Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion

Structure of higher-order interactions in social-ecological networks through Q-analysis of their neighbourhood and clique complex

This paper studies higher-order interactions in social-ecological networks, which formally represent interactions within the social and ecological units of an ecosystem. Many real-world social ecosystems exhibit not only pairwise interactions but also higher-order interactions among their units. Therefore, the conventional graph-theoretic description of networks falls short of capturing these higher-order interactions due to the inherent limitations of the graph definition. In this work, a mathematical framework for capturing the higher-order interactions of a social-ecological system has been given by incorporating notions from combinatorial algebraic topology. In order to achieve this, two different simplicial complexes, the clique and the neighbourhood complex, have been constructed from a pairwise social-ecological network. As a case study, the Q-analysis and a structural study of the interactions in the rural agricultural system of southern Madagascar have been done at various structural levels denoted by q. The results obtained by calculating all the structural vectors for both simplicial complexes, along with exciting results about the participation of facets of the clique complex at different q-levels, have been discussed. This work also establishes significant theorems concerning the dimension of the neighbourhood complex and clique complex obtained from the parent pairwise network.

Multiple interaction networks reveal that Lepidoptera larvae and adults prefer various host plants for diet and pollination

Plant-Lepidoptera interactions are often studied using the pollination or herbivore networks only. Lepidoptera species are involved in two types of plant-insect interactions because they are herbivores as larvae and pollinators as adults. The study of entangled networks is critical, since the interaction of different networks can affect the overall network and community stability. Here, we studied the interaction of plants and Lepidoptera on the Yongxing Island, South China Sea. A plant-lepidopteran pollination network and a plant-lepidopteran herbivore network were built by using data from flower-pollinator and leaf-herbivore interactions. We then combined the two networks into a single network. We measured plant composition similarity within each sub-network and across sub-networks for Lepidoptera species. Our findings indicate that the plant-Lepidoptera pollination network and the herbivory network share significant proportions of Lepidoptera but small proportions of plant assemblages. The pollination network had higher nestedness and connectance than the herbivore network. Agrius convolvuli was the most specialized species, while Zizina otis had the highest species strength in the pollination network. Most Lepidoptera species were highly specialized in the herbivore network and their importance positively correlated across the two networks. Furthermore, there was no dietary composition similarity between the two networks for most Lepidoptera species. Our findings highlight the visible structural difference between the pollination and the herbivore networks. Adult Lepidoptera selects different plants for oviposition and feeding, a strategy that may benefit their reproduction and survival by sustaining adequate resources for their two life stages and the diversity of both plants and insects in oceanic island communities.

Evolution of system connectivity to support food production in the Indus Basin in Pakistan

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Robustness in phenotypic plasticity and heterogeneity patterns enabled by EMT networks

Epithelial-mesenchymal plasticity (EMP) is a key arm of cancer metastasis and is observed across many contexts. Cells undergoing EMP can reversibly switch between three classes of phenotypes: epithelial (E), mesenchymal (M), and hybrid E/M. While a large number of multistable regulatory networks have been identified to be driving EMP in various contexts, the exact mechanisms and design principles that enable robustness in driving EMP across contexts are not yet fully understood. Here, we investigated dynamic and structural robustness in EMP networks with regard to phenotypic heterogeneity and plasticity. We use two different approaches to simulate these networks: a computationally inexpensive, parameter-independent continuous state space Boolean model, and an ODE-based parameter-agnostic framework (RACIPE), both of which yielded similar phenotypic distributions. While the latter approach is useful for measurements of plasticity, the former model enabled us to extensively investigate robustness in phenotypic heterogeneity. Using perturbations to network topology and by varying network parameters, we show that multistable EMP networks are structurally and dynamically more robust compared with their randomized counterparts, thereby highlighting their topological hallmarks. These features of robustness are governed by a balance of positive and negative feedback loops embedded in these networks. Using a combination of the number of negative and positive feedback loops weighted by their lengths, we identified a metric that can explain the structural and dynamical robustness of these networks. This metric enabled us to compare networks across multiple sizes, and the network principles thus obtained can be used to identify fragilities in large networks without simulating their dynamics. Our analysis highlights a network topology-based approach to quantify robustness in the phenotypic heterogeneity and plasticity emergent from EMP networks.

The fundamental benefits of multiplexity in ecological networks

A tipping point presents perhaps the single most significant threat to an ecological system as it can lead to abrupt species extinction on a massive scale. Climate changes leading to the species decay parameter drifts can drive various ecological systems towards a tipping point. We investigate the tipping-point dynamics in multi-layer ecological networks supported by mutualism. We unveil a natural mechanism by which the occurrence of tipping points can be delayed by multiplexity that broadly describes the diversity of the species abundances, the complexity of the interspecific relationships, and the topology of linkages in ecological networks. For a double-layer system of pollinators and plants, coupling between the network layers occurs when there is dispersal of pollinator species. Multiplexity emerges as the dispersing species establish their presence in the destination layer and have a simultaneous presence in both. We demonstrate that the new mutualistic links induced by the dispersing species with the residence species have fundamental benefits to the well-being of the ecosystem in delaying the tipping point and facilitating species recovery. Articulating and implementing control mechanisms to induce multiplexity can thus help sustain certain types of ecosystems that are in danger of extinction as the result of environmental changes.

