The effects of sea surface temperature anomalies on oceanic coral reef systems in the southwestern tropical Atlantic

In 2010, high sea surface temperatures that were recorded in several parts of the world and caused coral bleaching and coral mortality were also recorded in the southwest Atlantic Ocean, between latitudes 0°S and 8°S. This paper reports on coral bleaching and diseases in Rocas Atoll and Fernando de Noronha archipelago and examines their relationship with sea surface temperature (SST) anomalies recorded by PIRATA buoys located at 8°S30°W, 0°S35°W, and 0°S23°W. Adjusted satellite data were used to derive SST climatological means at buoy sites and to derive anomalies at reef sites. The whole region was affected by the elevated temperature anomaly that persisted through 2010, reaching 1.67 °C above average at reef sites and 1.83 °C above average at buoys sites. A significant positive relationship was found between the percentage of coral bleaching that was observed on reef formations and the corresponding HotSpot SST anomaly recorded by both satellite and buoys. These results indicate that the warming observed in the ocean waters was followed by a warming at the reefs. The percentage of bleached corals persisting after the subsidence of the thermal stress, and disease prevalence increased through 2010, after two periods of thermal stress. The in situ temperature anomaly observed during the 2009–2010 El Niño event was equivalent to the anomaly observed during the 1997–1998 El Niño event, explaining similar bleaching intensity. Continued monitoring efforts are necessary to further assess the relationship between bleaching severity and PIRATA SST anomalies and improve the use of this new dataset in future regional bleaching predictions.     

Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016

Amazon droughts have impacted regional ecosystem functioning as well as global carbon cycling. The severe dry‐season droughts in 2005 and 2010, driven by Atlantic sea surface temperature (SST) anomaly, have been widely investigated in terms of drought severity and impacts on ecosystems. Although the influence of Pacific SST anomaly on wet‐season precipitation has been well recognized, it remains uncertain to what extent the droughts driven by Pacific SST anomaly could affect forest greenness and photosynthesis in the Amazon. Here, we examined the monthly and annual dynamics of forest greenness and photosynthetic capacity when Amazon ecosystems experienced an extreme drought in 2015/2016 driven by a strong El Niño event. We found that the drought during August 2015–July 2016 was one of the two most severe meteorological droughts since 1901. Due to the enhanced solar radiation during this drought, overall forest greenness showed a small increase, and 21.6% of forests even greened up (greenness index anomaly ≥1 standard deviation). In contrast, solar‐induced chlorophyll fluorescence (SIF), an indicator of vegetation photosynthetic capacity, showed a significant decrease. Responses of forest greenness and photosynthesis decoupled during this drought, indicating that forest photosynthesis could still be suppressed regardless of the variation in canopy greenness. If future El Niño frequency increases as projected by earth system models, droughts would result in persistent reduction in Amazon forest productivity, substantial changes in tree composition, and considerable carbon emissions from Amazon.     

Spatio-temporal gap analysis of OBIS-SEAMAP project data: assessment and way forward

The OBIS-SEAMAP project has acquired and served high-quality marine mammal, seabird, and sea turtle data to the public since its inception in 2002. As data accumulated, spatial and temporal biases resulted and a comprehensive gap analysis was needed in order to assess coverage to direct data acquisition for the OBIS-SEAMAP project and for taxa researchers should true gaps in knowledge exist. All datasets published on OBIS-SEAMAP up to February 2009 were summarized spatially and temporally. Seabirds comprised the greatest number of records, compared to the other two taxa, and most records were from shipboard surveys, compared to the other three platforms. Many of the point observations and polyline tracklines were located in northern and central Atlantic and the northeastern and central-eastern Pacific. The Southern Hemisphere generally had the lowest representation of data, with the least number of records in the southern Atlantic and western Pacific regions. Temporally, records of observations for all taxa were the lowest in fall although the number of animals sighted was lowest in the winter. Oceanographic coverage of observations varied by platform for each taxa, which showed that using two or more platforms represented habitat ranges better than using only one alone. Accessible and published datasets not already incorporated do exist within spatial and temporal gaps identified. Other related open-source data portals also contain data that fill gaps, emphasizing the importance of dedicated data exchange. Temporal and spatial gaps were mostly a result of data acquisition effort, development of regional partnerships and collaborations, and ease of field data collection. Future directions should include fostering partnerships with researchers in the Southern Hemisphere while targeting datasets containing species with limited representation. These results can facilitate prioritizing datasets needed to be represented and for planning research for true gaps in space and time.     

