Grazing and climate interact to regulate greening trends in Mediterranean grasslands

A widespread increase in vegetation photosynthetic activity and biomass, known as greening, has been detected since the 1980s. While global climate change explains some of this trend, regional-level land-use management also plays a significant role. In grasslands, the fine-scale movement of livestock is a key driver of vegetation dynamics, likely affecting greening. Here, we investigate how spatial changes in grazing pressure interact with regional climate to determine long-term vegetation trends. Our study focuses on a Mediterranean mountainous region in south-eastern SpainF, with a long history of traditional grazing. We used GPS collar data from 35 livestock herds to create a high-resolution map of herbivory pressure. We then analysed vegetation dynamics from 1985 to 2024 using NDVI time series and evaluated vegetation responses to climate and herbivory with multivariate autoregressive models and logarithmic regressions. Greening was the dominant trend occurring in 90 % of the study ..., , # Data from: Grazing and climate interact to regulate greening trends in Mediterranean grasslands

Dataset DOI: [10.5061/dryad.0gb5mkmfq](https://doi.org/10.5061/dryad.0gb5mkmfq)

## Description of the data and file structure

This dataset contains the data on the 35 herbivory pressure index of 500 x 500 m grid cells across the study area, Los Campos de Hernán Perea and the greenness and climatic variable between 1985 and 2024. To create the herbivory index map, we calculated monthly utilisation distributions (UDs) for each herd using the kernel density estimator in the R package *amt* (Signer, 2018). Monthly NDVI was obtained from Landsat 5, 7, and 8 satellites ([www.usgs.gov](http://www.usgs.gov)), and precipitation and temperature data were sourced from the ERA5-Land dataset. All remote sensing data were processed using Google Earth Engine. We used a multivariate autoregressive state-space (MARSS) model implemented in the MARSS package in R to infer the underlying vegetation trends f...,