Project description:Nitrogen-fixing microorganisms (NFMs) are important components of soil N sinks and are influenced by multiple environmental factors. We established a random forest model optimized by the distributed delayed particle swarm optimization (RODDPSO) algorithm to analyze the global NFM data. Soil pH, organic carbon (OC), mean annual precipitation (MAP), altitude, and total phosphorus (TP) are factors with contributions greater than 10% to NFMs. pH, OC, and MAP are the top three factors at the global scale. The tipping points of pH and OC for the NFMs were 7.84 and 2.71%, respectively. The contribution of MAP first increased but then decreased with peak value at 1,265.65 mm. Under the scenario SSP 8.5, 12% of the NFMs increase occur in Africa in 2100; 16% and 36% of the NFMs decrease in North America and Oceania in 2100, respectively. Our work created a global NFMs map and identified the critical tipping points.
Project description:In an era of rapid climate change, there is a pressing need to understand how organisms will cope with faster and less predictable variation in environmental conditions. Here we develop a unifying model that predicts evolutionary responses to environmentally driven fluctuating selection and use this theoretical framework to explore the potential consequences of altered environmental cycles. We first show that the parameter space determined by different combinations of predictability and timescale of environmental variation is partitioned into distinct regions where a single mode of response (reversible phenotypic plasticity, irreversible phenotypic plasticity, bet-hedging, or adaptive tracking) has a clear selective advantage over all others. We then demonstrate that, although significant environmental changes within these regions can be accommodated by evolution, most changes that involve transitions between regions result in rapid population collapse and often extinction. Thus, the boundaries between response mode regions in our model correspond to evolutionary tipping points, where even minor changes in environmental parameters can have dramatic and disproportionate consequences on population viability. Finally, we discuss how different life histories and genetic architectures may influence the location of tipping points in parameter space and the likelihood of extinction during such transitions. These insights can help identify and address some of the cryptic threats to natural populations that are likely to result from any natural or human-induced change in environmental conditions. They also demonstrate the potential value of evolutionary thinking in the study of global climate change.
Project description:Most current cost-benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost-benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost-benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost-benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.
Project description:Research has documented increasing partisan division and extremist positions that are more pronounced among political elites than among voters. Attention has now begun to focus on how polarization might be attenuated. We use a general model of opinion change to see if the self-reinforcing dynamics of influence and homophily may be characterized by tipping points that make reversibility problematic. The model applies to a legislative body or other small, densely connected organization, but does not assume country-specific institutional arrangements that would obscure the identification of fundamental regularities in the phase transitions. Agents in the model have initially random locations in a multidimensional issue space consisting of membership in one of two equal-sized parties and positions on 10 issues. Agents then update their issue positions by moving closer to nearby neighbors and farther from those with whom they disagree, depending on the agents' tolerance of disagreement and strength of party identification compared to their ideological commitment to the issues. We conducted computational experiments in which we manipulated agents' tolerance for disagreement and strength of party identification. Importantly, we also introduced exogenous shocks corresponding to events that create a shared interest against a common threat (e.g., a global pandemic). Phase diagrams of political polarization reveal difficult-to-predict transitions that can be irreversible due to asymmetric hysteresis trajectories. We conclude that future empirical research needs to pay much closer attention to the identification of tipping points and the effectiveness of possible countermeasures.
Project description:There is widespread concern that anthropogenic global warming will trigger Arctic climate tipping points. The Arctic has a long history of natural, abrupt climate changes, which together with current observations and model projections, can help us to identify which parts of the Arctic climate system might pass future tipping points. Here the climate tipping points are defined, noting that not all of them involve bifurcations leading to irreversible change. Past abrupt climate changes in the Arctic are briefly reviewed. Then, the current behaviour of a range of Arctic systems is summarised. Looking ahead, a range of potential tipping phenomena are described. This leads to a revised and expanded list of potential Arctic climate tipping elements, whose likelihood is assessed, in terms of how much warming will be required to tip them. Finally, the available responses are considered, especially the prospects for avoiding Arctic climate tipping points.
