Global variations in ecosystem‐scale isohydricity

Abstract

Droughts are expected to become more frequent and more intense under climate change. Plant mortality rates and biomass declines in response to drought depend on stomatal and xylem flow regulation. Plants operate on a continuum of xylem and stomatal regulation strategies from very isohydric (strict regulation) to very anisohydric. Coexisting species may display a variety of isohydricity behaviors. As such, it can be difficult to predict how to model the degree of isohydricity at the ecosystem scale by aggregating studies of individual species. This is nonetheless essential for accurate prediction of ecosystem drought resilience. In this study, we define a metric for the degree of isohydricity at the ecosystem scale in analogy with a recent metric introduced at the species level. Using data from the AMSR-E satellite, this metric is evaluated globally based on diurnal variations in microwave vegetation optical depth (VOD), which is directly related to leaf water potential. Areas with low annual mean radiation are found to be more anisohydric. Except for evergreen broadleaf forests in the tropics, which are very isohydric, and croplands, which are very anisohydric, land cover type is a poor predictor of ecosystem isohydricity, in accordance with previous species-scale observations. It is therefore also a poor basis for parameterizing water stress response in land-surface models. For taller ecosystems, canopy height is correlated with higher isohydricity (so that rainforests are mostly isohydric). Highly anisohydric areas show either high or low underlying water use efficiency. In seasonally dry locations, most ecosystems display a more isohydric response (increased stomatal regulation) during the dry season. In several seasonally dry tropical forests, this trend is reversed, as dry-season leaf-out appears to coincide with a shift toward more anisohydric strategies. The metric developed in this study allows for detailed investigations of spatial and temporal variations in plant water behavior.

Plant Water Status Indicators for Irrigation Scheduling Associated with Iso- and Anisohydric Behavior: Vine and Plum Trees

“…Plants that preferentially put into action effective mechanisms to regulate their water content have a behavior known as isohydric [23,24], whereas those in which the mechanisms referred to above are less effective or non-existent are called anisohydric.…”

Water stress induced breakdown of carbon-water relations: indicators from diurnal FLUXNET patterns

“…These ecosystems may only exhibit higher hydraulic stresses, associated with both dryness and a more sensitive hydraulic structure. Temperate-continental and tropical climates all showed a low frequency of morning shifted days, even though they are occupied by large trees with cavitation susceptible vascular systems (Konings and Gentine, 2016), suggesting that these ecosystems show limited drought stress even with the hydraulic susceptibility. Similarly, the high 10 degree of variability in morning shifted frequencies between site-years in sub-tropical/Mediterranean and evergreen broad-and needle-leaf forests could either indicate variation in the response in hydraulic stress between sites, or that hydraulic stress is only expressed some years, leading to high and low frequencies within the same site.…”

“…Current estimates of isohydricity require repeated quantification of leaf water potential, which are currently restrained to the individual scale, i.e. from actual leaf measurements (Martínez-Vilalta et al, 2014) or to global scale, but only 0.5 degree resolution estimates from radar (Konings and Gentine, 2016). This limitation of large and small scales leaves a knowledge gap at the size of an eddy covariance footprint, hindering the study of ecosystem response to drought.…”

Ecosystem-dynamics link to hydrologic variations for different land-cover types

“…A clear understanding of the relationships between ecological and hydrological components is essential to estimate the dynamics of ecological processes (Santos and Negri 1997;Pielke et al 1998;Fisher et al 2009;Konings and Gentine 2017). However, the direct relationships between these components have not been intensively studied owing to the paucity of regional datasets (Méndez-Barroso et al 2009).…”