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Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
Temporal variation of natural light sources such as airglow limits the ability of night light sensors to detect changes in small sources of artificial light (such as villages). This study presents a method for correcting for this effect globally, using the satellite radiance detected from regions without artificial light emissions. We developed a routine to define an approximate grid of locations worldwide that do not have regular light emission. We apply this method with a 5 degree equally spaced global grid (total of 2016 individual locations), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB). This code could easily be adapted for other future global sensors. The correction reduces the standard deviation of data in the Earth Observation Group monthly DNB composites by almost a factor of two. The code and datasets presented here are available under an open license by GFZ Data Services, and are implemented in the Radiance Light Trends web application.
We report on the detection of very high energy (VHE; E > 100 GeV) gamma-ray emission from the BL Lac objects KUV 00311-1938 and PKS 1440-389 with the High Energy Stereoscopic System (H.E.S.S.). H.E.S.S. observations were accompanied or preceded by multiwavelength observations with Fermi/LAT, XRT and UVOT onboard the Swift satellite, and ATOM. Based on an extrapolation of the Fermi/LAT spectrum towards the VHE gamma-ray regime, we deduce a 95 per cent confidence level upper limit on the unknown redshift of KUV 00311-1938 of z < 0.98 and of PKS 1440-389 of z < 0.53. When combined with previous spectroscopy results, the redshift of KUV 00311-1938 is constrained to 0.51 <= z < 0.98 and of PKS 1440-389 to 0.14 (sic) z < 0.53.
Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.
Abdominal and general adiposity are independently associated with mortality, but there is no consensus on how best to assess abdominal adiposity. We compared the ability of alternative waist indices to complement body mass index (BMI) when assessing all-cause mortality. We used data from 352,985 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) and Cox proportional hazards models adjusted for other risk factors. During a mean follow-up of 16.1 years, 38,178 participants died. Combining in one model BMI and a strongly correlated waist index altered the association patterns with mortality, to a predominantly negative association for BMI and a stronger positive association for the waist index, while combining BMI with the uncorrelated A Body Shape Index (ABSI) preserved the association patterns. Sex-specific cohort-wide quartiles of waist indices correlated with BMI could not separate high-risk from low-risk individuals within underweight (BMI<18.5 kg/m(2)) or obese (BMI30 kg/m(2)) categories, while the highest quartile of ABSI separated 18-39% of the individuals within each BMI category, which had 22-55% higher risk of death. In conclusion, only a waist index independent of BMI by design, such as ABSI, complements BMI and enables efficient risk stratification, which could facilitate personalisation of screening, treatment and monitoring.
History without borders
(2020)
The origins and development of the arid and highly seasonal steppe-desert biome in Central Asia, the largest of its kind in the world, remain largely unconstrained by existing records. It is unclear how Cenozoic climatic, geological, and biological forces, acting at diverse spatial and temporal scales, shaped Central Asian ecosystems through time. Our synthesis shows that the Central Asian steppe-desert has existed since at least Eocene times but experienced no less than two regime shifts, one at the Eocene-Oligocene Transition and one in the mid-Miocene. These shifts separated three successive "stable states," each characterized by unique floral and faunal structures. Past responses to disturbance in the Asian steppe-desert imply that modern ecosystems are unlikely to recover their present structures and diversity if forced into a new regime. This is of concern for Asian steppes today, which are being modified for human use and lost to desertification at unprecedented rates.
Flood insurance coverage can enhance financial resilience of households to changing flood risk caused by climate change. However, income inequalities imply that not all households can afford flood insurance. The uptake of flood insurance in voluntary markets may decline when flood risk increases as a result of climate change. This increase in flood risk may cause substantially higher risk-based insurance premiums, reduce the willingness to purchase flood insurance, and worsen problems with the unaffordability of coverage for low-income households. A socio-economic tipping-point can occur when the functioning of a formal flood insurance system is hampered by diminishing demand for coverage. In this study, we examine whether such a tipping-point can occur in Europe for current flood insurance systems under different trends in future flood risk caused by climate and socio-economic change. This analysis gives insights into regional inequalities concerning the ability to continue to use flood insurance as an instrument to adapt to changing flood risk. For this study, we adapt the "Dynamic Integrated Flood and Insurance" (DIFI) model by integrating new flood risk simulations in the model that enable examining impacts from various scenarios of climate and socio-economic change on flood insurance premiums and consumer demand. Our results show rising unaffordability and declining demand for flood insurance across scenarios towards 2080. Under a high climate change scenario, simulations show the occurrence of a socio-economic tipping-point in several regions, where insurance uptake almost disappears. A tipping-point and related inequalities in the ability to use flood insurance as an adaptation instrument can be mitigated by introducing reforms of flood insurance arrangements.