The robustness of some Carboniferous fossil leaf venation networks to simulated damage.

Biological networks vary widely in their architecture and functional properties. Branching networks are good for transportation efficiency, while networks including loops offer good resistance to damage, and examples of these two topologies are found in leaf venation networks. The first plants with reticulate (loopy) leaf venation evolved in the Pennsylvanian of the Carboniferous, but the responses of different venation network architectures from this time period to damage are currently largely unknown. Here we address this issue with a computational analysis of venation network robustness that is focused on fossil leaves from the Pennsylvanian. We attacked fossil venation networks with simulated damage to individual vein segments and leaf blades. For both types of attack, branched venation networks are the least robust to damage, with greater robustness shown by the net-like reticulate networks found in the Pennsylvanian. A living angiosperm Betula alba was the most robust in our analysis. This may highlight a role for resistance to damage in the evolution of reticulate leaf venation in the Carboniferous, but further work is needed to answer the broader question of why reticulate leaf venation first evolved in the Pennsylvanian.

CIDER: Context-sensitive polarity measurement for short-form text.

Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general-purpose sentiment analysis methods are used. These perform well on average but miss the variation in meaning that happens across different contexts, for example, the word "active" has a very different intention and valence in the phrase "active lifestyle" versus "active volcano". This work presents a new approach, CIDER (Context Informed Dictionary and sEmantic Reasoner), which performs context-sensitive linguistic analysis, where the valence of sentiment-laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper, we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist unsupervised sentiment analysis techniques on a large collection of tweets about the weather. CIDER is also applicable to alternative (non-sentiment) linguistic scales. A case study on gender in the UK is presented, with the identification of highly gendered and sentiment-laden days. We have made our implementation of CIDER available as a Python package: https://pypi.org/project/ciderpolarity/.

Social Sensing of Heatwaves.

Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and "heatwaves" are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation.

Community evolution on Stack Overflow.

Question and answer (Q&A) websites are a medium where people can communicate and help each other. Stack Overflow is one of the most popular Q&A websites about programming, where millions of developers seek help or provide valuable assistance. Activity on the Stack Overflow website is moderated by the user community, utilizing a voting system to promote high quality content. The website was created on 2008 and has accumulated a large amount of crowd wisdom about the software development industry. Here we analyse this data to examine trends in the grouping of technologies and their users into different sub-communities. In our work we analysed all questions, answers, votes and tags from Stack Overflow between 2008 and 2020. We generated a series of user-technology interaction graphs and applied community detection algorithms to identify the biggest user communities for each year, to examine which technologies those communities incorporate, how they are interconnected and how they evolve through time. The biggest and most persistent communities were related to web development. In general, there is little movement between communities; users tend to either stay within the same community or not acquire any score at all. Community evolution reveals the popularity of different programming languages and frameworks on Stack Overflow over time. These findings give insight into the user community on Stack Overflow and reveal long-term trends on the software development industry.

Ideological biases in social sharing of online information about climate change.

Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

A social Beaufort scale to detect high winds using language in social media posts.

People often talk about the weather on social media, using different vocabulary to describe different conditions. Here we combine a large collection of wind-related Twitter posts (tweets) and UK Met Office wind speed observations to explore the relationship between tweet volume, tweet language and wind speeds in the UK. We find that wind speeds are experienced subjectively relative to the local baseline, so that the same absolute wind speed is reported as stronger or weaker depending on the typical weather conditions in the local area. Different linguistic tokens (words and emojis) are associated with different wind speeds. These associations can be used to create a simple text classifier to detect 'high-wind' tweets with reasonable accuracy; this can be used to detect high winds in a locality using only a single tweet. We also construct a 'social Beaufort scale' to infer wind speeds based only on the language used in tweets. Together with the classifier, this demonstrates that language alone is indicative of weather conditions, independent of tweet volume. However, the number of high-wind tweets shows a strong temporal correlation with local wind speeds, increasing the ability of a combined language-plus-volume system to successfully detect high winds. Our findings complement previous work in social sensing of weather hazards that has focused on the relationship between tweet volume and severity. These results show that impacts of wind and storms are found in how people communicate and use language, a novel dimension in understanding the social impacts of extreme weather.

