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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 shortlived 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. [ABSTRACT FROM AUTHOR]

The metabolism of a river basin is defined as the set of processes through which the basin maintains its structure and responds to its environment. Green (or biotic) metabolism is measured via transpiration and blue (or abiotic) metabolism through runoff. A principle of equal metabolic rate per unit area throughout the basin structure is developed and tested in a river basin characterized by large heterogeneities in precipitation, vegetation, soil, and geomorphology. This principle is suggested to have profound implications for the spatial organization of river basin hydrologic dynamics, including the minimization of energy expenditure known to control the scale-invariant characteristics of river networks over several orders of magnitude. Empirically derived, remarkably constant rates of average transpiration per unit area through the basin structure lead to a power law for the probability distribution of transpiration from a randomly chosen subbasin. The average runoff per unit area, evaluated for subbasins of a wide range of topological magnitudes, is also shown to be remarkably constant independently of size. A similar result is found for the rainfall after accounting for canopy interception. Allometric scaling of metabolic rates with size, variously addressed in the biological literature and network theory under the label of Kleiber's law, is similarly derived. The empirical evidence suggests that river basin metabolic activity is linked with the spatial organization that takes place around the drainage network and therefore with the mechanisms responsible for the fractal geometry of the network, suggesting a new coevolutionary framework for biological, geomorphological, and hydrologic dynamics. [ABSTRACT FROM AUTHOR]

A hydrologically driven hierarchical competition–colonization model is applied in an spatially explicit manner to investigate the effect of temporal (e.g., interannual rainfall fluctuations) and spatial (e.g., local dispersal) processes on vegetation patterns. Results suggest that interannual rainfall fluctuations extend the range of dispersal distances that result in spatial organization (e.g., clustering, self-similarity) and proportional abundances of vegetation different from those expected for the globally dispersing case. [Copyright &y& Elsevier]

A hierarchical competition-colonization model extensively used in ecological dynamics is driven by the stress conditions resulting from the annual fluctuations in rainfall in water-limited ecosystems. The results show that ecosystem composition is very sensitive to the inclusion of realistic amounts of interannual rainfall variability. The evolutionary dynamics of competing trees and grasses exhibit scale-invariance characteristics (i.e., a power law spectra) for the temporal changes in the relative density of the species. This temporal scaling results from the internal dynamics of the competition-colonization process, which is driven by Markovian annual rainfall amounts. The model was also implemented to approximately describe the conditions of two ecosystems characterized by grass-tree competition, one near La Copita, Texas, and one in Nylsvley, South Africa. Long-term simulations run for many different characteristics of the annual rainfall amounts show that the diversity of dynamic states in the tertiary grass/tree/bare soil system (i.e., its information entropy) is at a maximum near historically observed rainfall characteristics. This suggests that, under commonly observed interannual rainfall fluctuations, water-controlled ecosystems tend to self-organize in a manner that maximizes the richness of relative species abundances. [ABSTRACT FROM AUTHOR]

• Jitter noise allows to tackle soil moisture impacts of small-scale soil heterogeneities. • Spatial structure of soil moisture fields is key to savanna ecosystems. • Soil moisture patterns are drivers of ecological structure and function of savannas. • Clusters of soil moisture islands are characterized by robust scale-free structures. • Phase transition occurs whose order parameter depends on mean soil moisture. We study the spatial structure of soil moisture fields within savanna ecosystems, whose persistence is vital because it is the driver of the entire ecological structure and function. These include changes in the physical and biogeochemical conditions of the landscape, affecting vegetation state, soil composition, water fluxes, and solar radiation. We focus on computations of the probabilistic structure of islands of soil moisture, known empirically to be related to that of tree clusters, defined as crossing properties of simulated soil moisture fields. Rainfall is modelled via Cox-Isham space-time fields endowed with characteristic scales. Results show that clusters of soil moisture islands are characterized by robust scale-free structures in the region of a phase transition whose order parameter depends on mean soil moisture. Signatures of this fractal structure are well-defined power laws of size distributions of soil moisture clusters; their perimeters-vs-area relations; variance-vs- area of the fields. These characteristics allow for the estimation of the fractal dimension of the field, and its Hurst coefficient. From the general covariance equation of a fractal field, spatial simulations are possible because its mean and variance are known from the probabilistic structure of soil moisture at a point. Our results identify the statistics of hotspots of microbial activity deduced from proper moisture islands, unattainable otherwise, and thus may guide the design of field and remote observations. The critical order parameter characterizing the phase transition establishes where the fractal structure of soil moisture fields exists as a function of the climatic drivers, and the thresholds reflecting where vegetation survives in the field. An example of application of the phase transition diagram presented here is carried out with reference to the Nylsvley savanna in South Africa. [ABSTRACT FROM AUTHOR]

