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Josep Peñuelas

Publications and source records attributed to Josep Peñuelas.

3 recordsLinked to original sources

Anthropogenic disturbance expands the climatic limits of annual plant dominance

Disturbance regimes and nutrient inputs are changing worldwide, with consequences for the structure and functioning of plant communities. Classical life-history theory predicts that disturbance should shift communities from long-lived perennials toward short-lived annuals, and that nutrient enrichment may amplify this shift. However, these predictions have not been tested experimentally across broad environmental gradients. Here, using a global coordinated grassland experiment spanning 37 sites, we tested how physical disturbance, vegetation removal and shallow soil tillage, and fertilisation reshape annual-perennial balance, and whether disturbance relaxes the climatic limits of annual dominance. Disturbance nearly doubled the proportion of annual species and more than doubled the relative cover of annuals, whereas fertilisation had little influence and did not interact with disturbance. The disturbance-driven shift arose through contrasting pathways: in graminoids and legumes, it reflected the loss of perennial cover, while in forbs, the expansion of annual cover. In the absence of disturbance, annual dominance was restricted to systems with extremely hot and dry summers, but disturbance nearly tripled the extent of climate space in which annuals dominated. By rapidly reassembling after disturbance, annuals may help maintain vegetation cover, but their expansion also signals loss of perennial cover and the long-term ecosystem functions associated with it.

q-bio.OT↗

Global Crop-Specific Fertilization Dataset from 1961-2019

As global fertilizer application rates increase, high-quality datasets are paramount for comprehensive analyses to support informed decision-making and policy formulation in crucial areas such as food security or climate change. This study aims to fill existing data gaps by employing two machine learning models, eXtreme Gradient Boosting and HistGradientBoosting algorithms to produce precise country-level predictions of nitrogen ($N$), phosphorus pentoxide ($P_2O_5$), and potassium oxide ($K_2O$) application rates. Subsequently, we created a comprehensive dataset of 5-arcmin resolution maps depicting the application rates of each fertilizer for 13 major crop groups from 1961 to 2019. The predictions were validated by both comparing with existing databases and by assessing the drivers of fertilizer application rates using the model's SHapley Additive exPlanations. This extensive dataset is poised to be a valuable resource for assessing fertilization trends, identifying the socioeconomic, agricultural, and environmental drivers of fertilizer application rates, and serving as an input for various applications, including environmental modeling, causal analysis, fertilizer price predictions, and forecasting.

cs.CE↗

A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database ($> 45 \% $ of missing entries) and maximize the global representativeness of the trait dataset. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales.

stat.AP↗