Rimonabant improves cardiometabolic risk factors in overweight/obese patients irrespective of treatment with statins: Pooled data from the RIO program

peer reviewe

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation

Comparison of biodiversity between plantation and natural forests in Sabah using moths as indicators

The Malaysian state of Sabah, in northern Borneo, started massive monoculture forest plantations of fast-growing introduced tree species in the mid-1970's to replace part of the harvested tropical rain forest. Many people, particularly conservationists in the West, are very much against this sort of reforestation as they fear it would spell a permanent loss to the Bornean rain forest biodiversity. This project was carried out at the more established forest plantations of Sabah Softwoods Sdn. Bhd. in Brumas from 1991 to 1993, where fast-growing exotics namely Acacia mangium, Eucalyptus deglupta, Gmelina arborea, Paraserianthes (=Albizia) falcataria, Pinus caribaea, were studied to assess their biodiversity and these plantations were compared with the natural regenerating logged-over secondary forest in Brumas, as well as the primary forest in Danum Valley, by using light-trapped macromoths as indicators. The method of light-trapping as a reliable means of capturing moths was supported by canopy knockdown in the form of mist-blowing. Results obtained showed that for the year-long (January-December 1991) light-trap samples, the biodiversity values, as represented by Williams alpha (higher the value, higher the diversity), were unexpectedly high in the various plantation forests. Their alpha values ranged from the lowest in Acacia mangium with 208.14+-9.22, to the highest in Eucalyptus deglupta with 330.85+-16.37 which was even higher than the natural secondary forest with 314.53+-11.99, and certainly not inferior to the published values (300 to 350) from undisturbed Bornean forest of similar altitudes (below 500m). For the shorter month-long subsidiary samples (October/November 1992, January/February 1993), the alpha values of the samples from the lowland primary forest in Danum were not necessarily higher when compared with the similarly sampled disturbed forest habitats in Brumas, but despite its small samples, Danum produced some 33 species of macromoths which were never collected out of the 1680 species obtained from Brumas in the entire project. The main reason behind the surprisingly good diversity measures (as indicated by moths) in these forest plantations was the presence of an understorey of varying diversity under the canopy. It would appear that with the fast-growing introduced trees acting as light-demanding pioneers, many plant species ranging from herbs, shrubs, to saplings of native tree species, managed to germinate and grow more or less efficiently in the understorey. Eucalyptus deglupta had a more diverse understorey both in terms of plant species and architecture, which in turn supported a more diverse moth fauna. These findings are encouraging in terms of biodiversity conservation, as plantation forestry seems to be the only way forward for many developing countries like Malaysia.</p

Biodiversity hanging by a thread: the importance of fungal litter-trapping systems in tropical rainforests

The exceptionally high species richness of arthropods in tropical rainforests hinges on the complexity of the forest itself: that is, on features such as the high plant diversity, the layered nature of the canopy and the abundance and the diversity of epiphytes and litter. We here report on one important, but almost completely neglected, piece of this complex jigsaw—the intricate network of rhizomorph-forming fungi that ramify through the vegetation of the lower canopy and intercept falling leaf litter. We show that this litter-trapping network is abundant and intercepts substantial amounts of litter (257.3 kg ha?1): this exceeds the amount of material recorded in any other rainforest litter-trapping system. Experimental removal of this fungal network resulted in a dramatic reduction in both the abundance (decreased by 70.2 ± 4.1%) and morphospecies richness (decreased by 57.4 ± 5.1%) of arthropods. Since the lower canopy levels can contain the highest densities of arthropods, the proportion of the rainforest fauna dependent on the fungal networks is likely to be substantial. Fungal litter-trapping systems are therefore a crucial component of habitat complexity, providing a vital resource that contributes significantly to rainforest biodiversity

Oil palm expansion into rain forest greatly reduces ant biodiversity in canopy, epiphytes and leaf-litter

Oil palm cultivation is expanding rapidly into many of the world's most biodiverse tropical regions. One of the most functionally important and ecologically dominant animal groups in these environments is the ants. Here, we quantify the overall impacts of clear-felling lowland dipterocarp rainforest and conversion into oil palm plantation on ant diversity. At study sites in Sabah, Malaysia we collected ants from three microhabitats: 1 – the canopy, 2 – bird's nest ferns (Asplenium nidus complex, a common epiphyte in forest and oil palm), and 3 – leaf litter. We also measured temperature, humidity and light at collection sites to assess their impacts on ant community composition. Total ant species richness decreased from 309 to 110 (?64%) between forest and oil palm plantation. However, this impact was not the same across all microhabitats, with bird's nest ferns maintaining almost the same number of ant species in oil palm compared to forest (forest-oil palm, ferns: 36–35 (3% loss), canopy: 120–58 (52% loss), leaf litter: 216–56 (74% loss)). Relative abundance distributions remained the same for fern-dwelling ants, but became less even for oil palm ants in both the canopy and the leaf litter. These differences may be due in part to the ability of bird's nest ferns to provide a stable microclimate in hot, dry plantations. We also found that non-native ant species were more abundant in oil palm than in forest, and few forest ant species survived in plantations in any of the microhabitats. Only 59 of the 309 forest species persisted in oil palm plantations, corresponding to an 81% loss of forest species resulting from habitat conversion. Although oil palm supports many more ant species than has been previously reported, converting forest into plantation still leads to a dramatic reduction in species richness. The maintenance of forested areas is therefore vital for the conservation of ant biodiversity

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation