Nestedness of species assemblages occurs when thebiotas of sites with lower numbers of species tend to be subsets of the biotas at richer sites. We develop new quantitative and statistical techniques for measuring, testing, and comparing nestedness, and apply these methods to data from the literature. Significantly nonrandom nestedness was present in all 27 assemblages examined, and tended to be stronger in systems dominated by extinction, such as landbridge islands. Sets of assemblages that were very strongly nested were more likely to have greater species richness on one or a few large sites than on several smaller sites of equivalent total area - that is, to fall toward the "single large" side of the "Single Large Or Several Small" (SLOSS) continuum. Our analysis indicates that nestedness, when quantified as a single number for a presence-absence matrix, measures community-wide differences in incidence (the frequency of occurrence or "distribution" of species). Factors that lead to consistent differences among species in immigration or extinction rates cause strong patterns of nestedness of species assemblages. Nestedness is negatively related to beta diversity: nestedness is low when beta diversity is high, and vice versa. Conservation managers will thus seek to minimize nestedness and the development of nested structure in systems of nature reserves.
This review article enumerates, categorizes, and compares many of the methods that have been proposed to detect undocumented changepoints in climate data series. The methods examined include the standard normal homogeneity (SNH) test, Wilcoxon’s nonparametric test, two-phase regression (TPR) procedures, inhomogeneity tests, information criteria procedures, and various variants thereof. All of these methods have been proposed in the climate literature to detect undocumented changepoints, but heretofore there has been little formal comparison of the techniques on either real or simulated climate series. This study seeks to unify the topic, showing clearly the fundamental differences among the assumptions made by each procedure and providing guidelines for which procedures work best in different situations. It is shown that the common trend TPR and Sawa’s Bayes criteria procedures seem optimal for most climate time series, whereas the SNH procedure and its nonparametric variant are probably best when trend and periodic effects can be diminished by using homogeneous reference series. Two applications to annual mean temperature series are given. Directions for future research are discussed.
Changepoints (inhomogeneities) are present in many climatic time series. Changepoints are physically plausible whenever a station location is moved, a recording instrument is changed, a new method of data collection is employed, an observer changes, etc. If the time of the changepoint is known, it is usually a straightforward task to adjust the series for the inhomogeneity. However, an undocumented changepoint time greatly complicates the analysis. This paper examines detection and adjustment of climatic series for undocumented changepoint times, primarily from single site data. The two-phase regression model techniques currently used are demonstrated to be biased toward the conclusion of an excessive number of unobserved changepoint times. A simple and easily applicable revision of this statistical method is introduced.
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