BIOND LAB

This paper positions possibilities for human geographies of the sea. The growing volume of work under this banner has been largely qualitative in its approach, reflecting, in turn, the questions posed by oceanic scholars. These questions necessitate corresponding methods. Whilst this is not necessarily a problem, and the current corpus of work has offered many significant contributions, in making sense of the human dimensions of maritime worlds, other questions —and methods—may generate knowledge that is useful within this remit of work. This paper considers the place of quantitative approaches in posing lines of enquiry about shipping, one of the prominent areas of concern under the banner of ‘human geographies of the seas’. There is longstanding work in transport geographies concerned with shipping, logistics, freight movement and global connections, which embraces quantitative methods which could be bridged to ask fresh questions about oceanic spatial phenomena past and present. This paper reviews the state of the art of human geographies of the sea and transport geographies and navigates how the former field may be stimulated by some of the interests of the latter and a broader range of questions about society‐sea‐space relations. The paper focuses on Automatic Identification Systems (or AIS) as a potentially useful tool for connecting debates, and deepening spatial understandings of the seas and shipping beyond current scholarship. To advance the argument the example of shipping layups is used to illustrate or rather, position, the point.

Traditionally urban structure and development are monitored using infrequent high-quality datasets such as censuses. However, human culture is accelerating and aggregating, leading to ever-larger cities and an increased pace of urban development. Our modern interconnected world also provides us with new data sources that can be leveraged in the study of cities. However, these often noisy and unstructured sources of big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which is produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing for example the emergence and absorption of subcenters. In the future this method will allow the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency.

This paper positions possibilities for human geographies of the sea. The growing volume of work under this banner has been largely qualitative in its approach, reflecting, in turn, the questions posed by oceanic scholars. These questions necessitate corresponding methods. Whilst this is not necessarily a problem, and the current corpus of work has offered many significant contributions, in making sense of the human dimensions of maritime worlds, other questions —and methods—may generate knowledge that is useful within this remit of work. This paper considers the place of quantitative approaches in posing lines of enquiry about shipping, one of the prominent areas of concern under the banner of ‘human geographies of the seas’. There is longstanding work in transport geographies concerned with shipping, logistics, freight movement and global connections, which embraces quantitative methods which could be bridged to ask fresh questions about oceanic spatial phenomena past and present. This paper reviews the state of the art of human geographies of the sea and transport geographies and navigates how the former field may be stimulated by some of the interests of the latter and a broader range of questions about society‐sea‐space relations. The paper focuses on Automatic Identification Systems (or AIS) as a potentially useful tool for connecting debates, and deepening spatial understandings of the seas and shipping beyond current scholarship. To advance the argument the example of shipping layups is used to illustrate or rather, position, the point.

Many of the global challenges that confront humanity are interlinked in a dynamic complex network, with multiple feedback loops, nonlinear interactions and interdependencies that make it difficult, if not impossible, to consider individual threats in isolation. These challenges are mainly dealt with, however, by considering individual threats in isolation (at least in political terms). The mitigation of dual climate and biodiversity threats, for example, is linked to a univariate 1.5°C global warming boundary and a global area conservation target of 30% by 2030. The situation has been somewhat improved by efforts to account for interactions through multidimensional target setting, adaptive and open management and market-based decision pathways. But the fundamental problem still remains—that complex systems such as those formed by the network of global threats have emergent properties that are more than the sum of their parts. We must learn how to deal with or live with these properties if we are to find effective ways to cope with the threats, individually and collectively. Here, we argue that recent progresses in complex systems research and related fields have enhanced our ability to analyse and model such entwined systems to the extent that it offers the promise of a new approach to sustainability. We discuss how this may be achieved, both in theory and in practice, and how human cultural factors play an important but neglected role that could prove vital to achieving success.

By-catch. A noun. British English. “Unwanted fish and other sea animals caught in a fishing net along with the desired kind of fish.” Bycatch. Scientific definition. “Bycatch refers most often to those species incidentally taken in fishing operations aimed at other (target) species... [it] refers to species accidentally caught other than the target species, brought on board, dead or alive, and that can therefore be either released alive, discarded dead, or landed.” What happens when you spin a net out into a sea of data? What is returned? What is valuable (what is not)? What is by-catch (or bycatch)? What is unwanted, alongside that which is wanted? What is unintentional, caught by accident? And what should be discarded (or thrown back into the metaphorical waters)? What is kept? Landed. Why?

It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks.

Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time-series of taxonomically-rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning, to organize genomic information into potentially occupied bacterial metabolic niches over time. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time-series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon whereas others were occupied by multiple taxa, and this depended on season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems.