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.

Aim To describe the spatial variation in pteridophyte species richness; evaluate the importance of macroclimate, topography and within-grid cell range variables; assess the influence of spatial autocorrelation on the significance of the variables; and to test the prediction of the mid-domain effect.Location The Iberian Peninsula. MethodsWe estimated pteridophyte richness on a grid map with c . 2500 km 2 cell size, using published geocoded data of the individual species. Environmental data were obtained by superimposing the grid system over isoline maps of precipitation, temperature, and altitude. Mean and range values were calculated for each cell. Pteridophyte richness was related to the environmental variables by means of nonspatial and spatial generalized least squares models. We also used ordinary least squares regression, where a variance partitioning was performed to partial out the spatial component, i.e. latitude and longitude. Coastal and central cells were compared to test the mid-domain effect.Results Both spatial and nonspatial models showed that pteridophyte richness was best explained by a second-order polynomial of mean annual precipitation and a quadratic elevation-range term, although the relative importance of these two variables varied when spatial autocorrelation was accounted for. Precipitation range was weakly significant in a nonspatial multiple model (i.e. ordinary regression), and did not remain significant in spatial models. Richness is significantly higher along the coast than in the centre of the peninsula.Main conclusions Spatial autocorrelation affects the statistical significance of explanatory variables, but this did not change the biological interpretation of precipitation and elevation range as the main predictors of pteridophyte richness. Spatial and nonspatial models gave very similar results, which reinforce the idea that water availability and topographic relief control species richness in relatively high-energy regions. The prediction of the mid-domain effect is falsified.

Aim Climate-based models often explain most of the variation in species richness along broad-scale geographical gradients. We aim to: (1) test predictions of woody plant species richness on a regional spatial extent deduced from macroscale models based on water-energy dynamics; (2) test if the length of the climate gradients will determine whether the relationship with woody species richness is monotonic or unimodal; and (3) evaluate the explanatory power of a previously proposed 'water-energy' model and regional models at two grain sizes.Location The Iberian Peninsula.Methods We estimated woody plant species richness on grid maps with c. 2500 and 22,500 km 2 cell size, using geocoded data for the individual species. Generalized additive models were used to explore the relationships between richness and climatic, topographical and substrate variables. Ordinary least squares regression was used to compare regional and more general water-energy models in relation to grain size. Variation partitioning by partial regression was applied to find how much of the variation in richness was related to spatial variables, explanatory variables and the overlap between these two.Results Water-energy dynamics generate important underlying gradients that determine the woody species richness even over a short spatial extent. The relationships between richness and the energy variables were linear to curvilinear, whereas those with precipitation were nonlinear and non-monotonic. Only a small fraction of the spatially structured variation in woody species richness cannot be accounted for by the fitted variables related to climate, substrate and topography. The regional models accounted for higher variation in species richness than the water-energy models, although the water-energy model including topography performed well at the larger grain size. Elevation range was the most important predictor at all scales, probably because it corrects for 'climatic error' due to the unrealistic assumption that mean climate values are evenly distributed in the large grid cells. Minimum monthly potential evapotranspiration was the best climatic predictor at the larger grain size, but actual evapotranspiration was best at the smaller grain size. Energy variables were more important than precipitation individually. Precipitation was not a significant variable at the larger grain size when examined on its own, but was highly significant when an interaction term between itself and substrate was included in the model. Main conclusionsThe significance of range in elevation is probably because it corresponds to several aspects that may influence species diversity, such as climatic variability within grid cells, enhanced surface area, and location for refugia. The relative explanatory power of energy and water variables was high, and was influenced by the length of the climate gradient, substrate and grain size

A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling 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

Abstract. The relationships between biogeographical patterns and local‐scale patterns based on microscale features, such as topoclimate, are well known in plant biogeography. Here we present a method of determining this correspondence using constrained ordination and correlations. We examined compositional gradients at two different scales, biogeographical chorotypes, and diversity. Compositional data (124 taxa × 113 plots) were sampled at four regularly spaced sites in south‐eastern Spain. Longitude (LONGI) was used as a spatial variable representing an east–west climate gradient, together with a radiation index (RADIN), elevation, and a disturbance indicator. All factors correlated with the compositional gradients, but the local‐topoclimate factor (RADIN) and the broad‐scale factor (LONGI) were most important. These two, spatially independent factors were both correlated with the two first ordination axes, and therefore should relate to the same general trend in species‐turnover. There was a significant Spearman's rank correlation between the species order along these two gradients. This is interpreted as an ecological self‐similar pattern, i.e. coenoclines repeating at different scales. A consistent order of species along local‐ and broad‐scale coenoclines may indicate that similar operational factors act at several scales, here related to moisture and temperature. The distribution of Mediterraneo–Macaronesian, Mediterraneo–Saharo–Arabian and Ibero–Maghribian species confirmed the correspondence between the broad‐ and local‐scale gradients. The former group decreases in number with increasing aridity along both gradients, whereas the two latter groups increase. A discordant pattern was found with south‐eastern Iberian endemics, but this may be explained by several of them being edaphic (saxicolous) specialists. There is a significant decrease in species richness with high radiation, but the expected increase along the longitudinal gradient from west (dry) to east (moist) was not statistically significant. This may be due to the correspondence between high richness and disturbance, both occurring in the middle of the broad‐scale gradient.