The emergence and resilience of self-organized governance in coupled infrastructure systems.

Studies of small-scale, self-organized social-ecological systems have contributed to our understanding of successful governance of shared resources. However, the lack of formal analytically tractable models of such coupled infrastructure systems makes it difficult to connect this understanding to such concepts as stability, robustness, and resilience, which are increasingly important in considering such systems. In this paper, we mathematically operationalize a widely used conceptual framework via a stylized dynamical model. The model yields a wide range of system outcomes: sustainability or collapse, infrastructure at full or partial capacity, and social agents seeking outside opportunities or exclusively engaging in the system. The low dimensionality of the model enables us to derive these conditions in clear relationships of biophysical and social factors describing the coupled system. Analysis of the model further reveals regime shifts, trade-offs, and potential pitfalls that one may face in governing these self-organized systems. The intuition and insights derived from the model lay ground for more rigorous treatment of robustness and resilience of self-organized coupled infrastructure systems, which can lead to more effective governance.

Evolving biodiversity patterns in changing river networks.

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

Effect of infrastructure design on commons dilemmas in social-ecological system dynamics.

The use of shared infrastructure to direct natural processes for the benefit of humans has been a central feature of human social organization for millennia. Today, more than ever, people interact with one another and the environment through shared human-made infrastructure (the Internet, transportation, the energy grid, etc.). However, there has been relatively little work on how the design characteristics of shared infrastructure affect the dynamics of social-ecological systems (SESs) and the capacity of groups to solve social dilemmas associated with its provision. Developing such understanding is especially important in the context of global change where design criteria must consider how specific aspects of infrastructure affect the capacity of SESs to maintain vital functions in the face of shocks. Using small-scale irrigated agriculture (the most ancient and ubiquitous example of public infrastructure systems) as a model system, we show that two design features related to scale and the structure of benefit flows can induce fundamental changes in qualitative behavior, i.e., regime shifts. By relating the required maintenance threshold (a design feature related to infrastructure scale) to the incentives facing users under different regimes, our work also provides some general guidance on determinants of robustness of SESs under globalization-related stresses.

Testing the feasibility of a hypothetical whaling-conservation permit market in Norway.

A cap-and-trade system for managing whale harvests represents a potentially useful approach to resolve the current gridlock in international whale management. The establishment of whale permit markets, open to both whalers and conservationists, could reveal the strength of conservation demand, about which little is known. This lack of knowledge makes it difficult to predict the outcome of a hypothetical whale permit market. We developed a bioeconomic model to evaluate the influence of economic uncertainty about demand for whale conservation or harvest. We used simulations over a wide range of parameterizations of whaling and conservation demands to examine the potential ecological consequences of the establishment of a whale permit market in Norwegian waters under bounded (but substantial) economic uncertainty. Uncertainty variables were slope of whaling and conservation demand, participation level of conservationists and their willingness to pay for whale conservation, and functional forms of demand, including linear, quadratic, and log-linear forms. A whale-conservation market had the potential to yield a wide range of conservation and harvest outcomes, the most likely outcomes were those in which conservationists bought all whale permits.

Living in a network of scaling cities and finite resources.

Many urban phenomena exhibit remarkable regularity in the form of nonlinear scaling behaviors, but their implications on a system of networked cities has never been investigated. Such knowledge is crucial for our ability to harness the complexity of urban processes to further sustainability science. In this paper, we develop a dynamical modeling framework that embeds population-resource dynamics-a generalized Lotka-Volterra system with modifications to incorporate the urban scaling behaviors-in complex networks in which cities may be linked to the resources of other cities and people may migrate in pursuit of higher welfare. We find that isolated cities (i.e., no migration) are susceptible to collapse if they do not have access to adequate resources. Links to other cities may help cities that would otherwise collapse due to insufficient resources. The effects of inter-city links, however, can vary due to the interplay between the nonlinear scaling behaviors and network structure. The long-term population level of a city is, in many settings, largely a function of the city's access to resources over which the city has little or no competition. Nonetheless, careful investigation of dynamics is required to gain mechanistic understanding of a particular city-resource network because cities and resources may collapse and the scaling behaviors may influence the effects of inter-city links, thereby distorting what topological metrics really measure.

Neutral metacommunity models predict fish diversity patterns in Mississippi-Missouri basin.

