Complex Natural Systems Research Articles

Quantifying arsenate partitioning in aquatic systems: Narrowing the laboratory-real world gap with kinetic sediment extractions and the Diffuse Layer Model

Many studies have proposed that silicic acid and phosphate (PV) can displace arsenic sorbed to iron oxides leading to elevated As concentrations in aquatic systems. While surface complexation models are adept at quantifying sorption to synthetic oxides in laboratory systems their application to complex natural systems remains challenging. In this study we provide a systematic approach to developing a robust use of models for understanding AsV distribution in natural systems in which hydrated iron oxides are the main adsorptive phase. The Waikato River provides a useful laboratory for this work because it contains high H4SiO4, AsV and PV loadings due to geothermal and agricultural inputs. A 15min oxalate extraction and a 48h ethylenediaminetetraacetic acid (EDTA) extraction of river sediment contained the same ratios of As:Fe, P:Fe and Si:Fe. Both of these extracts target the poorly ordered iron oxide phases (typically ferrihydrite) and by following the release of elements over time in the EDTA extraction it was possible to demonstrate that the extracted As, P, and Si were associated with the ferrihydrite. This demonstrates for the first time that a single oxalate extraction can quantify ferrihydrite sorbed H4SiO4, As and PV and provides a basis to quantify the role of these ligands in inhibiting AsV sorption to sediments. The measured concentrations of ferrihydrite sorbed AsV, PV and H4SiO4 for the Waikato River suspended sediment allow for the informed selection of appropriate model parameters for applying the Diffuse Layer Model to the system. In this way it was possible to quantify the effect of the individual components in the river water on AsV sorption. This study provides an explanation for the observation that the proportion of sorbed As in the Waikato River is generally significantly lower than that observed in rivers closer to the world average concentrations. More generally the study provides a method to quantify the role of individual water chemistry components on AsV distribution in natural systems.

An integrated framework to assess complex cultural and natural heritage systems with Multi-Attribute Value Theory

Abstract Decisions related to integrated cultural and natural heritage are increasingly complex due to the multiple-use nature of goods and services provided by the environment, the difficulty in monetary evaluation of intangible heritage and the involvement of a large number of stakeholders. Multiple perspectives are thus required to align social and ecological values to promote sustainable solutions for heritage management. In this context, multi-attribute value techniques can be used to synthesize stakeholder preferences because they can accommodate conflicting, multidimensional and incommensurable objectives. The present paper proposes an experimentation employing the Multi-Attribute Value Theory (MAVT), which is a specific Multicriteria Analysis technique, in the domain of collective decisions about public goods. The objective of the work is to provide an integrated framework for planning and design of future actions according to both qualitative and quantitative elements, in order to help and support landscape and urban planners, policy and decision-makers, land managers and public organizations to manage complex territorial systems characterized by multiple values. To the knowledge of the authors, this is the first application of multi-attribute value techniques for dealing with intangible heritage characterized simultaneously by natural, cultural, ecological, historical and architectural values. The present contribution has thus an innovative and trans-disciplinary potential and may influence future applications referring to collective decisions about public goods.

Resilience and tipping points of an exploited fish population over six decades.

Complex natural systems with eroded resilience, such as populations, ecosystems and socio-ecological systems, respond to small perturbations with abrupt, discontinuous state shifts, or critical transitions. Theory of critical transitions suggests that such systems exhibit fold bifurcations featuring folded response curves, tipping points and alternate attractors. However, there is little empirical evidence of fold bifurcations occurring in actual complex natural systems impacted by multiple stressors. Moreover, resilience of complex systems to change currently lacks clear operational measures with generic application. Here, we provide empirical evidence for the occurrence of a fold bifurcation in an exploited fish population and introduce a generic measure of ecological resilience based on the observed fold bifurcation attributes. We analyse the multivariate development of Barents Sea cod (Gadus morhua), which is currently the world's largest cod stock, over six decades (1949-2009), and identify a population state shift in 1981. By plotting a multivariate population index against a multivariate stressor index, the shift mechanism was revealed suggesting that the observed population shift was a nonlinear response to the combined effects of overfishing and climate change. Annual resilience values were estimated based on the position of each year in relation to the fitted attractors and assumed tipping points of the fold bifurcation. By interpolating the annual resilience values, a folded stability landscape was fit, which was shaped as predicted by theory. The resilience assessment suggested that the population may be close to another tipping point. This study illustrates how a multivariate analysis, supported by theory of critical transitions and accompanied by a quantitative resilience assessment, can clarify shift mechanisms in data-rich complex natural systems.