Influences of temporal independence of data on modelling species distributions

Modelling species distributions has been widely used to understand present and future potential distributions of species, and can provide adaptation and mitigation information as references for conservation and management under climate change. However, various methods of data splitting to develop and validate functions of the models do not get enough attention, which may mislead the interpretation of predicted results. We used the Taiwanese endemic birds to test the influences of temporal independence of datasets on model performance and prediction. Training and testing data were considered to be independent if they were collected during different survey periods (1993–2004 and 2009–2010). The results indicated no significant differences of six model performance measures (AUC, kappa, TSS, accuracy, sensitivity, and specificity) among the combinations of training and testing datasets. Both species- and grid cell-based assessments differed significantly between predictions by the annual and pooled training data. We also found an average of 85.8% similarity for species presences and absences in different survey periods. The remaining dissimilarity was mostly caused by species observed in the late survey period but not in the early one. The method of data splitting, yielding training and testing data, is critical for resulting model species distributions. Even if similar model performance exists, different methods can lead to different species distributional maps. More attention needs to be given to this issue, especially when amplifying these models to project species distributions in a changing world.     

A study on the relationship between Atlantic sea surface temperature and Amazonian greenness

The growth of tropical rainforest in Amazon is critically vulnerable to the change in rainfall and radiation than in temperature, and that amount of rainfall and cloudiness in the northeast region of South American is strongly affected by the Atlantic sea surface temperature (SST). Results from recent model experiments for future climate projection have indicated a reduction of Amazonian greenness by a weakening of tropical vapor circulation system related with the change in SST. Therefore, the observational investigation of the relations between the Amazon greenness and Atlantic SST is fundamental to understand the response of Amazonian tropical forest to climate change. In this study, the effect of Atlantic SST on the spatial and temporal change of the Normalized Difference Vegetation Index (NDVI) in the Amazonian region is examined by using satellite remote sensing data for the period of 1981–2001. A strong correlation between NDVI and SST is found for certain regions in Amazon during the periods of 1980s and 1990s, respectively. In addition, strong correlations with NDVI lagging behind SST for two months and one year, respectively, are also identified from the interannual December-to-February (rain season) variations during 1981–2001. Despite these findings, the mechanisms behind the identified correlation remain unclear. Further analyses using observed precipitation and radiation data are required to understand the potential changes of Amazonian rainforest in the context of global warming.     

An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dry-season climate variability and change

Future changes in meridional sea surface temperature (SST) gradients in the tropical Atlantic could influence Amazon dry-season precipitation by shifting the patterns of moisture convergence and vertical motion. Unlike for the El Niño-Southern Oscillation, there are no standard indices for quantifying this gradient. Here we describe a method for identifying the SST gradient that is most closely associated with June–August precipitation over the south Amazon. We use an ensemble of atmospheric general circulation model (AGCM) integrations forced by observed SST from 1949 to 2005. A large number of tropical Atlantic SST gradient indices are generated randomly and temporal correlations are examined between these indices and June–August precipitation averaged over the Amazon Basin south of the equator. The indices correlating most strongly with June–August southern Amazon precipitation form a cluster of near-meridional orientation centred near the equator. The location of the southern component of the gradient is particularly well defined in a region off the Brazilian tropical coast, consistent with known physical mechanisms. The chosen index appears to capture much of the Atlantic SST influence on simulated southern Amazon dry-season precipitation, and is significantly correlated with observed southern Amazon precipitation.We examine the index in 36 different coupled atmosphere–ocean model projections of climate change under a simple compound 1% increase in CO₂. Within the large spread of responses, we find a relationship between the projected trend in the index and the Amazon dry-season precipitation trends. Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations. This suggests that the index would be of use in quantifying uncertainties in climate change in the region.     

Cholera Outbreak in Senegal in 2005: Was Climate a Factor?

Cholera is an acute diarrheal illness caused by Vibrio cholerae and occurs as widespread epidemics in Africa. In 2005, there were 31,719 cholera cases, with 458 deaths in the Republic of Senegal. We retrospectively investigated the climate origin of the devastating floods in mid-August 2005, in the Dakar Region of Senegal and the subsequent outbreak of cholera along with the pattern of cholera outbreaks in three other regions of that country. We compared rainfall patterns between 2002 and 2005 and the relationship between the sea surface temperature (SST) gradient in the tropical Atlantic Ocean and precipitation over Senegal for 2005. Results showed a specific pattern of rainfall throughout the Dakar region during August, 2005, and the associated rainfall anomaly coincided with an exacerbation of the cholera epidemic. Comparison of rainfall and epidemiological patterns revealed that the temporal dynamics of precipitation, which was abrupt and heavy, was presumably the determining factor. Analysis of the SST gradient showed that the Atlantic Ocean SST variability in 2005 differed from that of 2002 to 2004, a result of a prominent Atlantic meridional mode. The influence of this intense precipitation on cholera transmission over a densely populated and crowded region was detectable for both Dakar and Thiès, Senegal. Thus, high resolution rainfall forecasts at subseasonal time scales should provide a way forward for an early warning system in Africa for cholera and, thereby, trigger epidemic preparedness. Clearly, attention must be paid to both natural and human induced environmental factors to devise appropriate action to prevent cholera and other waterborne disease epidemics in the region.     

