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Abstract
Aim: The ecological literature posits that positive interactions are preponderant in stressful environments; however, the net balance between positive and negative interactions at the community level is still under debate. This study analysed the effect of trees on grass biomass in natural and cultivated woody systems distributed along a global aridity index (AI) gradient. Location: Global. Methods: We conducted a meta-analysis including eight natural [ver mas...]
dc.contributor.authorMazía, Noemí
dc.contributor.authorMoyano, Jaime
dc.contributor.authorPérez, Luis
dc.contributor.authorAguiar, Sebastián
dc.contributor.authorGaribaldi, Lucas Alejandro
dc.contributor.authorSchlichter, Tomas Miguel
dc.date.accessioned2017-10-30T13:41:25Z
dc.date.available2017-10-30T13:41:25Z
dc.date.issued2016-12
dc.identifier.issn1466-822X (Print)
dc.identifier.issn1466-8238 (Online)
dc.identifier.otherDOI: 10.1111/geb.12518
dc.identifier.urihttp://hdl.handle.net/20.500.12123/1621
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1111/geb.12518/abstract
dc.description.abstractAim: The ecological literature posits that positive interactions are preponderant in stressful environments; however, the net balance between positive and negative interactions at the community level is still under debate. This study analysed the effect of trees on grass biomass in natural and cultivated woody systems distributed along a global aridity index (AI) gradient. Location: Global. Methods: We conducted a meta-analysis including eight natural biomes and tree plantations distributed in five continents. The final database consisted of 93 data pairs across 65 locations spanning a gradient from AI = 0.1 to AI = 2.1, which covered annual precipitation ranging from 70 to 3500 mm. Effect size was calculated as the difference between above-ground grass biomass beneath and outside the tree canopy. We built linear models to evaluate the importance of different biotic and abiotic variables as potential drivers of the effect size. Multimodel inference, based on the Akaike information criterion (AICc) was used to select the best models. Results: The whole data set shows a shift from net facilitation to net competition along an increasing AI gradient. AI had the highest relative importance in explaining the sign and magnitude of the effect size. Tree characteristics (deciduous–evergreen and leguminous–non-leguminous) were the other predictive variables consistently included in almost all the 10 best models. Deciduous and leguminous trees enhanced grass biomass growing beneath them. Increasing soil sand content, C4 grasses and tropical and natural systems all increased the biomass of grasses growing beneath trees, but their relative importance was substantially lower than that of the AI and tree characteristics. Main conclusions: The results of our global meta-analysis showed that climatic context and the characteristics of benefactor trees both represent the main drivers of the sign and magnitude of tree–grass interactions. These findings may contribute to advancing knowledge of the mechanisms behind the global patterns.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceGlobal ecology and biogeography 25 (12) : 1510–1519. (December 2016)
dc.subjectMedio Ambientees_AR
dc.subjectEnvironmenteng
dc.subjectGramíneas
dc.subjectGrasseseng
dc.subjectArboles
dc.subjectTreeseng
dc.titleThe sign and magnitude of tree–grass interaction along a global environmental gradientes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/acceptedVersiones_AR
dc.description.filFil: Mazía, Noemí. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
dc.description.filFil: Moyano, Jaime. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
dc.description.filFil: Pérez, Luis. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.filFil: Schlichter, Tomas Miguel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
dc.subtypecientifico


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