Detrended partial-cross-correlation analysis: a new method for analyzing correlations in complex system.

In this paper, a new method, detrended partial-cross-correlation analysis (DPCCA), is proposed. Based on detrended cross-correlation analysis (DCCA), this method is improved by including partial-correlation technique, which can be applied to quantify the relations of two non-stationary signals (with influences of other signals removed) on different time scales. We illustrate the advantages of this method by performing two numerical tests. Test I shows the advantages of DPCCA in handling non-stationary signals, while Test II reveals the “intrinsic” relations between two considered time series with potential influences of other unconsidered signals removed. To further show the utility of DPCCA in natural complex systems, we provide new evidence on the winter-time Pacific Decadal Oscillation (PDO) and the winter-time Nino3 Sea Surface Temperature Anomaly (Nino3-SSTA) affecting the Summer Rainfall over the middle-lower reaches of the Yangtze River (SRYR). By applying DPCCA, better significant correlations between SRYR and Nino3-SSTA on time scales of 6 ~ 8 years are found over the period 1951 ~ 2012, while significant correlations between SRYR and PDO on time scales of 35 years arise. With these physically explainable results, we have confidence that DPCCA is an useful method in addressing complex systems.

Seasonal dynamics of zooplankton resource use revealed by carbon amino acid stable isotope values

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 531:143-154 (2015) - DOI: https://doi.org/10.3354/meps11319 Seasonal dynamics of zooplankton resource use revealed by carbon amino acid stable isotope values Jens M. Nielsen*, Monika Winder Department of Ecology, Environment and Plant Sciences, Stockholm University, 106 91 Stockholm, Sweden *Corresponding author: jens.nielsen@su.se ABSTRACT: Reliable tracer techniques are fundamental to retrieving accurate information on consumer dietary resource use in dynamic ecosystems. Analysis of δ13C values in essential amino acids has shown great promise in effectively capturing consumer food sources, since essential amino acids are not synthesized by heterotrophic organisms but instead routed directly from dietary sources to consumers, resulting in negligible 13C trophic discrimination. We assessed seasonal feeding patterns of a widespread key copepod species (Acartia spp.) in the northern Baltic proper using complementary approaches: bulk δ13C and δ15N values, δ13C values of essential amino acids, and quantitative phytoplankton taxonomic data. Our results showed distinct differences between Acartia and seston δ13C essential amino acid values measured at weekly to monthly sampling intervals, which indicated that Acartia preferentially utilized specific dietary resources that comprised only parts of the total phytoplankton composition (varying from 19.7% to 81.4% during the season). Results also indicated that care should be taken when inferring trophic position from bulk stable isotopes when consumers are highly selective, since isotope values of seston may not accurately reflect consumer specific diet resource uptake. Analysis of δ13C values in essential amino acids was shown to be a promising tool to accurately trace consumer resource use in complex natural systems. KEY WORDS: Stable isotopes · Amino acids · Acartia · Zooplankton · Phytoplankton · Food webs Full text in pdf format PreviousNextCite this article as: Nielsen JM, Winder M (2015) Seasonal dynamics of zooplankton resource use revealed by carbon amino acid stable isotope values. Mar Ecol Prog Ser 531:143-154. https://doi.org/10.3354/meps11319 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 531. Online publication date: July 02, 2015 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2015 Inter-Research.

Contribution to the Development of Simulation Model of Ship Turbine

Simulation modelling, performed by System Dynamics Modelling Approach and intensive use of computers, is one of the most convenient and most successful scientific methods of analysis of performance dynamics of nonlinear and very complex natural technical and organizational systems [1]. The purpose of this work is to demonstrate the successful application of system dynamics simulation modelling at analyzing performance dynamics of a complex system of ship’s propulsion system. Gas turbine is a complex non-linear system, which needs to be systematically investigated as a unit consisting of a number of subsystems and elements, which are linked by cause-effect (UPV) feedback loops (KPD), both within the propulsion system and with the relevant surrounding. In this paper the authors will present an efficient application of scientific methods for the study of complex dynamic systems called qualitative and quantitative simulation System Dynamics Methodology. Gas turbine will be presented by a set of non-linear differential equations, after which mental-verbal structural models and flowcharts in System dynamics symbols will be produced, and the performance dynamics in load condition will be simulated in POWERSIM simulation language.

Methodological problems in the modeling of ecosystems and ways of solutions

The great variety and complexity of natural ecosystems leads to methodological problems of simulation and lack of understanding of a common framework in the organization of ecosystems prevents the construction of the universal ecosystem models that are suitable for efficient integration in the model of the biosphere and for the successful solution of theoretical and practical problems. The aim of this work was to develop a systematic theoretical basis for universal description, analysis, and modeling of ecosystems as structural units of the biosphere as the highest-level ecosystem. Analysis of the literature and special studies of the authors demonstrated that a reliable basis for universal models is a multidisciplinary approach that includes three types of concepts (substrate, energy and information) and is based on such fundamental properties of living systems as the attractiveness, adaptability, fractal, network organization. It was found that the most promising are the methods of computer mathematics, based on adaptive networks. The network models can be formed using various tools for operation with artificial neural networks that were developed in neuroinformatics. The entire range of the model states, from excessive to minimally possible, can be investigated. In the network models, one can obtain arbitrarily complex static and dynamic regimes correctable both in the process of model tuning (identification) and during operation of the model (adaptation). The models based on the self-organizing adaptive networks can potentially reflect the most general and fundamental properties of the complex natural systems.

The Entropy Law and the Impossibility of Perpetual Economic Growth

Every production-recycling iteration accumulates an inevitable proportion of its matter-energy in the environment, lest the production process itself will be a system in perpetual motion, violating the second law of Thermodynamics. Such high-entropy matter depletes finite stocks of ecosystem services provided by the ecosphere, hence is incompatible with the long-term growth in the material scale of the economic process. Moreover, the complex natural systems governing such stocks respond to depletion by possibly sudden environmental transitions, thus hindering markets' very ability to adapt to the new equilibrium conditions. Consequently, uncertainty of critical resilience thresholds constrains material economic growth.

Secure routing in MANETs: a bio-inspired approach with honey bees

Mobile Ad Hoc Networks (MANETs) have created ample interest in recent times owing to their various advantages compared to the networks that require basic infrastructure for them to work. This paper elaborates various attacks that can be perpetrated on MANETs and their current solutions. Bio-inspired methods have recently gained importance in computing owing to the need for flexible, adaptable ways of solving engineering problems. Bio-inspired algorithms are based on the structure and functioning of complex natural systems and tend to solve problems in an adaptable and distributed fashion. Thus, bio-inspired techniques can accurately apply to MANETs to solve various issues in the design and working of MANETs. Our bee-inspired protocol is an on-demand path routing algorithm for MANETs inspired by the behavioural principles of honey bees. The algorithm works with multiple types of agents: scouts, workers and soldiers.

The driving forces of evolution

The problem we set is to identify the driving forces of evolution on the basis of well-known regularities from nuclear, atomic, and molecular physics, as well as biophysics. The results of new computer experiments were used to clarify the physical mechanisms of chemical kinetics. This made it possible to distinguish individual factors that direct the development of complex chemical systems toward sophistication. Such experiments make it possible to prove the efficiency of the evolutionary concept that was proposed by E.M. Galimov and to specify the conditions in which the evolutionary development of complex natural systems is possible.