Unravelling the Paradoxical Seasonal Food Scarcity in a Peasant Microregion of Mexico

Seasonal food scarcity during pre-harvest months is, widely, considered to be the principal manifestation of food insecurity, for some 600 million members of smallholder families, who rely on a variety of coping strategies. This paper analyses both the peasant-economy variables that explain the presence and intensity of seasonal food scarcity, and the coping strategies of 120 rural households in a microregion of southern Mexico. We, also, examine how supply networks for six archetypical foods of the peasant diet express robustness or vulnerability during seasons of abundance and scarcity. The method combines surveys, ethnographic fieldwork, statistical models and social network analyses. Results show that 74% of households experience at least one month of food scarcity annually, and 34% of shortages last more than six months. In total, 29% of affected households gather wild foods, and 14% use intense coping strategies, such as international migration, taking out rural loans, and parental food buffering. During scarce seasons, self-sufficiency networks for maize and beans contract, but still maintain the food supply of peasant households, while cash-consumption networks such as those of beef become accessible only to a small sector of economically differentiated households. In contrast to the vast majority of research, which simply reports the presence of seasonal food shortages and describes the coping strategies of rural households, this paper provides an in-depth analysis—based upon a novel methodological integration—of the socioeconomic, agrifood, and land tenure conditions that may determine why many peasant territories in the Global South face the “farmer–food-scarcity paradox”.

Potential for cascading impacts of environmental change and policy on indigenous culture

Global environmental and societal changes threaten the cultures of indigenous peoples and local communities (IPLC). Despite the importance of IPLC worldviews and knowledge to sustaining human well-being and biodiversity, risks to these cultural resources are commonly neglected in environmental governance, in part because impacts can be indirect and therefore difficult to evaluate. Here, we investigate the connectivity of values associated with the relationship Ngātiwai (a New Zealand Māori tribe) have with their environment. We show that mapping the architecture of values-environment relationships enables assessment of how deep into culture the impacts of environmental change or policy can cascade. Our results detail how loss of access to key environmental elements could potentially have extensive direct and cascading impacts on the cultural values of Ngātiwai, including environmental responsibilities. Thus, considering only direct effects of environmental change or policy on cultural resources, or treating IPLC social-ecological relations simplistically, can severely underestimate threats to cultures.

Differential Network Testing Reveals Diverging Dynamics of Organ System Interactions for Survivors and Non-survivors in Intensive Care Medicine

Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically based on specific concepts of association, and (2) the network characteristic employed to measure the difference. Using permutation-based tests, our approach is general and applicable to various overall, node-specific or edge-specific network difference characteristics. The methods are implemented in our freely available R software package DNT, along with an R Shiny application. In a study in intensive care medicine, we compare networks based on parameters representing main organ systems to evaluate the prognosis of critically ill patients in the intensive care unit (ICU), using data from the surgical ICU of the University Medical Centre Mannheim, Germany. We specifically consider both cross-sectional comparisons between a non-survivor and a survivor group and longitudinal comparisons at two clinically relevant time points during the ICU stay: first, at admission, and second, at an event stage prior to death in non-survivors or a matching time point in survivors. The non-survivor and the survivor networks do not significantly differ at the admission stage. However, the organ system interactions of the survivors then stabilize at the event stage, revealing significantly more network edges, whereas those of the non-survivors do not. In particular, the liver appears to play a central role for the observed increased connectivity in the survivor network at the event stage.

The Role of Language in Structuring Social Networks Following Market Integration in a Yucatec Maya Population

Language is the human universal mode of communication, and is dynamic and constantly in flux accommodating user needs as individuals interface with a changing world. However, we know surprisingly little about how language responds to market integration, a pressing force affecting indigenous communities worldwide today. While models of culture change often emphasize the replacement of one language, trait, or phenomenon with another following socioeconomic transitions, we present a more nuanced framework. We use demographic, economic, linguistic, and social network data from a rural Maya community that spans a 27-year period and the transition to market integration. By adopting this multivariate approach for the acquisition and use of languages, we find that while the number of bilingual speakers has significantly increased over time, bilingualism appears stable rather than transitionary. We provide evidence that when indigenous and majority languages provide complementary social and economic payoffs, both can be maintained. Our results predict the circumstances under which indigenous language use may be sustained or at risk. More broadly, the results point to the evolutionary dynamics that shaped the current distribution of the world's linguistic diversity.

Understanding Determinants of Hunting Trip Productivity in an Arctic Community

We examine factors underlying hunting productivity among Inuit in Ulukhaktok, Northwest Territories, Canada. Specifically, we focus on the role of gasoline use as the main variable of interest—commonly cited as a crucial determinant of hunting participation. Over the course of 12 months, 10 hunters recorded their on-the-land activities using a GPS tracking system, participatory mapping sessions, and bi-weekly interviews. A multivariable linear regression model (MvLRM) was applied to assess whether factors such as consumables used (i.e. heating fuel, gasoline, oil, food), distances traveled, or the number of companions on a trip were associated with the mass of edible foods returned to the community. Results indicate that, despite being positively associated with hunting trip productivity when assessed through a univariable linear regression model, gasoline is not a statistically significant determinant of standalone trip yield when adjusting for other variables in a multivariable linear regression. Instead, factors relating to seasonality, number of companions, and days on the land emerged as more significant and substantive drivers of productivity while out on the land. The findings do not suggest that access to, or the availability of, gasoline does not affect whether a hunting trip commences or is planned, nor that an increase in the amount of gasoline available to a hunter might increase the frequency of trips (and therefore annual productivity). Rather, this work demonstrates that the volume of gasoline used by harvesters on standalone hunting trips represent a poor a priori predictor of the edible weight that harvesters are likely to return to the community.