Focuses on the dynamical coupling between the soil moisture and atmospheric processes such as in the presence of a growing moist baroclinic wave. Formulation of model; Numerical simulations.

This review proceeds from Luna Leopold's and Ronald Shreve's lasting accomplishments dealing with the study of random-walk and topologically random channel networks. According to the random perspective, which has had a profound influence on the interpretation of natural landforms, nature's resiliency in producing recurrent networks and landforms was interpreted to be the consequence of chance. In fact, central to models of topologically random networks is the assumption of equal likelihood of any tree-like configuration. However, a general framework of analysis exists that argues that all possible network configurations draining a fixed area are not necessarily equally likely. Rather, a probability P (s) is assigned to a particular spanning tree configuration, say s, which can be generally assumed to obey a Boltzmann distribution: P(s) ∝ e[sup -H(s)/τ], where τ is a parameter and H (s) is a global property of the network configuration s related to energetic characters, i.e. its Hamiltonian. One extreme case is the random topology model where all trees are equally likely, i.e. the limit case for τ → ∞. The other extreme case is τ → ∞, and this corresponds to network configurations that tend to minimize their total energy dissipation to improve their likelihood. Networks obtained in this manner are termed optimal channel networks (OCNs). Observational evidence suggests that the characters of real river networks are reproduced extremely well by OCNs. Scaling properties of energy and entropy of OCNs suggest that large network development is likely to effectively occur at zero temperature (i.e. minimizing its Hamiltonian). We suggest a corollary of dynamic accessibility of a network configuration and speculate towards a thermodynamics of critical self-organization. We thus conclude that both chance and necessity are equally important ingredients for the dynamic origin of channel networks—and perh... [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Barrier islands are ubiquitous coastal features that create lowenergy 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 highelevation 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. [ABSTRACT FROM AUTHOR]

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, HR=p HsL0, defined in terms of deep-water significant wave height Hs and peak wavelength L0. 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. [ABSTRACT FROM AUTHOR]

Wetland shorelines around the world are susceptible to wave erosion. Previous work has suggested that the lateral erosion rate of their cliff-like edges can be predicted as a function of intercepting waves, and yet numerous field studies have shown that other factors, for example, tidal currents or masswasting of differing soil types, induce a wide range of variability. Our objective was to isolate the unique effects of wave heights, wavelengths, and water depths on lateral erosion rates and then synthesize a mechanistic understanding that can be applied globally. We found a potentially universal relationship, where the lateral erosion rates increase exponentially as waves increase in height but decrease exponentially as waves become longer in length. These findings suggest that wetlands and other sheltered coastlines likely experience outsized quantities of erosion, as compared to oceanic-facing coastlines. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A physically derived space-time mathematical representation of the soil moisture field is carried out via the soil moisture balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local soil and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time soil moisture field. The jitter is a multiplicative noise acting on the soil moisture dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of soil moisture at different spatial and temporal scales, is investigated. A case study fitting the derived model to a soil moisture dataset is presented in detail. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The analysis of soil water partitioning in seasonally dry climates necessarily requires careful consideration of the periodic climatic forcing at the intra-annual timescale in addition to daily scale variabilities. Here, we introduce three new extensions to a stochastic soil moisture model which yields seasonal evolution of soil moisture and relevant hydrological fluxes. These approximations allow seasonal climatic forcings (e.g. rainfall and potential evapotranspiration) to be fully resolved, extending the analysis of soil water partitioning to account explicitly for the seasonal amplitude and the phase difference between the climatic forcings. The results provide accurate descriptions of probabilistic soil moisture dynamics under seasonal climates without requiring extensive numerical simulations. We also find that the transfer of soil moisture between the wet to the dry season is responsible for hysteresis in the hydrological response, showing asymmetrical trajectories in the mean soil moisture and in the transient Budyko's curves during the 'dry-down' versus the 'rewetting' phases of the year. Furthermore, in some dry climates where rainfall and potential evapotranspiration are in-phase, annual evapotranspiration can be shown to increase because of inter-seasonal soil moisture transfer, highlighting the importance of soil water storage in the seasonal context. [ABSTRACT FROM AUTHOR]

Global change will simultaneously impact many aspects of climate, with the potential to exacerbate the risks posed by plant pathogens to agriculture and the natural environment; yet, most studies that explore climate impacts on plant pathogen ranges consider individual climatic factors separately. In this study, we adopt a stochastic modeling approach to address multiple pathways by which climate can constrain the range of the generalist plant pathogen Phytophthora cinnamomi (Pc): through changing winter soil temperatures affecting pathogen survival; spring soil temperatures and thus pathogen metabolic rates; and changing spring soil moisture conditions and thus pathogen growth rates through host root systems. We apply this model to the southwestern USA for contemporary and plausible future climate scenarios and evaluate the changes in the potential range of Pc. The results indicate that the plausible range of this pathogen in the southwestern USA extends over approximately 200 000 km2 under contemporary conditions. While warming temperatures as projected by the IPCC A2 and B1 emissions scenarios greatly expand the range over which the pathogen can survive winter, projected reductions in spring rainfall reduce its feasible habitat, leading to spatially complex patterns of changing risk. The study demonstrates that temperature and rainfall changes associated with possible climate futures in the southwestern USA have confounding impacts on the range of Pc, suggesting that projections of future pathogen dynamics and ranges should account for multiple pathways of climate-pathogen interaction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Plant pathogens often respond sensitively to changes in their environmental conditions and consequently represent a potentially important ecological response to global change. Although several studies have considered the effects of increased temperature and CO2 concentrations on plant pathogen risk, the effects of changing precipitation regimes have drawn less attention. Many classes of plant pathogen, however, are sensitive to changes in the water potential of their local environment. This study applied existing ecohydrological frameworks to connect precipitation, soil, and host properties with scenarios of pathogen risk, focusing on two water-sensitive pathogens: Phytophthora cinnamomi and Botryosphaeria doithidea. Simple models were developed to link the dynamics of these pathogens to water potentials. Model results demonstrated that the risk of host plants being colonized by the pathogens varied sensitively with soil and climate. The model was used to predict the distribution of Phytophthora in Western Australia and the severity of disease in horticultural blueberry trials with variable irrigation rates, illustrating potential applications of the framework. Extending the modeling framework to include spatial variation in hydrology, epidemic progression, and feedbacks between pathogens and soil moisture conditions may be needed to reproduce detailed spatial patterns of disease. At regional scales, the proposed modeling approach provides a tractable framework for coupling climatic drivers to ecosystem response while accounting for the probabilistic and variable nature of disease. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Detailed simulation studies, highly resolved in space and time, show that a physical relationship exists among instantaneous soil-moisture values integrated over different soil depths. This dynamic relationship evolves in time as a function of the hydrologic inputs and soil and vegetation characteristics. When depth-averaged soil moisture is sampled at a low temporal frequency, the structure of the relationship breaks down and becomes undetectable. Statistical measures can overcome the limitation of sampling frequency, and predictions of mean and variance for soil moisture can be defined over any soil averaging depth d. For a water-limited ecosystem, a detailed simulation model is used to compute the mean and variance of soil moisture for different averaging depths over a number of growing seasons. We present a framework that predicts the mean of soil moisture as a function of averaging depth given soil moisture over a shallow d and the average daily rainfall reaching the soil. [Copyright &y& Elsevier]

