Modeling

The goal of the Arctic LTER is to develop a predictive understanding of the responses of arctic landscapes to disturbance and climate change, using the concepts of biogeochemical and community openness for arctic terrestrial and freshwater ecosystems and the landscape connectivity among these ecosystems. Our approach includes modeling to first compare and contrast among ecosystems with very different physical and biological properties. To do this comparison we selected general model structures (e.g., Jacobian matrix models) and similar model structures (e.g., MEL model and a developing aquatic version) to compare and contrast key aspects of openness and connectivity and their effects on responses to disturbance and climate change. The second objective is to model characteristics unique to the individual components of the landscape (e.g., hillslope hydrology, energetics and size dimorphism in fish) and address these unique aspects in their response to disturbance and climate change. Our long-term objective is to develop a comprehensive understanding of the biogeochemistry and the community ecology in a diverse arctic landscape, and to predict how these ecosystems will change in the face of climate warming and the resultant more frequent and punctuated disturbances (fire and thermokarst).

Model Descriptions:

  • Terrestrial biogeochemical model: The MEL model is the main tool of the Arctic LTER for synthesizing and predicting changes in terrestrial biogeochemistry and assessing the consequences of biogeochemical openness. The model couples ecosystem C, N, P, and water cycles and operates at a plot scale on a daily time step (Rastetter et al. 2013, Pearce et al. 2015).

  • Aquatic biogeochemical model: We are applying models that integrate stream uptake and metabolism based on concepts of nutrient uptake velocity and nutrient spiraling (Wollheim et al. 2006, 2008, Peterson et al. 2013, Hale et al. 2014, Peterson &Ver Hoef 2014) to arctic streams to examine openness and connectivity.
  • Hydro-biogeochemical connectivity model: A major goal of the proposal is to examine the biogeochemical connectivity between terrestrial and aquatic ecosystems. This landscape-level connectivity is driven largely by hydrology. We are working with Bayani Cardenas (U. Texas) and Beth Neilson (Utah State) to develop a hillslope-riparian hydro chemical model that can link the terrestrial biogeochemical responses predicted by the MEL model to aquatic ecosystems (e.g., Merck et al. 2011, Merck & Neilson 2012).
  • General community model: To compare and contrast plant and animal communities in terrestrial, stream, and lake ecosystems, we will analyze Jacobian matrix models of food webs (e.g., Rooney et al. 2006); we have developed and tested these models with Toolik data (Ch.11 in Moore & de Ruiter 2012, Sistla et al. 2013). This approach melds empirical information into an abstract model structure that quantifies food web connections, feedback loops, and the effects of immigration and emigration in a way that can be compared across multiple, very different ecosystems (e.g., Rooney et al.2006).

REFERENCES

Hale, I. L., W. M. Wollheim, R. G. Smith, H. Asbjornsen, A. F. Brito, K. Broders, A. S. Grandy, and R. Rowe. 2014. A Scale-Explicit Framework for Conceptualizing the Environmental Impacts of Agricultural Land Use Changes. Sustainability 6:8432-8451.

Merck, M. F. & Neilson, B. T. Modelling in-pool temperature variability in a beaded arctic stream. Hydrological Processes 26:3921-3933, doi:10.1002/Hyp.8419 (2012).

Merck, M. F., B. T. Neilson, R. M. Cory, and G. W. Kling. 2011. Variability of in-stream and riparian storage in a beaded arctic stream. Hydrological Processes DOI:10.1002/hyp.8323.

Pearce, A.R., E.B. Rastetter, W.B. Bowden, M.C. Mack, Y. Jiang, and B.L. Kwiatkowski. 2015. Recovery of arctic tundra from thermal erosion disturbance is constrained by nutrient accumulation: a modeling analysis. Ecological Applications 25:1271-1289.

Peterson, E. E., and J. M. Ver Hoef. 2014. STARS: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journal of Statistical Software 56:1-17.

Peterson, E. E., J. M. Ver Hoef, D. J. Isaak, J. A. Falke, M. J. Fortin, C. E. Jordan, K. McNyset, P. Monestiez, A. S. Ruesch, A. Sengupta, N. Som, E. A. Steel, D. M. Theobald, C. E. Torgersen, and S. J. Wenger. 2013. Modelling dendritic ecological networks in space: an integrated network perspective. Ecology Letters 16:707-719.

Rastetter, E.B., R.D. Yanai, R.Q. Thomas, M.A. Vadeboncoeur, T.J. Fahey, M.C. Fisk, B.L. Kwiatkowski, and S.P. Hamburg. 2013. Recovery from Disturbance Requires Resynchronization of Ecosystem Nutrient Cycles. Ecological Applications 23:621-642.

Rooney, N., K.McCann, G.Gellner, and J.C.Moore. 2006. Structural asymmetry and the stability of diverse food webs. Nature 442: 265-269.

Sistla, S.A., J.C.Moore, R.T.Simpson, L.Gough, G.R.Shaver, and J.P.Schimel. 2013. Long-term warming restructures Arctic tundra without changing net soil carbon storage Nature 497: 615-618

Wollheim, W. M., C. J. Voosmarty, B. J. Peterson, S. P. Seitzinger, and C. S. Hopkinson. 2006. Relationship between river size and nutrient removal. Geophysical Research Letters 33.

Wollheim, W. M., C. J.Vorosmarty, A. F. Bouwman, P. Green, J. Harrison, E. Linder, B. J. Peterson, S. P. Seitzinger, and J. P. M. Syvitski. 2008. Global N removal by freshwater aquatic systems using a spatially distributed, within-basin approach. Global Biogeochemical Cycles 22.

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