Spatial synchrony in the response of a long range migratory species (Salmo salar) to climate change in the North Atlantic Ocean

A major challenge in understanding the response of populations to climate change is to separate the effects of local drivers acting independently on specific populations, from the effects of global drivers that impact multiple populations simultaneously and thereby synchronize their dynamics. We investigated the environmental drivers and the demographic mechanisms of the widespread decline in marine survival rates of Atlantic salmon (Salmo salar) over the last four decades. We developed a hierarchical Bayesian life cycle model to quantify the spatial synchrony in the marine survival of 13 large groups of populations (called stock units, SU) from two continental stock groups (CSG) in North America (NA) and Southern Europe (SE) over the period 1971–2014. We found strong coherence in the temporal variation in postsmolt marine survival among the 13 SU of NA and SE. A common North Atlantic trend explains 37% of the temporal variability of the survivals for the 13 SU and declines by a factor of 1.8 over the 1971–2014 time series. Synchrony in survival trends is stronger between SU within each CSG. The common trends at the scale of NA and SE capture 60% and 42% of the total variance of temporal variations, respectively. Temporal variations of the postsmolt survival are best explained by the temporal variations of sea surface temperature (SST, negative correlation) and net primary production indices (PP, positive correlation) encountered by salmon in common domains during their marine migration. Specifically, in the Labrador Sea/Grand Banks for populations from NA, 26% and 24% of variance is captured by SST and PP, respectively and in the Norwegian Sea for populations from SE, 21% and 12% of variance is captured by SST and PP, respectively. The findings support the hypothesis of a response of salmon populations to large climate‐induced changes in the North Atlantic simultaneously impacting populations from distant continental habitats.     

Species‐specific and temporal scale‐dependent responses of birds to drought

Global climate change is increasing the frequency and intensity of weather extremes, including severe droughts in many regions. Drought can impact organisms by inhibiting reproduction, reducing survival and abundance, and forcing range shifts. For birds, considering temporal scale by averaging drought‐related variables over different time lengths (i.e., temporal grains) captures different hydrologic attributes which may uniquely influence food supplies, vegetation greenness/structure, and other factors affecting populations. However, studies examining drought impacts on birds often assess a single temporal grain without considering that different species have different life histories that likely determine the temporal grain of their drought response. Furthermore, while drought is known to influence bird abundance and drive between‐year range shifts, less understood is whether it causes within‐range changes in species distributions. Our objectives were to (a) determine which temporal grain of drought (if any) is most related to bird presence/absence and whether this response is species specific; and (b) assess whether drought alters bird distributions by quantifying probability of local colonization and extinction as a function of drought intensity. We used North American Breeding Bird Survey data collected over 16 years, generalized linear mixed models, and dynamic occupancy models to meet these objectives. Different bird species responded to drought at different temporal grains, with most showing the strongest signal at annual or near‐annual grains. For all drought‐responsive species, increased drought intensity at any temporal grain always correlated with decreased occupancy. Additionally, colonization/extinction analyses indicated that one species, the dickcissel (Spiza americana), is more likely to colonize novel areas within the southern/core portion of its range during drought. Considering drought at different temporal grains, along with hydrologic attributes captured by each grain, may better reveal mechanisms behind drought impacts on birds and other organisms, and therefore improve understanding of how global climate change impacts species and the landscapes they inhabit.     

Temporal degradation of data limits biodiversity research

Spatial and/or temporal biases in biodiversity data can directly influence the utility, comparability, and reliability of ecological and evolutionary studies. While the effects of biased spatial coverage of biodiversity data are relatively well known, temporal variation in data quality (i.e., the congruence between recorded and actual information) has received much less attention. Here, we develop a conceptual framework for understanding the influence of time on biodiversity data quality based on three main processes: (1) the natural dynamics of ecological systems—such as species turnover or local extinction; (2) periodic taxonomic revisions, and; (3) the loss of physical and metadata due to inefficient curation, accidents, or funding shortfalls. Temporal decay in data quality driven by these three processes has fundamental consequences for the usage and comparability of data collected in different time periods. Data decay can be partly ameliorated by adopting standard protocols for generation, storage, and sharing data and metadata. However, some data degradation is unavoidable due to natural variations in ecological systems. Consequently, changes in biodiversity data quality over time need be carefully assessed and, if possible, taken into account when analyzing aging datasets.     

