Abstract. It is often claimed that we do not understand the forces driving the global diversity gradient. However, an extensive literature suggests that contemporary climate constrains terrestrial taxonomic richness over broad geographic extents. Here, we review the empirical literature to examine the nature and form of the relationship between climate and richness. Our goals were to document the support for the climatically based energy hypothesis, and within the constraints imposed by correlative analyses, to evaluate two versions of the hypothesis: the productivity and ambient energy hypotheses. Focusing on studies extending over 800 km, we found that measures of energy, water, or water-energy balance explain spatial variation in richness better than other climatic and non-climatic variables in 82 of 85 cases. Even when considered individually and in isolation, water/ energy variables explain on average over 60% of the variation in the richness of a wide range of plant and animal groups. Further, water variables usually represent the strongest predictors in the tropics, subtropics, and warm temperate zones, whereas energy variables (for animals) or water-energy variables (for plants) dominate in high latitudes. We conclude that the interaction between water and energy, either directly or indirectly (via plant productivity), provides a strong explanation for globally extensive plant and animal diversity gradients, but for animals there also is a latitudinal shift in the relative importance of ambient energy vs. water moving from the poles to the equator. Although contemporary climate is not the only factor influencing species richness and may not explain the diversity pattern for all taxonomic groups, it is clear that understanding water-energy dynamics is critical to future biodiversity research. Analyses that do not include water-energy variables are missing a key component for explaining broad-scale patterns of diversity.

Broad-scale variation in taxonomic richness is strongly correlated with climate. Many mechanisms have been hypothesized to explain these patterns; however, testable predictions that would distinguish among them have rarely been derived. Here, we examine several prominent hypotheses for climate-richness relationships, deriving and testing predictions based on their hypothesized mechanisms. The Ôenergy-richness hypothesisÕ (also called the Ômore individuals hypothesisÕ ) postulates that more productive areas have more individuals and therefore more species. More productive areas do often have more species, but extant data are not consistent with the expected causal relationship from energy to numbers of individuals to numbers of species. We reject the energy-richness hypothesis in its standard form and consider some proposed modifications. The Ôphysiological tolerance hypothesisÕ postulates that richness varies according to the tolerances of individual species for different sets of climatic conditions. This hypothesis predicts that more combinations of physiological parameters can survive under warm and wet than cold or dry conditions. Data are qualitatively consistent with this prediction, but are inconsistent with the prediction that species should fill climatically suitable areas. Finally, the Ôspeciation rate hypothesisÕ postulates that speciation rates should vary with climate, due either to faster evolutionary rates or stronger biotic interactions increasing the opportunity for evolutionary diversification in some regions. The biotic interactions mechanism also has the potential to amplify shallower, underlying gradients in richness. Tests of speciation rate hypotheses are few (to date), and their results are mixed.

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Aim We surveyed the empirical literature to determine how well six diversity hypotheses account for spatial patterns in species richness across varying scales of grain and extent.Location Worldwide.Methods We identified 393 analyses ('cases') in 297 publications meeting our criteria. These criteria included the requirement that more than one diversity hypothesis was tested for its relationship with species richness. We grouped variables representing the hypotheses into the following 'correlate types': climate/ productivity, environmental heterogeneity, edaphics/nutrients, area, biotic interactions and dispersal/history (colonization limitation or other historical or evolutionary effect). For each case we determined the 'primary' variable: the one most strongly correlated with taxon richness. We defined 'primacy' as the proportion of cases in which each correlate type was represented by the primary variable, relative to the number of times it was studied. We tested for differences in both primacy and mean coefficient of determination of the primary variable between the hypotheses and between categories of five grouping variables: grain, extent, taxon (animal vs. plant), habitat medium (land vs. water) and insularity (insular vs. connected).Results Climate/productivity had the highest overall primacy, and environmental heterogeneity and dispersal/history had the lowest. Primacy of climate/ productivity was much higher in large-grain and large-extent studies than at smaller scales. It was also higher on land than in water, and much higher in connected systems than in insular ones. For other hypotheses, differences were less pronounced. Throughout, studies on plants and animals showed similar patterns. Coefficients of determination of the primary variables differed little between hypotheses and across the grouping variables, the strongest effects being low means in the smallest grain class and for edaphics/nutrients variables, and a higher mean for water than for land in connected systems but vice versa in insular systems. We highlight areas of data deficiency. Main conclusionsOur results support the notion that climate and productivity play an important role in determining species richness at large scales, particularly for non-insular, terrestrial habitats. At smaller extents and grain sizes, the primacy of the different types of correlates appears to differ little from null expectation. In our analysis, dispersal/history is rarely the best correlate of species richness, but this may reflect the difficulty of incorporating historical factors into regression models, and the collinearity between past and current climates. Our findings are consistent with the view that climate determines the capacity for species richness. However, its influence is less evident at smaller spatial scales, probably because (1) studies small in extent tend to sample little climatic range, and (2) at large

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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.

Although there is no shortage of potential explanations for the large-scale patterns of biological diversity, the hypothesis that energy-related factors are the primary determinants is perhaps most extensively supported, especially in cold-temperate regions. By using unusually high-resolution biodiversity and environmental data that have not previously been available, we demonstrate that habitat heterogeneity, as measured by remotely sensed land cover variation, explains Canadian butterfly richness better than any energy-related variable we measured across spatial scales. Although species-richness predictability declines with progressively smaller quadrat sizes, as expected, we demonstrate that most variability (>90%) in butterfly richness may be explained by habitat heterogeneity with secondary contributions from climatic energy. We also find that patterns of community similarity across Canada are strongly related to patterns of habitat composition but not to differences in energy-related factors. Energy should still be considered significant but its main role may be through its effects on within-habitat diversity and perhaps, indirectly, on the sorts of habitats that may be found in a region. Effects of sampling intensity and spatial autocorrelation do not alter our findings.

Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations -Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions.