1,720,994 research outputs found
Maximum entropy model for business cycle synchronization
Full text linkThe global economy is a complex dynamical system, whose cyclical fluctuations can mainly be characterized by simultaneous recessions or expansions of major economies. Thus, the researches on the synchronization phenomenon are key to understanding and controlling the dynamics of the global economy. Based on a pairwise maximum entropy model, we analyze the business cycle synchronization of the G7 economic system. We obtain a pairwise-interaction network, which exhibits certain clustering structure and accounts for 45% of the entire structure of the interactions within the G7 system. We also find that the pairwise interactions become increasingly inadequate in capturing the synchronization as the size of economic system grows. Thus, higher-order interactions must be taken into account when investigating behaviors of large economic systems
Inferring plant ecosystem organization from species occurrences.
No full textIn this paper, we present an approach capable of extracting insights on ecosystem organization from merely occurrence (presence/absence) data. We extrapolate to the collective behavior by encapsulating some simplifying assumptions within a given set of constraints, and then examine their ecological implications. We show that by using the mean occurrence and co-occurrence of species as constraints, one is able to capture detailed statistics of a plant community distributed across a vast semiarid area of the United States. The approach allows us to quantify the species' effective couplings: Their frequencies exhibit a peak at zero and the minimal pairwise model is able to capture about 80% of the ecosystem structure. Our analysis reveals a relatively stronger impact of the species network on uncommon species and underscores the importance of species pairs experiencing positive couplings. Additionally, we study the associations among species and, interestingly, find that the frequencies of groups of different species, which the approach is able to capture, exhibit a power-law-like distribution
Wetland Ecohydrology: stochastic description of water level fluctuations across the soil surface
No full textWetlands provide a suite of social and ecological critical functions such as being habitats of disease-carrying vectors, providing buffer zones against hurricanes, controlling sediment transport, filtering nutrients and contaminants, and a repository of great biological diversity. More recently, wetlands have also been recognized as crucial for carbon storage in the context of global climate change. Despite such importance, quantitative approaches to many aspects of wetlands are far from adequate. Therefore, improving our quantitative understanding of wetlands is necessary to our ability to maintain, manage, and restore these invaluable environments. In wetlands, hydrologic factors and ecosystem processes interplay and generate unique characteristics and a delicate balance between biotic and abiotic elements. The main hydrologic driver of wetland ecosystems is the position of the water level that, being above or below ground, determines the submergence or exposure of soil. When the water level is above the soil surface, soil saturation and lack of oxygen causes hypoxia, anaerobic functioning of microorganisms and anoxic stress in plants, that might lead to the death of non-adapted organisms. When the water level lies below the soil surface, the ecosystem becomes groundwater-dependent, and pedological and physiological aspects play their role in the soil water balance. We propose here a quantitative description of wetland ecohydrology, through a stochastic process-based water balance, driven by a marked compound Poisson noise representing rainfall events. The model includes processes such as rainfall infiltration, evapotranspiration, capillary rise, and the contribution of external water bodies, which are quantified in a simple yet realistic way. The semi-analytical steady-state probability distributions of water level spanning across the soil surface are validated with data from the Everglades (Florida, USA). The model and its results allow for a quantitative analysis of the long term behavior of biotic and abiotic factors which depend on the position of the water level and enable the assessment of impacts of climate changes on the wetland ecosyste
Potential impacts of precipitation change on large-scale patterns of tree diversity
Full text linkForests are globally important ecosystems host to outstanding biological diversity. Widespread efforts have addressed the impacts of climate change on biodiversity in these ecosystems. We show that a metacommunity model founded on basic ecological processes offers direct linkage from large-scale forcing, such as precipitation, to tree diversity patterns of the Mississippi-Missouri River System and its subregions. We quantify changes in tree diversity patterns under various projected precipitation patterns, resulting in a range of responses. Uncertainties accompanying global climate models necessitate the use of scenarios of biodiversity. Here we present results from scenarios with the largest losses and gains in tree diversity. Our results suggest that species losses under scenarios with the most dramatic contractions tend to be greater in magnitude, spatial extent, and statistical significance than gains under alternative scenarios. These findings are expected to have important implications for conservation policy and resource management
Impact of Multiple Environmental Stresses on Wetland Vegetation Dynamics
