A Systems Model Approach to Determining Resilience Surrogates for Case Studies

Resilience theory offers a framework for understanding the dynamics of complex systems. However, operationalizing resilience theory to develop and test empirical hypotheses can be difficult. We present a method in which simple systems models are used as a framework to identify resilience surrogates for case studies. The process of constructing a systems model for a particular case offers a path for identifying important variables related to system resilience, including the slowly-changing variables and thresholds that often are keys to understanding the resilience of a system. We develop a four-step process for identifying resilience surrogates through development of systems models. Because systems model development is often a difficult step, we summarize four basic existing systems models and give examples of how each may be used to identify resilience surrogates. The construction and analysis of simple systems models provides a useful basis for guiding and directing the selection of surrogate variables that will offer appropriate empirical measures of resilience.     

DyRAM: An Active Reliable Multicast Framework for Data Distribution

Group communications (multicast) are foreseen to be one of the most critical yet challenging technologies to meet the exponentially growing demands for data distribution in a large variety of applications of the Internet (such as grid computing, web applications and distributed simulations). When reliability is required, there is no straightforward solutions and meeting the objectives of reliable multicast is not an easy task. Active networks open a new perspective in providing more efficient solutions for the problem of reliability. In this context, routers are able to perform customized computations on the packets flowing through them. In this paper, we propose a receiver-based (replier) local recovery multicast protocol with dynamic repliers elected on a per-packet basis. Designed to provide an efficient reliable multicast service without any cache facilities inside the network, our approach, uses low-overhead active services in routers. The current paper addresses the design, evaluation and the implementation of an efficient and scalable reliable multicast protocol noted DyRAM standing for Dynamic Replier Active reliable multicast.     

Grazing Management, Resilience, and the Dynamics of a Fire-driven Rangeland System

We developed a stylized mathematical model to explore the effects of physical, ecological, and economic factors on the resilience of a managed fire-driven rangeland system. Depending on grazing pressure, the model exhibits one of three distinct configurations: a fire-dominated, grazing-dominated, or shrub-dominated rangeland system. Transaction costs and costs due to shrub invasion, via their effect on grazing decisions, strongly influence which stable configuration is occupied. This, in turn, determines the resilience of the rangeland system. These results are used to establish conditions under which management for profit is consistent with the maintenance of resilience. Received 2 January 2001; accepted 11 June 2001.     

Knowledge, Learning and the Evolution of Conservation Practice for Social-Ecological System Resilience

There are two broadly conceptualized ways in which conservation knowledge may evolve: the depletion crisis model and the ecological understanding model. The first one argues that developing conservation thought and practice depends on learning that resources are depletable. Such learning typically follows a resource crisis. The second mechanism emphasizes the development of conservation practices following the incremental elaboration of environmental knowledge by a group of people. These mechanisms may work together. Following a perturbation, a society can self-organize, learn and adapt. The self-organizing process, facilitated by knowledge development and learning, has the potential to increase the resilience (capability to absorb disturbance and reorganize while undergoing change) of resource use systems. Hence, conservation knowledge can develop through a combination of long-term ecological understanding and learning from crises and mistakes. It has survival value, as it increases the resilience of integrated social--ecological systems to deal with change in ways that continue to sustain both peoples and their environments.     

Resilience indicators: prospects and limitations for early warnings of regime shifts

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.     

Plant Species Composition Effects on Belowground Properties and the Resistance and Resilience of the Soil Microflora to a Drying Disturbance

We hypothesised that plant species composition and richness would affect soil chemical and microbial community properties, and that these in turn would affect soil microbial resistance and resilience to an experimentally imposed drying disturbance. We performed a container experiment that manipulated the composition and species richness of common pasture plant species (Trifolium repens, Lolium perenne, and Plantago lanceolata) by growing them in monoculture, and in all the possible two and three-way combinations, along with an unplanted control soil. Experimental units were harvested at four different times over a 16-month period to determine the effect of plant community development and seasonal changes in temperature and moisture on belowground properties. Results showed that plant species composition influenced soil chemistry, soil microbial community properties and soil microbial resistance and resilience. Soil from planted treatments generally showed reduced soil microbial resistance to drying compared to unplanted control soils. Soils from under T. repens showed a higher resistance and resilience than the soils from under P. lanceolata, and a higher resistance than soils from under L. perenne. We suggest that differences across soils in either resource limitation or soil microbial community structure may be responsible for these results. Plant species richness rarely affected soil microbial community properties or soil microbial resistance and resilience, despite having some significant effects on plant community biomass and soil nitrogen contents in some harvests. The effect that treatments had for most variables differed between harvests, suggesting that results can be altered by the stage of plant community development or by extrinsic environmental factors that varied with harvest timing. These results in combination show that soil microbial resistance and resilience was affected by plant community composition, and the time of measurement, but was largely unrelated to plant species richness.     

