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Improving network approaches to the study of complex social-ecological interdependencies.

ABSTRACT: Achieving effective, sustainable environmental governance requires a better understanding of the causes and consequences of the complex patterns of interdependencies connecting people and ecosystems within and across scales. Network approaches for conceptualizing and analyzing these interdependencies offer one promising solution. Here, we present two advances we argue are needed to further this area of research: (i) a typology of causal assumptions explicating the causal aims of any given network-centric study of social-ecological interdependencies; (ii) unifying research design considerations that facilitate conceptualizing exactly what is interdependent, through what types of relationships, and in relation to what kinds of environmental problems. The latter builds on the appreciation that many environmental problems draw from a set of core challenges that re-occur across contexts. We demonstrate how these advances combine into a comparative heuristic that facilitates leveraging case-specific findings of social-ecological interdependencies to generalizable, yet context-sensitive, theories based on explicit assumptions of causal relationships.

SUBMITTER: Bodin O

PROVIDER: S-EPMC8943905 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Improving network approaches to the study of complex social-ecological interdependencies.

Bodin Ö Ö Alexander S M SM Baggio J J Barnes M L ML Berardo R R Cumming G S GS Dee L L Fischer A P AP Fischer M M Mancilla-Garcia M M Guerrero A A Hileman J J Ingold K K Matous P P Morrison T H TH Nohrstedt D D Pittman J J Robins G G Sayles J J

Nature sustainability 20190701 7

Achieving effective, sustainable environmental governance requires a better understanding of the causes and consequences of the complex patterns of interdependencies connecting people and ecosystems within and across scales. Network approaches for conceptualizing and analyzing these interdependencies offer one promising solution. Here, we present two advances we argue are needed to further this area of research: (i) a <i>typology of causal assumptions</i> explicating the causal aims of any given ...[more]

PMID: 35342825

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Project description:Communication between resource users has repeatedly been shown to be of significant importance in environmental management. The proposed causal mechanisms are numerous, ranging from the ability of users to share information to their ability to negotiate solutions to common problems and dilemmas. However, what is less known is under what conditions these potential causal mechanisms are important and if, in cases when different means other than communication were available, whether they would be more effective in accomplishing these objectives. An example of such an alternative could be that instead of (or in addition to) users being reliant on within-group communication to acquire useful information an intermediary-such as a public agency-could provide that for them. Furthermore, the different causal mechanisms making communication beneficial might not be independent, neither in respect to each other, nor in respect to other externally imposed means to facilitate better environmental management, and not in regards to different contextual factors. This study makes use of laboratory experiments in an innovative way to explore these questions and specifically test the relative importance of communication in managing complex social-ecological system characterized by common-pool resource dilemmas, ecological interdependencies, and asymmetric resource access-all characteristics being present simultaneously. We find that when resources users are confronted with such a complex challenge, the ability to communicate significantly increases individual and group performance. What is more surprising is the negative effect on overall outcomes that providing external information has on outcomes, when the users also have the ability to communicate. By analysing the content of the conversations we are able to suggest several possible explanations on how the combination of external information provisioning and user communications act to increase individual cognitive load and drives intra-group competition, leading to a significant reduction of individual and group outcomes.

Project description:Network medicine relies on different types of networks: from the molecular level of protein?protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein?protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs-including long non-coding RNAs (lncRNAs) -competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes-called switch genes-critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

Dataset Information

Improving network approaches to the study of complex social-ecological interdependencies.

Publications

Improving network approaches to the study of complex social-ecological interdependencies.

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