Temporal origin of nestedness in interaction networks

Nestedness is a common property of communication, finance, trade, and ecological networks. In networks with high levels of nestedness, the link positions of low-degree nodes (those with few links) form nested subsets of the link positions of high-degree nodes (those with many links), leading to matrix representations with characteristic upper triangular or staircase patterns. Recent theoretical work has connected nestedness to the functionality of complex systems and has suggested that it is a structural by-product of the skewed degree distributions often seen in empirical data. However, mechanisms for generating nestedness remain poorly understood, limiting the connections that can be made between system processes and observed network structures. Here, we show that a simple probabilistic model based on phenology—the timing of copresences among interaction partners—can produce nested structures and correctly predict around two-thirds of interactions in two fish market networks and around one-third of interactions in 22 plant–pollinator networks. Notably, the links most readily explained by frequent actor copresences appear to form a backbone of nested interactions, with the remaining interactions attributable to opportunistic interactions or preferences for particular interaction partners that are not routinely available

Predictor species: Improving assessments of rare species occurrence by modeling environmental co-responses

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species\u27 co-occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co-responses among species. For species from a European peat bog community, our approach consistently performs better than single-species models and better than conventional multi-species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices

Rapidly detecting disorder in rhythmic biological signals: A spectral entropy measure to identify cardiac arrhythmias

We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6 s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.Comment: 11 page

Reporting HIV in Papua New Guinea: Trends and Omissions from 2000 to 2010

This article presents the findings from a longitudinal content analysis on the reporting of HIV (Human Immunodeficiency Virus) in Papua New Guinea’s two national newspapers—The National and Post-Courier—in 2000, 2005 and 2010. The authors tried to answer two key questions: Did press coverage of the disease increase and did the topics change or remain the same? Data from the content analysis showed that coverage of the disease increased significantly during the ten-year study period, and that the framing of the disease moved beyond representing HIV as purely a health story to one that was linked to socio-economic conditions and cultural practices. The feature stories gradually showed more sensitivity to people living with HIV, while they recognised and challenged the social stigma still associated with the disease in much of the countr

Large herbivores transform plant-pollinator networks in an African savanna

Pollination by animals is a key ecosystem service1,2 and interactions between plants and their pollinators are a model system for studying ecological networks,3,4 yet plant-pollinator networks are typically studied in isolation from the broader ecosystems in which they are embedded. The plants visited by pollinators also interact with other consumer guilds that eat stems, leaves, fruits, or seeds. One such guild, large mammalian herbivores, are well-known ecosystem engineers5, 6, 7 and may have substantial impacts on plant-pollinator networks. Although moderate herbivory can sometimes promote plant diversity,8 potentially benefiting pollinators, large herbivores might alternatively reduce resource availability for pollinators by consuming flowers,9 reducing plant density,10 and promoting somatic regrowth over reproduction.11 The direction and magnitude of such effects may hinge on abiotic context—in particular, rainfall, which modulates the effects of ungulates on vegetation.12 Using a long-term, large-scale experiment replicated across a rainfall gradient in central Kenya, we show that a diverse assemblage of native large herbivores, ranging from 5-kg antelopes to 4,000-kg African elephants, limited resource availability for pollinators by reducing flower abundance and diversity; this in turn resulted in fewer pollinator visits and lower pollinator diversity. Exclusion of large herbivores increased floral-resource abundance and pollinator-assemblage diversity, rendering plant-pollinator networks larger, more functionally redundant, and less vulnerable to pollinator extinction. Our results show that species extrinsic to plant-pollinator interactions can indirectly and strongly alter network structure. Forecasting the effects of environmental change on pollination services and interaction webs more broadly will require accounting for the effects of extrinsic keystone species

Structure, dynamics, and robustness of ecological networks

Ecosystems are often made up of interactions between large numbers of species. They are considered complex systems because the behaviour of the system as a whole is not always obvious from the properties of the individual parts. A complex system can be represented by a network: a set of interconnected objects. In the case of ecological networks and food webs, the objects are species and the connections are interactions between species. Many complex systems are dynamic and exhibit intricate time series. Time series analysis has been developed to understand a wide range of natural phenomena. This thesis deals with the structure, dynamics, and robustness of ecological networks, the spatial dynamics of fluctuations in a social system, and the analysis of cardiac time series. Biodiversity on Earth is decreasing largely due to human-induced causes. My work looks at the effect of anthropogenic change on ecological networks. In Chapter Two, I investigate predator adaptation on food-web robustness following species extinctions. I identify a new theoretical category of species that may buffer ecosystems against environmental change. In Chapter Three, I study changes in parasitoid-host (consumer-resource) interaction frequencies between complex and simple environments. I show that the feeding preferences of parasitoid species actively change in response to habitat modification. Ecological networks are embedded in spatially-heterogeneous landscapes. In Chapter Four, I assess the role of geography on population fluctuations in an analogous social system. I demonstrate that fluctuations in the number of venture capital firms registered in cities in the United States of America are consistent with spatial and temporal contagion. Understanding how physiological signals vary through time is of interest to medical practitioners. In Chapter Five, I present a technique for quickly quantifying disorder in high frequency event series. Applying the algorithm to patient cardiac time series provides a rapid way to detect the onset of heart arrhythmia. Increasingly, answers to scientific questions lie at the intersection of traditional disciplines. This thesis applies techniques developed in physics and mathematics to problems in ecology and medicine

Correction: Bounds on Transient Instability for Complex Ecosystems.

[This corrects the article DOI: 10.1371/journal.pone.0157876.]

Distribution of upper bounds of the maximum amplitude of perturbations to the population vector Eq (9) for random community matrices generated with <i>S</i> = 100, <i>C</i> = 0.1 and <i>μ</i> = 1 and seven values of <i>σ</i> (10,000 realisations).

<p>Distributions are fat-tailed and the slope of the tail does not change with <i>σ</i>.</p

Regions of stability, transient instability and instability for a random community matrices with <i>S</i> = 100, <i>C</i> = 0.1 and <i>μ</i> = 1 as <i>σ</i> is varied.

<p>The y-axis is the lower bound of the maximum amplitude of perturbations to the population vector <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157876#pone.0157876.e020" target="_blank">Eq (12)</a>. Transient instability is observable as the curve crosses one at <i>σ</i>_ti ≈ 0.22 and instability is reached at . At the threshold of instability, the lower bound of the maximum amplitude is LB(<i>σ</i>_c) = 1.046 ± 0.006 (mean ± standard deviation). The shaded area represents the standard error over 100 realisations.</p