Data of various marine environmental factors collected and integrated by the National Center of Ocean Research (NCOR) were used to search for possible statistics relationship with chlorophyll concentration of the Kuroshio and its adjacent waters off eastern Taiwan. The seaWiFS chlorophyll concentration in natural logarithm were used as dependent variable in General Linear Model¡]GLM¡^analysis, followed by least square means (lsmeans) and cluster analysis. Study area ranged from 21.5¡CN and 121¡CE to 26.5¡CN and 125¡CE eastern Taiwan. Data were first assembled, screened, transformed to natural logarithm and reorganized into monthly averages for individual geographical grid points of 10¡¦X 10¡¦. The result of GLM analysis shows that all factors have significant relationship with chlorophyll concentration, more than 20 regression formulae were found with different combination of variable. Results of standard regression analysis show their order of importance as: latitude, depth, longitude, light, sst, east and west current on upper 20 meter(c20EW), north and south current on upper 20 meter(c20NS) and eddy kinetic energy(EKE), respectively. Results of lsmeans listing by latitude and by longitude showed that area with higher chlorophyll concentration are on high-latitude and low-longitude area but not between and area near east Taiwan tend to have high concentration and decreased eastward. Results of cluster analysis indicated that chlorophyll concentration of western longitude, and northern as well as southern latitude are different from other area.

Brassica napus is grown as an edible oil (canola) and an industrial oil (HEAR). Its fatty acid profile and chlorophyll concentration affect the quality. It is important to develop accurate and efficient methods to evaluate these traits. The first objective of this study was to improve the single nucleotide polymorphism (SNP) and sequence characterized amplified region (SCAR) molecular markers for erucic acid genotypes in the Bn-FAE1.1 and Bn-FAE1.2 genes in the A and C genomes in Brassica napus originally developed by Rahman et al. (2008). When put into practice, the error rate was unacceptably high. With the modifications that were made to the protocols, the overall accuracy remained relatively consistent indicating that further improvements are still required. The second objective was to develop a near infrared reflectance (NIR) based calibration equation for chlorophyll concentration in whole Brassica napus seeds. In this case, an equation was successfully created.

Brassica napus is grown as an edible oil (canola) and an industrial oil (HEAR). Its fatty acid profile and chlorophyll concentration affect the quality. It is important to develop accurate and efficient methods to evaluate these traits. The first objective of this study was to improve the single nucleotide polymorphism (SNP) and sequence characterized amplified region (SCAR) molecular markers for erucic acid genotypes in the Bn-FAE1.1 and Bn-FAE1.2 genes in the A and C genomes in Brassica napus originally developed by Rahman et al. (2008). When put into practice, the error rate was unacceptably high. With the modifications that were made to the protocols, the overall accuracy remained relatively consistent indicating that further improvements are still required. The second objective was to develop a near infrared reflectance (NIR) based calibration equation for chlorophyll concentration in whole Brassica napus seeds. In this case, an equation was successfully created.

No description available.

The fundamental components of a coral reef are coral, algae, and sand. At its simplest assessing the status of a coral reef may be reduced to quantifying the relative benthic cover of these three bottom-types. While in situ surveys can provide an accurate census on an individual reef scale (10s of meters), the only feasible method to surveys coral reefs on a reef tract (10-100s of kilometers) or worldwide scale is through the use of remote sensing. Remote sensing is a means of surveying entire ecosystems. A major issue in remote sensing of coastal environments is the confounding effects of the water column on the signal emerging from the water column. We used a simulation method to model differing levels of environmental parameters, which occur in marine ecosystems, with HydrolightEcolight 5. Simulated data were interpolated with actual bottom; type spectra to determine the accuracy of a classification function developed in MATLAB. The aim was to distinguish bottom-types as well as predict levels of water column parameters. The results of this study demonstrate that bottom-type (78% algae, 84% coral, and 94% sand) and chlorophyll concentration (85-90% across range) are well determined, while depth and suspended sediment load are not as well predicted (<70%) and has a tendency to slightly over predict depth.