Significance: Tropical moist forests harbor much of the world’s biodiversity, but this diversity is not evenly distributed globally, with tropical moist forests in the Neotropics and Indomalaya generally exhibiting much greater diversity than in the Afrotropics. Here, we assess the ubiquity of this “pantropical diversity disparity” (PDD) using the present-day distributions of over 150,000 species of plants and animals, and we compare these distributions with a spatial model of diversification combined with reconstructions of plate tectonics, temperature, and aridity. Our study demonstrates that differences in paleoenvironmental dynamics between continents, including mountain building, aridification, and global temperature fluxes, can explain the PDD by shaping spatial and temporal patterns of species origination and extinction, providing a close match to observed distributions of plants and animals.

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

Caves are home to unique and fragile biotas with high levels of endemism. However, little is known about how the biotic colonization of caves has developed over time, especially in caves from middle and low latitudes. Subtropical East Asia holds the world's largest karst landform with numerous ancient caves, which harbor a high diversity of cave-dwelling organisms and are regarded as a biodiversity hotspot. Here, we assess the temporal dynamics of biotic colonization of subtropical East Asian caves through a multi-taxon analysis with representatives of green plants, animals, and fungi. We then investigate the consequences of paleonviromental changes on the colonization dynamics of these caves in combination with reconstructions of vegetation, temperature, and precipitation. We discover that 88% of cave colonization events occurred after the Oligocene-Miocene boundary, and organisms from the surrounding forest were a major source for subtropical East Asian cave biodiversity. Biotic colonization of subtropical East Asian caves during the Neogene was subject to periods of acceleration and decrease, in conjunction with large-scale, seasonal climatic changes and evolution of local forests. This study highlights the long-term evolutionary interaction between surface and cave biotas; our climate-vegetation-relict model proposed for the subtropical East Asian cave biota may help explain the evolutionary origins of other mid-latitude subterranean biotas.

Pioneer naturalists such as Whewell, Lyell, Humboldt, Darwin and Wallace acknowledged the interactions between ecological and evolutionary forces, as well as the roles of continental movement, mountain formation and climate variations, in shaping biodiversity patterns. Recent developments in computer modelling and paleo‐environmental reconstruction have made it possible for scientists to study in silico how biodiversity emerges from eco‐evolutionary and environmental dynamic processes and their interactions. Simulating emergent biodiversity enables the experimentation of multiple interconnected hypotheses in a largely fragmented scientific landscape, with the final objective of successfully approximating natural mechanisms (i.e. hypothetical spatio–temporally unrestricted generalizations that hold across multiple empirical biodiversity patterns). This new interdisciplinary approach opens unprecedented scientific pathways, facilitating the communication and contemplation of causal implications of complex eco‐evolutionary and environmental interactions. In this review I provide a comprehensive overview of the available population‐based spatially explicit mechanistic eco‐evolutionary models (MEEMs) that rely on paleo‐environmental reconstructions, critically discussing their relevance and limitations for our understanding of biodiversity. To achieve this, I first introduce diverse biodiversity models and contextualize MEEMs. Second, I define MEEMs and synthesize the major insights from studies using MEEMs combined with deep‐time environmental dynamics (> 0.1 Ma). Lastly, I discuss the challenges and perspectives of solving long‐standing biodiversity enigmas by coupling eco‐evolutionary mechanisms with deep‐time environmental dynamics. Studies show that linking dynamic environments and eco‐evolutionary processes is necessary to reproduce multiple large‐scale biodiversity patterns simultaneously. Mechanisms related to adaptations (e.g. niche evolution), dispersal abilities and other eco‐evolutionary interactions (e.g. those resulting in speciation or extinction events) show universal importance, although their signatures across spatial and temporal scales remain largely unknown. Investigations with MEEMS spanning multiple levels of complexity in space and time foster interdisciplinary cooperation across the natural sciences and show promise for solving some of the enigmas in Earth's biodiversity.

South America is home to the highest freshwater fish biodiversity on Earth, and the hotspot of species richness is located in the western Amazon basin. The location of this hotspot is enigmatic, as it is inconsistent with the pattern observed in river systems across the world of increasing species richness towards a river’s mouth. Here we investigate the role of river capture events caused by Andean mountain building and repeated episodes of flooding in western Amazonia in shaping the modern-day richness pattern of freshwater fishes in South America, and in Amazonia in particular. To this end, we combine a reconstruction of river networks since 80 Ma with a mechanistic model simulating dispersal, allopatric speciation and extinction over the dynamic landscape of rivers and lakes. We show that Andean mountain building and consequent numerous small river capture events in western Amazonia caused freshwater habitats to be highly dynamic, leading to high diversification rates and exceptional richness. The history of marine incursions and lakes, including the Miocene Pebas mega-wetland system in western Amazonia, played a secondary role.