From single biobanks to international networks: developing e-governance

The future holds the possibility to link and network biobanks, existing biorepositories and reference databases for research purposes in ways that have not been possible before. There is the potential to develop 'research portals' that will enable researchers to access these research resources that are located around the globe with the click of a mouse. In this paper, I will argue that our current governance system for research is unable to provide all of the oversight and accountability mechanisms that are required for this new way of doing research that is based upon flows of data across international borders. For example, our current governance framework for research is nationally based, with a complex system of laws, policies and practice that can be unique to a jurisdiction. It is also evident that many of the nationally based governance bodies in this field do not have the legal powers or expertise to adjudicate on the complex issues, such as privacy and disclosure risks that are raised by cross-border data sharing. In addition, the conceptual underpinning of this research governance structure is based on the "one researcher, one project, one jurisdiction" model. In the conclusion of this paper, I lay out some preliminary ideas as to how this system has to change to accommodate research that is based on networks. I suggest that a move to digital governance mechanisms might be a start to making research governance systems more appropriate for the 21st century.     

The future of ocean governance

Ocean governance is complex and influenced by multiple drivers and actors with different worldviews and goals. While governance encompasses many elements, in this paper we focus on the processes that operate within and between states, civil society and local communities, and the market, including industry. Specifically, in this paper, we address the question of how to move towards more sustainable ocean governance aligning with the sustainable development goals (SDGs) and the UN Ocean Decade. We address three major risks to oceans that arise from governance-related issues: (1) the impacts of the overexploitation of marine resources; (2) inequitable distribution of access to and benefits from marine ecosystem services, and (3) inadequate or inappropriate adaptation to changing ocean conditions. The SDGs have been used as an underlying framework to develop these risks. We identify five drivers that may determine how ocean governance evolves, namely formal rules and institutions, evidence and knowledge-based decision-making, legitimacy of decision-making institutions, stakeholder engagement and participation, and empowering communities. These drivers were used to define two alternative futures by 2030: (a) ‘Business as Usual’—a continuation of current trajectories and (b) ‘More Sustainable Future’—optimistic, transformational, but technically achievable. We then identify what actions, as structured processes, can reduce the three major governance-related risks and lead to the More Sustainable Future. These actions relate to the process of co-creation and implementation of improved, comprehensive, and integrated management plans, enhancement of decision-making processes, and better anticipation and consideration of ambiguity and uncertainty.

     

A Conceptual Framework for Evaluating the Interaction of a Chemical and Nonchemical Stressor in Human Health Risk Assessments: A Case Study for Lead and Psychosocial Stress

Recent research has demonstrated that nonchemical stressors may alter the toxicity from chemical exposures. This may have public health implications for low socioeconomic status (SES) communities that may be disproportionately exposed to toxic chemicals and various types of community and personal stressors. Nonchemical stressors may introduce an important source of variability that needs to be considered by risk assessors. Herein, we propose a framework for determining if a chemical–nonchemical interaction exists and, if so, options for incorporating interaction information into risk assessments. We use the increasingly recognized interaction between lead and psychosocial stress to illustrate the framework. We found that lead exposure occurs disproportionately in low SES groups that also tend to face high levels of psychosocial stress; that stress and lead both affect neurodevelopment and that this occurs via similar pathways involving the hypothalamic-pituitary axis. Further, several epidemiological and experimental studies have provided evidence for an interaction between lead and psychosocial stress. The implications of this interaction for risk assessment are also discussed.     

An overview of data models for the analysis of biochemical pathways

Biochemical pathways such as metabolic, regulatory or signal transduction pathways can be viewed as interconnected processes forming an intricate network of functional and physical interactions between molecular species in the cell. The amount of information available on such pathways for different organisms is increasing very rapidly. This is offering the possibility of performing various analyses on the structure of the full network of pathways for one organism as well as across different organisms, and has therefore generated interest in developing databases for storing and managing this information. Analysing these networks remains far from straightforward owing to the nature of the databases, which are often heterogeneous, incomplete or inconsistent. Pathway analysis is hence a challenging problem in systems biology and in bioinformatics. Various forms of data models have been devised for the analysis of biochemical pathways. This paper presents an overview of the types of models used for this purpose, concentrating on those concerned with the structural aspects of biochemical networks. In particular, the different types of data models found in the literature are classified using a unified framework. In addition, how these models have been used in the analysis of biochemical networks is described. This enables us to underline the strengths and weaknesses of the different approaches, as well as to highlight relevant future research directions.     

