Estimating the tolerance of species to the effects of global environmental change

Global environmental change is affecting species distribution and their interactions with other species. In particular, the main drivers of environmental change strongly affect the strength of interspecific interactions with considerable consequences to biodiversity. However, extrapolating the effects observed on pair-wise interactions to entire ecological networks is challenging. Here we propose a framework to estimate the tolerance to changes in the strength of mutualistic interaction that species in mutualistic networks can sustain before becoming extinct. We identify the scenarios where generalist species can be the least tolerant. We show that the least tolerant species across different scenarios do not appear to have uniquely common characteristics. Species tolerance is extremely sensitive to the direction of change in the strength of mutualistic interaction, as well as to the observed mutualistic trade-offs between the number of partners and the strength of the interactions.Comment: Nature Communications 4, Article number: 2350, (2013

Spatial correlation as leading indicator of catastrophic shifts

Generic early-warning signals such as increased autocorrelation and variance have been demonstrated in time-series of systems with alternative stable states approaching a critical transition. However, lag times for the detection of such leading indicators are typically long. Here, we show that increased spatial correlation may serve as a more powerful early-warning signal in systems consisting of many coupled units. We first show why from the universal phenomenon of critical slowing down, spatial correlation should be expected to increase in the vicinity of bifurcations. Subsequently, we explore the applicability of this idea in spatially explicit ecosystem models that can have alternative attractors. The analysis reveals that as a control parameter slowly pushes the system towards the threshold, spatial correlation between neighboring cells tends to increase well before the transition. We show that such increase in spatial correlation represents a better early-warning signal than indicators derived from time-series provided that there is sufficient spatial heterogeneity and connectivity in the syste

Topics in crypto asset and blockchain finance

This thesis contributes to the crypto asset and blockchain empirical finance literature in three key areas: (i) market risk modelling, by developing simple volatility models which exhibit equal forecasting ability in terms of crypto asset tail risk measure and volatility forecasts, when compared against complex models; (ii) market manipulation, by extending a methodology derived from securities fraud litigation studies to identify blockchain transactions with a manipulative effect on crypto asset prices; (iii) crowdfunding via token offerings, by identifying factors of fundraising success using regression models, and exploring how these factors vary across time. Each of the above contributions is developed in a separate chapter. Firstly, the market risk modelling chapter provides extensive backtests of hourly and daily Value-at-Risk and Expected Shortfall forecasts regarded as best practice in the industry and used for regulatory approval. Results demonstrate that simpler models in the EWMA class are just as accurate as GARCH models for VaR and ES forecasting, and similarly when using average scores generated from proper univariate and multivariate scoring rules. Secondly, the market manipulation chapter examines large blockchain transactions of the tether stablecoin and assesses whether they produce positive abnormal returns for bitcoin prices. The methodology is adapted from single-firm event studies used in securities fraud litigation, using regression factor models. The chapter’s findings can be useful in determining materiality and estimating damages in legal cases of crypto asset market manipulation. Finally, the tokenomics of crowdfunding chapter examines the fundraising success of token offerings for the 2017 – early 2022 period, constituting one of the most comprehensive studies in this topic. We proxy fundraising success with the amount of funding raised and also by minimum funding target exceedance. Success factors are derived from the venture, token and offering characteristics, as well as additional common factors such as the price level of ether and the launchpad platforms used. The findings of this chapter provide insights as to the evolution of token offering success factors, with the choice of launchpad platform emerging as a new and significant factor and to some extent overshadowing the determinants previously documented in the relevant literature

Climate bifurcation during the last deglaciation?

There were two abrupt warming events during the last deglaciation, at the start of the Bølling-Allerød and at the end of the Younger Dryas, but their underlying dynamics are unclear. Some abrupt climate changes may involve gradual forcing past a bifurcation point, in which a prevailing climate state loses its stability and the climate tips into an alternative state, providing an early warning signal in the form of slowing responses to perturbations, which may be accompanied by increasing variability. Alternatively, short-term stochastic variability in the climate system can trigger abrupt climate changes, without early warning. Previous work has found signals consistent with slowing down during the last deglaciation as a whole, and during the Younger Dryas, but with conflicting results in the run-up to the Bølling-Allerød. Based on this, we hypothesise that a bifurcation point was approached at the end of the Younger Dryas, in which the cold climate state, with weak Atlantic overturning circulation, lost its stability, and the climate tipped irreversibly into a warm interglacial state. To test the bifurcation hypothesis, we analysed two different climate proxies in three Greenland ice cores, from the Last Glacial Maximum to the end of the Younger Dryas. Prior to the Bølling warming, there was a robust increase in climate variability but no consistent slowing down signal, suggesting this abrupt change was probably triggered by a stochastic fluctuation. The transition to the warm Bølling-Allerød state was accompanied by a slowing down in climate dynamics and an increase in climate variability. We suggest that the Bølling warming excited an internal mode of variability in Atlantic meridional overturning circulation strength, causing multi-centennial climate fluctuations. However, the return to the Younger Dryas cold state increased climate stability. We find no consistent evidence for slowing down during the Younger Dryas, or in a longer spliced record of the cold climate state before and after the Bølling-Allerød. Therefore, the end of the Younger Dryas may also have been triggered by a stochastic perturbation

