The Problem of Ecological Scaling in Spatially Complex, Nonequilibrium Ecological Systems

  • Samuel A. Cushman
  • Jeremy Littell
  • Kevin McGarigal


In the previous chapter we reviewed the challenges posed by spatial complexity and temporal disequilibrium to efforts to understand and predict the structure and dynamics of ecological systems. The central theme was that spatial variability in the environment and population processes fundamentally alters the interactions between species and their environments, largely invalidating the predictions of ideal models of community structure and population processes. In addition, we argued that temporal variability enormously amplifies the challenge of prediction, by altering and reversing species— species and species—environment relationships over time. Typically these fluctuations do not occur globally across space in synchrony; rather change in time is spatially dependent on location in the environment, and thus interacts in highly complex and nonlinear ways with spatial heterogeneity in influencing ecological processes. Given these challenges, we proposed focusing on the interactions between species and their immediate environments in the context of current and past conditions. However, given critical sensitivity of ecological processes to spatial and temporal factors, it is also necessary to consider their action within the context of a broader landscape of conditions, constraints and drivers. This therefore seems a catch-22, with fine-scale understanding of process required at the scale where ecological entities (e.g. organisms) directly interact with each other and their environments, and also integration of these fine-scale processes across complex and temporally varying broad-scale environments. This challenge fundamentally relates to scale and scaling ecological processes.


Coarse Scale Spatially Complex Aggregate Property Gradient Space Landsc Ecol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2010

Authors and Affiliations

  • Samuel A. Cushman
    • Jeremy Littell
      • 1
    • Kevin McGarigal
      • 2
    1. 1.Research Scientist, CSES Climate Impacts GroupUniversity of WashingtonSeattleUSA
    2. 2.Department of Natural Resources ConservationUniversity of MassachusettsAmherstUSA

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