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Yellow mosaic disease in winter wheat is usually attributed to the infection by bymoviruses or furoviruses; however, there is still limited information on whether other viral agents are also associated with this disease. To investigate the wheat viromes associated with yellow mosaic disease, we carried out de novo RNA sequencing (RNA-seq) analyses of symptomatic and asymptomatic wheat-leaf samples obtained from a field in Hokkaido, Japan, in 2018 and 2019. The analyses revealed the infection by a novel betaflexivirus, which tentatively named wheat virus Q (WVQ), together with wheat yellow mosaic virus (WYMV, a bymovirus) and northern cereal mosaic virus (a cytorhabdovirus). Basic local alignment search tool (BLAST) analyses showed that the WVQ strains (of which there are at least three) were related to the members of the genus Foveavirus in the subfamily Quinvirinae (family Betaflexiviridae). In the phylogenetic tree, they form a clade distant from that of the foveaviruses, suggesting that WVQ is a member of a novel genus in the Quinvirinae. Laboratory tests confirmed that WVQ, like WYMV, is potentially transmitted through the soil to wheat plants. WVQ was also found to infect rye plants grown in the same field. Moreover, WVQ-derived small interfering RNAs accumulated in the infected wheat plants, indicating that WVQ infection induces antiviral RNA silencing responses. Given its common coexistence with WYMV, the impact of WVQ infection on yellow mosaic disease in the field warrants detailed investigation. [ABSTRACT FROM AUTHOR]

We have estimated the number of sika deer, Cervus nippon, in Hokkaido, Japan, with the aim of developing a management program that will reduce the level of agricultural damage caused by these deer. A population index that is defined by the population divided by the population of 1993 is first estimated from the data obtained during a spotlight survey. A generalized linear mixed model (GLMM) with corner point constraints is used in this estimation. We then estimate the population from the index by evaluating the response of index to the known amount of harvest, including hunting. A stage-structured model is used in this harvest-based estimation. It is well-known that estimates of indices suffer from large observation errors when the probability of the observation fluctuates widely; therefore, we apply state-space modeling to the harvest-based estimation to remove the observation errors. We propose the use of Bayesian estimation with uniform prior-distributions as an approximation of the maximum likelihood estimation, without permitting an arbitrary assumption that the parameters fluctuate following prior-distributions. We are able to demonstrate that the harvest-based Bayesian estimation is effective in reducing the observation errors in sika deer populations, but the stage-structured model requires many demographic parameters to be known prior to running the analyses. These parameters cannot be estimated from the observed time-series of the index if there is insufficient data. We then construct a univariate model by simplifying the stage-structured model and show that the simplified model yields estimates that are nearly identical to those obtained from the stage-structured model. This simplification of the model simultaneously clarifies which parameter is important in estimating the population. [ABSTRACT FROM AUTHOR]

We evaluated relative density indices of sika deer ( Cervus nippon) to aid in population management. We monitored sika deer population trends from 1992 to 2002 in the eastern part of Hokkaido Island, northern Japan, using spotlight surveys, aerial surveys, catch per unit effort (CPUE), sighting per unit effort (SPUE), and cost of damage to agriculture and forestry. We assumed that the artificial bias in the spotlight index would be lower than in other indices, and compared temporal patterns of other indices to those produced using spotlight surveys using model II regression. There was a significant correlation between the damage cost index and the spotlight index, and both indices indicated consistent population trends. Managers used CPUE as a tool to determine hunting quota efficiency. The SPUE index had the smallest standard error among the indices, and the spotlight survey index had the second smallest standard error. Overall, the spotlight survey was the most useful index because its estimate error was small and it was precise in showing population trends; however, spotlight surveys did lead to underestimation once in 1994. The SPUE index seems to be effective in checking the validity of the spotlight index, but there are so many environmental and demographic uncertainties that several independent indices should be used and crosschecked for accurate evaluation of population trends. [ABSTRACT FROM AUTHOR]