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The relationship of network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines—from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Here, we show that one can distinguish two classes of functional connectivity—one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes—excitations, regular and chaotic oscillators—and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Ecology, Systems Biology, Neuroscience and Socio-Ecological Systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

The relationship of network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines—from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Here, we show that one can distinguish two classes of functional connectivity—one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes—excitations, regular and chaotic oscillators—and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Ecology, Systems Biology, Neuroscience and Socio-Ecological Systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

The relationship of network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines—from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Here, we show that one can distinguish two classes of functional connectivity—one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes—excitations, regular and chaotic oscillators—and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Ecology, Systems Biology, Neuroscience and Socio-Ecological Systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

Economic globalisation refers to flows of trade and capital among and between countries. A critical view on economic globalisation is that the historical forces influencing how and when a given country becomes integrated into global trade (and the world economy as a whole) condition the potential paths of development open to that country (e.g., Ref. [1]). Here, trade and other relations between countries act as structural mechanisms enabling wealthier, more core countries to maintain favourable terms of trade, which, in turn, negatively impact less developed, more peripheral ones in a variety of ways [2, 3].

Social learning is increasingly becoming a normative goal in natural resource management and policy. However, there remains little consensus over its meaning or theoretical basis. There are still considerable differences in understanding of the concept in the literature, including a number of articles published in Ecology & Society. Social learning is often conflated with other concepts such as participation and proenvironmental behavior, and there is often little distinction made between individual and wider social learning. Many unsubstantiated claims for social learning exist, and there is frequently confusion between the concept itself and its potential outcomes. This lack of conceptual clarity has limited our capacity to assess whether social learning has occurred, and if so, what kind of learning has taken place, to what extent, between whom, when, and how. This response attempts to provide greater clarity on the conceptual basis for social learning. We argue that to be considered social learning, a process must: (1) demonstrate that a change in understanding has taken place in the individuals involved; (2) demonstrate that this change goes beyond the individual and becomes situated within wider social units or communities of practice; and (3) occur through social interactions and processes between actors within a social network. A clearer picture of what we mean by social learning could enhance our ability to critically evaluate outcomes and better understand the processes through which social learning occurs. In this way, it may be possible to better facilitate the desired outcomes of social learning processes.