ArticleProceedings. Mathematical, physical, and engineering sciences2016
Assessing the robustness of spatial pattern sequences in a dryland vegetation model.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Numerical bifurcation analysis of turing and symmetry broken patterns of a PDE model for vegetation dynamics.Journal of mathematical biology · 2026Article
- Bounded risk disposition explains Turing patterns and tipping points during spatial contagions.Royal Society open science · 2024Article
- Pattern blending enriches the diversity of animal colorations.Science advances · 2020Article
- An integrodifference model for vegetation patterns in semi-arid environments with seasonality.Journal of mathematical biology · 2020Article
- Population mobility induced phase separation in SIS epidemic and social dynamics.Scientific reports · 2020Article
- Stabilizing a homoclinic stripe.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2018Article
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4 authors.
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Abstract
A particular sequence of patterns, 'gaps→labyrinth→spots', occurs with decreasing precipitation in previously reported numerical simulations of partial differential equation dryland vegetation models. These observations have led to the suggestion that this sequence of patterns can serve as an early indicator of desertification in some ecosystems. Because parameter values in the vegetation models can take on a range of plausible values, it is important to investigate whether the pattern sequence prediction is robust to variation. For a particular model, we find that a quantity calculated via bifurcation-theoretic analysis appears to serve as a proxy for the pattern sequences that occur in numerical simulations across a range of parameter values. We find in further analysis that the quantity takes on values consistent with the standard sequence in an ecologically relevant limit of the model parameter values. This suggests that the standard sequence is a robust prediction of the model, and we conclude by proposing a methodology for assessing the robustness of the standard sequence in other models and formulations.
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