River networks, seen as ecological corridors featuring connected and hierarchical dendritic landscapes for animals and plants, present unique challenges and opportunities for testing biogeographical theories and macroecological laws. Although local and basin-scale differences in riverine fish diversity have been analysed as functions of energy availability and habitat heterogeneity, scale-dependent environmental conditions and river discharge, a model that predicts a comprehensive set of system-wide diversity patterns has been hard to find. Here we show that fish diversity patterns throughout the Mississippi-Missouri River System are well described by a neutral metacommunity model coupled with an appropriate habitat capacity distribution and dispersal kernel. River network structure acts as an effective template for characterizing spatial attributes of fish biodiversity. We show that estimates of average dispersal behaviour and habitat capacities, objectively calculated from average runoff production, yield reliable predictions of large-scale spatial biodiversity patterns in riverine systems. The success of the neutral theory in two-dimensional forest ecosystems and here in dendritic riverine ecosystems suggests the possible application of neutral metacommunity models in a diverse suite of ecosystems. This framework offers direct linkage from large-scale forcing, such as global climate change, to biodiversity patterns.

Exploration of underlying patterns among conflict, socioeconomic and political factors.

The emergence of conflict is a complex issue with numerous drivers and interactions playing a role. Exploratory dimension-reduction techniques can reveal patterns of association in such complex data. In this study, an existing dataset was reanalyzed using factor analysis for mixed data to visualize the data in two-dimensional space to explore the conditions associated with high levels of conflict. The first dimension was strongly associated with resilience index, control of corruption, income, income inequality, and regime type, while the second dimension was strongly associated with oil production, regime type, conflict level, political terror level, and water stress. Hierarchical clustering from principal components was used to group the observations into five clusters. Country trajectories through the two-dimensional space provided examples of how movement in the first two dimensions reflected changes in conflict, political terror, regime type, and resilience index. These trajectories correspond to the evolution of themes in research on conflict, particularly in terms of considering the importance of climate or environmental variables in stimulating or sustaining conflict. Understanding conditions associated with high conflict can be helpful in guiding the development of future models for prediction and risk assessment.

Quantifying economic vulnerabilities induced by interdependent networks.

Under economic globalization, countries are linked through trade and investments. This economic interdependence creates vulnerabilities. The indirect vulnerability induced by interdependent networks of trade and investments can put a country's economy at risk, but this risk has yet to be systematically quantified and investigated. In this paper, we developed the novel Potential Indirect Vulnerability Index (PIVI) to capture how interdependencies between networks of trade and foreign direct investment (FDI) may induce economic vulnerabilities. The model consisted of three main components: a target country (the importer of goods), an investing country (the exporter of FDI), and the intermediary countries that export commodities to the target country and receive FDI from the investing country, serving as conduits of the vulnerabilities caused indirectly by the investing country. The PIVI quantifies the indirect vulnerabilities based on the product of two fractions: 1) the dependency of the target country on commodities from each intermediary country; and 2) the dependency of each intermediary country on FDI from the investing country. We demonstrated the utility of PIVI by examining the US economy's vulnerability to China using 2019 trade and FDI data. Several Asian countries and a mix of agricultural products and raw materials were identified as conduits through which China could potentially influence the US economy. Vietnam was a sizeable risk because, while it has been a primary source of many US imports, it also received about 30% of its FDI from China. The US policy makers might opt to increase diversity in trade partners or to promote investment in countries such as Vietnam. We also applied the PIVI analysis to critical minerals, identifying cobalt, tungsten, and copper as the most vulnerability-inducing among them. PIVI is a flexible metric than can be aggregated and modified to provide a more nuanced and focused assessment of an economy's vulnerability.

Triadic signatures of global human mobility networks.

Global refugee and migrant flows form complex networks with serious consequences for both sending and receiving countries as well as those in between. While several basic network properties of these networks have been documented, their finer structural character remains under-studied. One such structure is the triad significance profile (TSP). In this study, the TSPs of global refugee and migrant flow networks are assessed. Results indicate that the migrant flow network's size and TSP remain stable over the years; its TSP shares patterns with social networks such as trade networks. In contrast, the refugee network has been more dynamic and structurally unstable; its TSP shares patterns with networks in the information-processing superfamily, which includes many biological networks. Our findings demonstrate commonality between migrant and social networks as well as between refugee and biological networks, pointing to possible interdisciplinary collaboration-e.g., application of biological network theories to refugee network dynamics-, potentially furthering theoretical development with respect to both network theory and theories on human mobility.

