Abstract. We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) ln-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near À0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression, one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.
The Iberian Peninsula harbors about 50% of European plant and terrestrial vertebrate species and more than 30% of European endemic species. Despite the global recognition of its importance, the selection of protected areas has been ad hoc and the effectiveness of such choices has rarely been assessed. We compiled the most comprehensive distributional data set of Iberian terrestrial plant and vertebrate species available to date and used it to assess the degree of species representation within existing protected areas. Existing protected areas in Spain and Portugal reasonably represented the plant and animal species we considered (73-98%). Nevertheless, species of some groups (amphibians, reptiles, birds, and gymnosperms) did not accumulate in protected areas at a rate higher than expected by chance (p > 0.05). We determined that to conserve all vertebrate and plant species in the Iberian Peninsula, at least 36 additional areas are needed. Selection of additional areas for conservation would be facilitated if such areas coincided with sites of community importance (SCI) designated under the European Commission Habitats Directive. Additional areas required for full representation of the selected plant and animal species all coincide with SCI in Spain. Nevertheless, the degree of coincidence varies between 0.3% and 74.6%, and there is a possibility that important areas for conservation occur outside the SCI. Our results support the view that current SCI can be used for prioritization of areas for conservation, but a systematic reevaluation of conservation priorities in Spain and Portugal would be necessary to ensure that effective conservation of one of European's most important biodiversity regions is achieved.
Aim The geographical distributions of animal and plant species endemic to the Iberian Peninsula and Balearic Islands were analysed to locate and designate areas of endemicity.Location The Iberian Peninsula and the three largest Balearic Islands (Mallorca, Menorca and Ibiza) in the western Mediterranean, West Palaearctic region. MethodsThe information analysed consisted of presence/absence data of animal and plant species, recorded on a 100´100 km grid based on the UTM projection system. From a larger initial data set, a simpli®ed matrix of 480 species present in at least two quadrats was obtained, and processed to estimate the overall similarity patterns across land squares, and the areas of endemism. Two methods were employed to detect areas of endemism: Wagner Parsimony (PAE, or parsimony analysis of endemicity) and compatibility. A modi®cation of PAE, PAE±PCE (Parsimony analysis of endemicity with progressive character elimination) was applied to overcome some of the potential shortcomings of the method. ResultsThe results represent the ®rst attempt for a combined analysis of animal and plant distributions in the western Mediterranean. The proposed PAE±PCE procedure proved useful to identify areas of endemism that would have been otherwise overlooked. Up to thirty-six different areas of endemisms were identi®ed. Some of these represent concentric (hierarchically nested) structures, while other are partly overlapping sectors. The endemism areas, as derived from parsimony and compatibility analyses, generally ®t within the frame of the overall similarity approach.Main conclusions The areas of endemicity identi®ed often coincide with mountain sectors, and this may be of incidental interest for conservation policies as most natural preserves in the study area are located in mountain ranges. The conclusions are of interest for large scale approaches to the biogeography of the Mediterranean Basin, facilitating the selection of endemism areas for operative purposes. However, most of the best supported areas of endemism detected are relatively small, or overlap with neighbouring endemism areas. Hence, adopting large area units such as`Iberia' for historical analysis at a wider geographical scale may be risky, because such units may actually represent composite sectors of an heterogeneous nature. The distribution of the areas of endemism, as well as the results of the overall similarity classi®cation, share a number of features with previous sectorizations from independent, mostly phytogeographical, approaches. Parsimony analysis of endemicity is a potentially useful tool for identifying areas designated by species with congruent distributions, but (1) the results Correspondence: Enrique Garcõ Âa-Barros, have no direct historical implications (for phylogenetic information is not incorporated), and (2) unless modi®cations such as the PAE±PCE procedure are applied, the number of potential areas of endemism (in the sense stated above) will often be underestimated. It is also shown that, in a PAE, a`total evidence' approac...
Using an exhaustive data compilation, Iberian vascular plant species richness in 50x50 UTM grid cells was regressed against 24 explanatory variables (spatial, geographical, topographical, geological, climatic, land use and environmental diversity variables) using Generalized Linear Models and partial regression analysis in order to ascertain the relative contribution of primary, heterogeneous and spatially structured variables. The species richness variation accounted for by these variables is reasonably high (65% of total deviance). Little less than half of this variation is accounted for spatially structured variables. A purely spatial component of variation is hardly significant.The most significant variables are those related t o altitude, and particularly maximum altitude, whose cubic response reflects the occurrence of the maximum number of species at the highest altitudes. This result highlighted the importance of Iberian mountains as hotspots of diversity and the relevance of large and small scale historical factors in contemporary plant distribution patterns. Climatic or energy-related variables contributed little, whereas geological (calcareous and acid rocks) and, to a lesser extent, environmental heterogeneity variables (land use diversity and altitude range) seem to be more important. 0 2001 The Linnean Society of London ADDITIONAL KEY WORDS: diversity hotspotsgeographical patternsmodelling species richnessvariance partitioning.
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