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Citizen science is a research practice that relies on public contributions of data. The strong recognition of its educational value combined with the need for novel methods to handle subsequent large and complex data sets raises the question: Is citizen science effective at science? A quantitative assessment of the contributions of citizen science for its core purpose – scientific research – is lacking. We examined the contribution of citizen science to a review paper by ornithologists in which they formulated ten central claims about the impact of climate change on avian migration. Citizen science was never explicitly mentioned in the review article. For each of the claims, these ornithologists scored their opinions about the amount of research effort invested in each claim and how strongly the claim was supported by evidence. This allowed us to also determine whether their trust in claims was, unwittingly or not, related to the degree to which the claims relied primarily on data generated by citizen scientists. We found that papers based on citizen science constituted between 24 and 77% of the references backing each claim, with no evidence of a mistrust of claims that relied heavily on citizen-science data. We reveal that many of these papers may not easily be recognized as drawing upon volunteer contributions, as the search terms “citizen science” and “volunteer” would have overlooked the majority of the studies that back the ten claims about birds and climate change. Our results suggest that the significance of citizen science to global research, an endeavor that is reliant on long-term information at large spatial scales, might be far greater than is readily perceived. To better understand and track the contributions of citizen science in the future, we urge researchers to use the keyword “citizen science” in papers that draw on efforts of non-professionals. [ABSTRACT FROM AUTHOR]

This paper explores the determinants of carbon taxation by focusing on recent efforts to tax carbon in Washington State. The paper identifies citizen demand, energy interests, and tax structure as possible factors in the emergence of carbon taxation. Washington has relatively high demand for climate action, and is unique with respect to its high reliance on indirect taxation and its low level of local fossil fuel interests. I argue this context, especially the high reliance on indirect taxation, makes taxation more appealing than other carbon pricing alternatives. I then test whether local tax structure shapes preferences for carbon taxation using county level opinion data. I find evidence that support for carbon taxation is higher in states with higher gasoline taxes, but no effect from sales and sin taxes. I conclude summarizing various accounts of each initiative's failure, and highlight a few lessons from the efforts to tax carbon in Washington state. [ABSTRACT FROM AUTHOR]

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed “MDVM”, which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30–90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850–2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950–1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450–1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data. [ABSTRACT FROM AUTHOR]

There is currently widespread public misunderstanding about the degree of scientific consensus on human-caused climate change, both in the US as well as internationally. Moreover, previous research has identified important associations between public perceptions of the scientific consensus, belief in climate change and support for climate policy. This paper extends this line of research by advancing and providing experimental evidence for a “gateway belief model” (GBM). Using national data (N = 1104) from a consensus-message experiment, we find that increasing public perceptions of the scientific consensus is significantly and causally associated with an increase in the belief that climate change is happening, human-caused and a worrisome threat. In turn, changes in these key beliefs are predictive of increased support for public action. In short, we find that perceived scientific agreement is an important gateway belief, ultimately influencing public responses to climate change. [ABSTRACT FROM AUTHOR]

Detailed knowledge about the intra-urban air temperature variability within a city is crucial for the implementation of adaptation strategies to counteract the negative effects of urban heat stress. Various methods to model urban-rural temperature differences exist, but they often only cover certain periods (heatwave, hot day) or meteorological conditions (sunny and calm) due to computational limitations or limited data availability. Here, we present a land use regression approach to model nocturnal air temperature fields for every single night of the summers 2018 to 2020 in a city with complex terrain (Bern, Switzerland). Furthermore, we investigate the applicability of different model structures and straight-forward computable GIS variables to model cold air drainage, which exerts an important influence on the local-scale climate of cities with complex terrain. The geostatistical models are calibrated with in-situ data of a dense low cost air temperature measurement network and high resolution spatiotemporal (land use and meteorology) data, which are all publicly available. The resulting land use regression models are capable to model and map intra-urban air temperature differences with a good model performance (R²: 0.65-0.71; RMSE: 0.69-0.76 K). Evaluations with data from additional measurement stations and periods (summer 2021) show that the models are able to estimate different meteorological and spatial conditions, but that the representation of small-scale topographic features remains difficult. However, the comparatively low computational and financial effort needed to calculate nocturnal air temperature fields at daily basis enable new applications for cities with restricted resources for various areas of interest, such as urban planning (e.g. effect of heat mitigation policies) or heat risk management (e.g. analyze small-scale urban heat vulnerability). [ABSTRACT FROM AUTHOR]

