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Pair statistics clarify percolation properties of spatially explicit simulations
Jeffrey D Achter1, Colleen T Webb
1Department of Mathematics, Colorado State University, Fort Collins, 80523, USA. j.achter@colostate.edu
Theoretical Population Biology
|October 11, 2005
Summary
Population dispersal strategies, mixing short and long distances, critically influence spatial patterns. This study reveals how dispersal connects populations by controlling occupied neighboring sites, explaining clustering near the percolation threshold.
Area of Science:
- Ecology
- Population Dynamics
- Spatial Statistics
Background:
- Dispersal is a key factor shaping population spatial structure.
- Understanding the precise mechanisms of dispersal is crucial for ecological studies.
- Mixed dispersal strategies (short and long-distance) present complex spatial patterning.
Purpose of the Study:
- To investigate the precise mechanism by which mixed dispersal strategies influence population spatial patterning.
- To determine how dispersal modulates population connectivity.
- To identify the best metric for explaining population clustering.
Main Methods:
- Utilizing pair approximation techniques.
- Applying percolation theory to model population spread.
- Analyzing the proportion of simultaneously occupied neighboring sites.
Main Results:
- Dispersal controls population connectivity by modulating the proportion of occupied neighboring sites.
- The pair statistic (proportion of occupied neighbors) best explains population clustering near the percolation threshold.
- This metric is more effective than previously proposed metrics.
Conclusions:
- The proportion of occupied neighboring sites is a critical factor in population spatial patterning.
- Mixed dispersal strategies significantly impact population connectivity and clustering.
- The findings offer insights into general circumstances governing spatial structure.