Opportunities and constraints in women's resource security amid climate change: A case study of arid-living Namibian agro-pastoralists

OBJECTIVE

We describe the composition and variation of women's resource strategies in an arid-living Southern African agro-pastoralist society to gain insights into adaptation to climate-change-induced increased aridity.

METHODS

Using cross-sectional data from 210 women collected in 2009 across 28 agro-pastoralist villages in Kaokoveld Namibia, we conducted principal-component (PC) analysis of resource variables and constructed profiles of resource strategies from the major PCs. Next, we explored associations between key resource strategies and demographic measures and fitness proxies.

RESULTS

The first two PCs accounted for 43% of women's overall resource variation. PC1 reflects women's ability to access market resources via livestock trading, while PC2 captured women's direct food access. We found that market strategies were more common among married women and less common among women who have experienced child mortality. Women with higher subsistence security were more likely to be from the OvaHimba tribe and had a higher risk of gonorrhea exposure. We also qualitatively explored drought-induced pressure on women's livestock. Finally, we show that sexual networks were attenuated during drought, indicating strain on social support.

CONCLUSIONS

Our results highlight how agro-pastoralist women manage critical resources in unpredictable environments, and how resource strategies distribute among the women in our study. Goats as a commodity to obtain critical resources suggests that some women have flexibility during drought when gardens fail and cattle die. However, increased aridity and drought may eventually overwhelm husbandry practices in this region.

Kin Ties and Market Integration in a Yucatec Mayan Village

The importance of kin relationships varies with socioecological demands. Among subsistence agriculturalists, people commonly manage fluctuations in food availability by relying on family members to share resources and pool labor. However, the process of market integration may disrupt these support networks, which may begin to carry costs or liabilities in novel market environments. The current study aims to address (1) how kin are distributed in household support networks (2) how kin support varies as households become more engaged in market activities, and (3) how variation in kin support is associated with income disparities within a Yucatec Maya community undergoing rapid market integration. Using long-term census data combined with social networks and detailed household economic data, we find that household support networks are primarily composed of related households. Second, households engaged predominantly in wage labor rely less on kin support than agricultural or mixed economy households. Finally, kin support is associated with lower household net income and income per capita. Understanding how kin support systems shift over the course of market integration and in the face of new opportunities for social and economic production provides a unique window into the social and economic drivers of human family formation.

Topological clustering of multilayer networks

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

Topological clustering of multilayer networks

Multilayer network clustering is used in such diverse areas as optimal islanding of critical infrastructures, analysis of trade agreements, and monitoring ecological interaction patterns. We propose a perspective on multilayer network clustering based on the concept of shape. By invoking the machinery of topological data analysis, we first study a shape of each node neighborhood and then group nodes based on how similar shapes of their local neighborhoods are. The significance of this methodology can be viewed through an emerging problem of sustainability of house insurance to climate risks. The topological perspective opens possibilities for more systematic, robust, and mathematically rigorous integration of higher-order network properties and their interplay to the analysis of complex networks.

Robust feature collection and classification of network culture

The network provides a convenient mechanism for publishing and obtaining documents, and has now become a gathering place for all kinds of information. In the network, the amount of information increases exponentially, and how to dig useful patterns or knowledge from the massive network culture has become a hot topic for scholars. In data mining, in order to enable readers to quickly obtain the content of interest, research text classification, and automatically classify text data according to a certain classification model. Internet cultural text data has the characteristics of unstructured, subjective, high-dimensional, etc., which makes it difficult for text mining algorithms to extract effective and easy-to-understand classification rules, and the computational complexity is too high. This paper proposes a feature selection method based on robust features, using sample deviation and variance as the criteria for feature attributes to rank the importance of feature attributes, and select the best feature attribute subset. The experimental results show that the classification accuracy of the feature selection method based on sample deviation and variance proposed in this paper is higher than the traditional word frequency as the feature selection method, which proves the feasibility and superiority of the feature selection method proposed in this paper.

Linking subsistence harvest diversity and productivity to adaptive capacity in an Alaskan food sharing network

BACKGROUND

Although anthropogenic climate change poses existential challenges for Indigenous communities in the Arctic, these challenges are not entirely unprecedented. Over many generations, Arctic peoples have developed a wide range of behavioral strategies to navigate environmental change and uncertainty, and these strategies provide a foundation for contemporary adaptation.

AIMS

In this article, we focus on mixed cash-subsistence economies and the social networks that underlie them in Alaska. The patterns of food production, labor exchange, and food sharing in subsistence-oriented communities throughout Alaska are driven by the productivity of keystone households who regularly harvest and share resources within and between communities.

MATERIALS & METHODS

Building on previous research suggesting the critical importance of these networks to community resilience, we use network analysis to investigate whether patterns in resource transfers between households are associated with subsistence harvest diversity-the diversity of species harvested by a household unit. We use exponential random graph models to describe the structure of a sharing network from Aniak, Alaska, and model the links between harvest productivity, harvest diversity, and household position in this network.

RESULTS

Our results indicate that both productivity and diversity are positively associated with network connections, and that productivity alone provides an incomplete model of network structure.

