Stochastic dynamics of barrier island elevation.

Barrier islands are ubiquitous coastal features that create low-energy environments where salt marshes, oyster reefs, and mangroves can develop and survive external stresses. Barrier systems also protect interior coastal communities from storm surges and wave-driven erosion. These functions depend on the existence of a slowly migrating, vertically stable barrier, a condition tied to the frequency of storm-driven overwashes and thus barrier elevation during the storm impact. The balance between erosional and accretional processes behind barrier dynamics is stochastic in nature and cannot be properly understood with traditional continuous models. Here we develop a master equation describing the stochastic dynamics of the probability density function (PDF) of barrier elevation at a point. The dynamics are controlled by two dimensionless numbers relating the average intensity and frequency of high-water events (HWEs) to the maximum dune height and dune formation time, which are in turn a function of the rate of sea level rise, sand availability, and stress of the plant ecosystem anchoring dune formation. Depending on the control parameters, the transient solution converges toward a high-elevation barrier, a low-elevation barrier, or a mixed, bimodal, state. We find the average after-storm recovery time-a relaxation time characterizing barrier's resiliency to storm impacts-changes rapidly with the control parameters, suggesting a tipping point in barrier response to external drivers. We finally derive explicit expressions for the overwash probability and average overwash frequency and transport rate characterizing the landward migration of barriers.

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

Soil heterotrophic respiration (Rh) 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, Rh 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 Rh and identify primary predictors of its variability. We find that the temporal variability in Rh 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 Rh 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 Rh and the global carbon budget. In regions that are becoming wetter, Rh may increase faster than net primary productivity, thereby reducing the carbon storage capacity of terrestrial ecosystems.

Probabilistic structure of events controlling the after-storm recovery of coastal dunes.

Coastal dunes protect beach communities and ecosystems from rising seas and storm flooding and influence the stability of barrier islands by preventing overwashes and limiting barrier migration. Therefore, the degree of dune recovery after a large storm provides a simple measure of the short-term resiliency (and potential long-term vulnerability) of barrier islands to external stresses. Dune recovery is modulated by low-intensity/high-frequency high-water events (HWEs), which remain poorly understood compared to the low-frequency extreme events eroding mature dunes and dominating the short-term socio-economic impacts on coastal communities. Here, we define HWEs and analyze their probabilistic structure using time series of still-water level and deep-water wave data from multiple locations around the world. We find that HWEs overtopping the beach can be modeled as a marked Poisson process with exponentially distributed sizes or marks and have a mean size that varies surprisingly little with location. This homogeneity of global HWEs is related to the distribution of the extreme values of a wave-runup parameter, [Formula: see text], defined in terms of deep-water significant wave height [Formula: see text] and peak wavelength [Formula: see text] Furthermore, the characteristic beach elevation at any given location seems to be tied to a constant HWE frequency of about one event per month, which suggests a stochastic dynamics behind beach stabilization. Our results open the door to the development of stochastic models of beach, dune, and barrier dynamics, as well as a better understanding of wave-driven nuisance flooding.

Tree clusters in savannas result from islands of soil moisture.

Tree clusters in savannas are commonly found in sizes that follow power laws with well-established exponents. We show that their size distributions could result from the space-time probabilistic structure of soil moisture, estimated over the range of rainfall observed in semiarid savannas; patterns of soil moisture display islands whose size, for moisture thresholds above the mean, follows power laws. These islands are the regions where trees are expected to exist and they have a fractal structure whose perimeter-area relationship is the same as observed in field data for the clustering of trees. When the impact of fire and herbivores is accounted for, as acting through the perimeter of the tree clusters, the power law of the soil moisture islands is transformed into a power law with the same exponents observed in the tree cluster data.

Spatial patterning among savanna trees in high-resolution, spatially extensive data.