A comprehensive framework for the evaluation of metacommunity structure

The metacommunity framework is a powerful platform for evaluating patterns of species distribution in geographic or environmental space. Idealized patterns (checkerboard, Clementsian, evenly spaced, Gleasonian and nested distributions) give the framework shape. Each pattern represents an area in a multidimensional continuum of metacommunity structures; however, the current approach to analysis of spatial structure of metacommunities is incomplete. To address this, we describe additional non‐random structures and illustrate how they may be discerned via objective criteria. First, we distinguish three distinct forms of species loss in nested structures, which should improve identification of structuring mechanisms for nested patterns. Second, we define six quasi‐structures that are consistent with the conceptual underpinnings of Clementsian, Gleasonian, evenly spaced and nested distributions. Finally, we demonstrate how combinations of structures at smaller spatial extents may aggregate to form Clementsian structure at larger extents. These refinements should facilitate the identification of best‐fit patterns, associated structuring mechanisms, and informative scales of analysis and interpretation. This conceptual and analytical framework may be applied to network properties within communities (i.e. structure of interspecific interactions) and has broad application in ecology and biogeography.     

Motifs in bipartite ecological networks: uncovering indirect interactions

Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about bipartite ecological networks, we show that this shift is supported by analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight differences missed by the current index paradigm that may be ecologically important. We extend this finding to empirical plant–pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need to not rely solely on network‐ and species‐level indices for characterising the structure of bipartite ecological networks.     

Across ecosystem comparisons of size structure: methods,approaches and prospects

Understanding how ecological communities are structured and how this may vary between different types of ecosystems is a fundamental question in ecology. We develop a general framework for quantifying size‐structure within and among different ecosystem types (e.g. terrestrial, freshwater or marine), via the use of a suite of bivariate relationships between organismal size and properties of individuals, populations, assemblages, pair‐wise interactions, and network topology. Each of these relationships can be considered a dimension of size‐structure, along which real communities lie on a continuous scale. For example, the strength, slope, or elevation of the body mass‐versus‐abundance or predator size‐versus‐prey size relationships may vary systematically among ecosystem types. We draw on examples from the literature and suggest new ways to use allometries for comparing among ecosystem types, which we illustrate by applying them to published data. Finally, we discuss how dimensions of size‐structure are interconnected and how we could approach this complex hierarchy systematically. We conclude: (1) there are multiple dimensions of size‐structure; (2) communities may be size‐structured in some of these dimensions, but not necessarily in others; (3) across‐system comparisons via rigorous quantitative statistical methods are possible, and (4) insufficient data are currently available to illuminate thoroughly the full extent and nature of differences in size‐structure among ecosystem types.     

Edge-count probabilities for the identification of local protein communities and their organization

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.     

Exploring the causes and consequences of cooperative behaviour in wild animal populations using a social network approach

Understanding why individuals carry out behaviours that benefit others, especially genetically unrelated others, has been a major undertaking in many fields and particularly in biology. Here, we focus on the cooperation literature from natural populations and present the benefits of a social network approach in terms of how it can help to identify and understand factors that influence the maintenance and spread of cooperation, but are not easily captured when solely considering independent dyadic interactions. We describe how various routes to cooperation can be tested within the social network framework. Applying the social network approach to data from natural populations can help to uncover the evolutionary and ecological pressures that lead to differences in cooperation and other social processes.     

Optimizing Risk Management for the Sustainable Performance of the Regional Innovation System in Korea through Metamediation

In recent years, the concept of Regional Innovation Systems (RIS) has evolved into a widely used analytical framework for sustainable economic development. But in reality, the RIS does not seem to be workable due to the lack of governance. To reduce this operational risk, the emphasis on the structure as well as network management of the system should be merged into a more market-oriented as well as performance-oriented governance. In Korea, there are seven RISs among 14 regions nationwide. Empirically, they are not very sustainable. Thus, the article proposes the role of metamediary for the facilitation, network service provision, and collaboration of the system.     