Predicting spatial similarity of freshwater fish biodiversity.

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.

Modeling human migration driven by changing mindset, agglomeration, social ties, and the environment.

Migration is an adaptation strategy to unfavorable conditions and is governed by a complex set of socio-economic and environmental drivers. Here we identified important drivers relatively underrepresented in many migration models-CHanging mindset, Agglomeration, Social ties, and the Environment (CHASE)-and asked: How does the interplay between these drivers influence transient dynamics and long-term outcomes of migration? We addressed this question by developing and analyzing a parsimonious Markov chain model. Our findings suggest that these drivers interact in nonlinear and complex ways. The system exhibits legacy effects, highlighting the importance of including migrants' changing priorities. The increased characteristic population size of the system counter-intuitively leads to fewer surviving cities, and this effect is mediated by how fast migrants change their mindsets and how strong the social ties are. Strong social ties result in less diverse populations across cities, but this effect is influenced by how many cities remain. To our knowledge, this is the first time that these drivers are incorporated in one coherent, mechanistic, parsimonious model and the effects of their interplay on migration systematically studied. The complex interplay underscores the need to incorporate these drivers into mechanistic migration models and implement such models for real-world cases.

Evolution of dispersal in explicitly spatial metacommunities.

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.

Efficiency-fairness trade-offs in evacuation management of urban floods: The effects of the shelter capacity and zone prioritization.

With increasing flood risk, evacuation has become an important research topic in urban flood management. Urban flood evacuation is a complex problem due to i) the complex interactions among several components within a city and ii) the need to consider multiple, often competing, dimensions/objectives in evacuation analysis. In this study, we focused on the interplay between two such objectives: efficiency and fairness. We captured the evacuation process in a conceptual agent-based model (ABM), which was analyzed under different hard infrastructure and institutional arrangement conditions, namely, various shelter capacity distributions as a hard infrastructure property and simultaneous/staged evacuation as an institutional arrangement. Efficiency was measured as the time it takes for a person to evacuate to safety. Fairness was defined by how equally residents suffered from floods, and the level of suffering depended on the perceived risk and evacuation time. Our findings suggested that efficiency is more sensitive to the shelter capacity distribution, while fairness changes more notably according to the evacuation priority assigned to the divided zones in staged evacuation. Simultaneous evacuation generally tended to be more efficient but unfairer than staged evacuation. The efficiency-fairness trade-off was captured by Pareto-optimal strategies, among which uniform capacity cases led to a higher efficiency while prioritizing high-risk residents increases fairness. Strategies balancing efficiency and fairness featured a uniform capacity and prioritized high-risk residents at an intermediate time delay. These findings more clearly exposed the interactions between different factors and could be adopted as benchmarks to inform more complicated evacuation ABMs.

Intra-group decision-making in agent-based models.

Many agent-based models (ABMs) try to explain large-scale phenomena by reducing them to behaviors at lower scales. At these scales in social systems are functional groups such as households, religious congregations, coops and local governments. The intra-group dynamics of functional groups often generate inefficient or unexpected behavior that cannot be predicted by modeling groups as basic units. We introduce a framework for modeling intra-group decision-making and its interaction with social norms, using the household as our focus. We select phenomena related to women's empowerment in agriculture as examples influenced by both intra-household dynamics and gender norms. Our framework proves more capable of replicating these phenomena than two common types of ABMs. We conclude that it is not enough to build multi-scale models; explaining social behaviors entails modeling intra-scale dynamics.

A model of crop diversification under labor shocks.

As demands on agriculture increase, food producers will need to employ management strategies that not only increase yields but reduce environmental impacts. Modeling is a powerful tool for informing decision-making about current and future practices. We present a model to evaluate the effects of crop diversification on the robustness of simulated farms under labor shocks. We use an example inspired by the Florida production system of high-value, labor-intensive fruits. We find that crop diversification to high-value crops is a robust strategy when labor shocks are mild, and that crop diversification becomes less valuable as more simulated farms practice it. Based on our results, we suggest that crop diversification is a useful management strategy under specific conditions, but that policies designed to encourage crop diversification must consider broad effects as well as farm-level benefits.

