gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity

The macro-eco-evolutionary interplay between dispersal, competition and landscape structure in generating biodiversity

Theory links dispersal and diversity, predicting the highest diversity at intermediate dispersal levels. However, the modulation of this relationship by macro-eco-evolutionary mechanisms and competition within a landscape is still elusive. We examine the interplay between dispersal, competition and landscape structure in shaping biodiversity over 5 million years in a dynamic archipelago landscape. We model allopatric speciation, temperature niche, dispersal, competition, trait evolution and trade-offs between competitive and dispersal traits. Depending on dispersal abilities and their interaction with landscape structure, our archipelago exhibits two 'connectivity regimes', that foster speciation events among the same group of islands. Peaks of diversity (i.e. alpha, gamma and phylogenetic), occurred at intermediate dispersal; while competition shifted diversity peaks towards higher dispersal values for each connectivity regime. This shift demonstrates how competition can boost allopatric speciation events through the evolution of thermal specialists, ultimately limiting geographical ranges. Even in a simple landscape, multiple intermediate dispersal diversity relationships emerged, all shaped similarly and according to dispersal and competition strength. Our findings remain valid as dispersal- and competitive-related traits evolve and trade-off; potentially leaving identifiable biodiversity signatures, particularly when trade-offs are imposed. Overall, we scrutinize the convoluted relationships between dispersal, species interactions and landscape structure on macro-eco-evolutionary processes, with lasting imprints on biodiversity.This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

DeepDive: estimating global biodiversity patterns through time using deep learning

Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

Vrba was right: Historical climate fragmentation, and not current climate, explains mammal biogeography

Climate plays a crucial role in shaping species distribution and evolution over time. Dr Vrba's Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth.

Ecological niche conservatism spurs diversification in response to climate change

Lengthy debate has surrounded the theoretical and empirical science of whether climatic niche evolution is related to increased or decreased rates of biological diversification. Because species can persist for thousands to millions of years, these questions cross broad scales of time and space. Thus, short-term experiments may not provide comprehensive understanding of the system, leading to the emergence of contrasting opinions: niche evolution may increase diversity by allowing species to explore and colonize new geographic areas across which they could speciate; or, niche conservatism might augment biodiversity by supporting isolation of populations that may then undergo allopatric speciation. Here, we use a simulation approach to test how biological diversification responds to different rates and modes of niche evolution. We find that niche conservatism promotes biological diversification, whereas labile niches-whether adapting to the conditions available or changing randomly-generally led to slower diversification rates. These novel results provide a framework for understanding how Earth-life interactions produced such a diverse biota.

Novel community data in ecology-properties and prospects

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

Speed of thermal adaptation of terrestrial vegetation alters Earth's long-term climate

Earth's long-term climate is driven by the cycling of carbon between geologic reservoirs and the atmosphere-ocean system. Our understanding of carbon-climate regulation remains incomplete, with large discrepancies remaining between biogeochemical model predictions and the geologic record. Here, we evaluate the importance of the continuous biological climate adaptation of vegetation as a regulation mechanism in the geologic carbon cycle since the establishment of forest ecosystems. Using a model, we show that the vegetation's speed of adaptation to temperature changes through eco-evolutionary processes can strongly influence global rates of organic carbon burial and silicate weathering. Considering a limited thermal adaptation capacity of the vegetation results in a closer balance of reconstructed carbon fluxes into and out of the atmosphere-ocean system, which is a prerequisite to maintain habitable conditions on Earth's surface on a multimillion-year timescale. We conclude that the long-term carbon-climate system is more sensitive to biological dynamics than previously expected, which may help to explain large shifts in Phanerozoic climate.

Limited ecological opportunity influences the tempo of morphological evolution in birds

According to classic models of lineage diversification and adaptive radiation, phenotypic evolution should accelerate in the context of ecological opportunity and slow down when niches become saturated.1,2 However, only weak support for these ideas has been found in nature, perhaps because most analyses make the biologically unrealistic assumption that clade members contribute equally to reducing ecological opportunity, even when they occur in different continents or specialize on different habitats and diets. To view this problem through a different lens, we adapted a new phylogenetic modeling approach that accounts for the fact that competition for ecological opportunity only occurs between species that coexist and share similar habitats and diets. Applying this method to trait data for nearly all extant species of landbirds,3 we find a widespread signature of decelerating trait evolution in lineages adapted to similar habitats or diets. The strength of this pattern was consistent across latitudes when comparing tropical and temperate assemblages. Our results provide little support for the idea that increased diversity and tighter packing of niches accentuates evolutionary slowdowns in the tropics and instead suggest that limited ecological opportunity can be an important factor determining the rate of morphological diversification at a global scale.

