Abstract:

Determining the ecological consequences of interactions between slow changes in long-term climate means and amplified variability in climate is an important research frontier in plant ecology. We combined the recent approach of climate sensitivity functions with a revised hydrological ‘bucket model’ to improve predictions on how plant species will respond to changes in the mean and variance of groundwater resources.

We leveraged spatiotemporal variation in long-term datasets of riparian vegetation cover and groundwater levels to build the first groundwater sensitivity functions for common plant species of dryland riparian corridors. Our results demonstrate the value of this approach to identifying which plant species will thrive (or fail) in an increasingly variable climate layered with declining groundwater stores.

Riparian plant species differed in sensitivity to both the mean and variance in groundwater levels. Rio Grande cottonwood (Populus deltoides ssp. wislizenii) cover was predicted to decline with greater inter-annual groundwater variance, while coyote willow (Salix exigua) and other native wetland species were predicted to benefit from greater year-to-year variance. No non-native species were sensitive to groundwater variance, but patterns for Russian olive (Elaeagnus angustifolia) predict declines under deeper mean groundwater tables. Warm air temperatures modulated groundwater sensitivity for cottonwood, which was more sensitive to variability in groundwater in years/sites with warmer maximum temperatures than in cool sites/periods. Cottonwood cover declined most with greater intra-annual coefficients of variation (CV) in groundwater, but was not significantly correlated with inter-annual CV, perhaps due to the short time series (16 years) relative to cottonwood lifespan. In contrast, non-native tamarisk (Tamarix chinensis) cover increased with both intra- and inter-annual CV in groundwater. Altogether, our results predict that changes in groundwater variability and mean will affect riparian plant communities through the differential sensitivities of individual plant species to mean versus variance in groundwater stores.

Key words: climate change, climate variability, riparian, Rio Grande, New Mexico, cottonwood, willow, tamarisk