In savannas, predicting how vegetation varies is a longstanding challenge. Spatial patterning in vegetation may structure that variability, mediated by spatial interactions, including competition and facilitation. Here, we use unique high-resolution, spatially extensive data of tree distributions in an African savanna, derived from airborne Light Detection and Ranging (LiDAR), to examine tree-clustering patterns. We show that tree cluster sizes were governed by power laws over two to three orders of magnitude in spatial scale and that the parameters on their distributions were invariant with respect to underlying environment. Concluding that some universal process governs spatial patterns in tree distributions may be premature. However, we can say that, although the tree layer may look unpredictable locally, at scales relevant to prediction in, e.g., global vegetation models, vegetation is instead strongly structured by regular statistical distributions.

Role of co-occurring competition and facilitation in plant spacing hydrodynamics in water-limited environments.

Plant performance (i.e., fecundity, growth, survival) depends on an individual's access to space and resources. At the community level, plant performance is reflected in observable vegetation patterning (i.e., spacing distance, density) often controlled by limiting resources. Resource availability is, in turn, strongly dependent on plant patterning mediated by competitive and facilitative plant-plant interactions. Co-occurring competition and facilitation has never been specifically investigated from a hydrodynamic perspective. To address this knowledge gap, and to overcome limitations of field studies, three intermediate-scale laboratory experiments were conducted using a climate-controlled wind tunnel-porous media test facility to simulate the soil-plant-atmosphere continuum. The spacing between two synthetic plants, a design consideration introduced by the authors in a recent publication, was varied between experiments; edaphic and mean atmospheric conditions were held constant. The strength of the above- and belowground plant-plant interactions changed with spacing distance, allowing the creation of a hydrodynamic conceptual model based on established ecological theories. Greatest soil water loss was observed for the experiment with the smallest spacing where competition dominated. Facilitation dominated at the intermediate spacing; little to no interactions were observed for the largest plant spacing. Results suggest that there exists an optimal spacing distance range that lowers plant environmental stress, thus improving plant performance through reduced atmospheric demand and conservation of available soil water. These findings may provide a foundation for improving our understanding of many climatological, ecohydrological, and hydrological problems pertaining to the hydrodynamics of water-limited environments where plant-plant interactions and community self-organization are important.

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

The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend-for each season-on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons.

Evolving biodiversity patterns in changing river networks.

Biodiversity patterns are governed by landscape structure and dispersal strategies of residing organisms. Landscape, however, changes, and dispersal strategies evolve with it. It is unclear how these biological and geomorphological changes interplay to affect biodiversity patterns. Here we develop metacommunity models that allow for dispersal evolution and implement them in river networks with different structures, mimicking the geomorphological dynamics of fluvial landscape. For a given dispersal kernel, a more compact network structure, where local communities are closer to one another, results in biodiversity patterns characteristic of a more well-mixed environment. When dispersal evolution is present, however, organisms adopt more local dispersal strategies in a more compact network, counteracting the effects of the more well-mixed environment. The combined effects lead to biodiversity patterns different from when dispersal evolution is absent. These findings underscore the importance of taking the interplay between the evolution of dispersal, landscape, and biodiversity patterns into account when studying and managing biodiversity in changing landscape.

Geomorphic controls on elevational gradients of species richness.

Elevational gradients of biodiversity have been widely investigated, and yet a clear interpretation of the biotic and abiotic factors that determine how species richness varies with elevation is still elusive. In mountainous landscapes, habitats at different elevations are characterized by different areal extent and connectivity properties, key drivers of biodiversity, as predicted by metacommunity theory. However, most previous studies directly correlated species richness to elevational gradients of potential drivers, thus neglecting the interplay between such gradients and the environmental matrix. Here, we investigate the role of geomorphology in shaping patterns of species richness. We develop a spatially explicit zero-sum metacommunity model where species have an elevation-dependent fitness and otherwise neutral traits. Results show that ecological dynamics over complex terrains lead to the null expectation of a hump-shaped elevational gradient of species richness, a pattern widely observed empirically. Local species richness is found to be related to the landscape elevational connectivity, as quantified by a newly proposed metric that applies tools of complex network theory to measure the closeness of a site to others with similar habitat. Our theoretical results suggest clear geomorphic controls on elevational gradients of species richness and support the use of the landscape elevational connectivity as a null model for the analysis of the distribution of biodiversity.