Diversity across organisational scale emerges through dispersal ability and speciation dynamics in tropical fish

BACKGROUND

Biodiversity exists at different levels of organisation: e.g. genetic, individual, population, species, and community. These levels of organisation all exist within the same system, with diversity patterns emerging across organisational scales through several key processes. Despite this inherent interconnectivity, observational studies reveal that diversity patterns across levels are not consistent and the underlying mechanisms for variable continuity in diversity across levels remain elusive. To investigate these mechanisms, we apply a spatially explicit simulation model to simulate the global diversification of tropical reef fishes at both the population and species levels through emergent population-level processes.

RESULTS

We find significant relationships between the population and species levels of diversity which vary depending on both the measure of diversity and the spatial partitioning considered. In turn, these population-species relationships are driven by modelled biological trait parameters, especially the divergence threshold at which populations speciate.

CONCLUSIONS

To explain variation in multi-level diversity patterns, we propose a simple, yet novel, population-to-species diversity partitioning mechanism through speciation which disrupts continuous diversity patterns across organisational levels. We expect that in real-world systems this mechanism is driven by the molecular dynamics that determine genetic incompatibility, and therefore reproductive isolation between individuals. We put forward a framework in which the mechanisms underlying patterns of diversity across organisational levels are universal, and through this show how variable patterns of diversity can emerge through organisational scale.

Freshwater fish diversity in the western Amazon basin shaped by Andean uplift since the Late Cretaceous

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.

Thermodynamics of continental deformation

Continental deformation is known to be controlled by the interplay between tectonic and gravitational forces modulated by thermal relaxation-controlled lithospheric strength leading to oscillations around an equilibrium state, or to runaway extension. Using data-driven thermomechanical modelling of the Alpine Himalayan Collision Zone, we demonstrate how deviations from an equilibrium between mantle dynamics, plate-boundary forces, and the thermochemical configuration of the lithosphere control continental deformation. We quantify such balance between the internal energy of the plate and tectonic forces in terms of a critical crustal thickness, that match the global average of present-day continental crust. It follows that thicker intraplate domains than the critical crust (orogens) must undergo weakening due to their increased internal energy, and, in doing so, they dissipate the acquired energy within a diffused zone of deformation, unlike the localized deformation seen along plate boundaries. This evolution is controlled by a dissipative thermodynamic feedback loop between thermal and mechanical relaxation of the driving energy in the orogenic lithosphere. Exponentially growing energy states, leading to runaway extension are efficiently dampened by enhanced dissipation from radioactive heat sources. This ultimately drives orogens with their thickened radiogenic crust towards a final equilibrium state. Our results suggest a genetic link between the thermochemical state of the crust and the tectonic evolution of silicate Earth-like planets.

The Major Features of Macroevolution

Evolutionary dynamics operating across deep time leave footprints in the shapes of phylogenetic trees. For the last several decades, researchers have used increasingly large and robust phylogenies to study the evolutionary history of individual clades and to investigate the causes of the glaring disparities in diversity among groups. Whereas typically not the focal point of individual clade-level studies, many researchers have remarked on recurrent patterns that have been observed across many different groups and at many different time scales. Whereas previous studies have documented various such regularities in topology and branch length distributions, they have typically focused on a single pattern and used a disparate collection (oftentimes, of quite variable reliability) of trees to assess it. Here we take advantage of modern megaphylogenies and unify previous disparate observations about the shapes embedded in the Tree of Life to create a catalog of the "major features of macroevolution." By characterizing such a large swath of subtrees in a consistent way, we hope to provide a set of phenomena that process-based macroevolutionary models of diversification ought to seek to explain.