Climate reconstructions have contributed tremendously to our understanding of changes in the climate system and will continue to do so. However, in climate science the focus has partly shifted away from past seasonal and annual mean climate towards weather variability and extreme events. Weather events are more directly relevant for climate impacts and they capture the scale at which important processes take place. Weather reconstructions therefore help to better understand atmospheric processes, particularly during extreme events, to assess decadal-to-multidecadal climate variability through the lens of weather changes, and they allow impact modelling of past events. Consequently, attempts are currently undertaken to extend weather data sets far back into the past. In this review I discuss methods of weather reconstructions that are in use today. The methods range from expert analyses to data assimilation, from analog approaches to machine learning. Products range from weather types to four-dimensional fields. The methods complement each other as they are based on different assumptions and are based on different data sets. Weather reconstructions require more meteorological data than climate reconstructions. Additional data rescue efforts are therefore needed. [ABSTRACT FROM AUTHOR]

Increasing access to extensively replicated and broadly distributed tree-ring collections has led to a greater use of these large data sets to investigate climate forcing on tree growth. However, the number of chronologies added to large accessible databases is declining and few are updated, while chronologies are often sparsely distributed and are more representative of marginal growing environments. On the other hand, National Forest Inventories (NFI), although poorly replicated at the plot level as compared to classic dendrochronological sampling, contain a large amount of tree-ring data with high spatial density designed to be spatially representative of the forest cover. We propose an a posteriori approach to validating tree-ring measurements and dating, selecting individual tree-ring width time series, and building average chronologies at various spatial scales based on an extensive collection of ring width measurements of nearly 94,000 black spruce trees distributed over a wide area and collected as part of the NFI in the province of Quebec, Canada. Our results show that reliable signals may be derived at various spatial scales (from 37 to 583,000 km2) from NFI increment core samples. Signals from independently built chronologies are spatially coherent with each other and well-correlated with independent reference chronologies built at the stand level. We thus conclude that tree-ring data from NFIs provide an extraordinary opportunity to strengthen the spatial and temporal coverage of tree-ring data and to improve coordination with other contemporary measurements of forest growth to provide a better understanding of tree growth-climate relationships over broad spatial scales. [ABSTRACT FROM AUTHOR]

Species’ tolerance limits determine their capacity to tolerate climatic extremes and limit their potential distributions. Interspecific variation in thermal tolerances is often proposed to indicate climatic vulnerability and is, therefore, the subject of many recent meta-studies on differential capacities of species from climatically different habitats to deal with climate change. Most studies on thermal tolerances do not acclimate animals or use inconsistent, and insufficient, acclimation times, limiting our knowledge of the shape, duration and extent of acclimation responses. Consequently patterns in thermal tolerances observed in meta-analyses, based on data from the literature are based on inconsistent, partial acclimation and true trends may be obscured. In this study we describe time-course of complete acclimation of critical thermal minima in the tropical ectotherm Carlia longipes and compare it to the average acclimation response of other reptiles, estimated from published data, to assess how much acclimation time may contribute to observed differences in thermal limits. Carlia longipes decreased their lower critical thermal limits by 2.4°C and completed 95% of acclimation in 17 weeks. Wild populations did not mirror this acclimation process over the winter. Other reptiles appear to decrease cold tolerance more quickly (95% in 7 weeks) and to a greater extent, with an estimated average acclimation response of 6.1°C. However, without data on tolerances after longer acclimation times available, our capacity to estimate final acclimation state is very limited. Based on the subset of data available for meta-analysis, much of the variation in cold tolerance observed in the literature can be attributed to acclimation time. Our results indicate that (i) acclimation responses can be slow and substantial, even in tropical species, and (ii) interspecific differences in acclimation speed and extent may obscure trends assessed in some meta-studies. Cold tolerances of wild animals are representative of cumulative responses to recent environments, while lengthy acclimation is necessary for controlled comparisons of physiological tolerances. Measures of inconsistent, intermediate acclimation states, as reported by many studies, represent neither the realised nor the potential tolerance in that population, are very likely underestimates of species’ physiological capacities and may consequently be of limited value. [ABSTRACT FROM AUTHOR]

