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Richness Distribution (richness + distribution)

Distribution by Scientific Domains
Life Sciences75%

Kinds of Richness Distribution

  • species richness distribution
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    Selected Abstracts

    Modelling the species richness distribution for French Aphodiidae (Coleoptera, Scarabaeoidea)

    ECOGRAPHY, Issue 2 2004
    Jorge M. Lobo
    The species richness distribution of the French Aphodiidae was predicted using Generalized Linear Models to relate the number of species to spatial, topographic and climate variables. The entire French territory was studied, divided into 301 0.72×0.36 degree grid squares; the model was developed using 66 grid squares previously identified as well sampled. After eliminating nine outliers, the final model accounted for 74.8% of total deviance with a mean Jackknife predictive error of 10.5%. Three richest areas could be distinguished: the western head (Brittany), southwestern France, and, to a lesser extent, the northeastern region. Sampling effort should now be focused on the western head, where no square was correctly sampled, and on southwestern France, which was recognised as a diversity hotspot, both for Aphodiidae and for Scarabaeidae. The largest fraction of variability (37%) in the number of species was accounted for by the combined effect of the three groups of explanatory variables. After controlling for the effect of significant climate and topographic variables, spatial variables still explain 27% of variation in species richness, suggesting the existence of a spatial pattern in the distribution of species richness (greater diversity in western France) that can not be explained by the environmental variables considered here. We hypothesize that this longitudinal spatial pattern is due to the relevance of a western colonization pathway along the glacial-interglacial cycles, as well as by the barrier effect played by the Alps. [source]

    Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variables

    GLOBAL ECOLOGY, Issue 4 2002
    Jorge 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 and growth form richness along altitudinal gradients in the southwest Ethiopian highlands

    JOURNAL OF VEGETATION SCIENCE, Issue 4 2010
    Wana Desalegn
    Abstract Questions: Do growth forms and vascular plant richness follow similar patterns along an altitudinal gradient? What are the driving mechanisms that structure richness patterns at the landscape scale? Location: Southwest Ethiopian highlands. Methods: Floristic and environmental data were collected from 74 plots, each covering 400 m2. The plots were distributed along altitudinal gradients. Boosted regression trees were used to derive the patterns of richness distribution along altitudinal gradients. Results: Total vascular plant richness did not show any strong response to altitude. Contrasting patterns of richness were observed for several growth forms. Woody, graminoid and climber species richness showed a unimodal structure. However, each of these morphological groups had a peak of richness at different altitudes: graminoid species attained maximum importance at a lower elevations, followed by climbers and finally woody species at higher elevations. Fern species richness increased monotonically towards higher altitudes, but herbaceous richness had a dented structure at mid-altitudes. Soil sand fraction, silt, slope and organic matter were found to contribute a considerable amount of the predicted variance of richness for total vascular plants and growth forms. Main Conclusions: Hump-shaped species richness patterns were observed for several growth forms. A mid-altitudinal richness peak was the result of a combination of climate-related water,energy dynamics, species,area relationships and local environmental factors, which have direct effects on plant physiological performance. However, altitude represents the composite gradient of several environmental variables that were interrelated. Thus, considering multiple gradients would provide a better picture of richness and the potential mechanisms responsible for the distribution of biodiversity in high-mountain regions of the tropics. [source]