ArticleScientific data2024
Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Patterns and drivers of leaf carbon, nitrogen, and phosphorus stoichiometry in the desert steppe of the Ili River Basin.Frontiers in plant science · 2026Article
- Spatiotemporal dynamics and adaptive strategies of plant diversity and functional traits in desert steppe.Frontiers in plant science · 2026Article
- Plant traits and associated ecological data from Afromontane grasslands of Maloti-Drakensberg, South Africa.Scientific data · 2025Article
- Plant traits and associated ecological data from global change experiments and climate gradients in Norway.Scientific data · 2025Article
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62 authors.
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Abstract
Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families.
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