The assessment of glacier mass budget is crucial for assessing water reserves stored in glaciers. Derived glacier mass changes in the Muztag Ata and Kongur Tagh (MAKT) region in the eastern Pamir, northwestern China, is helpful in improving our knowledge of the dynamics of glaciers under a changing climate in High Mountain Asia. Here, glacier area and mass changes derived from remote sensing data are investigated for the period 1971/76–2013/14 for glaciers in MAKT. We have used ASTER images (2013/14), Cartosat-1 (2014) and Landsat, SRTM (Shuttle Radar Terrain Mission) digital elevation model (DEM) (2000), topographic maps (1971/76) and the first and second Chinese glacier inventories (CGIs). Our results indicated that the glacier area of MAKT decreased from 1018.3 ± 12.99 km2 in 1971/76 to 999.2 ± 31.22 km2 in 2014 (–1.9 ± 0.2%). Weak area shrinkage of glaciers by 2.5 ± 0.5 km2 (0.2 ± 0.1%) happened after 2000 and the period 2009–2014 even saw a slight expansion by 0.5 ± 0.1 km2 (0.1 ± 0.0%). The glaciers in this region have experienced an overall loss of –6.99 ± 0.80 km3 in ice volume or –0.15 ± 0.12 m water equivalent (w.e.) a–1 from 1971/76 to 2013/14. The mass budget of MAKT was –0.19 ± 0.19 m w.e. a−1 for the period ~1971/76–1999 and –0.14 ± 0.24 m w.e. a−1 during 1999–2013/2014. Similar to previous studies, there has been little mass change in the Pamir over recent decades despite such uncertainties. Glacier mass change showed spatial and temporal heterogeneity, with strong mass loss on debris-covered glaciers with an average of –0.32 ± 0.12 m w.e. a−1 from the 1970s to 2013/14. [ABSTRACT FROM AUTHOR]

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct ‘savanna-like’ state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available. [ABSTRACT FROM AUTHOR]

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale. [ABSTRACT FROM AUTHOR]

Background: Recent studies reported seasonal differences in gene expression in white blood cells, adipose tissue, and inflammatory biomarkers of the immune system. There is no data on the seasonal variations of these biomarkers in the US general population of both children and adults. Then aim of this study is to explore the seasonal trends in complete blood count (CBC), and C-reactive protein (CRP) in a large non-institutionalized US population. Methods: Seven cross-sectional data collected in the National Health and Nutrition Examination Survey (NHANES) during 1999–2012 were aggregated; participants reporting recent use of prescribed steroids, chemotherapy, immunomodulators and antibiotics were excluded. Linear regression models were used to compare levels of CBC and CRP between winter-spring (November-April) and summer-fall (May-October), adjusting for demographics, personal behavioral factors, and chronic disease conditions. Results: A total of 27,478 children and 36,644 adults (≥18 years) were included in the study. Levels of neutrophils, white blood cell count (WBC), and CRP were higher in winter-spring than summer-fall (p≤0.05). Red blood cell components were lower in winter-spring than in summer-fall, while the opposite was seen for platelets. Conclusions: This large population-based study found notable seasonal variations in blood cell composition and inflammatory biomarkers, with a more pro-inflammatory immune system seen in winter-spring than summer-fall. The red blood cell patterns could have implications for the observed cardio-vascular seasonality. [ABSTRACT FROM AUTHOR]

Incorporating warming disturbances into the design of marine protected areas (MPAs) is fundamental to developing appropriate conservation actions that confer coral reef resilience. We propose an MPA design approach that includes spatially- and temporally-varying sea-surface temperature (SST) data, integrating both observed (1985–2009) and projected (2010–2099) time-series. We derived indices of acute (time under reduced ecosystem function following short-term events) and chronic thermal stress (rate of warming) and combined them to delineate thermal-stress regimes. Coral reefs located on the Brazilian coast were used as a case study because they are considered a conservation priority in the southwestern Atlantic Ocean. We show that all coral reef areas in Brazil have experienced and are projected to continue to experience chronic warming, while acute events are expected to increase in frequency and intensity. We formulated quantitative conservation objectives for regimes of thermal stress. Based on these objectives, we then evaluated if/how they are achieved in existing Brazilian MPAs and identified priority areas where additional protection would reinforce resilience. Our results show that, although the current system of MPAs incorporates locations within some of our thermal-stress regimes, historical and future thermal refugia along the central coast are completely unprotected. Our approach is applicable to other marine ecosystems and adds to previous marine planning for climate change in two ways: (i) by demonstrating how to spatially configure MPAs that meet conservation objectives for warming disturbance using spatially- and temporally-explicit data; and (ii) by strategically allocating different forms of spatial management (MPA types) intended to mitigate warming impacts and also enhance future resistance to climate warming. [ABSTRACT FROM AUTHOR]

