Predicting walnut (Juglans spp.) crop yield using meteorological and airborne pollen data

Crop yield determines economy by influencing prices on the trade market, and so accurate forecasts of the yield are important for planning various aspects of agricultural production. The main aim of this study is to construct a model for predicting walnut yield in an important walnut production area (the region of Novi Sad in Northern Serbia). Relationships between the amount of walnuts produced annually (2000–2011) and abiotic (e.g. meteorological) and biotic (e.g. airborne pollen data) factors were examined using Pearson correlation analysis. Walnut yield data were then entered into linear regression models with variables that had the highest correlations. The models were constructed using 10 years of data, and tested using 2 years of data not included in constructing the model. This paper has shown that walnut yield is greatly dependent on weather conditions, particularly during fertilisation and seed growth, but the amount of available airborne pollen also plays an important role. The introduction of the seasonal pollen index, as a proxy for the amount of pollen available for fertilisation, improved the performance of models predicting walnut yield.     

Inheritance of reproductive phenology traits and related QTL identification in apricot

Reproductive phenological traits of great agronomical interest in apricot species, including flowering date, ripening date and fruit development period, were studied during 3 years in two F₁ progenies derived from the crosses ‘Bergeron’ × ‘Currot’ (B × C) and ‘Goldrich’ × ‘Currot’ (G × C). Results showed great variability and segregation in each population, confirming the polygenic nature and quantitative inheritance of all the studied traits. Genetic linkage maps were constructed combining SSR and SNP markers, using 87 markers in the ‘B × C’ population and 89 markers in ‘G × C’. The genetic linkage maps in both progenies show the eight linkage groups (LGs) of apricot, covering a distance of 394.9 cM in ‘Bergeron’ and of 414.3 cM in ‘Currot’. The ‘Goldrich’ and ‘Currot’ maps were of 353.5 and 422.3 cM, respectively. The average distance obtained between markers was thus 7.59 cM in ‘Bergeron’ and 7.53 cM in ‘Currot’, whereas the ‘Goldrich’ and ‘Currot’ averages were 5.6 and 7.5 cM, respectively. According to the polygenic nature of the studied phenology traits, QTLs linked to flowering date, ripening date and the fruit development period were identified during the 3 years of the study in all LGs except for LG 8. Among the QTLs identified, major QTLs for flowering and ripening date and the fruit development period were identified in LG 4, especially important in the ‘G × C’ population.     

Predicting insect phenology across space and time

Many species appear to be undergoing shifts in phenology, arising from climate change. To predict the direction and magnitude of future changes requires an understanding of how phenology depends on climatic variation. Species show large‐scale spatial variation in phenology (affected by differentiation among populations) as well as variation in phenology from year‐to‐year at the same site (affected predominantly by local plasticity). Teasing apart spatial and temporal variation in phenology should allow improved predictions of phenology under climate change. This study is the first to quantify large‐scale spatial and temporal variation in the entire emergence pattern of species, and to test the relationships found by predicting future data. We use data from up to 33 years of permanent transect records of butterflies in the United Kingdom to fit and test models for 15 butterfly species. We use generalized additive models to model spatial and temporal variation in the distribution of adult butterflies over the season, allowing us to capture changes in the timing of emergence peaks, relative sizes of peaks and/or number of peaks in a single analysis. We develop these models using data for 1973–2000, and then use them to predict phenologies from 2001 to 2006. For six of our study species, a model with only spatial variation in phenology is the best predictor of the future, implying that these species have limited plasticity. For the remaining nine species, the best predictions come from a model with both spatial and temporal variation in phenology; for four of these, growing degree‐days have similar effects over space and time, implying high levels of plasticity. The results show that statistical phenology models can be used to predict phenology shifts in a second time period, suggesting that it should be feasible to project phenologies under climate change scenarios, at least over modest time scales.     

New developments in CLAMP: Calibration using global gridded meteorological data

Climate Leaf Analysis Multivariate Program (CLAMP) is a versatile technique for obtaining quantitative estimates for multiple terrestrial palaeoclimate variables from woody dicot leaf assemblages. To date it has been most widely applied to the Late Cretaceous and Tertiary of the mid- to high latitudes because of concerns over the relative dearth of calibration sites in modern low-latitude warm climates, and the loss of information associated with the lack of marginal teeth on leaves in paratropical to tropical vegetation. This limits CLAMP's ability to quantify reliably climates at low latitudes in greenhouse worlds of the past.One of the reasons for the lack of CLAMP calibration samples from warm environments is the paucity of climate stations close to potential calibration vegetation sites at low latitudes. Agriculture and urban development have destroyed most lowland sites and natural vegetation is now largely confined to mountainous areas where climate stations are few and climatic spatial variation is high due to topographic complexity. To attempt to overcome this we have utilised a 0.5° × 0.5° grid of global interpolated climate data based on the data set of New et al. (1999) supplemented by the ERA40 re-analysis data for atmospheric temperature at upper levels. For each location, the 3-D climatology of temperature from the ECMWF re-analysis project was used to calculate the mean lower tropospheric lapse rate for each month of the year. The gridded data were then corrected to the altitude of the plant site using the monthly lapse rates. Corrections for humidity were also made. From this the commonly returned CLAMP climate variables were calculated. A bilinear interpolation scheme was then used to calculate the climate parameters at the exact lat/long of the site.When CLAMP analyses using the PHYSG3BR physiognomic data calibrated with the climate station based MET3BR were compared to analyses using the gridded data at the same locations (GRIDMET3BR), the results were indistinguishable in that they fell within the range of statistical uncertainty determined for each analysis. This opens the way to including natural vegetation anywhere in the world irrespective of the proximity of a meteorological station.     

Predicting xylem phenology in black spruce under climate warming

In the next century, the boreal ecosystems are projected to experience greater rates of warming than most other regions of the world. As the boreal forest constitutes a reservoir of trees of huge ecological importance and only partially known economic potential, any possible climate‐related change in plant growth and dynamics has to be promptly predicted and evaluated. A model for assessing xylem phenology in black spruce [Picea mariana (Mill.) B.S.P.] using daily temperatures and thermal thresholds was defined and applied to predict changes in onset, ending and duration of xylem growth under different warming scenarios with temperatures rising by up to 3 °C. This was achieved by collecting and analyzing a dataset obtained from a 7‐year monitoring of cambium phenology and wood formation on a weekly time‐scale in trees growing in four sites at different latitudes and altitudes in the Saguenay‐Lac‐Saint‐Jean region (Quebec, Canada). The onset of xylem growth occurred between mid‐May and early June while the end ranged between mid‐September and early October, resulting in a growing season of 101–141 days. The model predicted longer duration of xylem growth at higher temperatures, with an increase of 8–11 days/ °C, because of an earlier onset and later ending of growth. With an increase of 3 °C in the mean temperature during the year, the duration of xylem growth changed on average from 125 to 160 days. The predicted changes in cambial phenology could significantly affect future wood production of the boreal ecosystems.     

Predicting the spatial organization of chromosomes using epigenetic data

Chromosome folding can reinforce the demarcation between euchromatin and heterochromatin. Two new studies show how epigenetic data, including DNA methylation, can accurately predict chromosome folding in three dimensions. Such computational approaches reinforce the idea of a linkage between epigenetically marked chromatin domains and their segregation into distinct compartments at the megabase scale or topological domains at a higher resolution.Please see related articles: http://dx.doi.org/10.1186/s13059-015-0741-y and http://dx.doi.org/10.1186/s13059-015-0740-z     

Predicting mostly disordered proteins by using structure-unknown protein data

Background  

Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences.     