Borehole Imaging Technology Visualizes Photorealistically in Oil-Based Muds

Technology Update To estimate reserves, optimize well recovery, and place future development wells more accurately, operators require increasingly detailed understanding of complex oil and gas reservoirs. High-resolution geological data from the borehole— core samples and wireline microresistivity images—are essential to fully characterize reservoir architecture, guide and constrain reservoir models, and make timely decisions with precision and confidence. In unconventional resource plays, for example, geologists need to observe complex natural fracture systems, measure fracture density and direction, determine in-situ stresses, and calculate pressures required to initiate and propagate optimal hydraulic fracturing. In high-cost deepwater environments, exploration teams need to interpret a wide variety of sedimentary facies. Reservoirs often consist of thinly laminated sands or channels, which demand high-definition methods for visualization and interpretation. Typically, deepwater formations exhibit very low resistivities— from 1 ohm-m in shales to as low as 0.2 ohm-m in water-bearing sands. Operators often use whole cores to identify subtle sedimentary facies, but cores can be expensive and time-consuming to acquire. Thus, core samples are usually obtained only in select wells and in the most critical intervals. The resulting lack of geological detail can adversely affect reservoir analysis, interpretation, and modeling. On the other hand, microelectrical borehole images can be acquired continuously over the openhole interval at any depth or in any formation, with relative ease. Until recently, high-definition borehole imaging has been possible only in electrically conductive water-based muds (WBM). However, most deepwater wells and many unconventional shale wells today are drilled with high-performance, nonconductive oil-based muds (OBM). Legacy OBM borehole imaging tools, which were introduced in the last decade, are a significant technical advancement over the preceding dipmeter tools but still exhibit certain limitations. For example, spatial image resolution in OBM is nowhere near the quality obtained from WBM imagers. The lack of circumferential coverage of the borehole leaves large gaps that must be filled by inference.

Electromagnetic game modeling through Tensor Analysis of Networks and Game Theory

A complex system involves events coming from natural behaviors. Whatever is the complicated face of machines, they are still far from the complexity of natural systems. Currently, economy is one of the rare science trying to find out some ways to model human behavior. These attempts involve game theory and psychology. Our purpose is to develop a formalism able to take in charge both game and hardware modeling. We first present the Tensorial Analysis of Networks, used for the material part of the system. Then, we detail the mathematical objects defined in order to describe the evolution of the system and its gaming side. To illustrate the discussion we consider the case of a drone whose electronic can be disturbed by a radar field, but this drone must fly as near as possible close to this radar.

Editorial Comment on the Special Issue of “Information in Dynamical Systems and Complex Systems”

This special issue collects contributions from the participants of the “Information in Dynamical Systems and Complex Systems” workshop, which cover a wide range of important problems and new approaches that lie in the intersection of information theory and dynamical systems. The contributions include theoretical characterization and understanding of the different types of information flow and causality in general stochastic processes, inference and identification of coupling structure and parameters of system dynamics, rigorous coarse-grain modeling of network dynamical systems, and exact statistical testing of fundamental information-theoretic quantities such as the mutual information. The collective efforts reported here in reflect a modern perspective of the intimate connection between dynamical systems and information flow, leading to the promise of better understanding and modeling of natural complex systems and better/optimal design of engineering systems.

Numerical relations and skill level constrain co-adaptive behaviors of agents in sports teams.

