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Canonical correspondence analysis (CCA) is introduced as a multivariate extension of weighted averaging ordination, which is a simple method for arranging species along environmental variables. CCA constructs those linear combinations of environmental variables, along which the distributions of the species are max-imally separated. The eigenvalues produced by CCA measure this separation. As its name suggests, CCA is also a correspondence analysis technique, but one in which the ordination axes are constrained to be linear combinations of environmental variables. The ordination diagram generated by CCA visualizes not only a pattern of community variation (as in standard ordination) but also the main features of the distributions of species along the environmental variables. Applications demonstrate that CCA can be used both for detecting species-environment relations, and for investigating specific questions about the response of species to environmental variables. Questions in community ecology that have typically been studied by 'indirect ' gradient analysis (i.e. ordination followed by external interpretation of the axes) can now be answered more directly by CCA.

Abstract A method of deriving vegetation mapping units from quantitative data is described based on results from an ecological survey of Tongariro National Park. A particular aim was to develop a repeatable procedure. The method of classifying the samples uses a polythetic agglomerative technique in which the sorting strategy has as a priority the combining of similar entities that are closest together in the field. This allows class boundaries to be made more nearly coincidental with map boundaries. A naming system for vegetation mapping units is further refined from an earlier published system. The names convey both structural and compositional information about the vegetation in such a way that diagnostic field criteria for most mapping units are summarised by the unit names. Although emphasising cover estimates, both the classificatory method and naming system are independent of the sampling technique used to estimate cover. The method is suitable for a wide range of terrestrial habitats. Keywords growth forms; structural classification; Tongariro National Park; vegetation classification; vegetation mapping; vegetation naming; vegetation sampling

Biodiversity is often ill-defined and subjectively surveyed, resulting in inefficient and ambiguous estimates. Strengths and deficiencies of prevailing survey techniques are appraised through a review of selected literature. Analogies with forest inventory are used to suggest options for more efficient and rigorous biodiversity assessment. Techniques such as variable-probability and model-based sampling, especially when used in conjunction with generalized linear modelling, offer efficient alternatives to more traditional assessments based on quadrats and nested plots. Bayesian methods offer scope to combine expert and local knowledge with formal samples, and warrant further investigation. Suggestions for further research are given. 1.

Smits, A.J.M., de Lyon, M.J.H., van der Velde, G., Steentjes, P.L.M. and Roelofs, J.G.M., 1988. Distribution of three nymphaeid macrophytes (Nymphaea alba L., Nuphar lutea (L.) Sm. and Nymphoides peltata (Gmel.) O. Kuntze) in relation to alkalinity and uptake of inorganic car-bon. Aquat. Bot., 32: 45-62. The presence/absence data of Nymphaea alba L., Nuphar lutea (L.) Sm. and Nymphoides peltata (Gmel.) 0. Kuntze in 588 water bodies throughout The Netherlands were converted into logistic response curves with respect to alkalinity. For Nuphar lutea and Nymphoides peltata maximum probability of occurrence was found at 2.86 and 3.76 meq l- 1, respectively. Nymphaea alba has a broad ecological amplitude with respect to alkalinity in comparison with Nuphar lutea and Nymphoides peltata. Within the 95 % confidence interval no optimum for Nymphaea could be determined. Uptake capacity for dissolved inorganic carbon (DIC) was examined using the pH-drift tech-nique. In a 1-mM NaHC03 solution the DIC-extracting capacities of Nymphaea alba, Nuphar lutea and Nymphoides peltata were compared with those of the HCO ~-using macrophyte Pota-mogeton gramineus L. Unlike the situation for Potamogeton gramineus, no uptake of HCO~-could be registered by the floating and submerged laminae of the 3 nymphaeid species studied. A very limited uptake of HCO~- was measured when Nymphaea alba, Nuphar lutea and Nym-phoides peltata seedlings were cultivated in solutions with a high bicarbonate concentration. Un-der natural conditions uptake of HCO ~ can be neglected.

The indicator value and ecological amplitude of a species with respect to a quantitative environmental vari-able can be estimated from data on species occurrence and environment. A simple weighted averaging (WA) method for estimating these parameters is compared by simulation with the more elaborate method of Gaus-sian logistic regression (GLR), a form of the generalized linear model which fits a Gaussian-like species re-sponse curve to presence-absence data. The indicator value and the ecological amplitude are expressed by two parameters of this curve, termed the optimum and the tolerance, respectively. When a species is rare and has a narrow ecological amplitude- or when the distribution of quadrats along the environmental variable is reasonably even over the species ' range, and the number of quadrats is small- then WA is shown to ap-proach GLR in efficiency. Otherwise WA may give misleading results. GLR is therefore preferred as a practi-cal method for summarizing species ' distributions along environmental gradients. Formulas are given to cal-culate species optima and tolerances (with their standard errors), and a confidence interval for the optimum from the GLR output of standard statistical packages.

In 1999, the NASA Jet Propulsion Lab presented a proposal for a six satellite navigation and communication network for Mars called the Mars Network. This thesis investigates the performance of the Mars Network both theoretically, using figures of merit commonly applied to satellite navigation systems on Earth, and in the position domain using simulated observations.

This book was originally intended to be a ‘How to do it’ manual to guide foresters in the construction of growth models for the tropical moist forests. Unfortunately, modelling these forests isn’t that easy. There is no single ‘best’ way to build a model for these forests. Rather, many approaches can be used, and the best one to use depends on the data available, the time and expertise available to build the model, the computing resources available, and the inferences that are to be drawn from the model. So rather than writing a ‘cookbook’ with one or two recipes, I have chosen to give a detailed review, to illustrate to the reader the many approaches available, the requirements of and output from each, and their strengths and weaknesses. I also indicate what I believe to be the more promising approaches. Whilst this places more responsibility on the reader to choose and develop a suitable modelling methodology, it should also give the reader a better understanding and lead to better models and more reliable predictions. And I hope that better models will provide better information, greater understanding, and better management of the tropical moist forests.

Assessing the sustainability of timber harvests from natural forests: Limitations of indices based on successive harvests

Experimenting with various artificial neural network structures and feature sets is common practice when trying to improve classification accuracies. Combining multiple networks which employ different architectures and features is a popular alternative to choosing a single "best" network for the classification problem at hand. In this