: Distributed control in the environ networks of a seven-compartment model of nitrogen flow in the Neuse River Estuary, USA—Time series analysis," Ecological Modelling, Elsevier, vol. 206(1), pages 18-30.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Borrett, Stuart R. & Sheble, Laura & Moody, James & Anway, Evan C., 2018. "Bibliometric review of ecological network analysis: 2010–2016," Ecological Modelling, Elsevier, vol. 382(C), pages 63-82.
    2. Yang, Jin & Chen, Bin, 2016. "Energy–water nexus of wind power generation systems," Applied Energy, Elsevier, vol. 169(C), pages 1-13.
    3. Zhai, Mengyu & Huang, Guohe & Liu, Lirong & Zheng, Boyue & Guan, Yuru, 2020. "Inter-regional carbon flows embodied in electricity transmission: network simulation for energy-carbon nexus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    4. Schramski, J.R. & Patten, B.C. & Kazanci, C. & Gattie, D.K. & Kellam, N.N., 2009. "The Reynolds transport theorem: Application to ecological compartment modeling and case study of ecosystem energetics," Ecological Modelling, Elsevier, vol. 220(22), pages 3225-3232.
    5. Borrett, S.R. & Freeze, M.A. & Salas, A.K., 2011. "Equivalence of the realized input and output oriented indirect effects metrics in Ecological Network Analysis," Ecological Modelling, Elsevier, vol. 222(13), pages 2142-2148.
    6. Hines, David E. & Borrett, Stuart R., 2014. "A comparison of network, neighborhood, and node levels of analyses in two models of nitrogen cycling in the Cape Fear River Estuary," Ecological Modelling, Elsevier, vol. 293(C), pages 210-220.
    7. Yang, Zhifeng & Mao, Xufeng, 2011. "Wetland system network analysis for environmental flow allocations in the Baiyangdian Basin, China," Ecological Modelling, Elsevier, vol. 222(20), pages 3785-3794.
    8. Borrett, S.R. & Freeze, M.A., 2011. "Reconnecting environs to their environment," Ecological Modelling, Elsevier, vol. 222(14), pages 2393-2403.
    9. Zhang, Yan & Zheng, Hongmei & Fath, Brian D., 2015. "Ecological network analysis of an industrial symbiosis system: A case study of the Shandong Lubei eco-industrial park," Ecological Modelling, Elsevier, vol. 306(C), pages 174-184.
    10. Coskun, Huseyin, 2018. "Static Ecological System Measures," OSF Preprints g4xzt, Center for Open Science.
    11. Duan, Cuncun & Chen, Bin & Feng, Kuishuang & Liu, Zhu & Hayat, Tasawar & Alsaedi, Ahmed & Ahmad, Bashir, 2018. "Interregional carbon flows of China," Applied Energy, Elsevier, vol. 227(C), pages 342-352.
    12. Zhang, Yan & Yang, Zhifeng & Yu, Xiangyi, 2009. "Ecological network and emergy analysis of urban metabolic systems: Model development, and a case study of four Chinese cities," Ecological Modelling, Elsevier, vol. 220(11), pages 1431-1442.
    13. Bata, Seth A. & Borrett, Stuart R. & Patten, Bernard C. & Whipple, Stuart J. & Schramski, John R. & Gattie, David K., 2007. "Equivalence of throughflow- and storage-based environs," Ecological Modelling, Elsevier, vol. 206(3), pages 400-406.
    14. Li, Y. & Yang, Z.F., 2011. "Quantifying the sustainability of water use systems: Calculating the balance between network efficiency and resilience," Ecological Modelling, Elsevier, vol. 222(10), pages 1771-1780.
    15. Duan, Cuncun & Chen, Bin, 2017. "Energy–water nexus of international energy trade of China," Applied Energy, Elsevier, vol. 194(C), pages 725-734.
    16. Zhang, Xiaolin & Zhang, Yan & Wang, Yifan & Fath, Brian D., 2021. "Research progress and hotspot analysis for reactive nitrogen flows in macroscopic systems based on a CiteSpace analysis," Ecological Modelling, Elsevier, vol. 443(C).
    17. Lu, Jingzhao & Lu, Hongwei & Wang, Weipeng & Feng, SanSan & Lei, Kaiwen, 2021. "Ecological risk assessment of heavy metal contamination of mining area soil based on land type changes: An information network environ analysis," Ecological Modelling, Elsevier, vol. 455(C).
    18. Whipple, Stuart J. & Patten, Bernard C. & Borrett, Stuart R., 2014. "Indirect effects and distributed control in ecosystems," Ecological Modelling, Elsevier, vol. 293(C), pages 161-186.
    19. Jordán, Ferenc & Okey, Thomas A. & Bauer, Barbara & Libralato, Simone, 2008. "Identifying important species: Linking structure and function in ecological networks," Ecological Modelling, Elsevier, vol. 216(1), pages 75-80.
    20. Yang, Siyuan & Fath, Brian & Chen, Bin, 2016. "Ecological network analysis of embodied particulate matter 2.5 – A case study of Beijing," Applied Energy, Elsevier, vol. 184(C), pages 882-888.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:206:y:2007:i:1:p:1-17. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.