The environmental context for the origins of modern human diversity: a synthesis of regional variability in African climate 150,000-30,000 years ago

We synthesize African paleoclimate from 150 to 30 ka (thousand years ago) using 85 diverse datasets at a regional scale, testing for coherence with North Atlantic glacial/interglacial phases and northern and southern hemisphere insolation cycles. Two major determinants of circum-African climate variability over this time period are supported by principal components analysis: North Atlantic sea surface temperature (SST) variations and local insolation maxima. North Atlantic SSTs correlated with the variability found in most circum-African SST records, whereas the variability of the majority of terrestrial temperature and precipitation records is explained by local insolation maxima, particularly at times when solar radiation was intense and highly variable (e.g., 150-75 ka). We demonstrate that climates varied with latitude, such that periods of relatively increased aridity or humidity were asynchronous across the northern, eastern, tropical and southern portions of Africa. Comparisons of the archaeological, fossil, or genetic records with generalized patterns of environmental change based solely on northern hemisphere glacial/interglacial cycles are therefore imprecise.We compare our refined climatic framework to a database of 64 radiometrically-dated paleoanthropological sites to test hypotheses of demographic response to climatic change among African hominin populations during the 150-30 ka interval. We argue that at a continental scale, population and climate changes were asynchronous and likely occurred under different regimes of climate forcing, creating alternating opportunities for migration into adjacent regions. Our results suggest little relation between large scale demographic and climate change in southern Africa during this time span, but strongly support the hypothesis of hominin occupation of the Sahara during discrete humid intervals ∼135-115 ka and 105-75 ka. Hominin populations in equatorial and eastern Africa may have been buffered from the extremes of climate change by locally steep altitudinal and rainfall gradients and the complex and variable effects of increased aridity on human habitat suitability in the tropics. Our data are consistent with hominin migrations out of Africa through varying exit points from ∼140-80 ka.     

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability.     

Prediction of cell penetrating peptides by support vector machines

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.     

An integrative systematic approach to species diversity and distribution in the genus Mesophyllum (Corallinales,Rhodophyta) in Atlantic and Mediterranean Europe

For the first time, a comprehensive assessment of Mesophyllum species diversity and their distribution in Atlantic Europe and the Mediterranean Sea is presented based on molecular (COI-5P, psbA) and morphological data. The distribution ranges were redefined for the four species collected in this study: M. alternans, M. expansum, M. macroblastum and M. sphaericum. Mesophyllum sphaericum, which was previously known only from a single maerl bed in Galicia (NW Spain), is reported from the Mediterranean Sea. The known range of M. expansum (Mediterranean and Macaronesia) was extended to the Atlantic Iberian Peninsula. The occurrence of M. alternans was confirmed along the Atlantic French coast south to Algarve (southern Portugal). Mesophyllum lichenoides was only recorded from the Atlantic, whereas M. macroblastum appears to be restricted to the Mediterranean Sea. A positive correlation was observed between maximum Sea Surface Temperature (SSTmax) and the depth at which M. expansum was collected, suggesting that this species may compensate for higher SST by growing in deeper habitats where the temperature is lower. The latter indicates that geographic shifts in the distribution of coastal species as a result of global warming can possibly be mitigated by changes in the depth profile at which these species occur. Mesophyllum expansum, an important builder of Mediterranean coralligenous habitats, may be a good target species to assess its response to climate change.     

Dynamic graph convolution neural network based on spatial-temporal correlation for air quality prediction

Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents’ travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability.     

Use of extreme patient samples for outcome prediction from gene expression data

MOTIVATION: Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients' genotypic microarray data, predictions are made to estimate patients' survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients. RESULTS: We present a new computational method for patient outcome prediction. In the training phase of this method, we make use of two types of extreme patient samples: short-term survivors who got an unfavorable outcome within a short period and long-term survivors who were maintaining a favorable outcome after a long follow-up time. These extreme training samples yield a clear platform for us to identify relevant genes whose expression is closely related to the outcome. The selected extreme samples and the relevant genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a risk score that falls into one of the special pre-defined risk groups. We apply this method to several public datasets. In most cases, patients in high and low risk groups stratified by our method have clearly distinguishable outcome status as seen in their Kaplan-Meier curves. We also show that the idea of selecting only extreme patient samples for training is effective for improving the prediction accuracy when different gene selection methods are used.     