DISCUSSION

We suggest that subsistence harvest diversity may play a unique role in supporting adaptive capacity and resilience by maintaining the productivity of keystone households despite changing environments and sustaining social network structures that circulate resources throughout the community. Harvest diversity may also serve as a broad indicator of Indigenous ecological knowledge and a tangible representation of cultural practices, values, and worldviews that underlie subsistence in Alaska.

CONCLUSION

Greater attention to harvest diversity is important for understanding how subsistence networks adapt to environmental change and uncertainty linked to social and ecological dynamics of anthropogenic climate change.

Multilayer Connector Hub Mapping Reveals Key Brain Regions Supporting Expressive Language

Introduction: How components of the distributed brain networks that support cognition participate in typical functioning remains a largely unanswered question. An important subgroup of regions in the larger network are connector hubs, which are areas that are highly connected to several other functionally specialized sets of regions, and are likely important for sensorimotor integration. The present study attempts to characterize connector hubs involved in typical expressive language functioning using a data-driven, multimodal, full multilayer magnetoencephalography (MEG) connectivity-based pipeline. Methods: Twelve adolescents, 16-18 years of age (five males), participated in this study. Participants underwent MEG scanning during a verb generation task. MEG and structural connectivity were calculated at the whole-brain level. Amplitude/amplitude coupling (AAC) was used to compute functional connections both within and between discrete frequency bins. AAC values were then multiplied by a binary structural connectivity matrix, and then entered into full multilayer network analysis. Initially, hubs were defined based on multilayer versatility and subsequently reranked by a novel measure called delta centrality on interconnectedness (DCI). DCI is defined as the percent change in interfrequency interconnectedness after removal of a hub. Results: We resolved regions that are important for between-frequency communication among other areas during expressive language, with several potential theoretical and clinical applications that can be generalized to other cognitive domains. Conclusion: Our multilayer, data-driven framework captures nonlinear connections that span across scales that are often missed in conventional analyses. The present study suggests that crucial hubs may be conduits for interfrequency communication between action and perception systems that are crucial for typical functioning.

Social-Ecological Connectivity to Understand Ecosystem Service Provision across Networks in Urban Landscapes

Landscape connectivity is a critical component of dynamic processes that link the structure and function of networks at the landscape scale. In the Anthropocene, connectivity across a landscape-scale network is influenced not only by biophysical land use features, but also by characteristics and patterns of the social landscape. This is particularly apparent in urban landscapes, which are highly dynamic in land use and often in social composition. Thus, landscape connectivity, especially in cities, must be thought of in a social-ecological framework. This is relevant when considering ecosystem services—the benefits that people derive from ecological processes and properties. As relevant actors move through a connected landscape-scale network, particular services may “flow” better across space and time. For this special issue on dynamic landscape connectivity, we discuss the concept of social-ecological networks using urban landscapes as a focal system to highlight the importance of social-ecological connectivity to understand dynamic urban landscapes, particularly in regards to the provision of urban ecosystem services.

Re-Envisioning Sanitation As a Human-Derived Resource System

Sanitation remains a global challenge, both in terms of access to toilet facilities and resource intensity (e.g., energy consumption) of waste treatment. Overcoming barriers to universal sanitation coverage and sustainable resource management requires approaches that manage bodily excreta within coupled human and natural systems. In recent years, numerous analytical methods have been developed to understand cross-disciplinary constraints, opportunities, and trade-offs around sanitation and resource recovery. However, without a shared language or conceptual framework, efforts from individual disciplines or geographic contexts may remain isolated, preventing the accumulation of generalized knowledge. Here, we develop a version of the social-ecological systems framework modified for the specific characteristics of bodily excreta. This framework offers a shared vision for sanitation as a human-derived resource system, where people are part of the resource cycle. Through sanitation technologies and management strategies, resources including water, organics, and nutrients accumulate, transform, and impact human experiences and natural environments. Within the framework, we establish a multitiered lexicon of variables, characterized by breadth and depth, to support harmonized understanding and development of models and analytical approaches. This framework's refinement and use will guide interdisciplinary study around sanitation to identify guiding principles for sanitation that advance sustainable development at the nature-society interface.

Interdependent Networks: A Data Science Perspective

Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of interdependent networks, i.e., multi-layered networks with shared decision-making entities, and shared sensing infrastructures with interdisciplinary applications. The main challenge is how to develop data analytics solutions that are capable of enabling interdependent decision making. One of the emerging solutions is agent-based distributed decision making among heterogeneous agents and entities when their decisions are affected by multiple networks. We first provide a big picture of real-world interdependent networks in the context of smart city infrastructures. We then provide an outline of potential challenges and solutions from a data science perspective. We discuss potential hindrances to ensure reliable communication among intelligent agents from different networks. We explore future research directions at the intersection of network science and data science.

This article provides a holistic overview of interdependent cyber-physical-societal networks. We envision the subsequent research directions that require contribution of the data science community as well as interdisciplinary collaboration with network scientists, social scientists, computer scientists, and engineers to tackle the emerging problems raised by the notion of interdependent networks: (1) developing novel algorithms for data analytics and enabling interdependent decision making, (2) proposing holistic models that are capable of capturing the interdependence among human-centered multi-layer critical infrastructures, and (3) developing efficient solutions that are capable of finding globally optimum solutions using information from each network as well as modeling the interdependent information exchange. In addition to these directions, we outline policy and access-control issues, including conflict of interest among stakeholders and operators of each network. Successful implementation and development of an interdependent data analytics framework and its required algorithms will improve the quality of life of citizens by enabling globally optimum decision making, increasing efficiency, preserving privacy of intelligent agents, and reducing operational cost of interdependent networks. Further reading: Sustainable Interdependent Networks book series (interdependentnetworks.com) and Optimization, Learning, and Control for Interdependent Complex Networks (edited by M.H. Amini).