内蒙古草原羊草物候与气象因子的关系

研究温带草原植物物候与气象因子变化的关系,对于提高草原净初级生产量和碳收支估算的精度,掌握放牧的时宜及其区域差异,具有重要的科学意义和实践价值.该文利用内蒙古草原7个牧业气象试验站1983～2002年的羊草物候观测数据,对返青和黄枯日期与气温和降水量的关系进行了统计分析.结果表明:(1)所有站点的年平均气温均呈显著升高的趋势,多数站点的年降水量呈不显著减少的趋势;同时,各站羊草返青期和黄枯期均以显著提前的趋势占优势,多数站点的生长季节(返青到黄枯的期间)呈缩短的趋势.(2)在区域尺度上,羊草返青日期与返青前一个月均温的负相关最为显著,气温每升高1℃,返青期约提前2.4d;在站点尺度上,森林草原区站点的羊草返青期早晚主要受返青前一个月均温(负相关)的影响,典型草原区站点的羊草返青期早晚主要受前一年10月或11月到返青期累积降水量(负相关)的影响,森林草原区最北部额尔古纳右旗的返青期早晚受返青前一个月均温和前一年10月到返青期累积降水量的共同影响.(3)在区域尺度上,羊草黄枯日期与黄枯前一个月均温的负相关最为显著,气温每升高1℃,黄枯期约提前3.7d;在站点尺度上,羊草黄枯期早晚与前期气温以负相关为主,与前期降水量以正相关为主.     

Predicting phenology by integrating ecology,evolution and climate science

Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology – the timing of life‐history events. Phenology has well‐demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species’ reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.     

Photographic assessment of temperate forest understory phenology in relation to springtime meteorological drivers

Phenology shows sensitive responses to seasonal changes in atmospheric conditions. Forest understory phenology, in particular, is a crucial component of the forest ecosystem that interacts with meteorological factors, and ecosystem functions such as carbon exchange and nutrient cycling. Quantifying understory phenology is challenging due to the multiplicity of species and heterogeneous spatial distribution. The use of digital photography for assessing forest understory phenology was systematically tested in this study within a temperate forest during spring 2007. Five phenology metrics (phenometrics) were extracted from digital photos using three band algebra and two greenness percentage (image binarization) methods. Phenometrics were compared with a comprehensive suite of concurrent meteorological variables. Results show that greenness percentage cover approaches were relatively robust in capturing forest understory green-up. Derived spring phenology of understory plants responded to accumulated air temperature as anticipated, and with day-to-day changes strongly affected by estimated moisture availability. This study suggests that visible-light photographic assessment is useful for efficient forest understory phenology monitoring and allows more comprehensive data collection in support of ecosystem/land surface models.     

Predicting co-complexed protein pairs using genomic and proteomic data integration

Background  

Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.     

A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data

A raster or grid-based Geographic Information System with data on tsetse, trypanosomiasis, animal production, agriculture and land use has recently been developed in Togo. The area-wide sampling of tsetse fly, aided by satellite imagery, is the subject of two separate papers. This paper follows on a first paper, published in this journal, describing the generation of digital tsetse distribution and abundance maps and how these accord with the local climatic and agro-ecological setting. Such maps when combined with data on the disease, the hosts and their owners, should contribute to the knowledge of the spatial epidemiology of trypanosomiasis and assist planning of integrated control operations. Here we address the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. Different discriminant analysis models have been applied using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. The results confirm the potential of satellite data application and multivariate analysis for the prediction of the tsetse distribution and abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen and/or substitute one another. The analysis shows how the strategic incorporation of satellite imagery may minimize field collection of data. Field surveys may be modified and conducted in two stages, first concentrating on the expected fly distribution limits and thereafter on fly abundance. The study also shows that when applying satellite data, care should be taken in selecting the optimal number of predictor variables because this number varies with the amount of training data for predicting abundance and on the homogeneity of the distribution limits for predicting fly presence. Finally, it is suggested that in addition to the use of contemporary training data and predictor variables, training and predicted data sets should refer to the same eco-geographic zone.     

