Deforestation and the spatio-temporal distribution of savannah and forest members of the Simulium damnosum complex in southern Ghana and south-western Togo

Spatio-temporal data on cytotaxonomic identifications of larvae of different members of the Simulium damnosum complex collected from rivers in southern Ghana and south-western Togo from 1975 until 1997 were analysed. When the data were combined, the percentages of savannah blackflies (S. damnosum sensu stricto and S. sirbanum) in the samples were shown to have been progressively increasing since 1975. The increases were statistically significant (P < 0·001), but the rates of increase were not linear. Further analyses were conducted according to the collection seasons and locations of the samples, to account for possible biases such as savannah flies occurring further south in the dry season or a preponderance of later samples from northern rivers having more savannah flies. These analyses showed that the increasing trend was statistically significant (P< 0·0001) only during the periods April to June and October to December. The presence of adult savannah flies carrying infective larvae (L3) indistinguishable from those of Onchocerca volvulus in the study zone was confirmed by examinations of captured flies. The percentages of savannah flies amongst the human-biting populations and the percentages with L3s in the head were higher during dry seasons than wet seasons and the savannah species were found furthest south (5 °25′N) in the dry season. Comparisons of satellite images taken in 1973 and 1990 over a study area in south-western Ghana encompassing stretches of the Tano and Bia rivers demonstrated that there have been substantial increases in urban and savannah areas, at the expense of forest. This was so not only for the whole images but also for subsamples of the images taken at 1, 2, 4, 8 and 16 km distant from sites alongside the River Tano. At every distance from the river, the percentages of pixels classified as urban or savannah have increased in 1990 compared with 1973, while those classified as degraded or dense forest have decreased. The possibility that the proportionate increases in savannah forms of the vectors of onchocerciasis, and hence in the likelihood of the transmission of savannah strains of the disease in formerly forested areas, were related to the decreases in forest cover is discussed.

Towards the sustainable use of Europe’s forests: a centre for European forest science! – Some missing catalytic elements?

The Symposium, “Towards the sustainable use of Europe’s forests”, with sub-title “Forest ecosystem and landscape research: scientific challenges and opportunities” lists three fundamental substantive areas of research that are involved: Forest management and practices, Ecosystem processes and functional ecology, and Environmental economics and sociology. This paper argues that there are essential catalytic elements missing! Without these elements there is great danger that the aimed-for world leadership in the forest sciences will not materialize. What are the missing elements? All the sciences, and in particular biology, environmental sciences, sociology, economics, and forestry have evolved so that they include good scientific methodology. Good methodology is imperative in both the design and analysis of research studies, the management of research data, and in the interpretation of research finding. The methodological disciplines of Statistics, Modelling and Informatics (“SMI”) are crucial elements in a proposed Centre of European Forest Science, and the full involvement of professionals in these methodological disciplines is needed if the research of the Centre is to be world-class. Distributed Virtual Institute (DVI) for Statistics, Modelling and Informatics in Forestry and the Environment (SMIFE) is a consortium with the aim of providing world-class methodological support and collaboration to European research in the areas of Forestry and the Environment. It is suggested that DVI: SMIFE should be a formal partner in the proposed Centre for European Forest Science.

A new parameterization of Johnson's SB distribution with application to fitting forest tree diameter data

The SB distributional model of Johnson's 1949 paper was introduced by a transformation to normality, that is, z ~ N(0, 1), consisting of a linear scaling to the range (0, 1), a logit transformation, and an affine transformation, z = γ + δu. The model, in its original parameterization, has often been used in forest diameter distribution modelling. In this paper, we define the SB distribution in terms of the inverse transformation from normality, including an initial linear scaling transformation, u = γ′ + δ′z (δ′ = 1/δ and γ′ = �γ/δ). The SB model in terms of the new parameterization is derived, and maximum likelihood estimation schema are presented for both model parameterizations. The statistical properties of the two alternative parameterizations are compared empirically on 20 data sets of diameter distributions of Changbai larch (Larix olgensis Henry). The new parameterization is shown to be statistically better than Johnson's original parameterization for the data sets considered here.

Semi-Parametric Analysis of Extreme Forest Fires

Forest fires can cause extensive damage to natural resources and properties. They can also destroy wildlife habitat, affect the forest ecosystem and threaten human lives. In this paper incidences of extreme wildland fires are modelled by a point process model which incorporates time-trend. A model based on a generalised Pareto distribution is used to model data on acres of wildland burnt by extreme fire in the US since 1825. A semi-parametric smoothing approach, which is very useful in exploratory analysis of changes in extremes, is illustrated with the maximum likelihood method to estimate model parameters.

Differences in performance among four indices used to evaluate diversity in planktonic ecosystems

The performance of four common estimators of diversity are investigated using calanoid copepod data from the Continuous Plankton Recorder (CPR) survey. The region of the North Atlantic and the North Sea was divided into squares of 400 nautical miles for each 2-month period. For each 144 possible cases, Pielou's pooled quadrat method was performed with the aims of determining asymptotic diversity and investigating the CPR sample-size dependence of diversity estimators. It is shown that the performance of diversity indices may greatly vary in space and time (at a seasonal scale). This dependence is more pronounced in higher diverse environments and when the sample size is small. Despite results showing that all estimators underestimate the `actual' diversity, comparison of sites remained reliable from a few pooled CPR samples. Using more than one CPR sample, the Gini coefficient appears to be a better diversity estimator than any other indices and spatial or temporal comparisons are highly satisfactory. In situations where comparative studies are needed but only one CPR sample is available, taxonomic richness was the preferred method of estimating diversity. Recommendations are proposed to maximise the efficiency of diversity estimations with the CPR data.

Plankton, fisheries and climate change - insights into ocean ecosystems

This paper examines long term changes in the plankton of the North Atlantic and northwest European shelf seas and discusses the forcing mechanisms behind some observed interannual, decadal and spatial patterns of variability with a focus on climate change. Evidence from the Continuous Plankton Records suggests that the plankton integrates hydrometeorological signals and may be used as a possible index of climate change. Changes evident in the plankton are likely to have important effects on the carrying capacity of fisheries and are of relvance to eutrophication issues and to the assessment of biodiversity. The scale of the changes seen over the past five decades emphasises the importance of maintaining existing, and establishing new, long term and wide scale monitoring programmes of the world's oceans in initiatives such as the Global Ocean Observing System (GOOS).