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.

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

Predictable geographic patterns in the distribution of species richness, especially the latitudinal gradient, are intriguing because they suggest that if we knew what the controlling factors were we could predict species richness where empirical data is lacking (e.g. tropics). Based on analyses of the macro‐scale distribution of woody plant species richness in Southern Africa, one controlling factor appears to be climate‐based water‐energy dynamics. Using the regression models of climate's relationship to species richness in Southern Africa, I was able to describe an Interim General Model (IGM) and to predict first‐order macro‐scale geographic variations in woody plant species richness for the continent of Africa, as well as elsewhere in the world—exemplified using South America, the United States and China. In all cases, the geographic pattern of variation in species richness is in accord with geographic variations in vegetation (visual comparison with vegetation maps) and net primary productivity. What validation was possible (Africa and U.S.A.) suggests that the IGM provides ‘reasonable’ estimates for actual woody plant species richness where species richness is in relative equilibrium with climate. Areas of over‐ or under‐prediction support the contention of earlier workers that edaphic, topographic, historical, and dispersal factors need to be considered in a more complete explanation for spatio‐temporal variations in species richness. In addition to providing a means for systematically estimating woody plant species richness where present‐day empirical data is lacking, the Interim General Model may prove useful for modelling the effects of climate change (past/future) on species richness (and, by association, the vegetation).

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Recent studies at the macro-scale have demonstrated that geographic gradients in the richness of plants, in particular of woody plants such as trees and shrubs, can be viewed as by-products of water-energy dynamics. According to this view, they are climatic rather than latitudinal/longitudinal gradients, relating to coincident and predictable variations in planetary surface-atmosphere thermal dynamics and consequent patterns in biological activity. Previous analyses have shown that a twovariable model capturing the dynamic relationship between energy (heat/light) and water (rainfall) accounts for most of the variation in woody plant richness across southern Africa at species, genus, and family levels. Here we move towards a more complete explanation, while demonstrating how geographic analysis of residuals can be used to identify the type and sequence of additional variables for inclusion, either at the same or at more discrete scales of analysis. Residual geographic variation in richness from the two-variable model displays a geographic pattern unrelated to longitude and latitude. Regional clusters of under-and over-prediction point to macro-scale variation in topographic relief as a significant factor. When topographic relief is added as a third variable, the explanatory power (R 2 ) increases by 7 to 12%, and the subsequent pattern of variation in residuals becomes even more unpredictable. What clustering remains points to other macro-, and meso-or micro-scale variables that need to be considered. Such a top-down, trans-scalar approach permits systematic and objective development of more complete explanations, while the three-variable macro-scale model developed herein is the basis for a powerful research tool for ecologists, biogeographers, conservationists and bio-climatologists alike.

Climate has long been related to geographical differences in the distribution and diversity of life. What has eluded explanation is why this should be so. One emerging possibility is biological relativity to water–energy dynamics: the relative nature of biotic dynamics to changes in energy/matter conditions caused by changes in water (all states) while doing work, especially liquid water. The dynamic parameters involved – liquid water and optimal energy conditions – are independent of life, and have been shown to provide a simple, globally predictive explanation for co‐variation between climate and the species richness of woody plants. Here I elaborate on what I mean by ‘biological relativity to water–energy dynamics’ and how it should relate to the geography and evolution of life in general (terrestrial, subterranean, marine/aquatic biota). Working through a natural hierarchy of physical, geographical, ecological and biological first principles, I outline the hierarchical, abiotic → biotic conceptual framework within which this idea operates. The implications of this idea include the following. First, the biosphere is better conceptualized as a ‘subsphere’ of the liquid hydrosphere – a system within a system, wherein ‘life’ has all the unique physical properties of liquid water, plus unique emergent properties of its own. Second, the fundamental capacity for life to exist and be dynamic in all biotic systems is determined by the abiotic capacity for liquid water to exist and be dynamic, which is always relative to the capacity for water–energy dynamics in general. Third, liquid water–energy dynamics acts as a fundamental mechanism of evolution, while being a constant mechanism of natural selection. Fourth, over space and time, there should be first‐order predictable and/or systematic differences in the capacity for, operation and outcomes of, biotic dynamics globally (e.g. species richness), that necessarily dissolve into apparent chaos locally. Fifth, biological relativity to water–energy dynamics provides a fundamental and natural framework for operationalizing hierarchy theory and developing trans‐scalar explanations for the geography and evolution of life's diversity.

There have been few attempts to generate global models of climate–richness relationships, and fewer still that aim to predict richness rather than fitting a model to data. One such model, grounded on theory (biological relativity to water–energy dynamics) is the interim general model (IGM1) of the climatic potential for woody plant richness. Here we present a second‐generation model (IGM2), and genus and family versions of both models. IGM1 describes horizontal climate–richness relationships based on climate station data and systematic species range maps, with IGM2 additionally incorporating vertical changes in climate due to topographic relief. The IGMs are mathematical transformations of empirical relationships obtained for the southern subcontinent of Africa, whereby the re‐described regression models apply to the full range of global variation in all independent climate parameters. We undertake preliminary validation of the new IGMs, first by mapping the distribution and relative spatial variation in forecasted richness (per 25 000 km2) across the continent of Africa, then by evaluating the precision of forecasted values (actual vs. predicted) for an independent study system, the woody plants of Kenya. We also compare the IGMs with a recent example of purely statistical regression models of climate–richness relationships; namely, the “global” model of A. P. Francis and D. J. Currie for angiosperm family richness. We conclude that the IGMs are globally applicable and can provide a fundamental baseline for systematically estimating differences in (woody) plant richness and for exploring the hierarchy of subordinate relationships that should also contribute to differences in realized richness (mostly at more discrete scales of analysis). Further, we found that the model of Francis and Currie is useful for predicting angiosperm richness in Africa, on a conditional basis (somewhere, sometime); we examined the relationship that it describes between climate and richness. Lastly, we found that indices of available soil water used in “water‐budget” or “water‐balance” analyses are not proxies for available liquid water as a function of climatological dynamics.