Social-ecological Resilience Modeling: Water Stress Effects in the Bighorn Sheep Management System in Baja California Sur,Mexico

Bighorn sheep (Ovis canadensis) management involves ecological and socioeconomic aspects, creating a social-ecological system (SES). Social-ecological thresholds can be identified in the system to assess its specific resilience in response to climate stressors. Thus, the aim of this study is to build a dynamic model to assess whether this system is resilient to a particular stressor (water stress). In this study, the SES is considered resilient if the bighorn population is sufficiently large to provide economic revenue to landowners and promote conservation action. We validate and formalize this model by conducting semistructured interviews to Bonfil ejido landowners located in Baja California Sur (BCS), Mexico, and to experts in the field of recreational hunting and wildlife management. To explore the changes in specific resilience in this SES, we conduct simulations to assess the impact of rainfall variability patterns on the system. Our results indicate that rainfall variations with respect to the historical record have the potential to disrupt both the species and the local economy and that the lack of adaptive capacity in both harvest and conservation strategies may affect the dynamics of the whole SES. Finally, this paper explores how adaptive wildlife conservation management strategies can enhance the resilience of both subsystems in this SES.     

玉米转基因技术专利权人合作态势分析

转基因玉米是最重要的转基因主粮作物之一,其转基因技术具有一定的代表性。为了更好地了解和掌握玉米转基因技术领域研发主体合作情况,文章构建了基于专利权人合作网络的目标技术领域专利权人合作态势分析框架,并基于社会网络分析方法与技术,以世界范围内的转基因玉米领域的重要专利权人为分析对象,构建合作网络、分析整体合作特征、挖掘合作子网、探测领域内重要专利权人,从而从宏观、中观和微观三个层面客观展现玉米转基因技术领域专利权人合作态势,为科技战略规划提供一定的决策支撑。     

Living within dynamic social-ecological freshwater systems: System parameters and the role of ecological engineering

The objective of ecological engineering is to design sustainable ecosystems that integrate human communities and their natural environment for the benefit of both. In this paper, we illustrate how social-ecological modeling can be used as a tool to clarify this objective at a landscape scale for freshwater systems. Coupled social-ecological systems (SESs) are open, dynamic systems subject to both ecological and socioeconomic perturbations. Here we demonstrate the interactive effects of social and technological uncertainties on SES dynamics over time. Additionally, we integrate research on ecosystem stability, social-ecological modeling, and ecological engineering to offer guidance for research at the human-environment interface. Based on a case study of Lake Erie's Sandusky watershed, we use an integrated human-biophysical model to investigate the influence of two parameters on SES dynamics: (1) regional societal preferences that impact watershed management and (2) technological innovation that alters agricultural nutrient efficiency. Our results illustrate ways in which SES dynamics and optimum management strategies depend on societal preferences within the region, indicating a key area of uncertainty for future investigation. As guidance for SES restoration, our model results also illustrate the conditions under which technological change that increases nutrient efficiency on farms can and cannot create a win-win, or increase both human welfare and SES resistance to eutrophication simultaneously. Using these results, we elucidate the value of ecological engineering and offer guidance for assessments of ecological engineering projects using social-ecological modeling.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

Bees, farmers, tourists and hunters: conflict dynamics around Western Tanzania protected areas

Following important donor funding in Tanzania since the 1990s to support community based natural resource management, several cooperation agencies have implemented projects aiming at developing innovative conservation strategies combining protected and sustainable use areas. Based on data gathered in the Katavi Rukwa Lukwati and Ugalla core areas of Western Tanzania, this paper compares and analyses how projects developed their strategy and objectives to address conflicts between local population and conservation agents, and how this led to changes in conservation practices. The projects managed to achieve their objectives in conservation and poverty reduction at various degrees. Enhanced conflict resolution capacity involving private stakeholders, conservation agents and local communities, as well as improved collaboration between projects, helped to solve part of the conflicts. This was the case with the negotiation of rights of access for beekeepers to Rukwa game reserve. However, enduring sector based approaches continue to hinder opportunities for developing multiple use approaches. Contrasted results of the projects can be explained by factors inherent to projects’ planning and management, but also by factors that are beyond projects’ influence such as the historical and contemporary context in terms of governance of natural resources and more globally, of power relationships between the state, private organisations and the communities.     

