Error assessment in decision-tree models applied to vegetation analysis

Methods were developed to evaluate the performance of a decision-tree model used to predict landscape-level patterns of potential forest vegetation in central New York State. The model integrated environmental databases and knowledge on distribution of vegetation. Soil and terrain decision-tree variables were derived by processing state-wide soil geographic databases and digital terrain data. Variables used as model inputs were soil parent material, soil drainage, soil acidity, slope position, slope gradient, and slope azimuth. Landscapescale maps of potential vegetation were derived through sequential map overlay operations using a geographic information system (GIS). A verification sample of 276 field plots was analyzed to determine: (1) agreement between GIS-derived estimates of decision-tree variables and direct field measurements, (2) agreement between vegetation distributions predicted using GIS-derived estimates and using field observations, (3) effect of misclassification costs on prediction agreement, (4) influence of particular environmental variables on model predictions, and (5) misclassification rates of the decision-tree model. Results indicate that the prediction model was most sensitive to drainage and slope gradient, and that the imprecision of the input data led to a high frequency of incorrect predictions of vegetation. However, in many cases of misclassification the predicted vegetation was similar to that of the field plots so that the cost of errors was less than expected from the misclassification rate alone. Moreover, since common vegetation types were more accurately predicted than rare types, the model appears to be reasonably good at predicting vegetation for a randomly selected plot in the landscape. The error assessment methodology developed for this study provides a useful approach for determining the accuracy and sensitivity of landscape-scale environmental models, and indicates the need to develop appropriate field sampling procedures for verifying the predictions of such models.     

Safety and immunologic effects after inoculation of inactivated and combined live-inactivated dermatophytosis vaccines in cats

OBJECTIVE: To determine antidermatophyte immunologic effects of an experimental combined live-inactivated dermatophytosis vaccine (CLIDV) and a commercial inactivated dermatophytosis vaccine (IDV) in cats and to evaluate adverse effects associated with administration of these vaccines. ANIMALS: 20 healthy juvenile domestic shorthair cats. PROCEDURE: Cats were injected with 2 doses of CLIDV at the standard dosage or 1 dose of CLIDV at 10 times the standard dosage; IDV was administered at the manufacturer-recommended dosage. Cats were observed for illness and reactions at inoculation sites. Periodically, samples were obtained for fungal culture, lymphocyte blastogenesis test (LBT) as an indicator of cell-mediated immunity against dermatophyte antigens, and antidermatophyte IgG titers. Following vaccination, cats were challenge-exposed by topical application of Microsporum canis macroconidia and examined weekly for clinical signs of dermatophytosis. RESULTS: of 10 cats given CLIDV developed focal crusts at the injection site that resolved without treatment; these were areas of dermatophyte infection with the vaccine strain. Antidermatophyte IgG titers increased significantly with all vaccination protocols. Cellular immunity against M canis increased slightly and variably during the vaccination period and did not differ significantly between vaccinated and control cats. All cats developed dermatophyte infection after challenge exposure. Vaccination with CLIDV or IDV was associated with slightly reduced severity of initial infection. CONCLUSIONS AND CLINICAL RELEVANCE: Noculation with IDV or CLIDV did not provide prophylactic immunity against topical challenge exposure with M canis. Inoculation with either vaccine did not provide a more rapid cure of an established infection.     

Changes in landscape patterns in Georgia,USA

The objectives of this study were to determine how landscape patterns in Georgia, USA have changed through time and whether the spatial patterns varied by physiographic region. Historical aerial photography was used to analyze spatial patterns of land use from the 1930's to the 1980's. Land use patterns were quantified by: (1) mean number and size of patches; (2) fractal dimension of patches; (3) amount of edge between land uses; and (4) indices of diversity, dominance, and contagion. Forest cover increased in aerial extent and in mean patch size. The mean size of agricultural patches increased in the coastal plain and decreased in the mountains and piedmont. Edges between land uses decreased through time, indicating less dissection of the landscape. Fractal dimensions also decreased, indicating simpler patch shapes. Indices of diversity and dominance differed through time but not among regions; the contagion index differed among regions but not through time. A geographic trend of decreasing diversity and increasing dominance and contagion was observed from the mountains to the lower coastal plain. Landscape patterns exhibited the greatest changes in the piedmont region. Overall, the Georgia landscape has become less fragmented and more connected during the past 50 years. Changing patterns in the landscape may have implications for many ecological processes and resources.     

Consumer acceptance of transgenic crops

It has long been recognised that a key determinant of the future development of genetic engineering in food production is likely to be consumer acceptance. Much of the earlier research into acceptance issues has assumed that public perceptions of genetic engineering are defined by beliefs about the technology overall. However, it is now known that acceptance of novel products is unlikely to be related to general attitudes towards genetic engineering. People's perceptions of risk and benefit associated with particular products and applications will determine acceptance. There is a need to develop effective risk–benefit communication strategies, based on the best scientific information available, in order to enable the public to make informed choices about consuming the products of genetic engineering. It is also essential to develop effective communication methods if the public is to contribute to the wider debate about strategic development of genetic engineering. Issues of social context (such as trust in risk regulators) must also be considered. Providing information is likely to change people's attitudes, which will in turn generate further public concerns and create a demand for more information about genetic engineering. © 1998 SCI