Search

To capture the dependence in the upper tail of a time series, we develop non‐negative regularly varying time series models that are constructed similarly to classical non‐extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly varying time series via a measure of pairwise extremal dependencies, the tail pairwise dependence function (TPDF). We state consistency requirements among the finite‐dimensional collections of the elements of a regularly varying time series and show that the TPDF's value does not depend on the dimension of the random vector being considered. So that our models take non‐negative values, we use transformed‐linear operations. We show existence and stationarity of these models, and develop their properties such as the model TPDFs. We fit models to hourly windspeed and daily fire weather index data, and we find that the fitted transformed‐linear models produce better estimates of upper tail quantities than a traditional ARMA model, classical linear regularly varying models, a max‐ARMA model, and a Markov model. [ABSTRACT FROM AUTHOR]

Summary: Erwinia amylovora is the causal agent of fire blight, one of the most devastating diseases of apple and pear. Erwinia amylovora is thought to have originated in North America and has now spread to at least 50 countries worldwide. An understanding of the diversity of the pathogen population and the transmission to different geographical regions is important for the future mitigation of this disease. In this research, we performed an expanded comparative genomic study of the Spiraeoideae‐infecting (SI) E. amylovora population in North America and Europe. We discovered that, although still highly homogeneous, the genetic diversity of 30 E. amylovora genomes examined was about 30 times higher than previously determined. These isolates belong to four distinct clades, three of which display geographical clustering and one of which contains strains from various geographical locations (‘Widely Prevalent’ clade). Furthermore, we revealed that strains from the Widely Prevalent clade displayed a higher level of recombination with strains from a clade strictly from the eastern USA, which suggests that the Widely Prevalent clade probably originated from the eastern USA before it spread to other locations. Finally, we detected variations in virulence in the SI E. amylovora strains on immature pear, and identified the genetic basis of one of the low‐virulence strains as being caused by a single nucleotide polymorphism in hfq, a gene encoding an important virulence regulator. Our results provide insights into the population structure, distribution and evolution of SI E. amylovora in North America and Europe. [ABSTRACT FROM AUTHOR]

An intensive regional research campaign was conducted by the North American Carbon Program ( NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale ( PSU), to continental ( CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2-C sink estimates were generally slightly larger, 8-20% for PSU, 10-20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region. [ABSTRACT FROM AUTHOR]