Network analysis of regional livestock trade in West Africa.

In West Africa, long and complex livestock value chains connect producers mostly in the Sahel with consumption basins in urban areas and the coast. Regional livestock trade is highly informal and, despite recent efforts to understand animal movement patterns in the region, remains largely unrecorded. Using CILSS' database on intraregional livestock trade, we built yearly and overall weighted networks of animal movements between markets. We mapped and characterized the trade networks, identified market communities, key markets and their roles. Additionally, we compared the observed network properties with null-model generated ensembles. Most movements corresponded to cattle, were made by vehicle, and originated in Burkina Faso. We found that live animals in the central and eastern trade basins flow through well-defined, long distance trade corridors where markets tend to trade in a disassortive way with others in their proximity. Modularity-based communities indicated that both national and cross-border trade groups exist. The network's degree and link distributions followed a log-normal or a power-law distribution, and key markets located primarily in urban centers and near borders serve as hubs that give peripheral markets access to the regional network. The null model ensembles could not reproduce the observed higher-level properties, particularly the propinquity and highly negative assortativity, suggesting that other possibly spatial factors shape the structure of regional live animal trade. Our findings support eliminating cross-border impediments and improving the condition of the regional road network, which limit intraregional trade of and contribute to the high prices of food products in West Africa. Although with limitations, our study sheds light on the abstruse structure of regional livestock trade, and the role of trade communities and markets in West Africa.

Patterns of vegetation biodiversity: the roles of dispersal directionality and river network structure.

This paper investigates the importance of dispersal directionality and river network structure to biodiversity patterns. Our model results suggest that dispersal directionality plays a crucial role in determining biodiversity patterns, even more so than dispersal rates. Dispersal directionality heterogenizes the spatial distribution of abundance, which results in higher extinction rates of rare species and higher beta diversity. It induces a few species with very high abundances at the expense of many species with intermediate abundances, thereby lowering alpha and gamma diversities. The river network structure also increases beta diversity, i.e., more heterogeneous ecosystems, in comparison to typical two-dimensional landscapes. We find that the interplay between the dispersal directionality and network topology has important consequences on relative species abundance patterns and the distribution of alpha diversity.

Urban occupational structures as information networks: The effect on network density of increasing number of occupations.

Urban economies are composed of diverse activities, embodied in labor occupations, which depend on one another to produce goods and services. Yet little is known about how the nature and intensity of these interdependences change as cities increase in population size and economic complexity. Understanding the relationship between occupational interdependencies and the number of occupations defining an urban economy is relevant because interdependence within a networked system has implications for system resilience and for how easily can the structure of the network be modified. Here, we represent the interdependencies among occupations in a city as a non-spatial information network, where the strengths of interdependence between pairs of occupations determine the strengths of the links in the network. Using those quantified link strengths we calculate a single metric of interdependence-or connectedness-which is equivalent to the density of a city's weighted occupational network. We then examine urban systems in six industrialized countries, analyzing how the density of urban occupational networks changes with network size, measured as the number of unique occupations present in an urban workforce. We find that in all six countries, density, or economic interdependence, increases superlinearly with the number of distinct occupations. Because connections among occupations represent flows of information, we provide evidence that connectivity scales superlinearly with network size in information networks.

Size and stochasticity in irrigated social-ecological systems.

This paper presents a systematic study of the relation between the size of irrigation systems and the management of uncertainty. We specifically focus on studying, through a stylized theoretical model, how stochasticity in water availability and taxation interacts with the stochastic behavior of the population within irrigation systems. Our results indicate the existence of two key population thresholds for the sustainability of any irrigation system: or the critical population size required to keep the irrigation system operative, and N* or the population threshold at which the incentive to work inside the irrigation system equals the incentives to work elsewhere. Crossing irretrievably leads to system collapse. N* is the population level with a sub-optimal per capita payoff towards which irrigation systems tend to gravitate. When subjected to strong stochasticity in water availability or taxation, irrigation systems might suffer sharp population drops and irreversibly disintegrate into a system collapse, via a mechanism we dub 'collapse trap'. Our conceptual study establishes the basis for further work aiming at appraising the dynamics between size and stochasticity in irrigation systems, whose understanding is key for devising mitigation and adaptation measures to ensure their sustainability in the face of increasing and inevitable uncertainty.