Similar to other complex systems in nature (e.g., a hunting pack, flocks of birds), sports teams have been modeled as social neurobiological systems in which interpersonal coordination tendencies of agents underpin team swarming behaviors. Swarming is seen as the result of agent co-adaptation to ecological constraints of performance environments by collectively perceiving specific possibilities for action (affordances for self and shared affordances). A major principle of invasion team sports assumed to promote effective performance is to outnumber the opposition (creation of numerical overloads) during different performance phases (attack and defense) in spatial regions adjacent to the ball. Such performance principles are assimilated by system agents through manipulation of numerical relations between teams during training in order to create artificially asymmetrical performance contexts to simulate overloaded and underloaded situations. Here we evaluated effects of different numerical relations differentiated by agent skill level, examining emergent inter-individual, intra- and inter-team coordination. Groups of association football players (national – NLP and regional-level – RLP) participated in small-sided and conditioned games in which numerical relations between system agents were manipulated (5v5, 5v4 and 5v3). Typical grouping tendencies in sports teams (major ranges, stretch indices, distances of team centers to goals and distances between the teams' opposing line-forces in specific team sectors) were recorded by plotting positional coordinates of individual agents through continuous GPS tracking. Results showed that creation of numerical asymmetries during training constrained agents' individual dominant regions, the underloaded teams' compactness and each team's relative position on-field, as well as distances between specific team sectors. We also observed how skill level impacted individual and team coordination tendencies. Data revealed emergence of co-adaptive behaviors between interacting neurobiological social system agents in the context of sport performance. Such observations have broader implications for training design involving manipulations of numerical relations between interacting members of social collectives.

Fuzzy fractal dimension of complex networks

Complex networks are widely used to describe the structure of many complex systems in nature and society. The box-covering algorithm is widely applied to calculate the fractal dimension, which plays an important role in complex networks. However, there are two open issues in the existing box-covering algorithms. On the one hand, to identify the minimum boxes for any given size belongs to a family of Non-deterministic Polynomial-time hard problems. On the other hand, there exists randomness. In this paper, a fuzzy fractal dimension model of complex networks with fuzzy sets is proposed. The results are illustrated to show that the proposed model is efficient and less time consuming.

Linking Biophysical, Socioeconomic, and Political Effects of Climate Change on Agro-Ecosystems

ABSTRACT To meet the sustainability challenges of the future, we need leaders who are trained to work well in diverse, multidisciplinary teams and a populace that understands the biophysical and socioeconomic challenges facing humanity and how to meet the needs of its diverse stakeholders. With a goal of increasing climate literacy amongst college students, we developed a cooperative jigsaw activity to encourage students to explore the complexities of joint decision making when taking into account multiple perspectives. We found that undergraduate science (natural science and natural resources) students were engaged, drew on a variety of types of evidence to support claims about managing rangelands impacted by climate change, and referenced both complex social and natural systems in their postassessment.

A Synopsis of Short-Term Response to Alternative Restoration Treatments in Sagebrush-Steppe: The SageSTEP Project

The Sagebrush Steppe Treatment Evaluation Project (SageSTEP) is an integrated long-term study that evaluates ecological effects of alternative treatments designed to reduce woody fuels and to stimulate the herbaceous understory of sagebrush steppe communities of the Intermountain West. This synopsis summarizes results through 3 yr posttreatment. Woody vegetation reduction by prescribed fire, mechanical treatments, or herbicides initiated a cascade of effects, beginning with increased availability of nitrogen and soil water, followed by increased growth of herbaceous vegetation. Response of butterflies and magnitudes of runoff and erosion closely followed herbaceous vegetation recovery. Effects on shrubs, biological soil crust, tree cover, surface woody fuel loads, and sagebrush-obligate bird communities will take longer to be fully expressed. In the short term, cool wet sites were more resilient than warm dry sites, and resistance was mostly dependent on pretreatment herbaceous cover. At least 10 yr of posttreatment time will likely be necessary to determine outcomes for most sites. Mechanical treatments did not serve as surrogates for prescribed fire in how each influenced the fuel bed, the soil, erosion, and sage-obligate bird communities. Woody vegetation reduction by any means resulted in increased availability of soil water, higher herbaceous cover, and greater butterfly numbers. We identified several trade-offs (desirable outcomes for some variables, undesirable for others), involving most components of the study system. Trade-offs are inevitable when managing complex natural systems, and they underline the importance of asking questions about the whole system when developing management objectives. Substantial spatial and temporal heterogeneity in sagebrush steppe ecosystems emphasizes the point that there will rarely be a “recipe” for choosing management actions on any specific area. Use of a consistent evaluation process linked to monitoring may be the best chance managers have for arresting woodland expansion and cheatgrass invasion that may accelerate in a future warming climate.

