DOI: 10.5061/dryad.0gb5mkmfq
title: Data from: Grazing and climate interact to regulate greening trends in Mediterranean grasslands

description: 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 area. However, increasing grazing pressure diminished this greening
 tendency. We found a non-linear response: the greening trend reduced
 sharply even under low levels of grazing and then stabilized at medium to
 high intensities. Despite hotspots of grazing pressure, no significant
 browning (vegetation decline) was detected. Vegetation became more
 sensitive to climate under higher herbivory pressure. In heavily grazed
 areas, vegetation greenness responded more strongly to increases in
 precipitation and showed grater negative responses to rising temperatures.
 This suggests that grazing regulates how grassland vegetation responds to
 climatic shifts. Policy implications. Our results show that traditional
 grazing systems can maintain grassland stability without causing
 degradation, but they can also increase the ecosystem’s vulnerability to
 climate change. We provide clear evidence that GPS-based livestock
 monitoring is a powerful and scalable tool for sustainable rangeland
 management. It provides land managers with fine-scale data on grazing
 pressure, enabling them to identify areas at risk, enhance ecosystem
 resilience to climate change, and support both conservation goals and
 productive landscapes at local and regional scales.
description: # 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 from incomplete datasets without prior
 variance estimates . We also quantified how vegetation in each grid cell
 responds to the climatic covariates (precipitation and temperature). To
 assess vegetation trends modulated by herbivory, we identified the
 long-term trend (greening, browning, or no trend) in the 39-year NDVI time
 series for each grid cell using the Mann-Kendall test for trend detection,
 and the Theil–Sen to quantify the trend magnitude. ### Files and variables
 #### File: grazingData.csv **Description:** Parameters of the MARSS models
 (Hinrichsen and Holmes, 2009) resulting from time series data between 1985
 and 2024, based on 500 × 500 m cells distributed in a regular grid across
 the study area Los Campos de Hernán Perea. Id corresponds to the name of
 the resulting model for each grid cell; Herb indicates the herbivory index
 of each cell; Iter is the number of iterations until model convergence;
 Conv indicates whether the model converged; R.NDVI5, R.NDVI7, and R.NDVI8
 represent the observation noise matrices for the NDVI sensors from Landsat
 5, 7, and 8, respectively; Q refers to the hidden process noise; and rain
 and temperature indicate the effect of these covariates on the hidden NDVI
 process. Finally, Theil-Sen refers to the slope of the NDVI time series as
 estimated by the Theil-Sen and Mann-Kendall statistics. All parameters
 showed statistical support. ##### Variables * Id: Name of the resulting
 model for each grid cell * Herb: Herbivory index of each cell *
 Iter.: Number of iterations until MARSS model convergence used to built
 the NDVI time series of the cell * AIC: MARSS model AIC *
 Convergence: Indicative of the model convergence *
 R.NDVI5.Est: Observation noise estimate for the NDVI sensors from Landsat
 5 form the MARSS model used to built the time series of each cell *
 R.NDVI5.lowCI: Observation noise low credibility interval (CI) for the
 NDVI sensors from Landsat 5 form the MARSS model used to built the time
 series of each cell * R.NDVI5.highCI: Observation noise high credibility
 interval (CI) for the NDVI sensors from Landsat 5 form the MARSS model
 used to built the time series of each cell * R.NDVI7.Est: Observation
 noise estimate for the NDVI sensors from Landsat 7 form the MARSS model
 used to built the time series of each cell * R.NDVI7.lowCI: Observation
 noise low credibility interval (CI) for the NDVI sensors from Landsat 7
 form the MARSS model used to built the time series of each cell *
 R.NDVI7.highCI: Observation noise high credibility interval (CI) for the
 NDVI sensors from Landsat 7 form the MARSS model used to built the time
 series of each cell * R.NDVI8.Est: Observation noise estimate for the NDVI
 sensors from Landsat 8 form the MARSS model used to built the time series
 of each cell * R.NDVI8.lowCI: Observation noise low credibility interval
 (CI) for the NDVI sensors from Landsat 8 form the MARSS model used to
 built the time series of each cell * R.NDVI8.highCI: Observation noise
 high credibility interval (CI) for the NDVI sensors from Landsat 8 form
 the MARSS model used to built the time series of each cell * Q.Est: Hidden
 process noise estimate form the MARSS model used to built the time series
 of each cell * Q.lowCI: Hidden process noise low credibility interval (CI)
 form the MARSS model used to built the time series of each cell *
 Q.highCI: Hidden process noise high credibility interval (CI) form the
 MARSS model used to built the time series of each cell *
 Prec.Est: Precipitation estimate form the MARSS model used to built the
 time series of each cell * Prec.lowCI: Precipitation low credibility
 interval (CI) form the MARSS model used to built the time series of each
 cell * Prec.highCI: Precipitation high credibility interval (CI) form the
 MARSS model used to built the time series of each cell *
 Temp.Est: Temperature estimate form the MARSS model used to built the time
 series of each cell * Temp.lowCI: Temperature low credibility interval
 (CI) form the MARSS model used to built the time series of each cell *
 Temp.highCI: Temperature high credibility interval (CI) form the MARSS
 model used to built the time series of each cell * TheilSen: Slope of the
 NDVI time series as estimated by the Theil-Sen and Mann-Kendall statistics
 using MARSS model ## Code/software Data can be open using Excel or R ##
 Access information Other publicly accessible locations of the data: * None
 Data was derived from the following sources: * Monthly NDVI were 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 downloaded using Google Earth
 Engine.