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Chaos Solitons & Fractals
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License: Elsevier TDM
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Article . 2021
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Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands

Authors: Martinez-Garcia, R.; Cabal, C.; Calabrese, J.; Hernandez-Garcia, E.; Tarnita, C.; Lopez, C.; Bonachela, J.;

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

Abstract

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.

20 pages, 6 figures

Country
Germany
Keywords

Mathematical models, Competition, Statistical Mechanics (cond-mat.stat-mech), Populations and Evolution (q-bio.PE), FOS: Physical sciences, Scale-dependent feedback, Pattern Formation and Solitons (nlin.PS), Ecological transitions, Nonlinear Sciences - Pattern Formation and Solitons, Spatial self-organization, Ecological patterns, FOS: Biological sciences, Quantitative Biology - Populations and Evolution, Condensed Matter - Statistical Mechanics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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26
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