Algebraic connectivity may explain the evolution of gene regulatory networks

Gene expression is a result of the interplay between the structure, type, kinetics, and specificity of gene regulatory interactions, whose diversity gives rise to the variety of life forms. As the dynamic behavior of gene regulatory networks depends on their structure, here we attempt to determine structural reasons which, despite the similarities in global network properties, may explain the large differences in organismal complexity. We demonstrate that the algebraic connectivity, the smallest non-trivial eigenvalue of the Laplacian, of the directed gene regulatory networks decreases with the increase of organismal complexity, and may therefore explain the difference between the variety of analyzed regulatory networks. In addition, our results point out that, for the species considered in this study, evolution favours decreasing concentration of strategically positioned feed forward loops, so that the network as a whole can increase the specificity towards changing environments. Moreover, contrary to the existing results, we show that the average degree, the length of the longest cascade, and the average cascade length of gene regulatory networks cannot recover the evolutionary relationships between organisms. Whereas the dynamical properties of special subnetworks are relatively well understood, there is still limited knowledge about the evolutionary reasons for the already identified design principles pertaining to these special subnetworks, underlying the global quantitative features of gene regulatory networks of different organisms. The behavior of the algebraic connectivity, which we show valid on gene regulatory networks extracted from curated databases, can serve as an additional evolutionary principle of organism-specific regulatory networks.     

An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement

The adequate location of wells in oil and environmental applications has a significant economic impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infrastructure to realize an autonomic self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem. Wolfgang Bangerth is a postdoctoral research fellow at both the Institute for Computational Engineering and Sciences, and the Institute for Geophyics, at the University of Texas at Austin. He obtained his Ph.D. in applied mathematics from the University of Heidelberg, Germany in 2002. He is the project leader for the deal.II finite element library (http://www.dealii.org). Wolfgang is a member of SIAM, AAAS, and ACM. Hector Klie obtained his Ph.D. degree in Computational Science and Engineering at Rice University, 1996, he completed his Master and undergraduate degrees in Computer Science at the Simon Bolivar University, Venezuela in 1991 and 1989, respectively. Hector Klie's main research interests are in the development of efficient parallel linear and nonlinear solvers and optimization algorithms for large-scale transport and flow of porous media problems. He currently holds the position of Associate Director and Senior Research Associate in the Center for Subsurface Modeling at the Institute of Computational Science and Engineering at The University of Texas at Austin. Dr. Klie is current member of SIAM, SPE and SEG. Vincent Matossian obtained a Masters in applied physics from the French Université Pierre et Marie Curie. Vincent is currently pursuing a Ph.D. degree in distributed systems at the Department of Electrical and Computer Engineering at Rutgers University under the guidance of Manish Parashar. His research interests include information discovery and ad-hoc communication paradigms in decentralized systems. Manish Parashar is Professor of Electrical and Computer Engineering at Rutgers University, where he also is director of the Applied Software Systems Laboratory. He received a BE degree in Electronics and Telecommunications from Bombay University, India and MS and Ph.D. degrees in Computer Engineering from Syracuse University. He has received the Rutgers Board of Trustees Award for Excellence in Research (2004–2005), NSF CAREER Award (1999) and the Enrico Fermi Scholarship from Argonne National Laboratory (1996). His research interests include autonomic computing, parallel & distributed computing (including peer-to-peer and Grid computing), scientific computing, software engineering. He is a senior member of IEEE, a member of the IEEE Computer Society Distinguished Visitor Program (2004–2007), and a member of ACM. Mary Fanett Wheeler obtained her Ph.D. at Rice University in 1971. Her primary research interest is in the numerical solutions of partial differential systems with applications to flow in porous media, geomechanics, surface flow, and parallel computation. Her numerical work includes formulation, analysis and implementation of finite-difference/finite-element discretization schemes for nonlinear, coupled PDE's as well as domain decomposition iterative solution methods. She has directed the Center for Subsurface Modeling, The University of Texas at Austin, since its creation in 1990. Dr. Wheeler is recepient of the Ernest and Virginia Cockrell Chair in Engineering and is Professor in the Department of Aerospace Engineering & Engineering Mechanics and in the Department of Petroleum & Geosystems Engineering of The University of Texas     

Establishment of neuronal connectivity during development of the Drosophila larval visual system