A coupled energy and water balance model is used to simulate the effects of large tree canopies on soil moisture and water stress across a series of sites spanning a regional moisture gradient in southern Africa. The model tracks evapotranspiration from five components of the land surface at each site-the tree canopy, the grass under and between tree canopies, and the bare soil under and between tree canopies. The soil moisture dynamics are simulated at daily time steps and driven by a stochastic model of storm arrivals and storm depth. Evapotranspiration is modeled using the Priestley-Taylor approach, with potential evapotranspiration scaled by soil moisture availability. The soil moisture under tree canopies is compared to the soil moisture between tree canopies, and differences in average annual soil moisture stress conditions are analyzed at each site. The spatial distribution of large trees has important consequences for small-scale soil moisture dynamics across the rainfall gradient. The results indicate that tree canopies serve to reduce soil moisture stress of under-canopy vegetation in the middle of the rainfall gradient. At the dry end of the rainfall gradient, the effect of tree canopies on soil moisture is dependent on the amount of rainfall received in a given growing season. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the linkage between the drainage network and the patterns of soil water balance components determined by the organization of vegetation, soils and climate in a semiarid river basin. Research during the last 10 years has conclusively shown an increasing degree of organization and unifying principles behind the structure of the drainage network and the three-dimensional geometry of river basins. This cohesion exists despite the infinite variety of shapes and forms one observes in natural watersheds. What has been relatively unexplored in a quantitative and general manner is the question of whether or not the interaction of vegetation, soils, and climate also display a similar set of unifying characteristics among the very different patterns they presents in river basins. A recently formulated framework for the water balance at the daily level links the observed patterns of basin organization to the soil moisture dynamics. Using available geospatial data, we assign soil, climate, and vegetation properties across the basin and analyze the probabilistic characteristics of steady-state soil moisture distribution. We investigate the presence of organization through the analysis of the spatial patterns of the steady-state soil moisture distribution, as well as in the distribution of observed vegetation patterns, simulated vegetation dynamic water stress and hydrological fluxes such as transpiration. Here we show that the drainage network acts as a template for the organization of both vegetation and hydrological patterns, which exhibit self-affine characteristics in their distribution across the river basin. Our analyses suggest the existence of a balance between the large-scale determinants of vegetation pattern reflecting optimality in the response to water stress and the random small-scale patterns that arise from local factors and ecological legacies such as those caused by dispersal, disturbance, and founder effects. [Copyright &y& Elsevier]

Some essential features of the terrestrial hydrologic cycle and ecosystem response are singled out by confronting empirical observations of the soil water balance of different ecosystems with the results of a stochastic model of soil moisture dynamics. The simplified framework analytically describes how hydro climatic variability (especially the frequency and amount of rainfall events) concurs with soil and plant characteristics in producing the soil moisture dynamics that in turn impact vegetation conditions. The results of the model extend and help interpret the classical curve of Budyko, which relates evapotranspiration losses to a dryness index, describing the partitioning of precipitation into evapotranspiration, runofi and deep infiltration. They also provide a general classification of soil water balance of the world ecosystems based on two governing dimensionless groups summarizing the climate, soil, and vegetation conditions. The subsequent analysis of the links among soil moisture dynamics, plant water stress, and carbon assimilation offers an interpretation of recent manipulative field experiments on ecosystem response to shifts in the rainfall regime, showing that plant carbon assimilation crucially depends not only on the total rainfall during the growing season but also on the intermittency and magnitude of the rainfall events. [ABSTRACT FROM AUTHOR]

The governing equations of soil moisture dynamics, photosynthesis, and transpiration are reviewed and coupled to study the dependence of plant carbon assimilation on soil moisture. The model follows the scheme of the soil–plant–atmosphere continuum (SPAC) and uses a simplified model of the atmospheric boundary layer to arrive at an upscaled, parsimonious representation at the daily time scale. The analysis of soil moisture, transpiration, and carbon assimilation dynamics provides an assessment of the role of soil, plant, and boundary layer characteristics on the diurnal courses of photosynthesis and transpiration rates, while the subsequent upscaling at the daily level provides a functional dependence of stomatal conductance on soil moisture that is in good agreement with field experiments. The upscaled dependence of transpiration and carbon assimilation on soil moisture is used inof this paper to explore the impact of soil moisture dynamics on plant conditions when rainfall variability is explicitly considered. [ABSTRACT FROM AUTHOR]