Over the past 50 years, mean annual water temperature in northeastern U.S. estuaries has increased by approximately 1.2°C, with most of the warming recorded in the winter and early spring. A recent survey and synthesis of data from four locations in Southern Rhode Island has led us to hypothesize that this warming may be amplified in the shallow (<1 m), nearshore portions of these estuaries. While intertidal areas are not typically selected as locations for long-term monitoring, we compiled data from published literature, theses, and reports that suggest that enhanced warming may be occurring, perhaps at rates three times higher than deeper estuarine waters. Warmer spring waters may be one of the factors influencing biota residing in intertidal regions both in general as well as at our specific sites. We observed greater abundance of fish, and size of Menidia sp., in recent (2010–2012) seine surveys compared to similar collections in 1962. While any linkages are speculative and data are preliminary, taken together they suggest that shallow intertidal portions of estuaries may be important places to look for the effects of climate change. [ABSTRACT FROM AUTHOR]

Chinese thuja (Platycladus orientalis) has a wide but fragmented distribution in China. It is an important conifer tree in reforestation and plays important roles in ecological restoration in the arid mountains of northern China. Based on high-resolution environmental data for current and future scenarios, we modeled the present and future suitable habitat for P. orientalis, evaluated the importance of environmental factors in shaping the species´ distribution, and identified regions of high risk under climate change scenarios. The niche models showed that P. orientalis has suitable habitat of ca. 4.2×106 km2 across most of eastern China and identified annual temperature, monthly minimum and maximum ultraviolet-B radiation and wet-day frequency as the critical factors shaping habitat availability for P. orientalis. Under the low concentration greenhouse gas emissions scenario, the range of the species may increase as global warming intensifies; however, under the higher concentrations of emissions scenario, we predicted a slight expansion followed by contraction in distribution. Overall, the range shift to higher latitudes and elevations would become gradually more significant. The information gained from this study should be an useful reference for implementing long-term conservation and management strategies for the species. [ABSTRACT FROM AUTHOR]

Capturing conformational changes in proteins or protein-protein complexes is a challenge for both experimentalists and computational biologists. Solution nuclear magnetic resonance (NMR) is unique in that it permits structural studies of proteins under greatly varying conditions, and thus allows us to monitor induced structural changes. Paramagnetic effects are increasingly used to study protein structures as they give ready access to rich structural information of orientation and long-range distance restraints from the NMR signals of backbone amides, and reliable methods have become available to tag proteins with paramagnetic metal ions site-specifically and at multiple sites. In this study, we show how sparse pseudocontact shift (PCS) data can be used to computationally model conformational states in a protein system, by first identifying core structural elements that are not affected by the environmental change, and then computationally completing the remaining structure based on experimental restraints from PCS. The approach is demonstrated on a 27 kDa two-domain NS2B-NS3 protease system of the dengue virus serotype 2, for which distinct closed and open conformational states have been observed in crystal structures. By changing the input PCS data, the observed conformational states in the dengue virus protease are reproduced without modifying the computational procedure. This data driven Rosetta protocol enables identification of conformational states of a protein system, which are otherwise difficult to obtain either experimentally or computationally. [ABSTRACT FROM AUTHOR]

