Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands

Ricardo Martinez-Garcia, Ciro Cabal, Justin M. Calabrese, Emilio Hernández-García, Corina E. Tarnita, Cristóbal López, Juan A. Bonachela

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Self-organized spatial patterns of vegetation are frequent in drylands and, because pattern shape correlates with water availability, they have been suggested as important indicators of ecosystem health. However, the mechanisms underlying pattern emergence remain unclear. Some theories hypothesize that patterns could result from a water-mediated scale-dependent feedback (SDF) whereby interactions favoring plant growth dominate at short distances and growth–inhibitory interactions dominate in the long range. However, we know little about how the presence of a focal plant affects the fitness of its neighbors as a function of the inter-individual distance, which is expected to be highly ecosystem-dependent. This lack of empirical knowledge and system dependency challenge the relevance of SDF as a unifying theory for vegetation pattern formation. Assuming that plant interactions are always inhibitory and only their intensity is scale-dependent, alternative theories also recover the typical vegetation patterns observed in nature. Importantly, although these alternative hypotheses lead to visually indistinguishable patterns, they predict contrasting desertification dynamics, which questions the potential use of vegetation patterns as ecosystem-state indicators. To help resolve this issue, we first review existing theories for vegetation self-organization and their conflicting predictions about desertification dynamics. Second, we discuss potential empirical tests via manipulative experiments to identify pattern-forming mechanisms and link them to specific desertification dynamics. A comprehensive view of models, the mechanisms they intend to capture, and experiments to test them in the field will help to better understand both how patterns emerge and improve predictions on the fate of the ecosystems where they form.

Original languageEnglish (US)
Article number112881
JournalChaos, Solitons and Fractals
StatePublished - Jan 2023

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • General Mathematics
  • Mathematical Physics
  • General Physics and Astronomy
  • Applied Mathematics


  • Competition
  • Ecological patterns
  • Ecological transitions
  • Mathematical models
  • Scale-dependent feedback
  • Spatial self-organization


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