Frontiers of Biogeography

Spartocytisus supranubius shrubs covered in ice after an ice storm at the summit of La Palma, Canary Islands. This species is endemic to the high‐altitude parts of La Palma and Tenerife. Photograph taken by Richard Field on 19 February 2013. From Steinbauer et al. article in this issue.

The general dynamic model of oceanic island biogeography integrates temporal changes in ecological circumstances with diversification processes, and has stimulated current research in island biogeography. In the original publication, a set of testable hypotheses was analysed using regression models: specifically, whether island data for four diversity indices are consistent with the ‘B~ATT²’ model, in which B is a diversity index, A is log(area) and T is time. The four indices were species richness, the number and percentage of single‐island endemic species, and a diversification index. Whether the relationships between these indices and time are unimodal (i.e., ‘hump‐shaped’) was a key focus, based on the characteristic ontogeny of a volcanic oceanic island. However, the significance testing unintentionally used zero, rather than the mean of the diversity index, as the null hypothesis, greatly inflating F‐ values and reducing P‐values compared with the standard regression approach. Here we first re‐analyze the data used to evaluate the general dynamic model in the seminal paper, using the standard null hypothesis, to provide an important qualification of its empirical results. This supports the significance of about half the original tests, the rest becoming non‐significant but mostly suggestive of the hypothesized relationship. Then we expand the original analysis by testing additional, theoretically derived functional relationships between the diversity indices, island area and time, within the framework of the ATT² model and using a mixed‐effects modelling approach. This shows that species richness peaks earlier in island life‐cycles than endemism. Area has a greater effect on species richness and the number of single‐island endemics than on the proportion of single‐island endemics and the diversification index, and was always better fit as a log–log relationship than as a semi‐log one. Finally, the richness–time relationship is positively skewed, the initial rise happening much more quickly than the later decline.

Macroecology focuses on ecological questions at broad spatial and temporal scales, providing a statistical description of patterns in species abundance, distribution and diversity. More recently, historical components of these patterns have begun to be investigated more deeply. We tentatively refer to the practice of explicitly taking species history into account, both analytically and conceptually, as ‘evolutionary macroecology’. We discuss how the evolutionary dimension can be incorporated into macroecology through two orthogonal and complementary data types: fossils and phylogenies. Research traditions dealing with these data have developed more‐or‐less independently over the last 20–30 years, but merging them will help elucidate the historical components of diversity gradients and the evolutionary dynamics of species’ traits. Here we highlight conceptual and methodological advances in merging these two research traditions and review the viewpoints and toolboxes that can, in combination, help address patterns and unveil processes at temporal and spatial macro‐scales.