Socio-ecological connectivity differs in magnitude and direction across urban landscapes

Connectivity of social-ecological systems promotes resilience across urban landscapes. Community gardens are social-ecological systems that support food production, social interactions, and biodiversity conservation. We investigate how these hubs of ecosystem services facilitate socio-ecological connectivity and service flows as a network across complex urban landscapes. In three US cities (Baltimore, Chicago, New York City), we use community garden networks as a model system to demonstrate how biophysical and social features of urban landscapes control the pattern and magnitude of ecosystem service flows through these systems. We show that community gardens within a city are connected through biological and social mechanisms, and connectivity levels and spatial arrangement differ across cities. We found that biophysical connectivity was higher than social connectivity in one case study, while they were nearly equal in the other two. This higher social connectivity can be attributed to clustered distributions of gardens within neighborhoods (network modularity), which promotes neighborhood-scale connectivity hotspots, but produces landscape-scale connectivity coldspots. The particular patterns illustrate how urban form and social amenities largely shape ecosystem service flows among garden networks. Such socio-ecological analyses can be applied to enhance and stabilize landscape connectedness to improve life and resilience in cities.

Network Diffusion Promotes the Integrative Analysis of Multiple Omics

The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion—also referred to as network propagation—has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.

Environmental heterogeneity and commodity sharing in smallholder agroecosystems

Smallholder farmers undertake a number of strategies to cope with climate shocks in a community. The sharing of resources across households constitutes one coping mechanism when environmental shocks differentially impact households. This paper investigates commodity sharing dynamics among households in eight communities in an environmentally heterogeneous highland-lowland area in central Kenya. We use survey data and meteorological data to test whether commodity sharing, measured at the household level by net inflow of commodities, varies across a regional precipitation gradient, and we reveal how sharing fluctuates with rainfall over the course of a year. We find both precipitation and income to be significant predictors of households' net value of shared commodities. Specifically, farmers who live in drier areas with less income are more likely to receive more commodities than they give. We also find that the length of time a household has been established in the area is significantly related to commodity sharing. Further, commodity sharing follows the pattern of harvest and food storage over the course of the year, with households giving the most commodities at times when food storage levels are higher, that is, post-harvest. The study sheds light on the relationship between commodity sharing as a coping mechanism and environmental heterogeneity in a region prone to seasonal food insecurity.

Threshold concepts and sustainability: features of a contested paradigm

Threshold concepts describe the core concepts that people must master if they are to effectively think from within a new discipline or paradigm. Here, I discuss threshold concepts relevant to the science and practice of sustainability, unpacking the persistent challenges and critiques that sustainability has faced over the decades. Sustainability is immensely popular, but also endlessly critiqued as being naïve, vague, and easy to co-opt. I argue that these challenges can be traced to sustainability’s status as a robust, alternative world view to the industrial, neoliberal paradigm. The threshold concepts discussed below are troublesome, and new learners face significant challenges when trying to learn them and move into the paradigm. Here, I review five threshold concepts that are widely discussed as important to sustainability: complexity, collaborative institutions, multiple ways of knowing, no panaceas, and adaptability. This list is not intended as comprehensive but exemplary of sustainability as a pluralistic paradigm. Recognizing the special status of these and other threshold concepts within sustainability, and the linkages and dependencies among them, is an important advance for sustainability education and practice. I also offer some suggestions on classroom activities that have proved effective in helping people through the process of learning these concepts.

Cross-scale cooperation enables sustainable use of a common-pool resource

In social-ecological systems (SESs), social and biophysical dynamics interact within and between the levels of organization at multiple spatial and temporal scales. Cross-scale interactions (CSIs) are interdependences between processes at different scales, generating behaviour unpredictable at single scales. Understanding CSIs is important for improving SES governance, but they remain understudied. Theoretical models are needed that capture essential features while being simple enough to yield insights into mechanisms. In a stylized model, we study CSIs in a two-level system of weakly interacting communities harvesting a common-pool resource. Community members adaptively conform to, or defect from, a norm of socially optimal harvesting, enforced through social sanctioning both within and between communities. We find that each subsystem's dynamics depend sensitively on the other despite interactions being much weaker between subsystems than within them. When interaction is purely biophysical, stably high cooperation in one community can cause cooperation in the other to collapse. However, even weak social interaction can prevent the collapse of cooperation and instead cause collapse of defection. We identify conditions under which subsystem-level cooperation produces desirable system-level outcomes. Our findings expand evidence that collaboration is important for sustainably managing shared resources, showing its importance even when resource sharing and social relationships are weak.