The science case for an orbital mission to Uranus: Exploring the origins and evolution of ice giant planets

Giant planets helped to shape the conditions we see in the Solar System today and they account for more than 99% of the mass of the Sun’s planetary system. They can be subdivided into the Ice Giants (Uranus and Neptune) and the Gas Giants (Jupiter and Saturn), which differ from each other in a number of fundamental ways. Uranus, in particular is the most challenging to our understanding of planetary formation and evolution, with its large obliquity, low self-luminosity, highly asymmetrical internal field, and puzzling internal structure. Uranus also has a rich planetary system consisting of a system of inner natural satellites and complex ring system, five major natural icy satellites, a system of irregular moons with varied dynamical histories, and a highly asymmetrical magnetosphere. Voyager 2 is the only spacecraft to have explored Uranus, with a flyby in 1986, and no mission is currently planned to this enigmatic system. However, a mission to the uranian system would open a new window on the origin and evolution of the Solar System and would provide crucial information on a wide variety of physicochemical processes in our Solar System. These have clear implications for understanding exoplanetary systems. In this paper we describe the science case for an orbital mission to Uranus with an atmospheric entry probe to sample the composition and atmospheric physics in Uranus’ atmosphere. The characteristics of such an orbiter and a strawman scientific payload are described and we discuss the technical challenges for such a mission. This paper is based on a white paper submitted to the European Space Agency’s call for science themes for its large-class mission programme in 2013.

Surface complexation modeling of americium sorption onto volcanic tuff

Results of a surface complexation model (SCM) for americium sorption on volcanic rocks (devitrified and zeolitic tuff) are presented. The model was developed using PHREEQC and based on laboratory data for americium sorption on quartz. Available data for sorption of americium on quartz as a function of pH in dilute groundwater can be modeled with two surface reactions involving an americium sulfate and an americium carbonate complex. It was assumed in applying the model to volcanic rocks from Yucca Mountain, that the surface properties of volcanic rocks can be represented by a quartz surface. Using groundwaters compositionally representative of Yucca Mountain, americium sorption distribution coefficient (Kd, L/Kg) values were calculated as function of pH. These Kd values are close to the experimentally determined Kd values for americium sorption on volcanic rocks, decreasing with increasing pH in the pH range from 7 to 9. The surface complexation constants, derived in this study, allow prediction of sorption of americium in a natural complex system, taking into account the inherent uncertainty associated with geochemical conditions that occur along transport pathways.

Diversity of Arctic pelagic <i>Bacteria</i> with an emphasis on photoheterotrophs: a review

Abstract. The Arctic Ocean is a unique marine environment with respect to seasonality of light, temperature, perennial ice cover, and strong stratification. Other important distinctive features are the influence of extensive continental shelves and its interactions with Atlantic and Pacific water masses and freshwater from sea ice melt and rivers. These characteristics have major influence on the biological and biogeochemical processes occurring in this complex natural system. Heterotrophic bacteria are crucial components of marine food webs and have key roles in controlling carbon fluxes in the oceans. Although it was previously thought that these organisms relied on the organic carbon in seawater for all of their energy needs, several recent discoveries now suggest that pelagic bacteria can depart from a strictly heterotrophic lifestyle by obtaining energy through unconventional mechanisms that are linked to the penetration of sunlight into surface waters. These photoheterotrophic mechanisms may play a significant role in the energy budget in the euphotic zone of marine environments. Modifications of light and carbon availability triggered by climate change may favor the photoheterotrophic lifestyle. Here we review advances in our knowledge of the diversity of marine photoheterotrophic bacteria and discuss their significance in the Arctic Ocean gained in the framework of the Malina cruise.