Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.chaos.2022.112881 http://hdl.handle.net/11449/247938 |
Resumo: | 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. |
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Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylandsCompetitionEcological patternsEcological transitionsMathematical modelsScale-dependent feedbackSpatial self-organizationSelf-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.Agencia Estatal de InvestigaciónBundesministerium für Bildung und ForschungSimons FoundationAbdus Salam International Centre for Theoretical PhysicsPrinceton UniversityFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)National Science FoundationGordon and Betty Moore FoundationICTP South American Institute for Fundamental Research & Instituto de Física Teórica - Universidade Estadual PaulistaDepartment of Ecology and Evolutionary Biology Princeton UniversityDepartment of Biogrography and Global Change National Museum of Natural Sciences MNCN CSICCenter for Advanced Systems Understanding (CASUS) Helmholtz-Zentrum Dresden Rossendorf (HZDR)Department of Ecological Modelling Helmholtz Centre for Environmental Research – UFZDepartment of Biology University of MarylandIFISC Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB)Department of Ecology Evolution and Natural Resources Rutgers UniversityICTP South American Institute for Fundamental Research & Instituto de Física Teórica - Universidade Estadual PaulistaSimons Foundation: 2019/05523-8Abdus Salam International Centre for Theoretical Physics: 2019/24433-0Princeton University: 284558FY19Simons Foundation: DMS-2052616FAPESP: ICTP-SAIFR 2016/01343-7National Science Foundation: MCIN/AEI/10.13039/501100011033National Science Foundation: MDM-2017-0711Gordon and Betty Moore Foundation: RoL:FELS:EAGER-1838331Universidade Estadual Paulista (UNESP)Princeton UniversityCSICHelmholtz-Zentrum Dresden Rossendorf (HZDR)Helmholtz Centre for Environmental Research – UFZUniversity of MarylandInstituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB)Rutgers UniversityMartinez-Garcia, Ricardo [UNESP]Cabal, CiroCalabrese, Justin M.Hernández-García, EmilioTarnita, Corina E.López, CristóbalBonachela, Juan A.2023-07-29T13:29:58Z2023-07-29T13:29:58Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.chaos.2022.112881Chaos, Solitons and Fractals, v. 166.0960-0779http://hdl.handle.net/11449/24793810.1016/j.chaos.2022.1128812-s2.0-85142508095Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengChaos, Solitons and Fractalsinfo:eu-repo/semantics/openAccess2023-07-29T13:29:58Zoai:repositorio.unesp.br:11449/247938Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:03:45.263772Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
title |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
spellingShingle |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands Martinez-Garcia, Ricardo [UNESP] Competition Ecological patterns Ecological transitions Mathematical models Scale-dependent feedback Spatial self-organization |
title_short |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
title_full |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
title_fullStr |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
title_full_unstemmed |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
title_sort |
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands |
author |
Martinez-Garcia, Ricardo [UNESP] |
author_facet |
Martinez-Garcia, Ricardo [UNESP] Cabal, Ciro Calabrese, Justin M. Hernández-García, Emilio Tarnita, Corina E. López, Cristóbal Bonachela, Juan A. |
author_role |
author |
author2 |
Cabal, Ciro Calabrese, Justin M. Hernández-García, Emilio Tarnita, Corina E. López, Cristóbal Bonachela, Juan A. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Princeton University CSIC Helmholtz-Zentrum Dresden Rossendorf (HZDR) Helmholtz Centre for Environmental Research – UFZ University of Maryland Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB) Rutgers University |
dc.contributor.author.fl_str_mv |
Martinez-Garcia, Ricardo [UNESP] Cabal, Ciro Calabrese, Justin M. Hernández-García, Emilio Tarnita, Corina E. López, Cristóbal Bonachela, Juan A. |
dc.subject.por.fl_str_mv |
Competition Ecological patterns Ecological transitions Mathematical models Scale-dependent feedback Spatial self-organization |
topic |
Competition Ecological patterns Ecological transitions Mathematical models Scale-dependent feedback Spatial self-organization |
description |
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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:29:58Z 2023-07-29T13:29:58Z 2023-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.chaos.2022.112881 Chaos, Solitons and Fractals, v. 166. 0960-0779 http://hdl.handle.net/11449/247938 10.1016/j.chaos.2022.112881 2-s2.0-85142508095 |
url |
http://dx.doi.org/10.1016/j.chaos.2022.112881 http://hdl.handle.net/11449/247938 |
identifier_str_mv |
Chaos, Solitons and Fractals, v. 166. 0960-0779 10.1016/j.chaos.2022.112881 2-s2.0-85142508095 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Chaos, Solitons and Fractals |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808128748754567168 |