Social-ecological network analysis for sustainability sciences: a systematic review and innovative research agenda for the future

Social-ecological network (SEN) concepts and tools are increasingly used in human-environment and sustainability sciences. We take stock of this budding research area to further show the strength of SEN analysis for complex human-environment settings, identify future synergies between SEN and wider human-environment research, and provide guidance about when to use different kinds of SEN approaches and models. We characterize SEN research along a spectrum specifying the degree of explicit network representation of system components and dynamics. We then systematically review one end of this spectrum, what we term "fully articulated SEN" studies, which specifically model unique social and ecological units and relationships. Results show more focus on methodological advancement and applied ends. While there has been some development and testing of theories, this remains an area for future work and would help develop SENs as a unique field of research, not just a method. Authors have studied diverse systems, while mainly focused on the problem of social-ecological fit alongside a scattering of other topics. There is strong potential, however, to engage other issues central to human-environment studies. Analyzing the simultaneous effects of multiple social, environmental, and coupled processes, change over time, and linking network structures to outcomes are also areas for future advancement. This review provides a comprehensive assessment of (fully articulated) SEN research, a necessary step that can help scholars develop comparable cases and fill research gaps.

An approach to incorporating inferred connectivity of adult movement into marine protected area design with limited data

Marine protected areas (MPAs) are important conservation tools that can support the resilience of marine ecosystems. Many countries, including Canada, have committed to protecting at least 10% of their marine areas under the Convention on Biological Diversity's Aichi Target 11, which includes connectivity as a key aspect. Connectivity, the movement of individuals among habitats, can enhance population stability and resilience within and among MPAs. However, little is known about regional spatial patterns of marine ecological connectivity, particularly adult movement. We developed a method to assess and design MPA networks that maximize inferred connectivity within habitat types for adult movement when ecological data are limited. We used the Northern Shelf Bioregion in British Columbia, Canada, to explore two different approaches: (1) evaluating sites important for inferred regional connectivity (termed hotspots) and (2) assessing MPA network configurations based on their overlap with connectivity hotspots and interconnectedness between MPAs. To assess inferred connectivity via adult movement, we used two different threshold distances (15 and 50 km) to capture moderate home ranges, which are most appropriate to consider in MPA design. We applied graph theory to assess inferred connectivity within 16 habitat and depth categories (proxies for distinct ecological communities), and used novel multiplex network methodologies to perform an aggregated assessment of inferred connectivity. We evaluated inferred regional connectivity hotspots based on betweenness and eigenvector centrality metrics, finding that the existing MPA network overlapped a moderate proportion of these regional hotspots and identified key areas to be considered as candidate MPAs. Network density among existing MPAs was low within the individual habitat networks, as well as the multiplex. This work informs an ongoing MPA planning process, and approaches for incorporating connectivity into MPA design when data are limited, with lessons for other contexts.

Social-ecological alignment and ecological conditions in coral reefs

Complex social-ecological interactions underpin many environmental problems. To help capture this complexity, we advance an interdisciplinary network modeling framework to identify important relationships between people and nature that can influence environmental conditions. Drawing on comprehensive social and ecological data from five coral reef fishing communities in Kenya; including interviews with 648 fishers, underwater visual census data of reef ecosystem condition, and time-series landings data; we show that positive ecological conditions are associated with ‘social-ecological network closure’ – i.e., fully linked and thus closed network structures between social actors and ecological resources. Our results suggest that when fishers facing common dilemmas form cooperative communication ties with direct resource competitors, they may achieve positive gains in reef fish biomass and functional richness. Our work provides key empirical insight to a growing body of research on social-ecological alignment, and helps to advance an integrative framework that can be applied empirically in different social-ecological contexts.

The relationships between people can have important consequences for the systems they depend on. Here the authors show that when coral reef fishers face commons dilemmas, the formation of cooperative communication with competitors can lead to positive gains in reef fish biomass and functional richness.

Research on the synergy of urban system operation-Based on the perspective of urban metabolism

Cities have been posing threats to local environment due to intensive human activities. Disordered urban metabolism can lead to unhealthy urban system operation. Studying urban metabolism can identify the characteristics and structure of urban system operations, thereby promoting sustainable development. We construct an ecological network to model the energy/material flows among the sectors of urban system in Beijing, Shanghai, Tianjin, and Chongqing, which are the most representative cities of China. Ecological network analysis was introduced to survey the system's characteristic of structure and function. Results show that Shanghai's urban metabolic system has the highest synergism with a value of 5.04, followed by Beijing, Chongqing, and Tianjin. Different cities vary considerably in terms of the relationships among the sectors. In Beijing and Chongqing, there is mutualistic relationship between environment and primary industry. Transportation forms a mutualistic relationship with tertiary industry and other sectors Only in Chongqing. The good relationships between primary industry and construction are merely seen in Beijing and Shanghai. However, some relationships are similar in the four cities, for instance, all the relationships between primary industry and advanced industry, and between construction and advanced industry are competition; All the relationships between environment and transportation are mutualism. We further explored the network stability and found that the studied urban systems are all located in a stable status relatively. Our research could reveal the synergy of urban operation, and provide a theoretical basis for optimizing urban metabolic processes by regulating the flows and guidance for sustainable urban development.

Assessing diversity in multiplex networks

Diversity, understood as the variety of different elements or configurations that an extensive system has, is a crucial property that allows maintaining the system's functionality in a changing environment, where failures, random events or malicious attacks are often unavoidable. Despite the relevance of preserving diversity in the context of ecology, biology, transport, finances, etc., the elements or configurations that more contribute to the diversity are often unknown, and thus, they can not be protected against failures or environmental crises. This is due to the fact that there is no generic framework that allows identifying which elements or configurations have crucial roles in preserving the diversity of the system. Existing methods treat the level of heterogeneity of a system as a measure of its diversity, being unsuitable when systems are composed of a large number of elements with different attributes and types of interactions. Besides, with limited resources, one needs to find the best preservation policy, i.e., one needs to solve an optimization problem. Here we aim to bridge this gap by developing a metric between labeled graphs to compute the diversity of the system, which allows identifying the most relevant components, based on their contribution to a global diversity value. The proposed framework is suitable for large multiplex structures, which are constituted by a set of elements represented as nodes, which have different types of interactions, represented as layers. The proposed method allows us to find, in a genetic network (HIV-1), the elements with the highest diversity values, while in a European airline network, we systematically identify the companies that maximize (and those that less compromise) the variety of options for routes connecting different airports.