An integrative study of a meromictic lake ecosystem in Antarctica

In nature, the complexity and structure of microbial communities varies widely, ranging from a few species to thousands of species, and from highly structured to highly unstructured communities. Here, we describe the identity and functional capacity of microbial populations within distinct layers of a pristine, marine-derived, meromictic (stratified) lake (Ace Lake) in Antarctica. Nine million open reading frames were analyzed, representing microbial samples taken from six depths of the lake size fractionated on sequential 3.0, 0.8 and 0.1 μm filters, and including metaproteome data from matching 0.1 μm filters. We determine how the interactions of members of this highly structured and moderately complex community define the biogeochemical fluxes throughout the entire lake. Our view is that the health of this delicate ecosystem is dictated by the effects of the polar light cycle on the dominant role of green sulfur bacteria in primary production and nutrient cycling, and the influence of viruses/phage and phage resistance on the cooperation between members of the microbial community right throughout the lake. To test our assertions, and develop a framework applicable to other microbially driven ecosystems, we developed a mathematical model that describes how cooperation within a microbial system is impacted by periodic fluctuations in environmental parameters on key populations of microorganisms. Our study reveals a mutualistic structure within the microbial community throughout the lake that has arisen as the result of mechanistic interactions between the physico-chemical parameters and the selection of individual members of the community. By exhaustively describing and modelling interactions in Ace Lake, we have developed an approach that may be applicable to learning how environmental perturbations affect the microbial dynamics in more complex aquatic systems.     

Investigating microbial associations from sequencing survey data with co‐correspondence analysis

Microbial communities, which drive major ecosystem functions, consist of a wide range of interacting species. Understanding how microbial communities are structured and the processes underlying this is crucial to interpreting ecosystem responses to global change but is challenging as microbial interactions cannot usually be directly observed. Multiple efforts are currently focused to combine next‐generation sequencing (NGS) techniques with refined statistical analysis (e.g., network analysis, multivariate analysis) to characterize the structures of microbial communities. However, most of these approaches consider a single table of sequencing data measured for several samples. Technological advances now make it possible to collect NGS data on different taxonomic groups simultaneously for the same samples, allowing us to analyse a pair of tables. Here, an analytical framework based on co‐correspondence analysis (CoCA) is proposed to study the distributions, assemblages and interactions between two microbial communities. We show the ability of this approach to highlight the relationships between two microbial communities, using two data sets exhibiting various types of interactions. CoCA identified strong association patterns between autotrophic and heterotrophic microbial eukaryote assemblages, on the one hand, and between microalgae and viruses, on the other. We demonstrate also how CoCA can be used, complementary to network analysis, to reorder co‐occurrence networks and thus investigate the presence of patterns in ecological networks.     

Estimating co-extinction threats in terrestrial ecosystems

The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change—such as predicting which species will breach their thermal limits under different warming scenarios—with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.     

What is the role of predation on stability of natural communities? A theoretical investigation.

A basic question in ecology concerns the role of species interaction on dynamics of natural communities. In this framework, ecologists have considered predation, competition, mutualism, the three most important interactions, highlighting their specific effects on distribution and abundance of species, providing knowledge about phenomena like coexistence and extinction. This paper seeks to identify the effects of predation on stability of natural communities by mathematical models. Simple multispecies community models, organized in trophic levels, are analyzed by means of a qualitative technique, loop analysis, combined with a computer calculation procedure. Results do not support the hypothesis of predation as a stabilizing factor. Rather, the outcomes of the analysis suggest that predation may or may not stabilize a community. This depends on the predator's behaviour and on the network of the community.