Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province.

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Networked geographies of private landlordism: mapping flows of capital accumulation and rent extraction.

Recent years have seen the resurgence of private-rental housing as both a place to live, and a site of capital investment. Individual landlordism is a key feature of this resurgence. This paper aims to understand the networked geographies of private landlordism. Drawing on Dutch register data, containing geocoded information on the full population and housing stock, this paper is able to uniquely link private-rental units to their (landlord) owners. By linking landlords' places of residence and investment, flows of capital across space are visualized. Empirically, these findings show that, despite the liquidity of capital and the typical focus on transnational investments, most landlords invest locally or regionally, resulting in urban-regional networks of landlordism. These patterns pertain to landlord spatial strategies as well as the functioning of urban systems. Conceptually, findings demonstrate that property-based class relations between tenants and landlords are spatial relations as well, linking places of accumulation with places of extraction and reproducing spatial patterns of (dis)advantage.

Triadic percolation induces dynamical topological patterns in higher-order networks.

Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here, we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns, and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time dependent as in neuroscience.

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau.

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

Landscape configuration can flip species-area relationships in dynamic meta-food-webs.

Spatial and trophic processes profoundly influence biodiversity, yet ecological theories often treat them independently. The theory of island biogeography and related theories on metacommunities predict higher species richness with increasing area across islands or habitat patches. In contrast, food-web theory explores the effects of traits and network structure on coexistence within local communities. Exploring the mechanisms by which landscape configurations interact with food-web dynamics in shaping metacommunities is important for our understanding of biodiversity. Here, we use a meta-food-web model to explore the role of landscape configuration in determining species richness and show that when habitat patches are interconnected by dispersal, more species can persist on smaller islands than predicted by classical theory. When patch sizes are spatially aggregated, this effect flattens the slope of the species-area relationship. Surprisingly, when landscapes have random patch-size distributions, the slope of the species-area relationships can even flip and become negative. This could be explained by higher biomass densities of lower trophic levels that then support species occupying higher trophic levels, which only persist on small and well-connected patches. This highlights the importance of simultaneously considering landscape configuration and local food-web dynamics to understand drivers of species-area relationships in metacommunities.This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.

Connectivity at the human-wildlife interface: starling movements relate to carriage of E. coli.

Synanthropic bird species in human, poultry or livestock environments can increase the spread of pathogens and antibiotic-resistant bacteria between wild and domestic animals. We present the first telemetry-based spatial networks for a small songbird. We quantified landscape connectivity exerted by spotless starling movements, and aimed to determine if connectivity patterns were related to carriage of potential pathogens. We captured 28 starlings on a partridge farm in 2020 and tested them for Avian influenza virus, West Nile virus WNV, Avian orthoavulavirus 1, Coronavirus, Salmonella spp. and Escherichia coli. We did not detect any viruses or Salmonella, but one individual had antibodies against WNV or cross-reacting Flaviviruses. We found E. coli in 61 % (17 of 28) of starlings, 76 % (13 of 17) of which were resistant to gentamicin, 12 % (2 of 17) to cefotaxime/enrofloxacin and 6 % (1 of 17) were phenotypic extended spectrum beta-lactamase (ESBL) carriers. We GPS-tracked 17 starlings and constructed spatial networks showing how their movements (i.e. links) connect different farms with nearby urban and natural habitats (i.e. nodes with different attributes). Using E. coli carriage as a proxy for acquisition/dispersal of bacteria, we found differences across spatial networks constructed for E. coli positive (n = 7) and E. coli negative (n = 9) starlings. We used Exponential Random Graph Models to reveal significant differences between networks. In particular, an urban roost was more connected to other sites by movements of E. coli positive than by movements of E. coli negative starlings. Furthermore, an open pine forest used mainly for roosting was more connected to other sites by movements of E. coli negative than by movements of E. coli positive starlings. Using E. coli as a proxy for a potential pathogen carried by starlings, we reveal the pathways of spread that starlings could provide between farms, urban and natural habitats.

Does the brain behave like a (complex) network? I. Dynamics.

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

Spatial networks and the spread of COVID-19: results and policy implications from Germany.