Project description:Coverage of climate tipping points has rapidly increased over the past 20 years. Despite this upsurge, there has been precious little research into how the public perceives these abrupt and/or irreversible large-scale risks. This article provides a nationally representative view on public perceptions of climate tipping points and possible societal responses to them (n = 1773). Developing a mixed-methods survey with cultural cognition theory, it shows that awareness among the British public is low. The public is doubtful about the future effectiveness of humanity's response to climate change in general, and significantly more doubtful about its response to tipping points specifically. Significantly more people with an egalitarian worldview judge tipping points likely to be crossed and to be a significant threat to humanity. All possible societal responses received strong support. The article ends by considering the prospects for 'cultural tipping elements' to tip support for climate policies across divergent cultural worldviews.
Project description:Over a decade ago, a multidrug-resistant nosocomial fungus Candida auris emerged worldwide and has since become a significant challenge for clinicians and microbiologists across the globe. A resilient pathogen, C. auris survives harsh disinfectants, desiccation and high-saline environments. It readily colonizes the inanimate environment, susceptible patients and causes invasive infections that exact a high toll. Prone to misidentification by conventional microbiology techniques, C. auris rapidly acquires multiple genetic determinants that confer multidrug resistance. Whole-genome sequencing has identified four distinct clades of C. auris, and possibly a fifth one, in circulation. Even as our understanding of this formidable pathogen grows, the nearly simultaneous emergence of its distinct clades in different parts of the world, followed by their rapid global spread, remains largely unexplained. We contend that certain host-pathogen-environmental factors have been evolving along adverse trajectories for the last few decades, especially in regions where C. auris originally appeared, until these factors possibly reached a tipping point to compel the evolution, emergence and spread of C. auris. Comparative genomics has helped identify several resistance mechanisms in C. auris that are analogous to those seen in other Candida species, but they fail to fully explain how high-level resistance rapidly develops in this yeast. A better understanding of these unresolved aspects is essential not only for the effective management of C. auris patients, hospital outbreaks and its global spread but also for forecasting and tackling novel resistant pathogens that might emerge in the future. In this review, we discuss the emergence, spread and resistance of C. auris, and propose future investigations to tackle this resilient pathogen.
Project description:We live in an ever changing biosphere that faces continuous and often stressing environmental challenges. From this perspective, much effort is currently devoted to understanding how natural populations succeed or fail in adapting to evolving conditions. In a different context, many complex dynamical systems experience critical transitions where their dynamical behaviour or internal structure changes suddenly. Here we connect both approaches and show that in rough and correlated fitness landscapes, population dynamics shows flickering under small stochastic environmental changes, alerting of the existence of tipping points. Our analytical and numerical results demonstrate that transitions at the genomic level preceded by early-warning signals are a generic phenomenon in constant and slowly driven landscapes affected by even slight stochasticity. As these genomic shifts are approached, the time to reach mutation-selection equilibrium dramatically increases, leading to the appearance of hysteresis in the composition of the population. Eventually, environmental changes significantly faster than the typical adaptation time may result in population extinction. Our work points out several indicators that are at reach with current technologies to anticipate these sudden and largely unavoidable transitions.
Project description:Anthropogenic climate change profoundly alters the ocean's environmental conditions, which, in turn, impact marine ecosystems. Some of these changes are happening fast and may be difficult to reverse. The identification and monitoring of such changes, which also includes tipping points, is an ongoing and emerging research effort. Prevention of negative impacts requires mitigation efforts based on feasible research-based pathways. Climate-induced tipping points are traditionally associated with singular catastrophic events (relative to natural variations) of dramatic negative impact. High-probability high-impact ocean tipping points due to warming, ocean acidification, and deoxygenation may be more fragmented both regionally and in time but add up to global dimensions. These tipping points in combination with gradual changes need to be addressed as seriously as singular catastrophic events in order to prevent the cumulative and often compounding negative societal and Earth system impacts.
Project description:Complex and nonlinear ecological networks can exhibit a tipping point at which a transition to a global extinction state occurs. Using real-world mutualistic networks of pollinators and plants as prototypical systems and taking into account biological constraints, we develop an ecologically feasible strategy to manage/control the tipping point by maintaining the abundance of a particular pollinator species at a constant level, which essentially removes the hysteresis associated with a tipping point. If conditions are changing so as to approach a tipping point, the management strategy we describe can prevent sudden drastic changes. Additionally, if the system has already moved past a tipping point, we show that a full recovery can occur for reasonable parameter changes only if there is active management of abundance, again due essentially to removal of the hysteresis. This recovery point in the aftermath of a tipping point can be predicted by a universal, two-dimensional reduced model.