We used confocal microscopy in conjunction with specific antibodies and enhancer trap strains to investigate the development of specific neuronal connections in a simple model system, the larval visual system of Drosophila. We find that the establishment of axonal projections from the larval photoreceptor neurons to their central nervous system targets involves a series of discrete steps. During embryogenesis, the larval optic nerve contacts several different cell types, including optic lobe pioneer (OLP) neurons and a number of glial cells. We demonstrate that OLP neurons are present and project normally in glass (gl) mutant embryos in which the larval optic nerve fails to develop, suggesting that they do not depend on interactions with the larval optic nerve for differentiation and proper axonal projection. The OLPs fail to differentiate properly in disconnected (disco) mutant embryos, where appropriate connections between the larval optic nerve and its targets in the brain are not formed. The disco gene is expressed in the OLPs and may therefore act autonomously to direct the differentiation of these cells. Taken together, our results suggest that the OLPs act as an intermediate target required for the establishment of normal optic nerve projection and connectivity. © 1995 John Wiley & Sons, Inc.     

Sustainable Consumption of Food: A Framework for Analyzing Scenarios about Changes in Diets

This article describes the integration of life-cycle assessment methods with a new input-output model of the world economy to analyze the environmental and economic implications of alternative future diets. The article reviews findings by industrial ecologists about the energy and land required for the production and consumption of alternative foods and diets in several European countries. It also reviews attributes of foods and diets identified by nutritionists as reducing the risks of obesity and major chronic diseases related to the diets of the affluent. The predominantly plant-based Mediterranean-type diet emerges as a dietary scenario that could satisfy both sets of concerns. The likely implications for agriculture and for farm policies of a shift toward this diet from the current average diet in the United States are discussed and shown to be substantial. The one-country studies reviewed in the article provide substantial insights into the potential ramifications of dietary change. Many of the limitations of these studies could be overcome by conducting the analysis in a global framework that represented the relationships among consumption, production, and trade and the physical constraints within which they operate. Analysis of the environmental and economic implications of alternative scenarios describing healthy diets can help stimulate more intensive dialogue, debate, and action among the interested parties; such analysis can both benefit from and contribute to initiatives such as the World Health Organization's global strategy on diet and health, which intends to enlist the support of governments, corporations, and civil society.     

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.     

An Approach for the Accurate Measurement of Social Morality Levels

In the social sciences, computer-based modeling has become an increasingly important tool receiving widespread attention. However, the derivation of the quantitative relationships linking individual moral behavior and social morality levels, so as to provide a useful basis for social policy-making, remains a challenge in the scholarly literature today. A quantitative measurement of morality from the perspective of complexity science constitutes an innovative attempt. Based on the NetLogo platform, this article examines the effect of various factors on social morality levels, using agents modeling moral behavior, immoral behavior, and a range of environmental social resources. Threshold values for the various parameters are obtained through sensitivity analysis; and practical solutions are proposed for reversing declines in social morality levels. The results show that: (1) Population size may accelerate or impede the speed with which immoral behavior comes to determine the overall level of social morality, but it has no effect on the level of social morality itself; (2) The impact of rewards and punishment on social morality levels follows the “5∶1 rewards-to-punishment rule,” which is to say that 5 units of rewards have the same effect as 1 unit of punishment; (3) The abundance of public resources is inversely related to the level of social morality; (4) When the cost of population mobility reaches 10% of the total energy level, immoral behavior begins to be suppressed (i.e. the 1/10 moral cost rule). The research approach and methods presented in this paper successfully address the difficulties involved in measuring social morality levels, and promise extensive application potentials.     

Neuromodulation Integrating rTMS and Neurofeedback for the Treatment of Autism Spectrum Disorder: An Exploratory Study

Autism spectrum disorder (ASD) is a pervasive developmental disorder characterized by deficits in social interaction, language, stereotyped behaviors, and restricted range of interests. In previous studies low frequency repetitive transcranial magnetic stimulation (rTMS) has been used, with positive behavioral and electrophysiological results, for the experimental treatment in ASD. In this study we combined prefrontal rTMS sessions with electroencephalographic (EEG) neurofeedback (NFB) to prolong and reinforce TMS-induced EEG changes. The pilot trial recruited 42 children with ASD (~14.5 years). Outcome measures included behavioral evaluations and reaction time test with event-related potential (ERP) recording. For the main goal of this exploratory study we used rTMS-neurofeedback combination (TMS-NFB, N = 20) and waitlist (WTL, N = 22) groups to examine effects of 18 sessions of integrated rTMS-NFB treatment or wait period) on behavioral responses, stimulus and response-locked ERPs, and other functional and clinical outcomes. The underlying hypothesis was that combined TMS-NFB will improve executive functions in autistic patients as compared to the WTL group. Behavioral and ERP outcomes were collected in pre- and post-treatment tests in both groups. Results of the study supported our hypothesis by demonstration of positive effects of combined TMS-NFB neurotherapy in active treatment group as compared to control WTL group, as the TMS-NFB group showed significant improvements in behavioral and functional outcomes as compared to the WTL group.     

