Resource concentration in the vicinity of plants is observed in drylands as a result of various mechanisms, developed to cope with water scarcity. This often leads to self-organized spatial patterns that enhance drylands’ ecosystem resilience to environmental changes. Numerous vegetation dynamics models have been developed over the past few decades to study this pattern formation. Generally, they represent plant spatial spread as a diffusive process, which captures well species that reproduce via seed dispersal or through clonal growth following the “phalanx” strategy, characterized by slow, compact expansion. However, many dryland species exhibit “guerrilla” clonal growth, characterized by rapid, directional exploration of favourable areas, which is poorly captured by diffusion. To address this limitation, we introduce a novel term for lateral biomass expansion into a classical dryland model. We found conditions suitable for periodic patterns to emerge with a Turing analysis, aiming to test the stability of a uniform solution against uniform and periodic perturbations. However, numerically, these patterns could not be observed by perturbing the homogeneous equilibria with small perturbations, possibly because of the non-linearity of the guerrilla expansion term. Instead, remarkably, the model produced amorphous, far-from-equilibrium patterns when integrated along a rainfall precipitation gradient. These findings highlight the need to represent the diversity of clonal plant strategies in dryland ecosystem models, as they play an important role in pattern formation and, thus, may influence ecosystem resilience and responses to global environmental change. Furthermore, our results highlight the need to move beyond linear analyses when studying systems with nonlinear dispersal dynamics.

Guerrilla clonal growth strategy leads to amorphous pattern formation in a drylands vegetation model / Davin, Andrea; Von Hardenberg, Jost; Berghuis, Paul M. J.; Mayor, Ángeles G.; Magazzino, Enrico; Rietkerk, Max; Veerman, Frits; Baudena, Mara. - In: ECOLOGICAL MODELLING. - ISSN 0304-3800. - 515:(2026). [10.1016/j.ecolmodel.2026.111510]

Guerrilla clonal growth strategy leads to amorphous pattern formation in a drylands vegetation model

von Hardenberg, Jost;
2026

Abstract

Resource concentration in the vicinity of plants is observed in drylands as a result of various mechanisms, developed to cope with water scarcity. This often leads to self-organized spatial patterns that enhance drylands’ ecosystem resilience to environmental changes. Numerous vegetation dynamics models have been developed over the past few decades to study this pattern formation. Generally, they represent plant spatial spread as a diffusive process, which captures well species that reproduce via seed dispersal or through clonal growth following the “phalanx” strategy, characterized by slow, compact expansion. However, many dryland species exhibit “guerrilla” clonal growth, characterized by rapid, directional exploration of favourable areas, which is poorly captured by diffusion. To address this limitation, we introduce a novel term for lateral biomass expansion into a classical dryland model. We found conditions suitable for periodic patterns to emerge with a Turing analysis, aiming to test the stability of a uniform solution against uniform and periodic perturbations. However, numerically, these patterns could not be observed by perturbing the homogeneous equilibria with small perturbations, possibly because of the non-linearity of the guerrilla expansion term. Instead, remarkably, the model produced amorphous, far-from-equilibrium patterns when integrated along a rainfall precipitation gradient. These findings highlight the need to represent the diversity of clonal plant strategies in dryland ecosystem models, as they play an important role in pattern formation and, thus, may influence ecosystem resilience and responses to global environmental change. Furthermore, our results highlight the need to move beyond linear analyses when studying systems with nonlinear dispersal dynamics.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3008849