Traditional Food Practices, Attitudes, and Beliefs in Urban Alaska Native Women Receiving WIC Assistance

OBJECTIVE

To identify practices, attitudes, and beliefs associated with intake of traditional foods among Alaska Native women.

DESIGN

Cross-sectional study that measured traditional food intake; participation in food-sharing networks; presence of a hunter or fisherman in the home; the preference, healthfulness, and economic value of traditional foods; and financial barriers to obtaining these foods.

PARTICIPANTS

Purposive sample of 71 low-income Alaska Native women receiving Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) assistance in Anchorage, AK.

ANALYSIS

Bivariate and multivariate regression analyses.

RESULTS

Traditional foods contributed 4% of total daily calories. Given a choice, 63% of participants indicated that they would prefer half or more of the foods they ate to be traditional (ie, not store-bought). The majority of participants (64%) believed that traditional foods were healthier than store-bought foods. Of all participants, 72% relied on food-sharing networks for traditional foods; only 21% acquired traditional foods themselves. Participants who ate more traditional foods preferred traditional foods (B = .011 P = .02).

IMPLICATIONS FOR RESEARCH AND PRACTICE

Traditional food intake was low and findings suggested that Alaska Native women living in an urban setting prefer to consume more but are unable to do so. Future research might examine the effect of enhancing social networks and implementing policies that support traditional food intake.

The U.S. food-energy-water system: A blueprint to fill the mesoscale gap for science and decision-making

Food, energy, and water (FEW) are interdependent and must be examined as a coupled natural-human system. This perspective essay defines FEW systems and outlines key findings about them as a blueprint for future models to satisfy six key objectives. The first three focus on linking the FEW production and consumption to impacts on Earth cycles in a spatially specific manner in order to diagnose problems and identify potential solutions. The second three focus on describing the evolution of FEW systems to identify risks, thus empowering the FEW actors to better achieve the goals of resilience and sustainability. Four key findings about the FEW systems that guide future model development are (1) that they engage ecological, carbon, water, and nutrient cycles most powerfully among all human systems; (2) that they operate primarily at a mesoscale best captured by counties, districts, and cities; (3) that cities are hubs within the FEW system; and (4) that the FEW system forms a complex network.

The importance of cognitive diversity for sustaining the commons

Cognitive abilities underpin the capacity of individuals to build models of their environment and make decisions about how to govern resources. Here, we test the functional intelligences proposition that functionally diverse cognitive abilities within a group are critical to govern common pool resources. We assess the effect of two cognitive abilities, social and general intelligence, on group performance on a resource harvesting and management game involving either a negative or a positive disturbance to the resource base. Our results indicate that under improving conditions (positive disturbance) groups with higher general intelligence perform better. However, when conditions deteriorate (negative disturbance) groups with high competency in both general and social intelligence are less likely to deplete resources and harvest more. Thus, we propose that a functional diversity of cognitive abilities improves how effectively social groups govern common pool resources, especially when conditions deteriorate and groups need to re-evaluate and change their behaviors.

Social intelligence and general intelligence are two distinct cognitive abilities. Here, the authors show that groups of people with high competency in both social and general intelligence perform better in a resource-management task involving cooperation, and adjustment to unexpected ecological change.

Scaling properties of food flow networks

Food flows underpin the complex food supply chains that are prevalent in our increasingly globalized world. Recently, much effort has been devoted to evaluating the resources (e.g. water, carbon, nutrients) embodied in food trade. Now, research is needed to understand the scientific principles of the food commodity flows that underpin these virtual resource transfers. How do food flows vary with spatial scale? To address this question, we present an empirical analysis of food commodity flow networks across the full spectrum of spatial scales: global, national, and village. We discover properties of both scale invariance and scale dependence in food flow networks. The statistical distribution of node connectivity and mass flux are consistent across scales. Node connectivity follows a generalized exponential distribution, while node mass flux follows a Gamma distribution across scales. Similarly, the relationship between node connectivity and mass flux follows a power law across scales. However, the parameters of the distributions change with spatial scale. Mean node connectivity and mass flux increase with increasing scale. A core group of nodes exists at all scales, but node centrality increases as the spatial scale decreases, indicating that some households are more critical to village food exchanges than countries are to global trade. Remarkably, the structural network properties of food flows are consistent across spatial scales, indicating that a universal mechanism may underpin food exchange systems. In future research, this understanding can be used to develop theoretical models of food flow networks and to model food flows at resolutions for which empirical information is not available.

Sharing-based social capital associated with harvest production and wealth in the Canadian Arctic

Social institutions that facilitate sharing and redistribution may help mitigate the impact of resource shocks. In the North American Arctic, traditional food sharing may direct food to those who need it and provide a form of natural insurance against temporal variability in hunting returns within households. Here, network properties that facilitate resource flow (network size, quality, and density) are examined in a country food sharing network comprising 109 Inuit households from a village in Nunavik (Canada), using regressions to investigate the relationships between these network measures and household socioeconomic attributes. The results show that although single women and elders have larger networks, the sharing network is not structured to prioritize sharing towards households with low food availability. Rather, much food sharing appears to be driven by reciprocity between high-harvest households, meaning that poor, low-harvest households tend to have less sharing-based social capital than more affluent, high-harvest households. This suggests that poor, low-harvest households may be more vulnerable to disruptions in the availability of country food.