Global determinants of insect mitochondrial genetic diversity

Understanding global patterns of genetic diversity is essential for describing, monitoring, and preserving life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrates, which comprise only a small fraction of Earth's biodiversity. Here, we construct a global map of predicted insect mitochondrial genetic diversity from cytochrome c oxidase subunit 1 sequences, derived from open data. We calculate the mitochondrial genetic diversity mean and genetic diversity evenness of insect assemblages across the globe, identify their environmental correlates, and make predictions of mitochondrial genetic diversity levels in unsampled areas based on environmental data. Using a large single-locus genetic dataset of over 2 million globally distributed and georeferenced mtDNA sequences, we find that mitochondrial genetic diversity evenness follows a quadratic latitudinal gradient peaking in the subtropics. Both mitochondrial genetic diversity mean and evenness positively correlate with seasonally hot temperatures, as well as climate stability since the last glacial maximum. Our models explain 27.9% and 24.0% of the observed variation in mitochondrial genetic diversity mean and evenness in insects, respectively, making an important step towards understanding global biodiversity patterns in the most diverse animal taxon.

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

Paleoenvironments shaped the exchange of terrestrial vertebrates across Wallace's Line

Faunal turnover in Indo-Australia across Wallace's Line is one of the most recognizable patterns in biogeography and has catalyzed debate about the role of evolutionary and geoclimatic history in biotic interchanges. Here, analysis of more than 20,000 vertebrate species with a model of geoclimate and biological diversification shows that broad precipitation tolerance and dispersal ability were key for exchange across the deep-time precipitation gradient spanning the region. Sundanian (Southeast Asian) lineages evolved in a climate similar to the humid "stepping stones" of Wallacea, facilitating colonization of the Sahulian (Australian) continental shelf. By contrast, Sahulian lineages predominantly evolved in drier conditions, hampering establishment in Sunda and shaping faunal distinctiveness. We demonstrate how the history of adaptation to past environmental conditions shapes asymmetrical colonization and global biogeographic structure.

The build-up of the present-day tropical diversity of tetrapods

The extraordinary number of species in the tropics when compared to the extra-tropics is probably the most prominent and consistent pattern in biogeography, suggesting that overarching processes regulate this diversity gradient. A major challenge to characterizing which processes are at play relies on quantifying how the frequency and determinants of tropical and extra-tropical speciation, extinction, and dispersal events shaped evolutionary radiations. We address this question by developing and applying spatiotemporal phylogenetic and paleontological models of diversification for tetrapod species incorporating paleoenvironmental variation. Our phylogenetic model results show that area, energy, or species richness did not uniformly affect speciation rates across tetrapods and dispute expectations of a latitudinal gradient in speciation rates. Instead, both neontological and fossil evidence coincide in underscoring the role of extra-tropical extinctions and the outflow of tropical species in shaping biodiversity. These diversification dynamics accurately predict present-day levels of species richness across latitudes and uncover temporal idiosyncrasies but spatial generality across the major tetrapod radiations.

Linking human impacts to community processes in terrestrial and freshwater ecosystems

Human impacts such as habitat loss, climate change and biological invasions are radically altering biodiversity, with greater effects projected into the future. Evidence suggests human impacts may differ substantially between terrestrial and freshwater ecosystems, but the reasons for these differences are poorly understood. We propose an integrative approach to explain these differences by linking impacts to four fundamental processes that structure communities: dispersal, speciation, species-level selection and ecological drift. Our goal is to provide process-based insights into why human impacts, and responses to impacts, may differ across ecosystem types using a mechanistic, eco-evolutionary comparative framework. To enable these insights, we review and synthesise (i) how the four processes influence diversity and dynamics in terrestrial versus freshwater communities, specifically whether the relative importance of each process differs among ecosystems, and (ii) the pathways by which human impacts can produce divergent responses across ecosystems, due to differences in the strength of processes among ecosystems we identify. Finally, we highlight research gaps and next steps, and discuss how this approach can provide new insights for conservation. By focusing on the processes that shape diversity in communities, we aim to mechanistically link human impacts to ongoing and future changes in ecosystems.

Adding pattern and process to eco-evo theory and applications

Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies.