A critical investigation of cryptocurrency data and analysis

Less than half the crytocurrency papers published since January 2017 employ correct data

Probabilistic early warning signals

Ecological communities and other complex systems can undergo abrupt and long-lasting reorganization, a regime shift, when deterministic or stochastic factors bring them to the vicinity of a tipping point between alternative states. Such changes can be large and often arise unexpectedly. However, theoretical and experimental analyses have shown that changes in correlation structure, variance, and other standard indicators of biomass, abundance, or other descriptive variables are often observed prior to a state shift, providing early warnings of an anticipated transition. Natural systems manifest unknown mixtures of ecological and environmental processes, hampered by noise and limited observations. As data quality often cannot be improved, it is important to choose the best modeling tools available for the analysis. We investigate three autoregressive models and analyze their theoretical differences and practical performance. We formulate a novel probabilistic method for early warning signal detection and demonstrate performance improvements compared to nonprobabilistic alternatives based on simulation and publicly available experimental time series. The probabilistic formulation provides a novel approach to early warning signal detection and analysis, with enhanced robustness and treatment of uncertainties. In real experimental time series, the new probabilistic method produces results that are consistent with previously reported findings. Robustness to uncertainties is instrumental in the common scenario where mechanistic understanding of the complex system dynamics is not available. The probabilistic approach provides a new family of robust methods for early warning signal detection that can be naturally extended to incorporate variable modeling assumptions and prior knowledge.</p

Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings

Early detection of ecosystem regime shifts: A multiple method evaluation for management application

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change

Estimating the risk of species interaction loss in mutualistic communities

Funder: Royal Commission for the Exhibition of 1851 (GB)Funder: Cambridge TrustFunder: Cambridge Depatment of ZoologyFunder: Grantham Foundation for the Protection of the Environment; funder-id: http://dx.doi.org/10.13039/100008118Funder: Kenneth Miller TrustFunder: ArcadiaInteractions between species generate the functions on which ecosystems and humans depend. However, we lack an understanding of the risk that interaction loss poses to ecological communities. Here, we quantify the risk of interaction loss for 4,330 species interactions from 41 empirical pollination and seed dispersal networks across 6 continents. We estimate risk as a function of interaction vulnerability to extinction (likelihood of loss) and contribution to network feasibility, a measure of how much an interaction helps a community tolerate environmental perturbations. Remarkably, we find that more vulnerable interactions have higher contributions to network feasibility. Furthermore, interactions tend to have more similar vulnerability and contribution to feasibility across networks than expected by chance, suggesting that vulnerability and feasibility contribution may be intrinsic properties of interactions, rather than only a function of ecological context. These results may provide a starting point for prioritising interactions for conservation in species interaction networks in the future

Carriage of Methicillin-Resistant Staphylococcus Aureus at Hospital Admission

Abstract Objectives: To measure the prevalence of, and to establish predictors for, the nasal carriage of methicillin-resistant Staphylococcus aureus (MRSA) at hospital admission. To evaluate mannitol-salt agar with oxacillin for the simultaneous detection and identification of MRSA from nasal swabs. Design: Three-month prospective case-control survey, with data collected from interviews and computerized databases. The criterion standard for MRSA detection was culture on Mueller-Hinton agar with oxacillin 6 μg/mL (National Committee for Clinical Laboratory Standards method). Setting: 320-bed tertiary-care hospital. Patients: 387 patients screened within 24 hours after admission, including 10 MRSA carriers (cases), 291 patients with no S aureus, and 86 patients with methicillin-susceptible S aureus. Results: The prevalence of MRSA nasal carriage was 2.6%, whereas the prevalence of carriage was 3.1% when both nasal and wound cultures were performed. The significant predictors of carriage were a prior detection of MRSA, open wounds, diabetes mellitus, treatments by injection, prior nursing home stays, visits at home by a nurse, and prior antibiotic treatments. Cases had stayed for longer periods in hospitals and had received longer antibiotic treatments within a year. Eighty patients (including the 10 cases) had diabetes, had been exposed to healthcare facilities within a year, and had antibiotics within 6 months. The sensitivity and negative predictive value of nasal swabs on mannitol-salt agar with oxacillin were 60% and 71%, respectively. Conclusion: MRSA carriage on admission to the hospital may be an increasing and underestimated problem. Further studies are needed to develop and validate a sensitive and specific prediction rul