A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Process for producing biodiesel fuel with reduced viscosity and a cloud point below thirty-two (32) degrees fahrenheit

Triglycerides are reacted in a liquid phase reaction with methanol and a homogeneous basic catalyst. The reaction yields a spatially separated two phase result with an upper located non-polar phase consisting principally of non-polar methyl esters and a lower located phase consisting principally of glycerol and residual methyl esters. The glycerol phase is passed through a strong cationic ion exchanger to remove anions, resulting in a neutral product which is flashed to remove methanol and which is reacted with isobutylene in the presence of a strong acid catalyst to produce glycerol ethers. The glycerol ethers are then added back to the upper located methyl ethyl ester phase to provide an improved biodiesel fuel.