PS 65-88
Characterizing patchiness and scale of interactions in semi-arid vegetation patterns

Friday, August 15, 2014
Exhibit Hall, Sacramento Convention Center
Sumithra Sankaran, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
Sabiha Sachdeva, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
Ashwin Viswanathan, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
Vishwesha Guttal, Centre for Ecological sciences, Indian Institute of Science, Bangalore, India
Background/Question/Methods

Semi-arid ecosystems can exhibit striking vegetation patterns, which may have no characteristic size of patchiness. Elucidating local scale processes that generate these macroscopic patterns is of fundamental ecological importance. In addition, they may provide insights and tools to forecast the future dynamics of these highly vulnerable ecosystems. Recent studies have shown that, spatial patterns of semi-arid systems with no characteristic length scales can be explained by local facilitative interactions with global competition for resources (Kéfi S et. al., (2006). Nature 449(7159):213-217). However, no alternative models that could potentially explain similar patterns have been investigated. Here, our aim is to study whether such patterns could emerge from only local facilitative and local competitive processes and if so, how the involved processes could then be discerned.

Results/Conclusions

In this work, we compare and contrast the predictions of two spatially explicit models of vegetation patterns. In addition to the previous model, we analyse a model from the literature of non-equilibrium statistical physics in which both, facilitation and competition, occur at local scales (Lübeck S, (2006). Journal of Statistical Physics 123:193). We characterize spatial patterns by using the distribution of patch sizes and power spectrum analyses. We find that both models predict a power-law distribution of patch sizes. However, the two models differ in their prediction of characteristics of the power spectrum. Therefore, our results suggest that an analysis of spatial power spectra can be used to distinguish competitive processes occurring at different scales.