Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China

Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model

The stability of artificial sand-binding vegetation determines the success or failure of restoration of degraded ecosystem, accurately evaluating the stability of artificial sand-binding vegetation can provide evidence for the future management and maintenance of re-vegetated regions. In this paper, a novel data-driven evaluation model was proposed by combining statistical methods and a neural network model to evaluate the stability of artificial sand-binding vegetation in the southeastern margins of the Tengger Desert, where the evaluation indexes were selected from vegetation, soil moisture, and soil. The evaluation results indicate that the stability of the artificially re-vegetated belt established in different years (1956a, 1964a, 1981a, and 1987a) tend to be stable with the increase of sand fixation years, and the artificially re-vegetated belts established in 1956a and 1964a have almost the same stability, but the stability of the artificially re-vegetated belt established in 1981a and 1987a have a significant difference. The evaluation results are reliable and accurate, which can provide evidence for the future management of artificial sand-binding vegetation.

The Stem Sap Flow and Water Sources for Tamarix ramosissima in an Artificial Shelterbelt With a Deep Groundwater Table in Northwest China

The shelterbelt forest between oases and the desert plays a vital role in preventing aeolian disasters and desertification in arid regions of northwest China. Tamarix ramosissima (T. ramosissima), a typical perennial and native xerophyte shrub in Northwest China, grows naturally and is widely used in building artificial shelterbelt forests. The balance between water consumption and the availability of water determines the survival and growth of T. ramosissima. How T. ramosissima copes with extremely low rainfall and a deep groundwater table remains unknown. To answer this, the transpiration and the water sources of T. ramosissima were investigated by the heat balance and oxygen isotopic analysis method, respectively. Our results show that the daily T. ramosissima stem sap flow (SSF) was positively correlated with air temperature (Ta), photosynthetically active radiation (PAR), and the vapor pressure deficit (VPD). We found no significant relationship between the daily SSF and soil moisture in shallow (0-40 cm) and middle (40-160 cm) soil layers. Oxygen isotope results showed that T. ramosissima mainly sources (>90%) water from deep soil moisture (160-400 cm) and groundwater (910 cm). Diurnally, T. ramosissima SSF showed a hysteresis response to variations in PAR, Ta, and VPD, which suggests that transpiration suffers increasingly from water stress with increasing PAR, Ta, and VPD. Our results indicate that PAR, Ta, and VPD are the dominant factors that control T. ramosissima SSF, not precipitation and shallow soil moisture. Deep soil water and groundwater are the primary sources for T. ramosissima in this extremely water-limited environment. These results provide information that is essential for proper water resource management during vegetation restoration and ecological reafforestation in water-limited regions.

Variability of ecosystem carbon source from microbial respiration is controlled by rainfall dynamics

Soil heterotrophic respiration (R _h) represents an important component of the terrestrial carbon cycle that affects whether ecosystems function as carbon sources or sinks. Due to the complex interactions between biological and physical factors controlling microbial growth, R _h is uncertain and difficult to predict, limiting our ability to anticipate future climate trajectories. Here we analyze the global FLUXNET 2015 database aided by a probabilistic model of microbial growth to examine the ecosystem-scale dynamics of R _h and identify primary predictors of its variability. We find that the temporal variability in R _h is consistently distributed according to a Gamma distribution, with shape and scale parameters controlled only by rainfall characteristics and vegetation productivity. This distribution originates from the propagation of fast hydrologic fluctuations on the slower biological dynamics of microbial growth and is independent of biome, soil type, and microbial physiology. This finding allows us to readily provide accurate estimates of the mean R _h and its variance, as confirmed by a comparison with an independent global dataset. Our results suggest that future changes in rainfall regime and net primary productivity will significantly alter the dynamics of R _h and the global carbon budget. In regions that are becoming wetter, R _h may increase faster than net primary productivity, thereby reducing the carbon storage capacity of terrestrial ecosystems.

Vertical variations of soil water and its controlling factors based on the structural equation model in a semi-arid grassland

Soil water content (SWC) of a vertical profile plays an important role in the soil-plant-atmosphere continuum system through eco-hydrological process, which was controlled by multiple factors. Previous studies ignored soil water from a systematic perspective because of the lack of suitable methods to deal with interrelated factors. We developed a meta-model based on structural equation model (SEM) to identify the factors contributing to soil water, and the interactions among these factors, in a semi-arid grassland system. The model was based on the hypothesis that soil water is affected by hydrological variables (precipitation: P, evapotranspiration: E and underground water: GW), vegetation (vegetation coverage: VC and above ground biomass: AGB), and soil parameters (soil organic matter: SOM and bulk density: BD). E and AGB decrease soil water content, while VC and SOM help to retain soil water content. The proportion of explained variation in soil water increased with depth due to increasing stability. The most important contributors were AGB (r∂ = -0.15) and VC (r∂ = 0.39), and their contributions were opposite because their mechanisms differed. The accumulation of AGB in the growth season consumed soil water through root uptake. The contribution of AGB increased with depth, inferring that grassland species are xerophytes with deep roots to access soil water during drought. Coverage positively contributed to soil water, but its influence decreased with depth because its main effects (intercepting rainfall and providing shade) were at the surface. This systematic perspective of how hydrological, vegetation, and soil properties affect soil water will be useful to guide the management of semi-arid grasslands.

Water-limited vegetated ecosystems driven by stochastic rainfall: feedbacks and bimodality

In arid or semi-arid ecosystems, water availability is one of the primary controls on vegetation growth. When subsurface water resources are unavailable, the vegetation growth is dictated by the rainfall, and the random nature of the rainfall arrivals and quantities induces a probability distribution of soil moisture and vegetation biomass via the coupled dynamic equations of biomass balance and water balance. We have previously obtained an exact solution for these distributions under certain conditions, and shown that the mapping of rainfall variability to observed biomass variability can be successfully applied to a field site. Here, we expand upon our earlier theoretical work to show how the dynamics can give rise to more complicated, bimodal (and multimodal) structures in the biomass distribution when positive feedbacks between growth and water availability are included. We also derive some new analytical results for the crossing properties of this system, which enable us to determine on what time scale the effects of these feedbacks will be felt, and, relatedly, how long the system will take to cross between different modes.

Structural stability as a consistent predictor of phenological events

The timing of the first and last seasonal appearance of a species in a community typically follows a pattern that is governed by temporal factors. While it has been shown that changes in the environment are linked to phenological changes, the direction of this link appears elusive and context-dependent. Thus, finding consistent predictors of phenological events is of central importance for a better assessment of expected changes in the temporal dynamics of ecological communities. Here we introduce a measure of structural stability derived from species interaction networks as an estimator of the expected range of environmental conditions compatible with the existence of a community. We test this measure as a predictor of changes in species richness recorded on a daily basis in a high-arctic plant-pollinator community during two spring seasons. We find that our measure of structural stability is the only consistent predictor of changes in species richness among different ecological and environmental variables. Our findings suggest that measures based on the notion of structural stability can synthesize the expected variation of environmental conditions tolerated by a community, and explain more consistently the phenological changes observed in ecological communities.