Warming as a Driver of Vegetation Loss in the Sonoran Desert of California

Abstract

Dryland ecosystems cover large regions of the Earth and have important impacts on global biogeochemistry and the carbon cycle. The plant species that occupy dryland environments have traits that enable them to withstand harsh environmental conditions, and some researchers have hypothesized that dryland vegetation may be comparatively resilient to changing climate, while others have pointed out that dryland vegetation often operates close to the physiological limits of many species, implying a possible vulnerability to warming. Here we use the Landsat archive to analyze vegetation dynamics for part of the Sonoran Desert and adjacent mountains in southern California. We show that large decreases in vegetation cover occurred over the last 34 years (1984–2017), especially across the xeric portions of our study region, where we observed a normalized difference vegetation index (NDVI) decline of 1.1 ± 0.3% yr−1. Changes in precipitation explain most of the year‐to‐year variation but are unable to fully explain the observed long‐term decline in NDVI. Statistical models that combined summer temperature and mean annual precipitation explained more of the spatial and temporal structure of NDVI trends and implicate climate warming as an important driver of declining vegetation cover. The impact of warming contributed to a change in the precipitation‐vegetation relationship through time for this desert region, indicating a structural change in ecosystem function during the study period. These results suggest that recent climate change has already had significant impact on these drylands and highlight the potential for future warming to increase risks for dryland ecosystems in other regions.

Foundation plant species provide resilience and microclimatic heterogeneity in drylands

“…Climate change is palpable. In drylands including deserts and semi-arid grasslands and scrublands, the direct and indirect impacts of climate are profound 1 . Climate drivers relevant to plant, animals, and most dimensions of human interactions in these ecosystems are influenced through at least three key changes 2 .…”

Moisture and distribution of a keratophagous moth, Tinea occidentella

“…At the larger scale of the southwestern United States, the climate is warming and drying. The warming has caused more than three decades of loss of vegetation cover over the Sonoran Desert and mountains of Southern California (Hantson et al, 2021). A megadrought from 2000 to 2018 created the second driest pair of decades since 800 CE in southwestern North America, only the late‐1500s was dryer (Williams et al, 2020).…”

Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

“…The nearest neighbor resampling technique was used to resample the Landsat images into a pixel size of 30 m during image-to-image registration. Because of the radiometric differences between the Landsat 8 OLI detector and the previous Landsat detectors, we performed a bias correction on Landsat 8 imagery using a linear regression between the reflectance values of each band between Landsat 7 and Landsat 8 [30,31]. This step reduced the difference between Landsat 7 and Landsat 8, resulting in limited differences between the different sensors throughout the time period.…”

“…This step reduced the difference between Landsat 7 and Landsat 8, resulting in limited differences between the different sensors throughout the time period. We did not correct potential biases between Landsat 5 and 7 as radiometric differences between these sensors are relatively small [31,32].…”

“…First, in order to achieve long-term transparency monitoring, we combined the spectral data from three sensors (TM, ETM+, and OLS). Although the linear regression model was used for spectral correction [30,31], there are still uncertain spectral spots. Therefore, data continuity correction should be carried out under the background of low radiation to obtain more accurate and consistent transparency monitoring.…”