The coupled dynamics of soil moisture, transpiration, and assimilation are studied at the daily time scale by temporally upscaling the hourly time scale results obtained in a companion paper. The effects of soil and vegetation characteristics on soil moisture dynamics at the daily time scale and the parameters characterizing the dependence of transpiration and assimilation on soil water content are analyzed and discussed. The daily leaf carbon assimilation is then coupled to a stochastic soil moisture model to obtain a probabilistic description of the carbon assimilation during a growing season. The rainfall regime, in terms of both frequency and amount of precipitation, controls the mean assimilation during a growing season that reaches a maximum for an intermediate range of daily rainfall probabilities, indicating the existence of a rainfall regime that is most effective for plant productivity. The analysis of the duration and frequency of periods of no assimilation provides a measure of plant water stress as a function of the soil, vegetation, and climate characteristics. The results are in good agreement with the dynamic water stress defined in Porporato et al. on the basis of the crossing properties of the stochastic soil moisture dynamics. [ABSTRACT FROM AUTHOR]

Water-limited ecosystems are characterized by precipitation with low annual totals and significant temporal variability, transpiration that is limited by soil-moisture availability, and infiltration events that may only partially rewet the vegetation root zone. Average transpiration in such environments is controlled by precipitation, and accurate predictions of vegetation health require adequate representation of temporal variation in the timing and intensity of plant uptake. Complexities introduced by variability in depth of infiltration, distribution of roots, and a plant''s ability to compensate for spatially heterogeneous soil moisture suggest a minimum vertical resolution required for satisfactory representation of plant behavior.To explore the effect of vertical resolution on predictions of transpiration, we conduct a series of numerical experiments, comparing the results from models of varying resolution for a range of plant and climate conditions. From temporal and spatial scales of the underlying processes and desired output, we develop dimensionless parameters that indicate the adequacy of a finite-resolution model with respect to reproducing characteristics of plant transpiration over multiple growing seasons. These parameters may be used to determine the spatial resolution required to predict vegetation health in water-limited ecosystems. [Copyright &y& Elsevier]

The spatial pattern of vegetation patchiness may follow universal characteristic rules when the system is close to critical transitions between alternative states, which improves the anticipation of ecosystem-level state changes which are currently difficult to detect in real systems. However, the spatial patterning of vegetation patches in temperature-driven ecosystems have not been investigated yet. Here, using high-resolution imagery from 1972 to 2013 and a stochastic cellular automata model, we show that in a North American coastal ecosystem where woody plant encroachment has been happening, the size distribution of woody patches follows a power law when the system approaches a critical transition, which is sustained by the local positive feedbacks between vegetation and the surrounding microclimate. Therefore, the observed power law distribution of woody vegetation patchiness may be suggestive of critical transitions associated with temperature-driven woody plant encroachment in coastal and potentially other ecosystems. Huang et al. use satellite imagery spanning over 40 years to investigate the spatial patterning of vegetation patches in a North American coastal ecosystem. They find that woody plant encroachment follows a power law when approaching critical transition points, which may inform future ecological monitoring of coastal systems. [ABSTRACT FROM AUTHOR]

Highlights • This study addresses interplay of evolution in dispersal, landscape, and diversity. • In a more compact network, evolution leads to more local dispersal strategies. • The more local dispersals counteract effects of the more well-mixed environment. • Such interplay has differing effects onlocal and system-level diversity patterns. Abstract 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. [ABSTRACT FROM AUTHOR]

Abstract: Aim: Vegetation is sensitive to mean annual precipitation (MAP), but the sensitivity of vegetation to precipitation variability (PV) is less clear. Tropical ecosystems are likely to experience increased PV in the future. Here we assessed the importance, magnitude and mechanism of PV effects on tree cover in the context of covarying environmental drivers such as fire, temperature and soil properties. Location: Tropical land. Time period: 2000–2010. Major taxa studied: Trees. Methods: We compiled climate, soil and remotely‐sensed tree cover data over tropical land. We then comprehensively assessed the contribution of PV at different time‐scales to tropical tree cover variations and estimated the sensitivity of tree cover to PV changes by conducting rolling‐window regression and variance decomposition analyses. We further adopted a mechanistic modelling approach to test whether water competition between trees and grasses can explain the observed effect of PV. Results: We find that PV contributes 33–56% to the total explained spatial variation (65–79%) in tree cover. The contribution of PV depends on MAP and is highest under intermediate MAP (500–1,500 mm). Tree cover generally increases with rainy day frequency and wet season length but shows mixed responses to inter‐annual PV. Based on the estimated sensitivity, tropical tree cover can decrease by 3–5% overall and by up to 20% in Amazonia under a 20% decrease in rainy days. Mechanistic modelling analysis reproduced the continental differences in tree cover along an MAP gradient. Main conclusions: Under intermediate rainfall regimes (500–1,500 mm), PV can be a more important determinant of tropical tree cover than conventionally proposed drivers such as MAP and fire. The effect of PV likely results from the sensitivity of tree–grass competition to the temporal distribution of water resources. These results show that climate variability can strongly shape the biosphere. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 km³/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%. [ABSTRACT FROM AUTHOR]

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 km3/y irrigation water savings, 41 km3/y total). Finally, we identify specific provinces (e.g.. Inner Mongolia) and products (e.g., corn) that show high potential for irrigation productivity improvements. [ABSTRACT FROM AUTHOR]

Waterborne pathogens cause many possibly lethal human diseases. We derive the condition for pathogen invasion and subsequent disease outbreak in a territory with specific, space-inhomogeneous characteristics (hydrological, ecological, demographic, and epidemiological). The criterion relies on a spatially explicit model accounting for the density of susceptible and infected individuals and the pathogen concentration in a network of communities linked by human mobility and the water system. Pathogen invasion requires that a dimensionless parameter, the dominant eigenvalue of a generalized reproductive matrix J0, be larger than unity. Conditions for invasion are studied while crucial parameters (population density distribution, contact and water contamination rates, pathogen growth rates) and the characteristics of the networks (connectivity, directional transport, water retention times, mobility patterns) are varied. We analyze both simple, prototypical test cases and realistic landscapes, in which optimal channel networks mimic the water systems and gravitational models describe human mobility. Also, we show that the dominant eigenvector of J0 effectively portrays the geography of epidemic outbreaks, that is, the areas of the studied territory that will be initially affected by an epidemic. This is important for planning an efficient spatial allocation of interventions (e.g., improving sanitation and providing emergency aid and medicines). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: A synthesis is presented highlighting the importance of hydrologic variables and dynamics to biodiversity patterns. The focus of this paper is the key hydrologic controls crucial towards quantifying the impacts of climate changes on the distribution of species. Specifically, we highlight the hydrologic controls operating on the carrying capacity, niche formation, and dispersal dynamics. This synthesis will facilitate avenues of future research and is connected to issues of major practical importance, such as the integration of the structure of river networks into conservation strategies and the evaluations of the impacts of climate change on biodiversity. [Copyright &y& Elsevier]

Understanding, predicting, and controlling outbreaks of waterborne diseases are crucial goals of public health policies, but pose challenging problems because infection patterns are influenced by spatial structure and temporal asynchrony. Although explicit spatial modeling is made possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is still lacking. Here we show that the requirement that all the local reproduction numbers R0 be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix G0, explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number Λ0 (the dominant eigenvalue of G0) must be larger than unity. We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of G0. Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology, and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. We study the ex post reliability of predictions of the 2010-2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. We consider the impact of different approaches to the modeling of spatial spread of Vibrio cholerae and mechanisms of cholera transmission, accounting for the dynamics of susceptible and infected individuals within different local human communities. To explain resurgences of the epidemic, we go on to include waning immunity and a mechanism explicitly accounting for rainfall as a driver of enhanced disease transmission. The formal comparative analysis is carried out via the Akaike information criterion (AIC) to measure the added information provided by each process modeled, discounting for the added parameters. A generalized model for Haitian epidemic cholera and the related uncertainty is thus proposed and applied to the year-long dataset of reported cases now available. The model allows us to draw predictions on longer-term epidemic cholera in Haiti from multiseason Monte Carlo runs, carried out up to January 2014 by using suitable rainfall fields forecasts. Lessons learned and open issues are discussed and placed in perspective. We conclude that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control. [ABSTRACT FROM AUTHOR]

