Remotely sensing potential climate change tipping points across scales.

Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.

The origins of the Guinness stout yeast.

Beer is made via the fermentation of an aqueous extract predominantly composed of malted barley flavoured with hops. The transforming microorganism is typically a single strain of Saccharomyces cerevisiae, and for the majority of major beer brands the yeast strain is a unique component. The present yeast used to make Guinness stout brewed in Dublin, Ireland, can be traced back to 1903, but its origins are unknown. To that end, we used Illumina and Nanopore sequencing to generate whole-genome sequencing data for a total of 22 S. cerevisiae yeast strains: 16 from the Guinness collection and 6 other historical Irish brewing. The origins of the Guinness yeast were determined with a SNP-based analysis, demonstrating that the Guinness strains occupy a distinct group separate from other historical Irish brewing yeasts. Assessment of chromosome number, copy number variation and phenotypic evaluation of key brewing attributes established Guinness yeast-specific SNPs but no specific chromosomal amplifications. Our analysis also demonstrated the effects of yeast storage on phylogeny. Altogether, our results suggest that the Guinness yeast used today is related to the first deposited Guinness yeast; the 1903 Watling Laboratory Guinness yeast.

Destabilisation of the Subpolar North Atlantic prior to the Little Ice Age.

The cooling transition into the Little Ice Age was the last notable shift in the climate system prior to anthropogenic global warming. It is hypothesised that sea-ice to ocean feedbacks sustained an initial cooling into the Little Ice Age by weakening the subpolar gyre circulation; a system that has been proposed to exhibit bistability. Empirical evidence for bistability within this transition has however been lacking. Using statistical indicators of resilience in three annually-resolved bivalve proxy records from the North Icelandic shelf, we show that the subpolar North Atlantic climate system destabilised during two episodes prior to the Little Ice Age. This loss of resilience indicates reduced attraction to one stable state, and a system vulnerable to an abrupt transition. The two episodes preceded wider subpolar North Atlantic change, consistent with subpolar gyre destabilisation and the approach of a tipping point, potentially heralding the transition to Little Ice Age conditions.

A resilience sensing system for the biosphere.

We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems-loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.

Resilience of countries to COVID-19 correlated with trust.

We characterized > 150 countries' resilience to COVID-19 as the nationwide decay rate of daily cases or deaths from peak levels. Resilience to COVID-19 varies by a factor of ~ 40 between countries for cases/capita and ~ 25 for deaths/capita. Trust within society is positively correlated with country-level resilience to COVID-19, as is the adaptive increase in stringency of government interventions when epidemic waves occur. By contrast, countries where governments maintain greater background stringency tend to have lower trust within society and tend to be less resilient. All countries where > 40% agree "most people can be trusted" achieve a near complete reduction of new cases and deaths, but so do several less-trusting societies. As the pandemic progressed, resilience tended to decline, as adaptive increases in stringency also declined. These results add to evidence that trust can improve resilience to epidemics and other unexpected disruptions, of which COVID-19 is unlikely to be the last.

Quantitatively monitoring the resilience of patterned vegetation in the Sahel.

Patterning of vegetation in drylands is a consequence of localized feedback mechanisms. Such feedbacks also determine ecosystem resilience-i.e. the ability to recover from perturbation. Hence, the patterning of vegetation has been hypothesized to be an indicator of resilience, that is, spots are less resilient than labyrinths. Previous studies have made this qualitative link and used models to quantitatively explore it, but few have quantitatively analysed available data to test the hypothesis. Here we provide methods for quantitatively monitoring the resilience of patterned vegetation, applied to 40 sites in the Sahel (a mix of previously identified and new ones). We show that an existing quantification of vegetation patterns in terms of a feature vector metric can effectively distinguish gaps, labyrinths, spots, and a novel category of spot-labyrinths at their maximum extent, whereas NDVI does not. The feature vector pattern metric correlates with mean precipitation. We then explored two approaches to measuring resilience. First we treated the rainy season as a perturbation and examined the subsequent rate of decay of patterns and NDVI as possible measures of resilience. This showed faster decay rates-conventionally interpreted as greater resilience-associated with wetter, more vegetated sites. Second we detrended the seasonal cycle and examined temporal autocorrelation and variance of the residuals as possible measures of resilience. Autocorrelation and variance of our pattern metric increase with declining mean precipitation, consistent with loss of resilience. Thus, drier sites appear less resilient, but we find no significant correlation between the mean or maximum value of the pattern metric (and associated morphological pattern types) and either of our measures of resilience.

