The resilience of the Amazon rainforest to climate and land-use change is crucial for biodiversity, regional climate and the global carbon cycle. Deforestation and climate change, via increasing dry-season length and drought frequency, may already have pushed the Amazon close to a critical threshold of rainforest dieback. Here, we quantify changes of Amazon resilience by applying established indicators (for example, measuring lag-1 autocorrelation) to remotely sensed vegetation data with a focus on vegetation optical depth (1991–2016). We find that more than three-quarters of the Amazon rainforest has been losing resilience since the early 2000s, consistent with the approach to a critical transition. Resilience is being lost faster in regions with less rainfall and in parts of the rainforest that are closer to human activity. We provide direct empirical evidence that the Amazon rainforest is losing resilience, risking dieback with profound implications for biodiversity, carbon storage and climate change at a global scale.

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ATP : citrate lyase has been found in 13 strains of yeast (representing six genera) which are capable of accumulating lipid to above 20% of their biomass. The enzyme is absent in 10 other yeasts which do not accumulate lipid. The presence of the enzyme is therefore directly correlated to the phenomenon of oleaginicity. The enzyme is located in the cytosol fraction of the yeasts and is probably the sole means of producing acetyl-CoA in most oleaginous yeasts. The specific activity of the enzyme correlates with the specific rate of lipid synthesis as determined in nitrogen-limited chemostat cultures of Lipomyces starkeyi, though not with the lipid content of the cells. From this and by calculation, it may be inferred that the enzyme is possibly the rate-limiting reaction for lipid biosynthesis.

“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.

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.

The resilience of the Amazon rainforest to climate and land-use change is of critical importance for biodiversity, regional climate, and the global carbon cycle. Some models project future climate-driven Amazon rainforest dieback and transition to savanna1. Deforestation and climate change, via increasing dry-season length2,3 and drought frequency – with three 1-in-100-year droughts since 20054-6 – may already have pushed the Amazon close to a critical threshold of rainforest dieback7,8. However, others argue that CO2 fertilization should make the forest more resilient9,10. Here we quantify Amazon resilience by applying established indicators11 to remotely-sensed vegetation data with focus on vegetation optical depth (1991-2016), which correlates well with broadleaf tree coverage. We find that the Amazon rainforest has been losing resilience since 2003, consistent with the approach to a critical transition. Resilience is being lost faster in regions with less rainfall, and in parts of the rainforest that are closer to human activity. Given observed increases in dry-season length2,3 and drought frequency4-6, and expanding areas of land use change, loss of resilience is likely to continue. We provide direct empirical evidence that the Amazon rainforest is losing stability, risking dieback with profound implications for biodiversity, carbon storage and climate change at a global scale.

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.

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