Projections of coral cover and habitat change on turbid reefs under future sea-level rise.

Global sea-level rise (SLR) is projected to increase water depths above coral reefs. Although the impacts of climate disturbance events on coral cover and three-dimensional complexity are well documented, knowledge of how higher sea levels will influence future reef habitat extent and bioconstruction is limited. Here, we use 31 reef cores, coupled with detailed benthic ecological data, from turbid reefs on the central Great Barrier Reef, Australia, to model broad-scale changes in reef habitat following adjustments to reef geomorphology under different SLR scenarios. Model outputs show that modest increases in relative water depth above reefs (Representative Concentration Pathway (RCP) 4.5) over the next 100 years will increase the spatial extent of habitats with low coral cover and generic diversity. More severe SLR (RCP8.5) will completely submerge reef flats and move reef slope coral communities below the euphotic depth, despite the high vertical accretion rates that characterize these reefs. Our findings suggest adverse future trajectories associated with high emission climate scenarios which could threaten turbid reefs globally and their capacity to act as coral refugia from climate change.

Student engagement and wellbeing over time at a higher education institution.

Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students' subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records.

Scaling laws in geo-located Twitter data.

Twitter has become an important platform for geo-spatial analyses, providing high-volume spatial data on a wide variety of social processes. Understanding the relationship between population density and Twitter activity is therefore of key importance. This study reports a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power law functions with exponents greater than one. These relations are consistent with each other and hold across a range of spatial scales. This implies that population density can accurately predict Twitter activity, but importantly, it also implies that correct predictions are not given by a naive linear scaling analysis. The observed super-linearity has implications for any spatial analyses performed with Twitter data and is important for understanding the relationship between Twitter use and demographics. For example, the robustness of this relationship means that we can identify 'anomalous' geographic areas that deviate from the observed trend, identifying several towns with high/low usage relative to expectation; using the scaling relationship we are able to show that these anomalies are not caused by age structure, as has been previously proposed. Proper consideration of this scaling relationship will improve robustness in future geo-spatial studies using Twitter.

The human geography of Twitter: Quantifying regional identity and inter-region communication in England and Wales.

Given the centrality of regions in social movements, politics and public administration, here we aim to quantitatively study regional identity, cross-region communication and sentiment. This paper presents a new methodology to study social interaction within and between social-geographic regions, and then applies the methodology to a case study of England and Wales. We use a social network, built from geo-located Twitter data, to identify contiguous geographical regions with a shared social identity and then investigate patterns of communication within and between them. In contrast to other approaches (e.g. using phone call data records or online friendship networks), use of Twitter data provides message contents as well as social connections. This allows us to investigate not only the volume of communication between locations, but also the sentiment and vocabulary used in the messages. For example, our case study shows: a significant dialect difference between England and Wales; that regions tend to be more positive about themselves than about others, with the South being more 'self-regarding' than the North; and that people talk politics much more between regions than within. This study demonstrates how social media can be used to quantify regional identity and inter-region communications and sentiment, exposing these previously hard-to-observe geographic concepts to analysis.

@choo: Tracking Pollen and Hayfever in the UK Using Social Media.

Allergic rhinitis (hayfever) affects a large proportion of the population in the United Kingdom. Although relatively easily treated with medication, symptoms nonetheless have a substantial adverse effect on wellbeing during the summer pollen season. Provision of accurate pollen forecasts can help sufferers to manage their condition and minimise adverse effects. Current pollen forecasts in the UK are based on a sparse network of pollen monitoring stations. Here, we explore the use of "social sensing" (analysis of unsolicited social media content) as an alternative source of pollen and hayfever observations. We use data from the Twitter platform to generate a dynamic spatial map of pollen levels based on user reports of hayfever symptoms. We show that social sensing alone creates a spatiotemporal pollen measurement with remarkable similarity to measurements taken from the established physical pollen monitoring network. This demonstrates that social sensing of pollen can be accurate, relative to current methods, and suggests a variety of future applications of this method to help hayfever sufferers manage their condition.

Alternative mechanisms for Gaia.