Relation between rainfall intensity and savanna tree abundance explained by water use strategies.

Tree abundance in tropical savannas exhibits large and unexplained spatial variability. Here, we propose that differentiated tree and grass water use strategies can explain the observed negative relation between maximum tree abundance and rainfall intensity (defined as the characteristic rainfall depth on rainy days), and we present a biophysical tree-grass competition model to test this idea. The model is founded on a premise that has been well established in empirical studies, namely, that the relative growth rate of grasses is much higher compared with trees in wet conditions but that grasses are more susceptible to water stress and lose biomass more quickly in dry conditions. The model is coupled with a stochastic rainfall generator and then calibrated and tested using field observations from several African savanna sites. We show that the observed negative relation between maximum tree abundance and rainfall intensity can be explained only when differentiated water use strategies are accounted for. Numerical experiments reveal that this effect is more significant than the effect of root niche separation. Our results emphasize the importance of vegetation physiology in determining the responses of tree abundance to climate variations in tropical savannas and suggest that projected increases in rainfall intensity may lead to an increase in grass in this biome.

Decreased water limitation under elevated CO2 amplifies potential for forest carbon sinks.

Increasing atmospheric CO2 concentrations and changing rainfall regimes are creating novel environments for plant communities around the world. The resulting changes in plant productivity and allocation among tissues will have significant impacts on forest carbon storage and the global carbon cycle, yet these effects may depend on mechanisms not included in global models. Here we focus on the role of individual-level competition for water and light in forest carbon allocation and storage across rainfall regimes. We find that the complexity of plant responses to rainfall regimes in experiments can be explained by individual-based competition for water and light within a continuously varying soil moisture environment. Further, we find that elevated CO2 leads to large amplifications of carbon storage when it alleviates competition for water by incentivizing competitive plants to divert carbon from short-lived fine roots to long-lived woody biomass. Overall, we find that plant dependence on rainfall regimes and plant responses to added CO2 are complex, but understandable. The insights developed here will serve as an important foundation as we work to predict the responses of plants to the full, multidimensional reality of climate change, which involves not only changes in rainfall and CO2 but also changes in temperature, nutrient availability, and disturbance rates, among others.

Balancing water resource conservation and food security in China.

China's economic growth is expected to continue into the next decades, accompanied by sustained urbanization and industrialization. The associated increase in demand for land, water resources, and rich foods will deepen the challenge of sustainably feeding the population and balancing agricultural and environmental policies. We combine a hydrologic model with an economic model to project China's future food trade patterns and embedded water resources by 2030 and to analyze the effects of targeted irrigation reductions on this system, notably on national agricultural water consumption and food self-sufficiency. We simulate interprovincial and international food trade with a general equilibrium welfare model and a linear programming optimization, and we obtain province-level estimates of commodities' virtual water content with a hydrologic model. We find that reducing irrigated land in regions highly dependent on scarce river flow and nonrenewable groundwater resources, such as Inner Mongolia and the greater Beijing area, can improve the efficiency of agriculture and trade regarding water resources. It can also avoid significant consumption of irrigation water across China (up to 14.8 km(3)/y, reduction by 14%), while incurring relatively small decreases in national food self-sufficiency (e.g., by 3% for wheat). Other researchers found that a national, rather than local, water policy would have similar effects on food production but would only reduce irrigation water consumption by 5%.

River networks as ecological corridors: A coherent ecohydrological perspective.

