Network structure and the biology of populations

May, Robert M.

doi:10.1016/j.tree.2006.03.013

Cited by 281 publications

(238 citation statements)

References 67 publications

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“…Anderson and May 38 established the theoretical case for focusing preventative action on 'super-spreaders' within the network to limit the potential for system-wide spread. Although initially applied in the study of contagious diseases, such as HIV/AIDs, this same insight has since been applied in managing the dynamics of the world wide web, power grids and biological ecosystems 8,39 .…”

Section: Setting Systemic Regulatory Requirementsmentioning

confidence: 99%

Systemic risk in banking ecosystems

Haldane

May

2011

Nature

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1,171

754

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In the run-up to the recent financial crisis, an increasingly elaborate set of financial instruments emerged, intended to optimize returns to individual institutions with seemingly minimal risk. Essentially no attention was given to their possible effects on the stability of the system as a whole. Drawing analogies with the dynamics of ecological food webs and with networks within which infectious diseases spread, we explore the interplay between complexity and stability in deliberately simplified models of financial networks. We suggest some policy lessons that can be drawn from such models, with the explicit aim of minimizing systemic risk.

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Section: Setting Systemic Regulatory Requirementsmentioning

confidence: 99%

Systemic risk in banking ecosystems

Haldane

May

2011

Nature

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1,171

754

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“…The boxes on the right show unconnected species. 2002; May, 2006). Loosely speaking, this means that there are many nodes with only one or few connections, but also some nodes with many more connections than the average degree.…”

Section: Network Characterisationmentioning

confidence: 99%

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Faisal

Dondelinger

Husmeier

et al. 2010

Ecological Informatics

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The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism. Understanding the networks that form the systems is of growing importance, e.g. to understand how species will respond to climate change, or to predict potential knock-on effects of a biological control agent. In recent years, a variety of summary statistics for characterising the global and local properties of such networks have been derived, which provide a measure for gauging the accuracy of a mathematical model for network formation processes. However, the critical underlying assumption is that the true network is known. This is not a straightforward task to accomplish, and typi- * Corresponding author. Tel: +44 (0) cally requires minute observations and detailed field work. More importantly, knowledge about species interactions is restricted to specific kinds of interactions. For instance, while the interactions between pollinators and their host plants are amenable to direct observation, other types of species interactions, like those mentioned above, are not, and might not even be clearly defined from the outset. To discover information about complex ecological systems efficiently, new tools for inferring the structure of networks from field data are needed. In the present study, we investigate the viability of various statistical and machine learning methods recently applied in molecular systems biology: graphical Gaussian models, L1-regularised regression with least absolute shrinkage and selection operator (LASSO), sparse Bayesian regression and Bayesian networks. We have assessed the performance of these methods on data simulated from food webs of known structure, where we combined a niche model with a stochastic population model in a 2-dimensional lattice.We assessed the network reconstruction accuracy in terms of the area under the receiver operator characteristics (ROC) curve, which was typically in the range between 0.75 and 0.9, corresponding to the recovery of about 60% of the true species interactions at a false prediction rate of 5%. We also applied the models to presence/absence data for 39 European warblers, and found that the inferred species interactions showed a weak yet significant correlation with phylogenetic similarity scores, which tended to weakly increase when including bio-climate covariates and allowing for spatial autocorrelation. Our findings demonstrate that relevant patterns in ecological networks can be identified from large-scale spatial data sets with machine learning methods, and that these methods have the potential to contribute novel important tools for gaining deeper insight into the structure and stability of ecosystems.

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“…Recent syntheses have used network theoretical analysis to understand the functioning of diverse sets of complex ecological systems (May 2006;Montoya et al 2006). These analyses have suggested that emergent characteristics such as system-level responses to disturbance can be predicted from the structure of a network and the strength of interactions among network elements (Grant et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Diversity at Network Nodes: River Confluences Increase Vegetation-Patch Diversity~!2010-03-22~!2010-04-30~!2010-07-07~!

Osawa¹,

Mitsuhashi²,

Niwa³

et al. 2010

TOECOLJ

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Although dendritic networks within ecosystems have typically been considered a special case of network topology, they have attracted a great deal of attention in recent years. These systems exhibit unique features in that both the nodes and branches provide distinct habitats. Within a river discontinuum context, river confluences, which are nodes of dendritic river networks, are hypothesised to have particular hydrodynamic traits that create heterogeneous habitats through a unique disturbance regime, although this hypothesis has not yet been tested. We tested this hypothesis using a vegetation data set collected from 14 river basin systems in Hyogo Prefecture, Japan. We compared vegetation-patch diversity between confluence and single-flow areas using hierarchical Bayesian models. Our results demonstrated greater vegetation-patch diversity in confluence areas compared to single-flow areas. Our findings support the hypothesis that confluences result in highly heterogeneous habitats. To the best of our knowledge, this is the first empirical report to demonstrate that river confluences have high vegetation-patch diversity. We conclude that network nodes play an important role in maintaining the biodiversity of river networks.

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Network structure and the biology of populations

Cited by 281 publications

References 67 publications

Systemic risk in banking ecosystems

Systemic risk in banking ecosystems

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Enhanced Diversity at Network Nodes: River Confluences Increase Vegetation-Patch Diversity~!2010-03-22~!2010-04-30~!2010-07-07~!

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