A long-standing objection to the Gaia hypothesis has been a perceived lack of plausible mechanisms by which life on Earth could come to regulate its abiotic environment. A null hypothesis is survival by pure chance, by which any appearance of regulation on Earth is illusory and the persistence of life simply reflects the weak anthropic principle - it must have occurred for intelligent observers to ask the question. Recent work has proposed that persistence alone increases the chance that a biosphere will acquire further persistence-enhancing properties. Here we use a simple quantitative model to show that such 'selection by survival alone' can indeed increase the probability that a biosphere will persist in the future, relative to a baseline of pure chance. Adding environmental feedback to this model shows either an increased or decreased survival probability depending on the initial conditions. Feedback can hinder early life becoming established if initial conditions are poor, but feedback can also prevent systems from diverging too far from optimum environmental conditions and thus increase survival rates. The outstanding question remains the relative importance of each mechanism for the historical and continued persistence of life on Earth.

Selection for Gaia across Multiple Scales.

Recently postulated mechanisms and models can help explain the enduring 'Gaia' puzzle of environmental regulation mediated by life. Natural selection can produce nutrient recycling at local scales and regulation of heterogeneous environmental variables at ecosystem scales. However, global-scale environmental regulation involves a temporal and spatial decoupling of effects from actors that makes conventional evolutionary explanations problematic. Instead, global regulation can emerge by a process of 'sequential selection' in which systems that destabilize their environment are short-lived and result in extinctions and reorganizations until a stable attractor is found. Such persistence-enhancing properties can in turn increase the likelihood of acquiring further persistence-enhancing properties through 'selection by survival alone'. Thus, Earth system feedbacks provide a filter for persistent combinations of macroevolutionary innovations.

Social sensing of floods in the UK.

"Social sensing" is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes 'relevance' filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.

Multiple states of environmental regulation in well-mixed model biospheres.

The Gaia hypothesis postulates that life influences Earth's feedback mechanisms to form a self regulating system. This provokes the question: how can global self-regulation evolve? Most models demonstrating environmental regulation involving life have relied on alignment between local selection and global regulation. In these models environment-improving individuals or communities spread to outcompete environment degrading individuals/communities, leading to global regulation, but this depends on local differences in environmental conditions. In contrast, well-mixed components of the Earth system, such as the atmosphere, lack local environmental differentiation. These previous models do not explain how global regulation can emerge in a system with no well defined local environment, or where the local environment is overwhelmed by global effects. We present a model of self-regulation by 'microbes' in an environment with no spatial structure. These microbes affect an abiotic 'temperature' as a byproduct of metabolism. We demonstrate that global self-regulation can arise in the absence of spatial structure in a diverse ecosystem without localised environmental effects. We find that systems can exhibit nutrient limitation and two temperature limitation regimes where the temperature is maintained at a near constant value. During temperature regulation, the total temperature change caused by the microbes is kept near constant by the total population expanding or contracting to absorb the impacts of new mutants on the average affect on the temperature per microbe. Dramatic shifts between low temperature regulation and high temperature regulation can occur when a mutant arises that causes the sign of the temperature effect to change. This result implies that self-regulating feedback loops can arise without the need for spatial structure, weakening criticisms of the Gaia hypothesis that state that with just one Earth, global regulation has no mechanism for developing because natural selection requires selection between multiple entities.

FALCON: a software package for analysis of nestedness in bipartite networks.

Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an "adaptive ensemble" method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts.

On the origin of planetary-scale tipping points.

Tipping points are recognised in many systems, including ecosystems and elements of the climate system. But can the biosphere as a whole tip and, if so, how? Past global tipping points were rare and occurred in the coupled planetary-scale dynamics of the Earth system, not in the local-scale dynamics of its weakly interacting component ecosystems. Yet, evolutionary innovations have triggered past global transformations, suggesting that tipping point theory needs to go beyond bifurcations and networks to include evolution.

Phage-induced diversification improves host evolvability.

BACKGROUND: Bacteriophage (viruses that infect bacteria) are of key importance in ecological processes at scales from biofilms to biogeochemical cycles. Close interaction can lead to antagonistic coevolution of phage and their hosts. Selection pressures imposed by phage are often frequency-dependent, such that rare phenotypes are favoured; this occurs when infection depends on some form of genetic matching. Also, resistance to phage often affects host fitness by pleiotropy (whereby mutations conferring resistance affect the function of other traits) and/or direct costs of resistance mechanisms. RESULTS: Here a simple model of bacteria and bacteriophage coevolving in a resource-limited chemostat is used to study the effect of coevolving phage on the evolution of bacterial hosts. Density-dependent mortality from phage predation limits the density of any single bacterial strain, preventing competitive exclusion by faster-growing strains. Thus multiple strains can coexist by partitioning resources and stable high diversity is created by negative frequency-dependent selection from phage. Standing bacterial diversity promotes adaptation in dynamic environments, since it increases the likelihood of a pre-existing genotype being suited to altered conditions. In addition, frequency-dependent selection for resistance creates transient local trade-offs between growth rate and resistance that allow bacterial strains to adapt across fitness valleys. Thus bacterial populations that (in the absence of phage) would have been trapped at sub-optimal local peaks in the adaptive landscape are able (in the presence of phage) to reach alternate higher peaks than could have been reached by mutation alone. CONCLUSIONS: This study shows that reasonable assumptions for coevolution of bacteria and phage create conditions in which phage increase the evolutionary potential of their hosts. Thus phage, in contrast to their deleterious effects on individual host cells, can confer an evolutionary benefit to bacterial populations. These findings have implications for the role of phage in ecosystem processes, where they have mainly been considered as a mortality factor; these results suggest that on long timescales phage may actually increase bacterial productivity by aiding the evolution of faster-growing strains. Furthermore, these results suggest that the therapeutic use of phage to treat bacterial infections (phage therapy) could have unintended negative side-effects.

Coevolutionary diversification creates nested-modular structure in phage-bacteria interaction networks.

Phage and their bacterial hosts are the most diverse and abundant biological entities in the oceans, where their interactions have a major impact on marine ecology and ecosystem function. The structure of interaction networks for natural phage-bacteria communities offers insight into their coevolutionary origin. At small phylogenetic scales, observed communities typically show a nested structure, in which both hosts and phages can be ranked by their range of resistance and infectivity, respectively. A qualitatively different multi-scale structure is seen at larger phylogenetic scales; a natural assemblage sampled from the Atlantic Ocean displays large-scale modularity and local nestedness within each module. Here, we show that such 'nested-modular' interaction networks can be produced by a simple model of host-phage coevolution in which infection depends on genetic matching. Negative frequency-dependent selection causes diversification of hosts (to escape phages) and phages (to track their evolving hosts). This creates a diverse community of bacteria and phage, maintained by kill-the-winner ecological dynamics. When the resulting communities are visualized as bipartite networks of who infects whom, they show the nested-modular structure characteristic of the Atlantic sample. The statistical significance and strength of this observation varies depending on whether the interaction networks take into account the density of the interacting strains, with implications for interpretation of interaction networks constructed by different methods. Our results suggest that the apparently complex community structures associated with marine bacteria and phage may arise from relatively simple coevolutionary origins.

Natural selection for costly nutrient recycling in simulated microbial metacommunities.

Recycling of essential nutrients occurs at scales from microbial communities to global biogeochemical cycles, often in association with ecological interactions in which two or more species utilise each others' metabolic by-products. However, recycling loops may be unstable; sequences of reactions leading to net recycling may be parasitised by side-reactions causing nutrient loss, while some reactions in any closed recycling loop are likely to be costly to participants. Here we examine the stability of nutrient recycling loops in an individual-based ecosystem model based on microbial functional types that differ in their metabolism. A supplied nutrient is utilised by a "source" functional type, generating a secondary nutrient that is subsequently used by two other types-a "mutualist" that regenerates the initial nutrient at a growth rate cost, and a "parasite" that produces a refractory waste product but does not incur any additional cost. The three functional types are distributed across a metacommunity in which separate patches are linked by a stochastic diffusive migration process. Regions of high mutualist abundance feature high levels of nutrient recycling and increased local population density leading to greater export of individuals, allowing the source-mutualist recycling loop to spread across the system. Individual-level selection favouring parasites is balanced by patch-level selection for high productivity, indirectly favouring mutualists due to the synergistic productivity benefits of the recycling loop they support. This suggests that multi-level selection may promote nutrient cycling and thereby help to explain the apparent ubiquity and stability of nutrient recycling in nature.