Predicting protein post-translational modifications using meta-analysis of proteome scale data sets

Protein post-translational modifications are an important biological regulatory mechanism, and the rate of their discovery using high throughput techniques is rapidly increasingly. To make use of this wealth of sequence data, we introduce a new general strategy designed to predict a variety of post-translational modifications in several organisms. We used the motif-x program to determine phosphorylation motifs in yeast, fly, mouse, and man and lysine acetylation motifs in man. These motifs were then scanned against proteomic sequence data using a newly developed tool called scan-x to globally predict other potential modification sites within these organisms. 10-fold cross-validation was used to determine the sensitivity and minimum specificity for each set of predictions, all of which showed improvement over other available tools for phosphoprediction. New motif discovery is a byproduct of this approach, and the phosphorylation motif analyses provide strong evidence of evolutionary conservation of both known and novel kinase motifs.     

Predicting the sensitivity of butterfly phenology to temperature over the past century

Studies to date have documented substantial variation among species in the degree to which phenology responds to temperature and shifts over time, but we have a limited understanding of the causes of such variation. Here, we use a spatially and temporally extensive data set (ca. 48 000 observations from across Canada) to evaluate the utility of museum collection records in detecting broad‐scale phenology‐temperature relationships and to test for systematic differences in the sensitivity of phenology to temperature (days °C^?1) of Canadian butterfly species according to relevant ecological traits. We showed that the timing of flight season predictably responded to temperature both across space (variation in average temperature from site to site in Canada) and across time (variation from year to year within each individual site). This reveals that collection records, a vastly underexploited resource, can be applied to the quantification of broad‐scale relationships between species' phenology and temperature. The timing of the flight season of earlier fliers and less mobile species was more sensitive to temperature than later fliers and more mobile species, demonstrating that ecological traits can account for some of the interspecific variation in species' phenological sensitivity to temperature. Finally, we found that phenological sensitivity to temperature differed across time and space implying that both dimensions of temperature will be needed to translate species' phenological sensitivity to temperature into accurate predictions of species' future phenological shifts. Given the widespread temperature sensitivity of flight season timing, we can expect long‐term temporal shifts with increased warming [ca. 2.4 days °C^?1 (0.18 SE)] for many if not most butterfly species.     

Predicting a change in the order of spring phenology in temperate forests

The rise in spring temperatures over the past half‐century has led to advances in the phenology of many nontropical plants and animals. As species and populations differ in their phenological responses to temperature, an increase in temperatures has the potential to alter timing‐dependent species interactions. One species‐interaction that may be affected is the competition for light in deciduous forests, where early vernal species have a narrow window of opportunity for growth before late spring species cast shade. Here we consider the Marsham phenology time series of first leafing dates of thirteen tree species and flowering dates of one ground flora species, which spans two centuries. The exceptional length of this time series permits a rare comparison of the statistical support for parameter‐rich regression and mechanistic thermal sensitivity phenology models. While mechanistic models perform best in the majority of cases, both they and the regression models provide remarkably consistent insights into the relative sensitivity of each species to forcing and chilling effects. All species are sensitive to spring forcing, but we also find that vernal and northern European species are responsive to cold temperatures in the previous autumn. Whether this sensitivity reflects a chilling requirement or a delaying of dormancy remains to be tested. We then apply the models to projected future temperature data under a fossil fuel intensive emissions scenario and predict that while some species will advance substantially others will advance by less and may even be delayed due to a rise in autumn and winter temperatures. Considering the projected responses of all fourteen species, we anticipate a change in the order of spring events, which may lead to changes in competitive advantage for light with potential implications for the composition of temperate forests.     

Application of meteorological data to agroclimatological mapping

During the years 1969–72 a comprehensive agroclimatic survery was carried out in Aust-Agder, a county of approximately 9000 km² in the southernmost part of Norway. Meteorological data were collected from some 70 stations grouped in 11 model locations. In the analysis the agricultural purposes behind the survey are born in mind and importance is attached to simple and direct methods that will allow extrapolations within the region with sufficient confidence. The geographical variations in growth climate is expressed by average radiation and temperature conditions, and by estimates of plant-available soil moisture.Presented at the Eighth International Congress of Biometeorology, 9–14 September 1979, Shefayim, Israel.