Robustness of a Neural Network Model for Differencing

A neural network, originally proposed as a model for nuclei in the auditory brainstem, uses gradients of cell thresholds to reliably compute the difference of inputs over wide input ranges. The encoding of difference is linear even though the individual components of the network are finite, saturating, nonlinear devices highly dependent on input level. Theorems are proven that explain the linear dependence of network output on difference and that show the robustness of the network to perturbations of the threshold gradients. There is some evidence that the network exists in the neural tissue of the auditory brainstem.     

An Analytical Framework for Contrasting Patient and Provider Views of the Process of Chronic Disease Management

Medical anthropologists involved in clinical research are often asked to help explain patients' "noncompliance" with treatment recommendations. The clinical literature on "noncompliance" tends to problematize only the patient's perspective, treating the provider's perspective as an uncontroversial point of departure. Explicating the articulation between provider and patient assumptions, expectations, and perceptions in managing chronic illness is an area well suited to the unique perspective of medical anthropologists. In this article we present an analytical framework for contrasting patient and provider goals, strategies, and evaluation criteria in chronic illness management, using examples from research on type 2 diabetes care in South Texas. This approach goes beyond contrasting patient and provider concepts and explanations of the illness itself and examines their contrasting views within the dynamic process of long-term care. This approach may prove especially useful for research aimed at a clinical audience, since it maintains a clinically relevant focus while giving serious consideration to the patient's perspective.     

Resilience of Alternative Stable States during the Recovery of Shallow Lakes from Eutrophication: Lake Veluwe as a Case Study

In this paper we analyze a long-term dataset on the recovery from eutrophication of Lake Veluwe (The Netherlands). Clear hysteresis was observed in a number of ecosystem variables: the route to recovery differed significantly from the route that led to loss of clear water. The macrophyte dominated state disappeared in the late 1960s at TP above 0.20 mg l⁻¹, whereas its return occurred at less than 0.10 mg TP l⁻¹. Several regime shifts resulting in the occurrence of three alternative stable states were observed over a period of 30 years. The turbid state showed resistance to change, despite a strong and prompt reduction in Chl-a following reduction of external P-loading. The most important component that determined hysteresis in the return to clear water was not internal P-loading, but a high level of nonalgal light attenuation (through sediment resuspension) maintained by the interaction between wind and benthivorous fish. Although Chara was able to re-colonize the most shallow parts of the lake, recovery stalled and for a number of years clear (above charophyte beds) and turbid (deeper parts of the lake) water co-existed, as a separate alternative state on route to full recovery. Lake-wide clear water was re-established after bream density had been reduced substantially. This allowed a return of zebra mussels to the lake, whose high filtration capacity helped in maintaining clear water. In this study, we were able to identify the main drivers of hysteresis and regime shifts, although formal demonstration of cause and effect was not possible on the basis of field data alone. We argue that resilience of the present clear water state of Lake Veluwe very much depends on sizable populations of a few keystone species, especially Chara (stoneworts) and Dreissena (zebra mussels), and that careful management of these species is equally important as control of nutrients. Lake management should strive to maintain and strengthen resilience of the ecosystem, and this should offer protection against a renewed collapse of the clear state.     

Testing the consistency of connectivity patterns for a widely dispersing marine species

Connectivity is widely recognized as an important component in developing effective management and conservation strategies. Although managers are generally most interested in demographic, rather than genetic connectivity, new analytic approaches are able to provide estimates of both demographic and genetic connectivity measures from genetic data. Combining such genetic data with mathematical models represents a powerful approach for accurately determining patterns of population connectivity. Here, we use microsatellite markers to investigate the genetic population structure of the New Zealand Rock Lobster, Jasus edwardsii, which has one of the longest known larval durations of all marine species (>2 years), a very large geographic range (>5500 km), and has been the subject of extensive dispersal modeling. Despite earlier mitochondrial DNA studies finding homogeneous genetic structure, the mathematical model suggests that there are source-sink dynamics for this species. We found evidence of genetic structure in J. edwardsii populations with three distinct genetic groups across New Zealand and a further Australian group; these groups and patterns of gene flow were generally congruent with the earlier mathematical model. Of particular interest was the consistent identification of a self-recruiting population/region from both modeling and genetic approaches. Although there is the potential for selection and harvesting to influence the patterns we observed, we believe oceanographic processes are most likely responsible for the genetic structure observed in J. edwardsii. Our results, using a species at the extreme end of the dispersal spectrum, demonstrate that source-sink population dynamics may still exist for such species.