Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general ‘‘phase change’ ’ phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like F ST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.

A contagion index was proposed by O'Neill et al. (1988) to quantify spatial patterns of landscapes. However, this index is insensitive to changes in spatial pattern. We present a new contagion index that corrects an error in the mathematical formulation of the original contagion index. The error is identified mathematically. The contagion indices (both original and new) are then evaluated against simulated landscapes.

Cellular automata (CA) are fundamental computational models of spatial phenomena, in which space is represented by a discrete lattice of cells. Each cell concurrently interacts with its neighborhood which, in traditional CA, is limited to the cell's nearest neighbors. In this paper we discuss generalized cellular automata (GCA), an important but unexplored class of CA, in which the cell's interaction domain extends beyond the nearest neighbors. The computational power necessary to run large scale CA (and GCA) models has only recently been available thanks to parallel processing. This paper focuses on implementation and performance of GCA in biological modeling. In particular, we present results of simulating the spread of epidemics and the creation of spatial infection patterns that are important for disease control.

Measures for sparse best–basis selection are analyzed and shown to fit into a general framework based on majorization, Schur-concavity, and concavity. This framework facilitates the analysis of algorithm performance and clarifies the relationships between existing proposed concentration measures useful for sparse basis selection. It also allows one to define new concentration measures, and several general classes of measures are proposed and analyzed in this paper. Admissible measures are given by the Schur-concave functions, which are the class of functions consistent with the so-called Lorentz ordering (a partial ordering on vectors also known as majorization). In particular, concave functions form an important subclass of the Schur-concave functions which attain their minima at sparse solutions to the best basis selection problem. A general affine scaling optimization algorithm obtained from a special factorization of the gradient function is developed and proved to converge to a sparse solution for measures chosen from within this subclass.

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.

Abstract: Watershed response and recovery from a wildfire which burned 95 percent of the Williams Creek watershed in 1979 were monitored. Ground cover reduced to 11 percent by the fire increased to 80 percent by 1983. Grasses seeded for erosion control provided less than 10 percent cover until 3 years following the fire, and no significant difference in ground cover was found between seeded and unseeded transects. The average area of three channel cross sections on Williams Creek increased by 20 percent 4 years after the fire, but had returned to immediate postfire conditions by 1985. Benthic invertebrate sampling indicated the fire had a substantial impact on water quality for several years after the fire, and that recovery was incomplete through 1987. Comparable findings of incomplete recovery are presented for four additional California watersheds burned up to 23 years ago. Precipitation averages 100 cm annually (mostly as snow above 1750 m) and supports a perennial stream. The stream channel is steep and cascading, dominated by bedrock above 1450 m. Lower stretches of the creek are alluvial. On the afternoon of September 18, 1979, a wildfire began to burn in the drainage. Pushed by strong winds, fire moved at rates of 2000 m per hour, and was not controlled until approximately 95 percent of the watershed had been burned. Fire intensity was rated as high on two-thirds of the burned area. Emergency watershed rehabilitation measures included seeding a mixture of orchard grass, slender wheatgrass, tall fescue and timothy with fertilizer on 390 ha of the burn. METHODS

In this paper we compare and analyze the various diversity measures used in the Evolutionary Computation field. While each measure looks quite different from the others in form, we surprisingly found that the same basic method underlies all of them: the distance between all possible pairs of chromosomes/organisms in the population. This is true even of the Shannon entropy of gene frequencies. We then associate the different varieties of EC diversity measures to different diversity measures used in Biology. Finally we give an On ( ) implementation for each of the diversity measures (where n is the population size), despite their basis in an 1

All biologists who sample natural communities are plagued with the problem of how well a sample reflects a community’s “true ” diversity. New genetic techniques have revealed extensive microbial diversity that was previously undetected with culture-dependent methods and morphological identification (reviewed in references 2 and 46), but exhaustive inventories of microbial communities still remain impractical. As a result, we must rely on samples to inform us about the actual diversity of microbial communities. Ecologists studying the diversity of macroorganisms also face this estimation problem and have designed tools to deal with the problems of sampling (14, 25, 36). Sparked by the availability of microbial diversity data, interest is emerging in applying these tools to microbes. Reliable estimates of microbial diversity would offer a means to address once intractable

Aim To determine the best-fit model of species--area relationships for Mediterraneantype plant communities and evaluate how community structure affects these species--area models.

Systematists expect their hypotheses to be asymptotically precise. As the number of phylogenetically informative characters for a set of taxa increases, the relationships implied should stabilize on some topology. If true, this increasing stability should clearly manifest itself if an index of congruence is plotted against the accumulating number of characters.