Random forests, a novel approach for discrimination of fish populations using parasites as biological tags

Due to the complexity of host-parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of fish. This novel approach is applied to a dataset with a complex structure comprising 763 parasite infracommunities in population samples of Atlantic cod, Gadus morhua, from the spawning/feeding areas in five regions in the North East Atlantic (Baltic, Celtic, Irish and North seas and Icelandic waters). The learning behaviour of RF is evaluated in comparison with two other algorithms applied to class assignment problems, the linear discriminant function analysis (LDA) and artificial neural networks (ANN). The three algorithms are used to develop predictive models applying three cross-validation procedures in a series of experiments (252 models in total). The comparative approach to RF, LDA and ANN algorithms applied to the same datasets demonstrates the competitive potential of RF for developing predictive models since RF exhibited better accuracy of prediction and outperformed LDA and ANN in the assignment of fish to their regions of sampling using parasite community data. The comparative analyses and the validation experiment with a 'blind' sample confirmed that RF models performed more effectively with a large and diverse training set and a large number of variables. The discrimination results obtained for a migratory fish species with largely overlapping parasite communities reflects the high potential of RF for developing predictive models using data that are both complex and noisy, and indicates that it is a promising tool for parasite tag studies. Our results suggest that parasite community data can be used successfully to discriminate individual cod from the five different regions of the North East Atlantic studied using RF.     

Association between environmental factors, smolt size and the survival of wild and reared Atlantic salmon from the Simojoki River in the Baltic Sea

The survival of Atlantic salmon Salmo salar in the Baltic Sea was examined in relation to smolt traits (length and origin) and annual environmental factors [sea surface temperature (SST) and seasonal North Atlantic Oscillation (NAO) index], and prey fish abundance (herring Clupea harengus and sprat Sprattus sprattus) in the main basin and the southern Gulf of Bothnia. The study was based on recapture data for Carlin‐tagged hatchery‐reared and wild smolts from the Simojoki, a river flowing into the northern Gulf of Bothnia. The survival of the wild and reared groups was analysed using an ANOVA model and a stepwise regression model, with the arcsin‐transformed proportion of recaptured fish as the response variable. The results demonstrated a combined influence of smolt traits and environmental factors on survival. For the reared Atlantic salmon released in 1986–1998 (28 groups), the increasing annual mean SST in July in the southern Gulf of Bothnia and increasing mean smolt size improved survival. If the SST in July was excluded from the model, the NAO index in May to July also had a positive effect on survival (P < 0·10). The log₁₀‐transformed abundance of 0+ year herring in the southern Gulf of Bothnia entered the model (P < 0·15) if the SST and NAO index were excluded. For the wild Atlantic salmon released in 1972–1993 (21 groups), only the increasing SST in July showed a significant association with improved survival (P = 0·004). Prey fish abundance in the main basin of the Baltic Sea had no influence on the survival of reared or wild smolt groups. The interaction between smolt size and the SST in July was not significant. The origin was a better, but not a significant, predictor of marine survival compared to the smolt size or the SST in July. The mean recapture rate of the wild groups was twice that of the reared groups in the whole data. The results suggest that cold summers in the Gulf of Bothnia reduce the survival of young Atlantic salmon in both wild and reared groups. The larger smolt size of the reared groups compared with the wild groups to some extent compensated for their lower ability to live in the wild.     

Changes and Relationships of Climatic and Hydrological Droughts in the Jialing River Basin,China

The comprehensive assessment of climatic and hydrological droughts in terms of their temporal and spatial evolutions is very important for water resources management and social development in the basin scale. To study the spatial and temporal changes of climatic and hydrological droughts and the relationships between them, the SPEI and SDI are adopted to assess the changes and the correlations of climatic and hydrological droughts by selecting the Jialing River basin, China as the research area. The SPEI and SDI at different time scales are assessed both at the entire Jialing River basin and at the regional levels of the three sub basins. The results show that the SPEI and SDI are very suitable for assessing the changes and relationships of climatic and hydrological droughts in large basins. Based on the assessment, for the Jialing River basin, climatic and hydrological droughts have the increasing tendency during recent several decades, and the increasing trend of climatic droughts is significant or extremely significant in the western and northern basin, while hydrological drought has a less significant increasing trend. Additionally, climatic and hydrological droughts tend to increase in the next few years. The results also show that on short time scales, climatic droughts have one or two months lag impact on hydrological droughts in the north-west area of the basin, and have one month lag impact in south-east area of the basin. The assessment of climatic and hydrological droughts based on the SPEI and SDI could be very useful for water resources management and climate change adaptation at large basin scale.     

Global discriminative learning for higher-accuracy computational gene prediction

Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM) in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.