Mito-nuclear selection induces a trade-off between species ecological dominance and evolutionary lifespan

Mitochondrial and nuclear genomes must be co-adapted to ensure proper cellular respiration and energy production. Mito-nuclear incompatibility reduces individual fitness and induces hybrid infertility, which can drive reproductive barriers and speciation. Here, we develop a birth-death model for evolution in spatially extended populations under selection for mito-nuclear co-adaptation. Mating is constrained by physical and genetic proximity, and offspring inherit nuclear genomes from both parents, with recombination. The model predicts macroscopic patterns including a community's species diversity, species abundance distribution, speciation and extinction rates, as well as intraspecific and interspecific genetic variation. We explore how these long-term outcomes depend upon the parameters of reproduction: individual fitness governed by mito-nuclear compatibility, constraints on mating compatibility and ecological carrying capacity. We find that strong selection for mito-nuclear compatibility reduces the equilibrium number of species after a radiation, increasing species' abundances and simultaneously increasing both speciation and extinction rates. The negative correlation between species diversity and diversification rates in our model agrees with the broad empirical pattern of lower diversity and higher speciation/extinction rates in temperate regions, compared to the tropics. We conclude that these empirical patterns may be caused in part by latitudinal variation in metabolic demands and corresponding variation in selection for mito-nuclear function.

Spatiotemporal influences of climate and humans on muskox range dynamics over multiple millennia

Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution have included an overreliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Here, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia and in its expansion in North America using process-explicit macroecological models. The approach integrates modern and fossil occurrence records, ancient DNA, spatiotemporal reconstructions of past climatic change, species-specific population ecology, and the growth and spread of anatomically modern humans. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years require high dispersal abilities, large maximum densities, and a small Allee effect. Analyses of validated process-explicit projections indicate that climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses show that the range collapse and extinction of the muskox in Europe (~13,000 years ago) was likely caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities probably combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene-Holocene transition signals a vulnerability of this species to future increased warming. By better establishing the ecological processes that shaped the distribution of the muskox through space and time, we show that process-explicit macroecological models have important applications for the future conservation and management of this iconic species in a warming Arctic.

Mapping species diversification metrics in macroecology: Prospects and challenges

The intersection of macroecology and macroevolution is one of today’s most active research in biology. In the last decade, we have witnessed a steady increment of macroecological studies that use metrics attempting to capture macroevolutionary processes to explain present-day biodiversity patterns. Evolutionary explanations of current species richness gradients are fundamental for understanding how diversity accumulates in a region. Although multiple hypotheses have been proposed to explain the patterns we observe in nature, it is well-known that the present-day diversity patterns result from speciation, extinction, colonization from nearby areas, or a combination of these macroevolutionary processes. Whether these metrics capture macroevolutionary processes across space is unknown. Some tip-rate metrics calculated directly from a phylogenetic tree (e.g., mean root distance -MRD-; mean diversification rate -mDR-) seem to return very similar geographical patterns regardless of how they are estimated (e.g., using branch lengths explicitly or not). Model-based tip-rate metrics —those estimated using macroevolutionary mixtures, e.g., the BAMM approach— seem to provide better net diversification estimates than only speciation rates. We argue that the lack of appropriate estimates of extinction and dispersal rates in phylogenetic trees may strongly limit our inferences about how species richness gradients have emerged at spatial and temporal scales. Here, we present a literature review about this topic and empirical comparisons between select taxa with several of these metrics. We implemented a simple null model approach to evaluate whether mapping of these metrics deviates from a random sampling process. We show that phylogenetic metrics by themselves are relatively poor at capturing speciation, extinction, and dispersal processes across geographical gradients. Furthermore, we provide evidence of how parametric biogeographic methods can improve our inference of past events and, therefore, our conclusions about the evolutionary processes driving biodiversity patterns. We recommend that further studies include several approaches simultaneously (e.g., spatial diversification modeling, parametric biogeographic methods, simulations) to disentangle the relative role of speciation, extinction, and dispersal in the generation and maintenance of species richness gradients at regional and global scales.

Understanding the relationship between dispersal and range size

The drivers of variability in species range sizes remain an outstanding enigma in ecology. The theoretical expectation of a positive dispersal-range size relationship has received mixed empirical support, despite dispersal being one of the most prominent hypothesised predictors of range size. Here, we synthesised results from 86 studies examining the dispersal-range size relationship for plants and animals in marine, terrestrial and freshwater realms. Overall, our meta-analysis showed that dispersal positively affects range size, but its effect is dependent on the clade and dispersal proxy studied. Moreover, despite potential differences in habitat connectivity, we did not find an effect of realm on the dispersal-range size relationship. Finally, the strength of the dispersal-range size relationship was dependent on latitude, range size metric and the taxonomic breadth of the study clade. Our synthesis emphasizes the importance of developing a mechanistic understanding of the trait to dispersal to range size relationship, considering the complexity of dispersal departure, transfer and settlement, as well as evolutionary components such as time for range expansion, speciation and past geological-environmental dynamics. We, therefore, call for a more integrative view of the dispersal process and its causal relationship with range size.

