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Richness Variation (richness + variation)
Selected AbstractsWhy do mountains support so many species of birds?ECOGRAPHY, Issue 3 2008Adriana Ruggiero Although topographic complexity is often associated with high bird diversity at broad geographic scales, little is known about the relative contributions of geomorphologic heterogeneity and altitudinal climatic gradients found in mountains. We analysed the birds in the western mountains of the New World to examine the two-fold effect of topography on species richness patterns, using two grains at the intercontinental extent and within temperate and tropical latitudes. Birds were also classified as montane or lowland, based on their overall distributions in the hemisphere. We estimated range in temperature within each cell and the standard deviation in elevation (topographic roughness) based on all pixels within each cell. We used path analysis to test for the independent effects of topographic roughness and temperature range on species richness while controlling for the collinearity between topographic variables. At the intercontinental extent, actual evapotranspiration (AET) was the primary driver of species richness patterns of all species taken together and of lowland species considered separately. In contrast, within-cell temperature gradients strongly influenced the richness of montane species. Regional partitioning of the data also suggested that range in temperature either by itself or acting in combination with AET had the strongest "effect" on montane bird species richness everywhere. Topographic roughness had weaker "effects" on richness variation throughout, although its positive relationship with richness increased slightly in the tropics. We conclude that bird diversity gradients in mountains primarily reflect local climatic gradients. Widespread (lowland) species and narrow-ranged (montane) species respond similarly to changes in the environment, differing only in that the richness of lowland species correlates better with broad-scale climatic effects (AET), whereas mesoscale climatic variation accounts for richness patterns of montane species. Thus, latitudinal and altitudinal gradients in species richness can be explained through similar climatic-based processes, as has long been argued. [source] Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variablesGLOBAL ECOLOGY, Issue 4 2002Jorge M. Lobo Abstract Aim To predict French Scarabaeidae dung beetle species richness distribution, and to determine the possible underlying causal factors. Location The entire French territory has been studied by dividing it into 301 grid cells of 0.72 × 0.36 degrees. Method Species richness distribution was predicted using generalized linear models to relate the number of species with spatial, topographic and climate variables in grid squares previously identified as well sampled (n = 66). The predictive function includes the curvilinear relationship between variables, interaction terms and the significant third-degree polynomial terms of latitude and longitude. The final model was validated by a jack-knife procedure. The underlying causal factors were investigated by partial regression analysis, decomposing the variation in species richness among spatial, topographic and climate type variables. Results The final model accounts for 86.2% of total deviance, with a mean jack-knife predictive error of 17.7%. The species richness map obtained highlights the Mediterranean as the region richest in species, and the less well-explored south-western region as also being species-rich. The largest fraction of variability (38%) in the number of species is accounted for by the combined effect of the three groups of explanatory variables. The spatially structured climate component explains 21% of variation, while the pure climate and pure spatial components explain 14% and 11%, respectively. The effect of topography was negligible. Conclusions Delimiting the adequately inventoried areas and elaborating forecasting models using simple environmental variables can rapidly produce an estimate of the species richness distribution. Scarabaeidae species richness distribution seems to be mainly influenced by temperature. Minimum mean temperature is the most influential variable on a local scale, while maximum and mean temperature are the most important spatially structured variables. We suggest that species richness variation is mainly conditioned by the failure of many species to go beyond determined temperature range limits. [source] Plant species richness of nature reserves: the interplay of area, climate and habitat in a central European landscapeGLOBAL ECOLOGY, Issue 4 2002Petr Py Abstract Aim To detect regional patterns of plant species richness in temperate nature reserves and determine the unbiased effects of environmental variables by mutual correlation with operating factors. Location The Czech Republic. Methods Plant species richness in 302 nature reserves was studied by using 14 explanatory variables reflecting the reserve area, altitude, climate, habitat diversity and prevailing vegetation type. Backward elimination of explanatory variables was used to analyse the data, taking into account their interactive nature, until the model contained only significant terms. Results A minimal adequate model with reserve area, mean altitude, prevailing vegetation type and habitat diversity (expressed as the number of major habitat types in the reserve) accounted for 53.9% of the variance in species number. After removing the area effect, habitat diversity explained 15.6% of variance, while prevailing vegetation type explained 29.6%. After removing the effect of both area and vegetation type, the resulting model explained 10.3% of the variance, indicating that species richness further increased with habitat diversity, and most obviously towards warm districts. After removing the effects of area, habitat diversity and climatic district, the model still explained 9.4% of the variance, and showed that species richness (i) significantly decreased with increasing mean altitude and annual precipitation, and with decreasing January temperature in the region of the mountain flora, and (ii) increased with altitudinal range in regions of temperate and thermophilous flora. Main conclusions We described, in quantitative terms, the effects of the main factors that might be considered to be determining plant species richness in temperate nature reserves, and evaluated their relative importance. The direct habitat effect on species richness was roughly equal to the direct area effect, but the total direct and indirect effects of area slightly exceeded that of habitat. It was shown that the overall effect of composite variables such as altitude or climatic district can be separated into particular climatic variables, which influence the richness of flora in a context-specific manner. The statistical explanation of richness variation at the level of families yielded similar results to that for species, indicating that the system of nature conservation provides similar degrees of protection at different taxonomic levels. [source] Measurements of area and the (island) species,area relationship: new directions for an old patternOIKOS, Issue 10 2008Kostas A. Triantis The species,area relationship is one of the strongest empirical generalizations in geographical ecology, yet controversy persists about some important questions concerning its causality and application. Here, using more accurate measures of island surface size for five different island systems, we show that increasing the accuracy of the estimation of area has negligible impact on the fit and form of the species,area relationship, even though our analyses included some of the most topographically diverse island groups in the world. In addition, we show that the inclusion of general measurements of environmental heterogeneity (in the form of the so-called choros model), can substantially improve the descriptive power of models of island species number. We suggest that quantification of other variables, apart from area, that are also critical for the establishment of biodiversity and at the same time have high explanatory power (such as island age, distance, productivity, energy, and environmental heterogeneity), is necessary if we are to build up a more predictive science of species richness variation across island systems. [source] | ||||||||||