Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

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

Estimating belowground plant abundance with DNA metabarcoding

Most work on plant community ecology has been performed above ground, neglecting the processes that occur in the soil. DNA metabarcoding, in which multiple species are computationally identified in bulk samples, can help to overcome the logistical limitations involved in sampling plant communities belowground. However, a major limitation of this methodology is the quantification of species’ abundances based on the percentage of sequences assigned to each taxon. Using root tissues of five dominant species in a semi‐arid Mediterranean shrubland (Bupleurum fruticescens, Helianthemum cinereum, Linum suffruticosum, Stipa pennata and Thymus vulgaris), we built pairwise mixtures of relative abundance (20%, 50% and 80% biomass), and implemented two methods (linear model fits and correction indices) to improve estimates of root biomass. We validated both methods with multispecies mixtures that simulate field‐collected samples. For all species, we found a positive and highly significant relationship between the percentage of sequences and biomass in the mixtures (R2 = .44–.66), but the equations for each species (slope and intercept) differed among them, and two species were consistently over‐ and under‐estimated. The correction indices greatly improved the estimates of biomass percentage for all five species in the multispecies mixtures, and reduced the overall error from 17% to 6%. Our results show that, through the use of post‐sequencing quantification methods on mock communities, DNA metabarcoding can be effectively used to determine not only species’ presence but also their relative abundance in field samples of root mixtures. Importantly, knowledge of these aspects will allow us to study key, yet poorly understood, belowground processes

Global shift in a key plant trait indicates a change in biosphere function

In the face of climate change, understanding the dynamic responses of vegetation is crucial for predicting shifts in biosphere functioning. Plant functional traits, particularly leaf mass per area (LMA), are critical links between plant metabolism, vegetation responses to climate change, and the broader exchanges of energy and matter within the biosphere. Despite their importance, a comprehensive, predictive understanding of traits and biosphere changes is hampered by spatial and temporal gaps in trait observations. Here, we introduce a novel remote sensing method for the global, continuous mapping of LMA and its historical shifts. Consistent with ecological theory predicting a widespread decrease in LMA with global warming, our findings reveal a global LMA reduction of 6.5-7.6 % between 1985 and 2019, primarily due to increasing temperatures. This decrease varies among biomes, with evergreen conifer and tropical forests showing the most significant declines. Due to LMA connections with carbon metabolism in ecosystems, a global decrease in LMA points to a quickening of the carbon cycle, including largely unexplored contributions to increased global photosynthesis in recent decades. Collectively, these results signal an ongoing widespread and profound transformation in the functioning of the biosphere resulting from climate-related changes in vegetation and its traits

Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food

The origins of agriculture were key events in human history, during which people came to depend for their food on small numbers of animal and plant species. However, the biological traits determining which species were domesticated for food provision, and which were not, are unclear. Here, we investigate the phylogenetic distribution of livestock and crops, and compare their phenotypic traits with those of wild species. Our results indicate that phylogenetic clustering is modest for crop species but more intense for livestock. Domesticated species explore a reduced portion of the phenotypic space occupied by their wild counterparts and have particular traits in common. For example, herbaceous crops are globally characterized by traits including high leaf nitrogen concentration and tall canopies, which make them fast-growing species and proficient competitors. Livestock species are relatively large mammals with low basal metabolic rates, which indicate moderate to slow life histories. Our study therefore reveals ecological differences in domestication potential between plants and mammals. Domesticated plants belong to clades with traits that are advantageous in intensively managed high-resource habitats, whereas domesticated mammals are from clades adapted to moderately productive environments. Combining comparative phylogenetic methods with ecologically relevant traits has proven useful to unravel the causes and consequences of domestication

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

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

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

International audienceAlpine 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. © The Author(s) 2024