The inheritance and expression patterns of the cry1Ab gene were studied in the progenies derived from different Bt ( Bacillus thuringiensis) transgenic japonica rice lines under field conditions. Both Mendelian and distorted segregation ratios were observed in some selfed and crossed F(2) populations. Crosses between japonica intra-subspecies had no significant effect on the segregation ratios of the cry1Ab gene, but crossing between japonica and indicainter-subspecies led to distorted segregation of the cry1Ab gene in the F(2)population. Field-release experiments indicated that the cry1Ab gene was stably transmitted in an intact manner via successive sexual generations, and the concentration of the Cry1Ab protein was kept quantitatively stable up to the R(6)generation. The cry1Ab gene, driven by the maize ubiquitinpromoter, displayed certain kinds of spatial and temporal expression patterns under field conditions. The content of the Cry1Ab protein varied in different tissues of the main stems, the primary tillers and the secondary tillers. Higher levels of the Cry1Ab protein were found in the stems, leaves and leaf sheaths than in the roots, while the lowest level was detected in grains at the maturation stage. The content of the Cry1Ab protein in the leaves peaked at the booting stage and was lowest at the heading stage. Furthermore, the Cry1Ab content of cry1Ab expression in different tissues of transgenic rice varied individually with temperature.

The eutrophication of the coastal waters of Zhejiang Province has become one of the main contamination threats to the region's coastal marine ecosystems. Accordingly, the comprehensive characterization of the eutrophication status in terms of improved quantitative methods is valuable for local risk assessment and policy making. A novelty of this work is that the spatial distributions of chemical oxygen demand, dissolved inorganic nitrogen, and dissolved inorganic phosphorus were estimated across space by the Bayesian maximum entropy (BME) method. The BME estimates were found to have the best cross‐validation performance compared to ordinary kriging and inverse distance weighted techniques. Based on the BME maps, it was found that about 25.95%, 19.18%, 20.53%, and 34.34% of these coastal waters were oligotrophic, mesotrophic, eutrophic, and hypereutrophic. Another novelty of the present work is that comprehensive stochastic site indicators (SSI) were introduced in the quantitative characterization of the eutrophication risk in the Zhejiang coastal waters under conditions of in situ uncertainty. The results showed that the level of the eutrophication index (EI) increased almost linearly with increasing threshold values; and that 71%, 51%, and 19% of coastal locations separated by various spatial lags experience considerable mesotrophic, eutrophic, and hypereutrophic risks, respectively. The average EI values over the subregions of the Zhejiang coastal waters graded as “oligotrophic or higher,” “eutrophic or higher,” and “hypereutrophic” were about 11.14, 14.28, and 25.34, respectively. Our results also revealed that the joint eutrophication strength between coastal locations in the Zhejiang region was consistently greater than the combined strength of independent eutrophications at these locations (we termed this situation “positive quadrant eutrophication dependency”). It was found that a critical eutrophication threshold ζcr ≈ 8.38 exists so that below ζcr the spatial eutrophication dependency in the Zhejiang coastal waters increases with ζ, whereas above ζcr the opposite is true. Moreover, the eutrophication dependency decreases as the separation distance δs increases. Interestingly, at distances δs smaller than a critical distance δscr ≈ 15  km, the eutrophication locations are concentrated in the coastal waters of the Zhejiang province rather than being dispersed (this observation holds even for large thresholds ζ). Elasticity analysis of eutrophication indicators offered a quantitative measure of the excess eutrophication change in the Zhejiang coastal waters caused by a threshold change (the larger the elasticity is, the more sensitive eutrophication is to threshold changes). The above findings can contribute to an improved understanding of seawater quality and provide a practical approach for the identification of critical coastal water regions.