Spatial networks are known to be informative about the spatiotemporal transmission dynamics of COVID-19. Using district-level panel data from Germany that cover the first 22 weeks of 2020, we show that mobility, commuter and social networks all predict the spatiotemporal propagation of the epidemic. The main innovation of our approach is that it incorporates the whole network and updated information on case numbers across districts over time. We find that when disease incidence increases in network neighbouring regions, case numbers in the home district surge one week later. The magnitude of these network transmission effects is comparable to within-district transmission, illustrating the importance of networks as drivers of local disease dynamics. After the introduction of containment policies in mid-March, network transmission intensity drops substantially. Our analysis suggests that this reduction is primarily due to a change in quality-not quantity-of interregional movements. This implies that blanket mobility restrictions are not a prerequisite for containing the interregional spread of COVID-19.

Nonoptimal component placement of the human connectome supports variable brain dynamics.

Neural systems are shaped by multiple constraints, balancing region communication with the cost of establishing and maintaining physical connections. It has been suggested that the lengths of neural projections be minimized, reducing their spatial and metabolic impact on the organism. However, long-range connections are prevalent in the connectomes across various species, and thus, rather than rewiring connections to reduce length, an alternative theory proposes that the brain minimizes total wiring length through a suitable positioning of regions, termed component placement optimization. Previous studies in nonhuman primates have refuted this idea by identifying a nonoptimal component placement, where a spatial rearrangement of brain regions in silico leads to a reduced total wiring length. Here, for the first time in humans, we test for component placement optimization. We show a nonoptimal component placement for all subjects in our sample from the Human Connectome Project (N = 280; aged 22-30 years; 138 females), suggesting the presence of constraints-such as the reduction of processing steps between regions-that compete with the elevated spatial and metabolic costs. Additionally, by simulating communication between brain regions, we argue that this suboptimal component placement supports dynamics that benefit cognition.

Expanding network ecology in freshwater ecosystems.

Research in freshwater ecosystems has always had a strong focus on ecological interactions. The vast majority of studies, however, have investigated trophic interactions and food webs, overlooking a wider suite of non-trophic interactions (e.g. facilitation, competition, symbiosis and parasitism) and the ecological networks they form. Without a complete understanding of all potential interactions, ranging from mutualistic through to antagonistic, we may be missing important ecological processes with consequences for ecosystem assembly, structure and function. Ecological networks can be constructed at different scales, from genes to ecosystems, but also local to global, and as such there is significant opportunity to put them to work in freshwater research. To expand beyond food webs, we need to leverage technological and methodological advances and look to recent research in marine and terrestrial systems-which are far more advanced in terms of detecting, measuring and contextualising ecological interactions. Future studies should look to emerging technologies to aid in merging the wide range of ecological interactions in freshwater ecosystems into networks to advance our understanding and ultimately increase the efficacy of conservation, management, restoration and other applications.

Resource predictability modulates spatial-use networks in an endangered scavenger species.

BACKGROUND: Changes in human-induced resource availability can alter the behaviour of free-living species and affect their foraging strategies. The future European Landfill Waste Directive and Circular Economy Action Plan will reduce the number of predictable anthropogenic food subsidies (PAFS), above all, by closing landfills to preclude negative effects on human health. Obligate avian scavengers, the most threatened group of birds worldwide, are the most likely group of species that will be forced to change their behaviour and use of space in response to landfill site closures. Here, we examine the possible consequences of these management decisions on the foraging patterns of Egyptian vultures (Neophron percnopterus) in an expanding population in the Iberian Peninsula. METHODS: We tracked 16 individuals in 2018-2021, including breeders and non-breeders, and, using a combination of spatial-use and spatial-network modelling, assessed landscape connectivity between key resources based on movement patterns. We then carried out simulations of future scenarios based on the loss of PAFS to predict likely changes in the movement patterns of both non-breeders and breeders. RESULTS: Our results show that foraging strategies in non-breeders and breeders differ significantly: non-breeders performed more dispersal movements than breeding birds across a spatial-use network. Non-breeding and breeding networks were found to be vulnerable to the removal of central foraging areas containing landfill sites, a highly predictable resource, while perturbation analysis showed dissimilar foraging responses to the gradual reduction of other predictable resources. Under a context of the non-availability of landfills for breeders and non-breeders, vultures will increase their use of extensive livestock as a trophic resource. CONCLUSIONS: Future environmental policies should thus extend the areas used by scavengers in which livestock carcasses are allowed to remain in the wild, a strategy that will also mitigate the lack of food caused by any reduction in available waste if landfills close. In general, our results emphasize the capabilities of a spatial network approaches to address questions on movement ecology. They can be used to infer the behavioural response of animal species and, also demonstrate the importance of applying such approaches to endangered species conservation within a context of changing humanized scenarios.