Global freshwater resources are under increasing pressure from economic development, population growth, and climate change. The international trade of water-intensive products (e.g., agricultural commodities) or virtual water trade has been suggested as a way to save water globally. We focus on the virtual water trade network associated with international food trade built with annual trade data and annual modeled virtual water content. The evolution of this network from 1986 to 2007 is analyzed and linked to trade policies, socioeconomic circumstances, and agricultural efficiency. We find that the number of trade connections and the volume of water associated with global food trade more than doubled in 22 years. Despite this growth, constant organizational features were observed in the network. However, both regional and national virtual water trade patterns significantly changed. Indeed, Asia increased its virtual water imports by more than 170%, switching from North America to South America as its main partner, whereas North America oriented to a growing intraregional trade. A dramatic rise in China's virtual water imports is associated with its increased soy imports after a domestic policy shift in 2000. Significantly, this shift has led the global soy market to save water on a global scale, but it also relies on expanding soy production in Brazil, which contributes to deforestation in the Amazon. We find that the international food trade has led to enhanced savings in global water resources over time, indicating its growing efficiency in terms of global water use. [ABSTRACT FROM AUTHOR]

Abstract: We apply an evolutionary game theoretic approach to the evolution of dispersal in explicitly spatial metacommunities, using a flexible parametric class of dispersal kernels, namely 2Dt kernels, and study the resulting evolutionary dynamics and outcomes. We observe strong selective pressure on mean dispersal distance (i.e., the first moment), and weaker, but significant, one on the shape of dispersal kernel (i.e., higher moments). We investigate the effects of landscape topology and spatial heterogeneity on the resulting ‘optimal’ dispersal kernels. The shape—importantly the tail structure—and stability of evolutionarily optimal dispersal strategies are strongly affected by landscape topology or connectivity. Specifically, the results suggest that the optimal dispersal kernels in the river network topology have heavier tails and are stable, while those in the direct topology, where organisms are allowed to travel directly from one location to another, have relatively thin tails and may be unstable. We also find that habitat spatial heterogeneity enables coexistence and controls spatial distribution of distinct groups of dispersal strategies and that alteration in topology alone may not be sufficient to change such coexistence. This work provides a tool to translate environmental changes such as global climate change and human intervention into changes in dispersal behavior, which in turn may lead to important alterations of biodiversity and biological invasion patterns. [ABSTRACT FROM AUTHOR]

Water controls the dynamics of terrestrial ecosystems directly, as a resource for the biota, and indirectly, as a driver for abiotic processes on the Earth's surface, in the atmosphere, and belowground. The biota, in turn, modulate several hydrological processes and the rate of the water cycle. Here we review recent advances related to fundamental processes and feedbacks emerging from the interactions among hydrologic processes and ecosystems, with a particular focus on soil moisture dynamics and river flow. Most terrestrial vegetation interacts with hydrological processes through the soil-water balance, which is affected by soil properties, random climate drivers, and feedbacks with the biota. River flow enhances the ecohydrological connectivity of the landscape, spreading sediments, nutrients, propagules, and waterborne disease through waterways. [ABSTRACT FROM AUTHOR]

A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we analyze the analytical predictions of a general model suitable for describing the spatial biodiversity similarity in river ecosystems. and benchmark them against the empirical occurrence data of freshwater fish species collected in the Mississippi-Missouri river system. Encapsulating immigration, emigration, and stochastic noise, and without resorting to species abundance data, the model is able to reproduce the observed probability distribution of the Jaccard similarity index at any given distance. In addition to providing an excellent agreement with the empirical data, this approach accounts for heterogeneities of different subbasins, suggesting a strong dependence of biodiversity similarity on their respective climates. Strikingly, the model can also predict the actual probability distribution of the Jaccard similarity index for any distance when considering just a relatively small sample. The proposed framework supports the notion that simplified macroecological models are capable of predicting fundamental patterns-a theme at the heart of modern community ecology. [ABSTRACT FROM AUTHOR]