The present study features the estimation of number of generations of tobacco caterpillar, Spodoptera litura. Fab. on peanut crop at six locations in India using MarkSim, which provides General Circulation Model (GCM) of future data on daily maximum (T.max), minimum (T.min) air temperatures from six models viz., BCCR-BCM2.0, CNRM-CM3, CSIRO-Mk3.5, ECHams5, INCM-CM3.0 and MIROC3.2 along with an ensemble of the six from three emission scenarios (A2, A1B and B1). This data was used to predict the future pest scenarios following the growing degree days approach in four different climate periods viz., Baseline-1975, Near future (NF) -2020, Distant future (DF)-2050 and Very Distant future (VDF)—2080. It is predicted that more generations would occur during the three future climate periods with significant variation among scenarios and models. Among the seven models, 1–2 additional generations were predicted during DF and VDF due to higher future temperatures in CNRM-CM3, ECHams5 & CSIRO-Mk3.5 models. The temperature projections of these models indicated that the generation time would decrease by 18–22% over baseline. Analysis of variance (ANOVA) was used to partition the variation in the predicted number of generations and generation time of S. litura on peanut during crop season. Geographical location explained 34% of the total variation in number of generations, followed by time period (26%), model (1.74%) and scenario (0.74%). The remaining 14% of the variation was explained by interactions. Increased number of generations and reduction of generation time across the six peanut growing locations of India suggest that the incidence of S. litura may increase due to projected increase in temperatures in future climate change periods. [ABSTRACT FROM AUTHOR]

Climate change threatens biodiversity worldwide, however predicting how particular species will respond is difficult because climate varies spatially, complex factors regulate population abundance, and species vary in their susceptibility to climate change. Studies need to incorporate these factors with long-term data in order to link climate change to population abundance. We used 40 years of lizard abundance data and local climate data from Barro Colorado Island to ask how climate, total lizard abundance and cohort-specific abundance have changed over time, and how total and cohort-specific abundance relate to climate variables including those predicted to make the species vulnerable to climate change (i.e. temperatures exceeding preferred body temperature). We documented a decrease in lizard abundance over the last 40 years, and changes in the local climate. Population growth rate was related to the previous years’ southern oscillation index; increasing following cooler-wetter, la niña years, decreasing following warmer-drier, el nino years. Within-year recruitment was negatively related to rainfall and minimum temperature. This study simultaneously identified climatic factors driving long-term population fluctuations and climate variables influencing short-term annual recruitment, both of which may be contributing to the population decline and influence the population’s future persistence. [ABSTRACT FROM AUTHOR]

In recent decades, many marine populations have experienced major declines in abundance, but we still know little about where management interventions may help protect the highest levels of marine biodiversity. We used modeled spatial distribution data for nearly 12,500 species to quantify global patterns of species richness and two measures of endemism. By combining these data with spatial information on cumulative human impacts, we identified priority areas where marine biodiversity is most and least impacted by human activities, both within Exclusive Economic Zones (EEZs) and Areas Beyond National Jurisdiction (ABNJ). Our analyses highlighted places that are both accepted priorities for marine conservation like the Coral Triangle, as well as less well-known locations in the southwest Indian Ocean, western Pacific Ocean, Arctic and Antarctic Oceans, and within semi-enclosed seas like the Mediterranean and Baltic Seas. Within highly impacted priority areas, climate and fishing were the biggest stressors. Although new priorities may arise as we continue to improve marine species range datasets, results from this work are an essential first step in guiding limited resources to regions where investment could best sustain marine biodiversity. [ABSTRACT FROM AUTHOR]

Understanding the factors driving innovation in energy technologies is of critical importance to mitigating climate change and addressing other energy-related global challenges. Low levels of innovation, measured in terms of energy patent filings, were noted in the 1980s and 90s as an issue of concern and were attributed to limited investment in public and private research and development (R&D). Here we build a comprehensive global database of energy patents covering the period 1970–2009, which is unique in its temporal and geographical scope. Analysis of the data reveals a recent, marked departure from historical trends. A sharp increase in rates of patenting has occurred over the last decade, particularly in renewable technologies, despite continued low levels of R&D funding. To solve the puzzle of fast innovation despite modest R&D increases, we develop a model that explains the nonlinear response observed in the empirical data of technological innovation to various types of investment. The model reveals a regular relationship between patents, R&D funding, and growing markets across technologies, and accurately predicts patenting rates at different stages of technological maturity and market development. We show quantitatively how growing markets have formed a vital complement to public R&D in driving innovative activity. These two forms of investment have each leveraged the effect of the other in driving patenting trends over long periods of time. [ABSTRACT FROM AUTHOR]