Spatiotemporal reconstruction of ancient road networks through sequential cost-benefit analysis.

The construction of ancient road networks spanned generations and exhibits temporal path dependence that is not fully captured by established network formation models that are used to support archaeological reasoning. We introduce an evolutionary model that captures explicitly the sequential nature of road network formation: A central feature is that connections are added successively and according to an optimal cost-benefit trade-off with respect to existing connections. In this model, the network topology emerges rapidly from early decisions, a trait that makes it possible to identify plausible road construction orders in practice. Based on this observation we develop a method to compress the search space of path-dependent optimization problems. We use this method to show that the model's assumptions on ancient decision-making allow the reconstruction of partially known road networks from the Roman era in good detail and from sparse archaeological evidence. In particular, we identify missing links in the major road network of ancient Sardinia that are in good agreement with expert predictions.

Study of spatialtemporal changes in Chinese forest eco-space and optimization strategies for enhancing carbon sequestration capacity through ecological spatial network theory.

The conservation of forest ecosystems and the enhancement of carbon sequestration capacity play a crucial role in maintaining ecological balance and human development. However, with excessive deforestation, the flow of energy and information within the ecosystem has changed, which in turn has led to changes in the topological properties and carbon sequestration capacity of forest ecosystems. In order to better investigate the nature and carbon sequestration capacity of forest ecological space in mainland China during 2000-2018, we constructed a time-series Chinese forest ecological spatial network based on complex network theory and graph theory, combined with the modified minimal cumulative resistance model (MCR). By combining the net primary productivity (NPP) values obtained from the Boreal Ecosystem Productivity Simulator (BEPS) model of existing scholars, we further explored the relationship between topology and carbon sequestration capacity within forest ecosystems, and proposed strategies and suggestions for optimization. The results show that forest ecological sources and ecological corridors showed an increasing trend and resistance values decreased year by year during 2000-2018, especially in the western region, indicating that ecological restoration projects in western China have achieved certain effects. However, the stability of forest ecosystems has been decreasing year by year, and the forest carbon sequestration capacity in western China is also decreasing. Through correlation analysis, we found that carbon sequestration capacity showed highly significant positive correlation with closeness centrality, harmonic closeness centrality, clustering, and eigen centrality, and carbon sequestration capacity showed highly significant negative correlation with betweeness centrality. Through Principal Components Analysis (PCA), we suggest that consolidating small patches in the northeast, reducing the number of redundant ecological corridors, adding stepping stone patches to shorten the length of ecological corridors, and increasing ecological corridors in non-northeast areas are conducive to enhancing plant carbon sequestration capacity. This study provides theoretical support and ecological engineering recommendations for China to achieve its strategic goals of carbon neutrality and carbon peaking.

Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks.

We examine how spatial heterogeneity combines with mobility network structure to influence vector-borne disease dynamics. Specifically, we consider a Ross-Macdonald-type disease model on n spatial locations that are coupled by host movement on a strongly connected, weighted, directed graph. We derive a closed form approximation to the domain reproduction number using a Laurent series expansion, and use this approximation to compute sensitivities of the basic reproduction number to model parameters. To illustrate how these results can be used to help inform mitigation strategies, as a case study we apply these results to malaria dynamics in Namibia, using published cell phone data and estimates for local disease transmission. Our analytical results are particularly useful for understanding drivers of transmission when mobility sinks and transmission hot spots do not coincide.

Local topological features of robust supply networks.

The design of robust supply and distribution systems is one of the fundamental challenges at the interface of network science and logistics. Given the multitude of performance criteria, real-world constraints, and external influences acting upon such a system, even formulating an appropriate research question to address this topic is non-trivial. Here we present an abstraction of a supply and distribution system leading to a minimal model, which only retains stylized facts of the systemic function and, in this way, allows us to investigate the generic properties of robust supply networks. On this level of abstraction, a supply and distribution system is the strategic use of transportation to eliminate mismatches between production patterns (i.e., the amounts of goods produced at each production site of a company) and demand patterns (i.e., the amount of goods consumed at each location). When creating networks based on this paradigm and furthermore requiring the robustness of the system with respect to the loss of transportation routes (edge of the network) we see that robust networks are built from specific sets of subgraphs, while vulnerable networks display a markedly different subgraph composition. Our findings confirm a long-standing hypothesis in the field of network science, namely, that network motifs-statistically over-represented small subgraphs-are informative about the robust functioning of a network. Also, our findings offer a blueprint for enhancing the robustness of real-world supply and distribution systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s41109-022-00470-2.