This paper draws together several lines of argument to suggest that an ecohydrological framework, i.e. laboratory, field and theoretical approaches focused on hydrologic controls on biota, has contributed substantially to our understanding of the function of river networks as ecological corridors. Such function proves relevant to: the spatial ecology of species; population dynamics and biological invasions; the spread of waterborne disease. As examples, we describe metacommunity predictions of fish diversity patterns in the Mississippi-Missouri basin, geomorphic controls imposed by the fluvial landscape on elevational gradients of species' richness, the zebra mussel invasion of the same Mississippi-Missouri river system, and the spread of proliferative kidney disease in salmonid fish. We conclude that spatial descriptions of ecological processes in the fluvial landscape, constrained by their specific hydrologic and ecological dynamics and by the ecosystem matrix for interactions, i.e. the directional dispersal embedded in fluvial and host/pathogen mobility networks, have already produced a remarkably broad range of significant results. Notable scientific and practical perspectives are thus open, in the authors' view, to future developments in ecohydrologic research.

Evolution and selection of river networks: statics, dynamics, and complexity.

Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal channel networks (OCNs) are feasible optimal configurations of a spanning network mimicking landscape evolution and network selection through imperfect searches for dynamically accessible states. OCNs are spanning loopless configurations, however, only under precise physical requirements that arise under the constraints imposed by river dynamics--every spanning tree is exactly a local minimum of total energy dissipation. It is remarkable that dynamically accessible configurations, the local optima, stabilize into diverse metastable forms that are nevertheless characterized by universal statistical features. Such universal features explain very well the statistics of, and the linkages among, the scaling features measured for fluvial landforms across a broad range of scales regardless of geology, exposed lithology, vegetation, or climate, and differ significantly from those of the ground state, known exactly. Results are provided on the emergence of criticality through adaptative evolution and on the yet-unexplored range of applications of the OCN concept.

Water resources transfers through Chinese interprovincial and foreign food trade.

China's water resources are under increasing pressure from socioeconomic development, diet shifts, and climate change. Agriculture still concentrates most of the national water withdrawal. Moreover, a spatial mismatch in water and arable land availability--with abundant agricultural land and little water resources in the north--increases water scarcity and results in virtual water transfers from drier to wetter regions through agricultural trade. We use a general equilibrium welfare model and linear programming optimization to model interprovincial food trade in China. We combine these trade flows with province-level estimates of commodities' virtual water content to build China's domestic and foreign virtual water trade network. We observe large variations in agricultural water-use efficiency among provinces. In addition, some provinces particularly rely on irrigation vs. rainwater. We analyze the virtual water flow patterns and the corresponding water savings. We find that this interprovincial network is highly connected and the flow distribution is relatively homogeneous. A significant share of water flows is from international imports (20%), which are dominated by soy (93%). We find that China's domestic food trade is efficient in terms of rainwater but inefficient regarding irrigation, meaning that dry, irrigation-intensive provinces tend to export to wetter, less irrigation-intensive ones. Importantly, when incorporating foreign imports, China's soy trade switches from an inefficient system to a particularly efficient one for saving water resources (20 km(3)/y irrigation water savings, 41 km(3)/y total). Finally, we identify specific provinces (e.g., Inner Mongolia) and products (e.g., corn) that show high potential for irrigation productivity improvements.

Signs of critical transition in the Everglades wetlands in response to climate and anthropogenic changes.

The increasing pressure of climatic change and anthropogenic activities is predicted to have major effects on ecosystems around the world. With their fragility and sensitivity to hydrologic shifts and land-use changes, wetlands are among the most vulnerable of such ecosystems. Focusing on the Everglades National Park, we here assess the impact of changes in the hydrologic regime, as well as habitat loss, on the spatial configuration of vegetation species. Because the current structuring of vegetation clusters in the Everglades exhibits power-law behavior and such behavior is often associated with self-organization and dynamics occurring near critical transition points, the quantification and prediction of the impact of those changes on the ecosystem is deemed of paramount importance. We implement a robust model able to identify the main hydrologic and local drivers of the vegetation species spatial structuring and apply it for quantitative assessment. We find that shifts in the hydropatterns will mostly affect the relative abundance of species that currently colonize specific hydroperiod niches. Habitat loss or disruption, however, would have a massive impact on all plant communities, which are found to exhibit clear threshold behaviors when a given percentage of habitable habitat is lost.

