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Exploring Aggregation in Space and Time

  • Monica G. Turner
  • Robert V. O’Neill

Summary

Population and ecosystem processes are heterogeneous in both time and space, and every ecological study requires some level of aggregation or abstraction. Aggregating organism or environmental dynamics is challenging because the processes occur at a variety of spatial and temporal scales, and the scale-dependent effects of aggregation are not well understood. We used a spatially explicit individual-based simulation model of winter foraging and survival of free-ranging ungulates in northern Yellowstone National Park to explore effects of aggregation in space, in time, and of individual animals on model predictions. Aggregation in space was examined by (1) varying the heterogeneity represented in forage abundance across the landscape and (2) eliminating spatial heterogeneity in the accumulation of snow. Results suggest that any aggregation that averages the broad-scale patterns of forage biomass availability underestimates ungulate survival. Aggregation in time was examined by varying the temporal grain used to simulate snow accumulation through the winter. Ungulate survival was not sensitive to this temporal grain, probably because the response remained linear within the range explored. Aggregation of individuals was done by varying the number of individuals contained within ungulate groups assumed to contain identical individuals. Aggregating across individuals was reasonable for small group sizes but led to substantial underestimates of survival for large group sizes. The effect of aggregation on an ecosystem or population parameter is a function of the question asked and a specified spatial and temporal scale. Even successful aggregation of processes will be reliable only if dynamic thresholds are not crossed, if keystone species are not eliminated, and if feedback loops remain intact.

Keywords

Group Size Grid Cell Temporal Scale Spatial Heterogeneity Snow Depth 
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 Science+Business Media Dordrecht 1995

Authors and Affiliations

  • Monica G. Turner
  • Robert V. O’Neill

There are no affiliations available

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