Networks behind the morphology and structural design of living systems.

Technological advances in imaging techniques and biometric data acquisition have enabled us to apply methods of network science to study the morphology and structural design of organelles, organs, and tissues, as well as the coordinated interactions among them that yield a healthy physiology at the level of whole organisms. We here review research dedicated to these advances, in particular focusing on networks between cells, the topology of multicellular structures, neural interactions, fluid transportation networks, and anatomical networks. The percolation of blood vessels, structural connectivity within the brain, the porous structure of bones, and relations between different anatomical parts of the human body are just some of the examples that we explore in detail. We argue and show that the models, methods, and algorithms developed in the realm of network science are ushering in a new era of network-based inquiry into the morphology and structural design of living systems in the broadest possible terms. We also emphasize that the need and applicability of this research is likely to increase significantly in the years to come due to the rapid progress made in the development of bioartificial substitutes and tissue engineering.

Extracting spatial networks from capture-recapture data reveals individual site fidelity patterns within a marine mammal's spatial range.

Estimating the impacts of anthropogenic disturbances requires an understanding of the habitat-use patterns of individuals within a population. This is especially the case when disturbances are localized within a population's spatial range, as variation in habitat use within a population can drastically alter the distribution of impacts.Here, we illustrate the potential for multilevel binomial models to generate spatial networks from capture-recapture data, a common data source used in wildlife studies to monitor population dynamics and habitat use. These spatial networks capture which regions of a population's spatial distribution share similar/dissimilar individual usage patterns, and can be especially useful for detecting structured habitat use within the population's spatial range.Using simulations and 18 years of capture-recapture data from St. Lawrence Estuary (SLE) beluga, we show that this approach can successfully estimate the magnitude of similarities/dissimilarities in individual usage patterns across sectors, and identify sectors that share similar individual usage patterns that differ from other sectors, that is, structured habitat use. In the case of SLE beluga, this method identified multiple clusters of individuals, each preferentially using restricted areas within their summer range of the SLE.Multilevel binomial models can be effective at estimating spatial structure in habitat use within wildlife populations sampled by capture-recapture of individuals, and can be especially useful when sampling effort is not evenly distributed. Our finding of a structured habitat use within the SLE beluga summer range has direct implications for estimating individual exposures to localized stressors, such as underwater noise from shipping or other activities.

Network Models and Simulation Analytics for Multi-scale Dynamics of Biological Invasions.

Globalization and climate change facilitate the spread and establishment of invasive species throughout the world via multiple pathways. These spread mechanisms can be effectively represented as diffusion processes on multi-scale, spatial networks. Such network-based modeling and simulation approaches are being increasingly applied in this domain. However, these works tend to be largely domain-specific, lacking any graph theoretic formalisms, and do not take advantage of more recent developments in network science. This work is aimed toward filling some of these gaps. We develop a generic multi-scale spatial network framework that is applicable to a wide range of models developed in the literature on biological invasions. A key question we address is the following: how do individual pathways and their combinations influence the rate and pattern of spread? The analytical complexity arises more from the multi-scale nature and complex functional components of the networks rather than from the sizes of the networks. We present theoretical bounds on the spectral radius and the diameter of multi-scale networks. These two structural graph parameters have established connections to diffusion processes. Specifically, we study how network properties, such as spectral radius and diameter are influenced by model parameters. Further, we analyze a multi-pathway diffusion model from the literature by conducting simulations on synthetic and real-world networks and then use regression tree analysis to identify the important network and diffusion model parameters that influence the dynamics.

Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar.

Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.

JUE Insight: The Geography of Travel Behavior in the Early Phase of the COVID-19 Pandemic.

We use U.S. county-level location data derived from smartphones to examine travel behavior and its relationship with COVID-19 cases in the early stages of the outbreak. People traveled less overall and notably avoided areas with relatively larger outbreaks. A doubling of new cases in a county led to a 3 to 4 percent decrease in trips to and from that county. Without this change in travel activity, exposure to out-of-county virus cases could have been twice as high at the end of April 2020. Limiting travel-induced exposure was important because such exposure generated new cases locally. We find a one percent increase in case exposure from travel led to a 0.21 percent increase in new cases added within a county. This suggests the outbreak would have spread faster and to a greater degree had travel activity not dropped accordingly. Our findings imply that the scale and geographic network of travel activity and the travel response of individuals are important for understanding the spread of COVID-19 and for policies that seek to control it.