The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole.

Eukaryotic phytoplankton are responsible for at least 20% of annual global carbon fixation. Their diversity and activity are shaped by interactions with prokaryotes as part of complex microbiomes. Although differences in their local species diversity have been estimated, we still have a limited understanding of environmental conditions responsible for compositional differences between local species communities on a large scale from pole to pole. Here, we show, based on pole-to-pole phytoplankton metatranscriptomes and microbial rDNA sequencing, that environmental differences between polar and non-polar upper oceans most strongly impact the large-scale spatial pattern of biodiversity and gene activity in algal microbiomes. The geographic differentiation of co-occurring microbes in algal microbiomes can be well explained by the latitudinal temperature gradient and associated break points in their beta diversity, with an average breakpoint at 14 °C ± 4.3, separating cold and warm upper oceans. As global warming impacts upper ocean temperatures, we project that break points of beta diversity move markedly pole-wards. Hence, abrupt regime shifts in algal microbiomes could be caused by anthropogenic climate change.

Abrupt changes in Great Britain vegetation carbon projected under climate change.

Past abrupt 'regime shifts' have been observed in a range of ecosystems due to various forcing factors. Large-scale abrupt shifts are projected for some terrestrial ecosystems under climate change, particularly in tropical and high-latitude regions. However, there is very little high-resolution modelling of smaller-scale future projected abrupt shifts in ecosystems, and relatively less focus on the potential for abrupt shifts in temperate terrestrial ecosystems. Here, we show that numerous climate-driven abrupt shifts in vegetation carbon are projected in a high-resolution model of Great Britain's land surface driven by two different climate change scenarios. In each scenario, the effects of climate and CO2 combined are isolated from the effects of climate change alone. We use a new algorithm to detect and classify abrupt shifts in model time series, assessing the sign and strength of the non-linear responses. The abrupt ecosystem changes projected are non-linear responses to climate change, not simply driven by abrupt shifts in climate. Depending on the scenario, 374-1,144 grid cells of 1.5 km × 1.5 km each, comprising 0.5%-1.5% of Great Britain's land area show abrupt shifts in vegetation carbon. We find that abrupt ecosystem shifts associated with increases (rather than decreases) in vegetation carbon, show the greatest potential for early warning signals (rising autocorrelation and variance beforehand). In one scenario, 89% of abrupt increases in vegetation carbon show increasing autocorrelation and variance beforehand. Across the scenarios, 81% of abrupt increases in vegetation carbon have increasing autocorrelation and 74% increasing variance beforehand, whereas for decreases in vegetation carbon these figures are 56% and 47% respectively. Our results should not be taken as specific spatial or temporal predictions of abrupt ecosystem change. However, they serve to illustrate that numerous abrupt shifts in temperate terrestrial ecosystems could occur in a changing climate, with some early warning signals detectable beforehand.

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.

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.

Exploring uncertainty of Amazon dieback in a perturbed parameter Earth system ensemble.

The future of the Amazon rainforest is unknown due to uncertainties in projected climate change and the response of the forest to this change (forest resiliency). Here, we explore the effect of some uncertainties in climate and land surface processes on the future of the forest, using a perturbed physics ensemble of HadCM3C. This is the first time Amazon forest changes are presented using an ensemble exploring both land vegetation processes and physical climate feedbacks in a fully coupled modelling framework. Under three different emissions scenarios, we measure the change in the forest coverage by the end of the 21st century (the transient response) and make a novel adaptation to a previously used method known as "dry-season resilience" to predict the long-term committed response of the forest, should the state of the climate remain constant past 2100. Our analysis of this ensemble suggests that there will be a high chance of greater forest loss on longer timescales than is realized by 2100, especially for mid-range and low emissions scenarios. In both the transient and predicted committed responses, there is an increasing uncertainty in the outcome of the forest as the strength of the emissions scenarios increases. It is important to note however, that very few of the simulations produce future forest loss of the magnitude previously shown under the standard model configuration. We find that low optimum temperatures for photosynthesis and a high minimum leaf area index needed for the forest to compete for space appear to be precursors for dieback. We then decompose the uncertainty into that associated with future climate change and that associated with forest resiliency, finding that it is important to reduce the uncertainty in both of these if we are to better determine the Amazon's outcome.

Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change.

Marine ecosystems are sensitive to stochastic environmental variability, with higher-amplitude, lower-frequency--i.e., "redder"--variability posing a greater threat of triggering large ecosystem changes. Here we show that fluctuations in the Pacific Decadal Oscillation (PDO) index have slowed down markedly over the observational record (1900-present), as indicated by a robust increase in autocorrelation. This "reddening" of the spectrum of climate variability is also found in regionally averaged North Pacific sea surface temperatures (SSTs), and can be at least partly explained by observed deepening of the ocean mixed layer. The progressive reddening of North Pacific climate variability has important implications for marine ecosystems. Ecosystem variables that respond linearly to climate forcing will have become prone to much larger variations over the observational record, whereas ecosystem variables that respond nonlinearly to climate forcing will have become prone to more frequent "regime shifts." Thus, slowing down of North Pacific climate variability can help explain the large magnitude and potentially the quick succession of well-known abrupt changes in North Pacific ecosystems in 1977 and 1989. When looking ahead, despite model limitations in simulating mixed layer depth (MLD) in the North Pacific, global warming is robustly expected to decrease MLD. This could potentially reverse the observed trend of slowing down of North Pacific climate variability and its effects on marine ecosystems.

Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model.

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic early warning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that early warning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.

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.

Carbohydrate utilization and the lager yeast transcriptome during brewery fermentation.

The fermentable carbohydrate composition of wort and the manner in which it is utilized by yeast during brewery fermentation have a direct influence on fermentation efficiency and quality of the final product. In this study the response of a brewing yeast strain to changes in wort fermentable carbohydrate concentration and composition during full-scale (3275 hl) brewery fermentation was investigated by measuring transcriptome changes with the aid of oligonucleotide-based DNA arrays. Up to 74% of the detectable genes showed a significant (p

The oxidative stress response of a lager brewing yeast strain during industrial propagation and fermentation.

Commercial brewing yeast strains are exposed to a number of potential stresses including oxidative stress. The aim of this investigation was to measure the physiological and transcriptional changes of yeast cells during full-scale industrial brewing processes with a view to determining the environmental factors influencing the cell's oxidative stress response. Cellular antioxidant levels and genome-wide transcriptional changes were monitored throughout an industrial propagation and fermentation. The greatest increase in cellular antioxidants and transcription of antioxidant-encoding genes occurred as the rapidly fermentable sugars glucose and fructose were depleted from the growth medium (wort) and the cell population entered the stationary phase. The data suggest that, contrary to expectation, the oxidative stress response is not influenced by changes in the dissolved oxygen concentration of wort but is initiated as part of a general stress response to growth-limiting conditions, even in the absence of oxygen. A mechanism is proposed to explain the changes in antioxidant response observed in yeast during anaerobic fermentation. The available data suggest that the yeast cell does not experience oxidative stress during industrial brewery handling. This information may be taken into consideration when setting parameters for industrial brewery fermentation.