Opinion formation in multiplex networks with general initial distributions

We study opinion dynamics over multiplex networks where agents interact with bounded confidence. Namely, two neighbouring individuals exchange opinions and compromise if their opinions do not differ by more than a given threshold. In literature, agents are generally assumed to have a homogeneous confidence bound. Here, we study analytically and numerically opinion evolution over structured networks characterised by multiple layers with respective confidence thresholds and general initial opinion distributions. Through rigorous probability analysis, we show analytically the critical thresholds at which a phase transition takes place in the long-term consensus behaviour, over multiplex networks with some regularity conditions. Our results reveal the quantitative relation between the critical threshold and initial distribution. Further, our numerical simulations illustrate the consensus behaviour of the agents in network topologies including lattices and, small-world and scale-free networks, as well as for structure-dependent convergence parameters accommodating node heterogeneity. We find that the critical thresholds for consensus tend to agree with the predicted upper bounds in Theorems 4 and 5 in this paper. Finally, our results indicate that multiplexity hinders consensus formation when the initial opinion configuration is within a bounded range and, provide insight into information diffusion and social dynamics in multiplex systems modeled by networks.

Kinship underlies costly cooperation in Mosuo villages

The relative importance of social evolution theories such as kin selection, direct reciprocity and need-based transfers in explaining real-world cooperation is the source of much debate. Previous field studies of cooperation in human communities have revealed variability in the extent to which each of these theories explains human sociality in different contexts. We conducted multivariate social network analyses predicting costly cooperation-labouring on another household's farm-in 128 082 dyads of Mosuo farming households in southwest China. Through information-theoretic model selection, we tested the roles played by genealogical relatedness, affinal relationships (including reproductive partners), reciprocity, relative need, wealth, household size, spatial proximity and gift-giving in an economic game. The best-fitting model included all factors, along with interactions between relatedness and (i) reciprocity, (ii) need, (iii) the presence of own children in another household and (iv) proximity. Our results show how a real-world form of cooperation was driven by kinship. Households tended to help kin in need (but not needy non-kin) and travel further to help spatially distant relatives. Households were more likely to establish reciprocal relationships with distant relatives and non-kin but closer kin cooperated regardless of reciprocity. These patterns of kin-driven cooperation show the importance of inclusive fitness in understanding human social behaviour.

The socio-cultural benefits and costs of the traditional hunting of dugongsDugong dugonand green turtlesChelonia mydasin Torres Strait, Australia

Signatory states of the Convention on Biological Diversity must ‘protect and encourage the customary use of biological resources in accordance with traditional cultural practices that are compatible with conservation or sustainable use requirements’. Thus the management of traditional hunting of wildlife must balance the sustainability of target species with the benefits of hunting to traditional communities. Conservation policies usually define the values associated with wild meats in terms of income and nutrition, neglecting a wide range of social and cultural values that are important to traditional hunting communities. We elicited the community-defined benefits and costs associated with the traditional hunting of dugongsDugong dugonand green turtlesChelonia mydasfrom communities on two islands in Torres Strait, Australia. We then used cognitive mapping and multidimensional scaling to identify separable groups of benefits (cultural services, provisioning services, and individual benefits) and demonstrate that traditional owners consider the cultural services associated with traditional hunting to be significantly more important than the provisioning services. Understanding these cultural values can inform management actions in accordance with the Convention on Biological Diversity. If communities are unable to hunt, important cultural benefits are foregone. Based on our results, we question the appropriateness of conservation actions focused on prohibiting hunting and providing monetary compensation for the loss of provisioning services only.

Do-it-yourself networks: a novel method of generating weighted networks

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.

Social-ecological network analysis of scale mismatches in estuary watershed restoration

Resource management boundaries seldom align with environmental systems, which can lead to social and ecological problems. Mapping and analyzing how resource management organizations in different areas collaborate can provide vital information to help overcome such misalignment. Few quantitative approaches exist, however, to analyze social collaborations alongside environmental patterns, especially among local and regional organizations (i.e., in multilevel governance settings). This paper develops and applies such an approach using social-ecological network analysis (SENA), which considers relationships among and between social and ecological units. The framework and methods are shown using an estuary restoration case from Puget Sound, United States. Collaboration patterns and quality are analyzed among local and regional organizations working in hydrologically connected areas. These patterns are correlated with restoration practitioners' assessments of the productivity of their collaborations to inform network theories for natural resource governance. The SENA is also combined with existing ecological data to jointly consider social and ecological restoration concerns. Results show potentially problematic areas in nearshore environments, where collaboration networks measured by density (percentage of possible network connections) and productivity are weakest. Many areas also have high centralization (a few nodes hold the network together), making network cohesion dependent on key organizations. Although centralization and productivity are inversely related, no clear relationship between density and productivity is observed. This research can help practitioners to identify where governance capacity needs strengthening and jointly consider social and ecological concerns. It advances SENA by developing a multilevel approach to assess social-ecological (or social-environmental) misalignments, also known as scale mismatches.