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The worldwide decline in taxonomists has a broad impact on biology and society. Learning from general historical patterns of science and understanding social changes caused by growing economies, we propose changes in priorities for training taxonomists to reverse these losses. Academically trained professionals, parataxonomists (local assistants trained by professional biologists), youths educated with an emphasis on natural history, and self-supported expert amateurs are the major sources of taxonomists. Recruiting effort from each category is best determined by public attitudes toward education, as well as the availability of discretionary funds and leisure time. Instead of concentrating on descriptions of species and narrow studies of morphology and DNA, the duties of the few professional taxonomists of the future also will be to use cyberspace and a wide range of skills to recruit, train, and provide direction for expert amateurs, young students, parataxonomists, the general public, and governments. [ABSTRACT FROM AUTHOR]

Informally gathered species lists are a potential source of data for conservation biology, but most remain unused because of questions of reliability and statistical issues. We applied two alternative analytical methods (contingency tests and occupancy modeling) to a 35-year data set (1973–2007) to test hypotheses about local bird extinction. We compiled data from bird lists collected by expert amateurs and professional scientists in a 2-km2 fragment of lowland tropical forest in coastal Ecuador. We tested the effects of the following on local extinction: trophic level, sociality, foraging specialization, light tolerance, geographical range area, and biogeographic source. First we assessed extinction on the basis of the number of years in which a species was not detected on the site and used contingency tests with each factor to compare the frequency of expected and observed extinction events among different species categories. Then we defined four multiyear periods that reflected different stages of deforestation and isolation of the study site and used occupancy modeling to test extinction hypotheses singly and in combination. Both types of analyses supported the biogeographic source hypothesis and the species-range hypothesis as causes of extinction; however, occupancy modeling indicated the model incorporating all factors except foraging specialization best fit the data. [ABSTRACT FROM AUTHOR]

Species richness patterns of tiger beetles (Coleoptera: Cicindelidae) were analyzed using a grid of 407 squares (137.5 km per side) across northwestern South America (Guyana, Venezuela, Colombia, Ecuador, Peru, Bolivia and western Brazil). Reliable data on species numbers were available for only 149 of the squares. Using a trend surface model (a model used to represent the mean of a spatial process by a polynomial function of spatial coordinates) as well as altitudinal relief and biogeographical influence for each square, we predicted the number of tiger beetle species likely to occur in intermediate squares for which no or unreliable data were available. The resultant spatial patterns of species richness were compared to similar analyses for temperate areas of North America. Intercontinental comparisons and a more complete pattern of species numbers in South America are useful in developing an understanding of general spatial patterns and in the environmental management of species richness. [ABSTRACT FROM AUTHOR]

Abstract: Due to the structuring forces and large-scale physical processes that shape our biosphere, we often find that environmental and ecological data are either spatially or temporally—or both spatially and temporally—dependent. When these data are analyzed, statistical techniques and models are frequently applied that were developed for independent data. We describe some of the detrimental consequences, such as inefficient parameter estimators, biased hypothesis test results, and inaccurate predictions, of ignoring spatial and temporal data dependencies, and we cite an example of adverse statistical results occurring when spatial dependencies were disregarded. We also discuss and recommend available techniques used to detect and model spatial and temporal dependence, including variograms, covariograms, autocorrelation and partial autocorrelation plots, geostatistical techniques, Gaussian autoregressive models, K functions, and ARIMA models, in environmental and ecological research to avoid the aforementioned difficulties. [ABSTRACT FROM AUTHOR]

Summary A comparison of species richness patterns of butterflies and birds was made using data from two grids of squares (small squares 137.5 km on a side and large squares 275 km on a side) covering western North America. Using geostatistical procedures, we found that the spatial patterns of species richness of these two taxa were related. The influence of grain size on the strength of this relationship was investigated by analysing the two data sets. For both data sets, the number of butterfly species in a square was a statistically significant predictor of the corresponding number of bird species. However, cross-validation techniques showed that the marginal improvement in prediction accuracy due to including butterflies as a predictor was greater in the large-square data. We explored the effect of areal extent on cross-taxon congruencies by investigating species richness patterns in four subsets of the small-square data. In regions with smaller areal extent, the cross-taxon congruence patterns were not substantially different from the pattern found in the full data set. Finally, using data-splitting techniques, we explored the relationships between prediction accuracy of species richness, sample size, areal extent of the sample, and grain size. [ABSTRACT FROM AUTHOR]

We used birds, butterflies, tiger beetles, mean annual precipitation, and spatial statistical models to investigate the applicability of using indicators of species richness for conservation planning on a continental scale. The models were applied to data collected on three grids of squares (each square 275 or 350 km on a side) covering North America, the Indian subcontinent, and Australia. We applied spatial statistical modeling techniques to determine the viability of using a single or multiple indicators to predict spatial patterns of species diversity of ecologically and phylogenetically unrelated taxa. Spatial models are optimal for these analyses because species data typically are not spatially independent, primarily owing to dispersion effects. Furthermore, spatial models can be used to predict species numbers in areas where no observed data are available. We found that the number of tiger beetle species is a useful indicator of the number of butterfly species in North America and of the number of bird species on the Indian subcontinent, but it is not so useful as an indicator of either the number of bird or butterfly species in Australia or of the number of bird species in North America. We also found that the number of butterfly species is a useful indicator of the number of bird species in North America and Australia and that mean annual precipitation is useful for predicting the number of butterfly species in Australia. Although the general model used on all three continental areas is the same, the relative importance of potential indicators in predicting spatial patterns of other taxa changes from continent to continent. We attribute this change largely to differential biogeographical and ecological history, which must be taken into account in the selection and testing of potential indicators. [ABSTRACT FROM AUTHOR]