Resilience of river flow regimes.

Landscape and climate alterations foreshadow global-scale shifts of river flow regimes. However, a theory that identifies the range of foreseen impacts on streamflows resulting from inhomogeneous forcings and sensitivity gradients across diverse regimes is lacking. Here, we derive a measurable index embedding climate and landscape attributes (the ratio of the mean interarrival of streamflow-producing rainfall events and the mean catchment response time) that discriminates erratic regimes with enhanced intraseasonal streamflow variability from persistent regimes endowed with regular flow patterns. Theoretical and empirical data show that erratic hydrological regimes typical of rivers with low mean discharges are resilient in that they hold a reduced sensitivity to climate fluctuations. The distinction between erratic and persistent regimes provides a robust framework for characterizing the hydrology of freshwater ecosystems and improving water management strategies in times of global change.

Maximum entropy production, carbon assimilation, and the spatial organization of vegetation in river basins.

The spatial organization of functional vegetation types in river basins is a major determinant of their runoff production, biodiversity, and ecosystem services. The optimization of different objective functions has been suggested to control the adaptive behavior of plants and ecosystems, often without a compelling justification. Maximum entropy production (MEP), rooted in thermodynamics principles, provides a tool to justify the choice of the objective function controlling vegetation organization. The application of MEP at the ecosystem scale results in maximum productivity (i.e., maximum canopy photosynthesis) as the thermodynamic limit toward which the organization of vegetation appears to evolve. Maximum productivity, which incorporates complex hydrologic feedbacks, allows us to reproduce the spatial macroscopic organization of functional types of vegetation in a thoroughly monitored river basin, without the need for a reductionist description of the underlying microscopic dynamics. The methodology incorporates the stochastic characteristics of precipitation and the associated soil moisture on a spatially disaggregated framework. Our results suggest that the spatial organization of functional vegetation types in river basins naturally evolves toward configurations corresponding to dynamically accessible local maxima of the maximum productivity of the ecosystem.

Hydroperiod regime controls the organization of plant species in wetlands.

With urban, agricultural, and industrial needs growing throughout the past decades, wetland ecosystems have experienced profound changes. Most critically, the biodiversity of wetlands is intimately linked to its hydrologic dynamics, which in turn are being drastically altered by ongoing climate changes. Hydroperiod regimes, e.g., percentage of time a site is inundated, exert critical control in the creation of niches for different plant species in wetlands. However, the spatial signatures of the organization of plant species in wetlands and how the different drivers interact to yield such signatures are unknown. Focusing on Everglades National Park (ENP) in Florida, we show here that cluster sizes of each species follow a power law probability distribution and that such clusters have well-defined fractal characteristics. Moreover, we individuate and model those signatures via the interplay between global forcings arising from the hydroperiod regime and local controls exerted by neighboring vegetation. With power law clustering often associated with systems near critical transitions, our findings are highly relevant for the management of wetland ecosystems. In addition, our results show that changes in climate and land management have a quantifiable predictable impact on the type of vegetation and its spatial organization in wetlands.

Dendritic connectivity controls biodiversity patterns in experimental metacommunities.

Biological communities often occur in spatially structured habitats where connectivity directly affects dispersal and metacommunity processes. Recent theoretical work suggests that dispersal constrained by the connectivity of specific habitat structures, such as dendrites like river networks, can explain observed features of biodiversity, but direct evidence is still lacking. We experimentally show that connectivity per se shapes diversity patterns in microcosm metacommunities at different levels. Local dispersal in isotropic lattice landscapes homogenizes local species richness and leads to pronounced spatial persistence. On the contrary, dispersal along dendritic landscapes leads to higher variability in local diversity and among-community composition. Although headwaters exhibit relatively lower species richness, they are crucial for the maintenance of regional biodiversity. Our results establish that spatially constrained dendritic connectivity is a key factor for community composition and population persistence.