No full textThis research quantifies the impacts of climate change on the dynamics of wetland vegetation under the effect of multiple stresses, such as drought, water-logging, shade and nutrients. The effects of these stresses are investigated through a mechanistic model that captures the co-evolving nature between marsh emergent plant species and their resources (water, nitrogen, light, and oxygen). The model explicitly considers the feedback mechanisms between vegetation, light and nitrogen dynamics as well as the specific dynamics of plant leaves, rhizomes, and roots. Each plant species is characterized by three independent traits, namely leaf nitrogen (N) content, specific leaf area, and allometric carbon (C) allocation to rhizome storage, which govern the ability to gain and maintain resources as well as to survive in a particular multi-stressed environment. The modeling of plant growth incorporates C and N into the construction of leaves and roots, whose amount of new biomass is determined by the dynamic plant allocation scheme. Nitrogen is internally recycled between pools of plants, litter, humus, microbes, and mineral N. The N dynamics are modeled using a parallel scheme, with the major modifications being the calculation of the aerobic and anoxic periods and the incorporation of the anaerobic processes. A simple hydrologic model with stochastic rainfall is used to describe the water level dynamics and the soil moisture profile. Soil water balance is evaluated at the daily time scale and includes rainfall, evapotranspiration and lateral flow to/from an external water body, with evapotranspiration loss equal to the potential value, governed by the daily average condition of atmospheric water demand. The resulting feedback dynamics arising from the coupled system of plant-soil-microbe are studied in details and species’ fitnesses in the 3-D trait space are compared across various rainfall patterns with different mean and fluctuations. The model results are then compared with those from experiments and field studies reported in the literature, providing insights about the physiological features that enable plants to thrive in different wetland environments and climate regimes
Hydrological drivers of wetland vegetational biodiversity patterns within Everglades National Park, Florida
No full textThe influence of hydrological dynamics on vegetational biodiversity and structuring of wetland environments is of growing interest as wetlands are modified by human alteration and the increasing threat from climate change. Hydrology has long been considered a driving force in shaping wetland communities as the frequency of inundation along with the duration and depth of flooding are key determinants of wetland structure. We attempt to link hydrological dynamics with vegetational distribution and species richness across Everglades National Park(ENP) using two publicly available datasets. The first, the Everglades Depth Estimation Network (EDEN),is a water-surface model which determines the median daily measure of water level across a 400mX400m grid over seven years of measurement. The second is a vegetation map and classification system at the 1:15,000 scale which categorizes vegetation within the Everglades into 79 community types. From these data, we have studied the probabilistic structure of the frequency, duration, and depth of hydroperiods. Preliminary results indicate that the percentage of time a location is inundated is a principal structuring variable with individual communities responding differently. For example, sawgrass appears to be more of a generalist community as it is found across a wide range of time inundated percentages while spike rush has a more restricted distribution and favors wetter environments disproportionately more than predicted at random. Further, the diversity of vegetation communities (e.g. a measure of biodiversity) found across a hydrologic variable does not necessarily match the distribution function for that variable on the landscape. For instance, the number of communities does not differ across the percentage of time inundated. Different measures of vegetation biodiversity such as the local number of community types are also studied at different spatial scales with some characteristics, like the slope of the semi-logarithmic relation between rank and occupancy, found to be robust to scale changes. The ENP offers an expansive natural environment in which to study how vegetational dynamics and community composition are affected by hydrologic variables from the small scale (at the individual community level) to the large(biodiversity measures at differing spatial scales)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A neutral metapopulation model of biodiversity in river networks
No full textIn this paper, we develop a stochastic, discrete, structured meta population model to explore the dynamics and patterns of biodiversity of riparian vegetation. In the model, individual plants spread along a branched network via directional dispersal and undergo neutral ecological drift. Simulation results suggest that in comparison to 2-D landscapes with non-directional dispersal, river networks with directional dispersal have lower local (alpha) and overall (gamma) diversities, but higher between-community (beta) diversity, implying that riparian species are distributed in a more localized pattern and more vulnerable to local extinction. The relative abundance patterns also change, such that higher percentages of species are in low-abundance, or rare, classes, accompanied by concave rank-abundance curves. In contrast to existing theories, the results Suggest that in river networks, increased directional dispersal reduces alpha diversity. These altered patterns and trends result from the combined effects of directionality of dispersal and river network structure, whose relative importance is in need of continuing study. In addition, riparian communities obeying neutral dynamics seem to exhibit abrupt changes where large tributaries confluence; this pattern may provide a signature to identify types of interspecific dynamics in river networks
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