The insertion/deletion (I/D) polymorphism of the angiotensin converting enzyme (ACE), commonly associated with many diseases, is believed to have affected human adaptation to environmental changes during the out-of-Africa expansion. APOBEC3B (A3B), a member of the cytidine deaminase family APOBEC3s, also exhibits a variable gene insertion/deletion polymorphism across world populations. Using data available from published reports, we examined the global geographic distribution of ACE and A3B genotypes. In tracking the modern human dispersal routes of these two genes, we found that the variation trends of the two I/D polymorphisms were directly correlated. We observed that the frequencies of ACE insertion and A3B deletion rose in parallel along the expansion route. To investigate the presence of a correlation between the two polymorphisms and the effect of their interaction on human health, we analyzed 1199 unrelated Chinese adults to determine their genotypes and other important clinical characteristics. We discovered a significant difference between the ACE genotype/allele distribution in the A3B DD and A3B II/ID groups (P = 0.045 and 0.015, respectively), indicating that the ACE Alu I allele frequency in the former group was higher than in the latter group. No specific clinical phenotype could be associated with the interaction between the ACE and A3B I/D polymorphisms. A3B has been identified as a powerful inhibitor of Alu retrotransposition, and primate A3 genes have undergone strong positive selection (and expansion) for restricting the mobility of endogenous retrotransposons during evolution. Based on these findings, we suggest that the ACE Alu insertion was enabled (facilitated) by the A3B deletion and that functional loss of A3B provided an opportunity for enhanced human adaptability and survival in response to the environmental and climate challenges arising during the migration from Africa. [ABSTRACT FROM AUTHOR]

Recent analyses have shown that significant changes have occurred in patterns of sea ice seasonality in West Antarctica since 1979, with wide-ranging climatic, biological and biogeochemical consequences. Here, we provide the first detailed report on long-term change and variability in annual timings of sea ice advance, retreat and resultant ice season duration in East Antarctica. These were calculated from satellite-derived ice concentration data for the period 1979/80 to 2009/10. The pattern of change in sea ice seasonality off East Antarctica comprises mixed signals on regional to local scales, with pockets of strongly positive and negative trends occurring in near juxtaposition in certain regions e.g., Prydz Bay. This pattern strongly reflects change and variability in different elements of the marine “icescape”, including fast ice, polynyas and the marginal ice zone. A trend towards shorter sea-ice duration (of 1 to 3 days per annum) occurs in fairly isolated pockets in the outer pack from∼95–110°E, and in various near-coastal areas that include an area of particularly strong and persistent change near Australia's Davis Station and between the Amery and West Ice Shelves. These areas are largely associated with coastal polynyas that are important as sites of enhanced sea ice production/melt. Areas of positive trend in ice season duration are more extensive, and include an extensive zone from 160–170°E (i.e., the western Ross Sea sector) and the near-coastal zone between 40–100°E. The East Antarctic pattern is considerably more complex than the well-documented trends in West Antarctica e.g., in the Antarctic Peninsula-Bellingshausen Sea and western Ross Sea sectors. [ABSTRACT FROM AUTHOR]

Background: Association between bacillary dysentery (BD) disease and temperature has been reported in some studies applying Poisson regression model, however the effect estimation might be biased due to the data autocorrelation. Furthermore the temperature effect distributed in the time of different lags has not been studied either. The purpose of this work was to obtaining the association between the BD counts and the climatic factors such as temperature in the form of the weighted averages, concerning the autocorrelation pattern of the model residuals, and to make short term predictions using the model. The data was collected in the city of Shanghai from 2004 to 2008. Methods: We used mixed generalized additive model (MGAM) to analyze data on bacillary dysentery, temperature and other covariates with autoregressive random effect. Short term predictions were made using MGAM with the moving average of the BD counts. Main Results: Our results showed that temperature was significant linearly associated with the logarithm of BD count for temperature in the range from 12°C to 22°C. Optimal weights in the temperature effect have been obtained, in which the one of 1-day-lag was close to 0, and the one of 2-days-lag was the maximum (p-value of the difference was less than 0.05). The predictive model was showing good fitness on the internal data with R2 value 0.875, and the good short term prediction effect on the external data with correlation coefficient to be 0.859. Conclusion: According to the model estimation, corresponding Risk Ratio to affect BD was close to 1.1 when temperature effect goes up for 1°C in the range from 12°C to 22°C. And the 1-day incubation period could be inferred from the model estimation. Good prediction has been made using the predictive MGAM. [ABSTRACT FROM AUTHOR]