Biotic colonization of subtropical East Asian caves through time

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.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Temperature-dependent evolutionary speed shapes the evolution of biodiversity patterns across tetrapod radiations

Biodiversity varies predictably with environmental energy around the globe, but the underlaying mechanisms remain incompletely understood. The evolutionary speed hypothesis predicts that environmental kinetic energy shapes variation in speciation rates through temperature- or life history-dependent rates of evolution. To test whether variation in evolutionary speed can explain the relationship between energy and biodiversity in birds, mammals, amphibians, and reptiles, we simulated diversification over 65 million years of geological and climatic change with a spatially explicit eco-evolutionary simulation model. We modelled four distinct evolutionary scenarios in which speciation-completion rates were dependent on temperature (M1), life history (M2), temperature and life history (M3), or were independent of temperature and life-history (M0). To assess the agreement between simulated and empirical data, we performed model selection by fitting supervised machine learning models to multidimensional biodiversity patterns. We show that a model with temperature-dependent rates of speciation (M1) consistently had the strongest support. In contrast to statistical inferences, which showed no general relationships between temperature and speciation rates in tetrapods, we demonstrate how process-based modelling can disentangle the causes behind empirical biodiversity patterns. Our study highlights how environmental energy has played a fundamental role in the evolution of biodiversity over deep time.

Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity

The processes that give rise to species richness gradients are not well understood, but may be linked to resource-based limits on the number of species a region can support. Ecological limits placed on regional species richness should also affect population demography, suggesting that these processes could also generate genetic diversity gradients. If true, we might better understand how broad-scale biodiversity patterns are formed by identifying the common causes of genetic diversity and species richness. We develop a hypothetical framework based on the consequences of regional variation in ecological limits set by resource availability and heterogeneity to simultaneously explain spatial patterns of species richness and neutral genetic diversity. Repurposing raw genotypic data spanning 38 mammal species sampled across 801 sites in North America, we show that estimates of genome-wide genetic diversity and species richness share spatial structure. Notably, species richness hotspots tend to harbor lower levels of within-species genetic variation. A structural equation model encompassing eco-evolutionary processes related to resource availability, habitat heterogeneity, and contemporary human disturbance supports the spatial patterns we detect. These results suggest broad-scale patterns of species richness and genetic diversity could both partly be caused by intraspecific demographic and evolutionary processes acting simultaneously across species.

Earth history events shaped the evolution of uneven biodiversity across tropical moist forests

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.

Far from a uniform band, the biodiversity found across Earth’s tropical moist forests varies widely between the high diversity of the Neotropics and Indomalaya and the relatively lower diversity of the Afrotropics. Explanations for this variation across different regions, the “pantropical diversity disparity” (PDD), remain contentious, due to difficulty teasing apart the effects of contemporary climate and paleoenvironmental history. Here, we assess the ubiquity of the PDD in over 150,000 species of terrestrial plants and vertebrates and investigate the relationship between the present-day climate and patterns of species richness. We then investigate the consequences of paleoenvironmental dynamics on the emergence of biodiversity gradients using a spatially explicit model of diversification coupled with paleoenvironmental and plate tectonic reconstructions. Contemporary climate is insufficient in explaining the PDD; instead, a simple model of diversification and temperature niche evolution coupled with paleoaridity constraints is successful in reproducing the variation in species richness and phylogenetic diversity seen repeatedly among plant and animal taxa, suggesting a prevalent role of paleoenvironmental dynamics in combination with niche conservatism. The model indicates that high biodiversity in Neotropical and Indomalayan moist forests is driven by complex macroevolutionary dynamics associated with mountain uplift. In contrast, lower diversity in Afrotropical forests is associated with lower speciation rates and higher extinction rates driven by sustained aridification over the Cenozoic. Our analyses provide a mechanistic understanding of the emergence of uneven diversity in tropical moist forests across 110 Ma of Earth’s history, highlighting the importance of deep-time paleoenvironmental legacies in determining biodiversity patterns.