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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. ISSN:0036-8075 ISSN:1095-9203

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., Ecography, 2023 (4), ISSN:0906-7590, ISSN:1600-0587

The documentation of biodiversity distribution through species range identification is crucial for macroecology, biogeography, conservation, and restoration. However, for plants, species range maps remain scarce and often inaccurate. We present a novel approach to map species ranges at a global scale, integrating polygon mapping and species distribution modelling (SDM). We develop a polygon mapping algorithm by considering distances and nestedness of occurrences. We further apply an SDM approach considering multiple modelling algorithms, complexity levels, and pseudo-absence selections to map the species at a high spatial resolution and intersect it with the generated polygons. We use this approach to construct range maps for all 1957 species of Fagales and Pinales with data compilated from multiple sources. We construct high-resolution global species richness maps of these important plant clades, and document diversity hotspots for both clades in southern and south-western China, Central America, and Borneo. We validate the approach with two representative genera, Quercus and Pinus, using previously published coarser range maps, and find good agreement. By efficiently producing high-resolution range maps, our mapping approach offers a new tool in the field of macroecology for studying global species distribution patterns and supporting ongoing conservation efforts., Entomologia Experimentalis et Applicata, 170 (6), ISSN:0013-8703, ISSN:1570-7458

The estimation of diversification rates is one of the most vividly debated topics in modern systematics, with considerable controversy surrounding the power of phylogenetic and fossil-based approaches in estimating extinction. Van Valen's seminal work from 1973 proposed the "Law of constant extinction,” which states that the probability of extinction of taxa is not dependent on their age. This assumption of age-independent extinction has prevailed for decades with its assessment based on survivorship curves, which, however, do not directly account for the incompleteness of the fossil record, and have rarely been applied at the species level. Here, we present a Bayesian framework to estimate extinction rates from the fossil record accounting for age-dependent extinction (ADE). Our approach, unlike previous implementations, explicitly models unobserved species and accounts for the effects of fossil preservation on the observed longevity of sampled lineages. We assess the performance and robustness of our method through extensive simulations and apply it to a fossil data set of terrestrial Carnivora spanning the past 40 myr. We find strong evidence of ADE, as we detect the extinction rate to be highest in young species and declining with increasing species age. For comparison, we apply a recently developed analogous ADE model to a dated phylogeny of extant Carnivora. Although the phylogeny-based analysis also infers ADE, it indicates that the extinction rate, instead, increases with increasing taxon age. The estimated mean species longevity also differs substantially, with the fossil-based analyses estimating 2.0 myr, in contrast to 9.8 myr derived from the phylogeny-based inference. Scrutinizing these discrepancies, we find that both fossil and phylogeny-based ADE models are prone to high error rates when speciation and extinction rates increase or decrease through time. However, analyses of simulated and empirical data show that fossil-based inferences are more robust. This study shows that an accurate estimation of ADE from incomplete fossil data is possible when the effects of preservation are jointly modeled, thus allowing for a reassessment of Van Valen's model as a general rule in macroevolution.

Supplementary Information # gen3sis: a general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity ## **Description**: Notes, Figures, Animations, Scripts and Data for gen3sis publication. ### **Notes**: - Notes_S1.pdf: Notes of case study: The emergence of the LDG from environmental changes of the Cenozoic. - Note_S2.pdf: Notes of case study: Does trait evolution impact biodiversity dynamics? - Note_S3.pdf: Pseudo-code: gen3sis ### **Figures**: - Figure_S1.pdf: Divergence increase per time step d_i against the normalized occupied niche of isolated populations for models (A) M1, M2, M4 and M5, which assume temperature-independent divergence; and (B) M3, which assumes temperature-dependent divergence, where divergence relates to the mean of the realized temperature with three different d_power values. - Figure_S2.pdf: Non-exhaustive probability density functions of the explored dispersal parameters in a Weibull distribution with shape ɸ of 1, 2 and 5 and Ψ of 550, 650, 750 and 850. Data presented available in S2 Data. - Figure_S3.pdf: Models (i.e. M1, M2, M3, M4, and M5) (A) Kernel density estimate of the same explored parameters (i.e. divergence threshold and dispersal scale) for selected simulations based on a Pearson correlation of simulated vs. best observed (i.e. cor > 0.4) and (B) performance quantified with the Bayesian Information Criterion (BIC). Omitted values from the parameter space were simulations generating an unacceptable best Pearson correlation to the empirical data (r ≤ 0.4), too many species (> 35,000) or a weak richness gradient (< 20 species between minimal and maximal alpha-richness). Data presented available in S3 Data. - Figure_S4.pdf: Summary statistics of the model fit to empirical data with and without environmental dynamics for (A) a Pearson correlation of standardized mean species number per latitude (LDGcurve), (B) a Pearson correlation of spatial alpha-diversity, and (C) the exact difference between lineage through time curves (nLTT). Data presented available in S2 Data. - Figure_S5.pdf: Standardized mean species number per latitude (LDGcurve ) for empirical data (i.e. terrestrial mammals, birds, amphibians and reptiles) and best matching simulation from models (A) M1, (B) M2, (C) M3, (D) M4 and (E) M5. Data presented available in S4 Data. - Figure_S6.pdf: Frequencies of Pearson correlation between simulated standardized mean species number per latitude (LDGcurve ) against best matching empirical LDGcurve for each dynamic landscape L1 (in blue) and L2 (in pink) for models (A) M1, (B) M2, (C) M3, (D) M4 and (E) M5. Models M4 and M5 are the only ones producing correlations > 0.5. Data presented available in S3 Data. - Figure_S7.pdf: Effects of grid cell size on simulations of M2 L1. (A) Correlation of grid cell, LDG slope and other summary statistics. (B) Simulated LDG slope and grid cell size, showing a significant effect of spatial resolution on LDG slope. Data presented available in S5 Data. - Figure_S8.pdf: Frequencies of simulated normalized LDG slope (histogram) with empirical LDG for four main groups (dashed grey line) and acceptance range (red line). Frequencies for models (A) M1, (B) M2, (C) M3, (D) M4 and (E)M5 with total frequency and frequency discriminated for each landscape, i.e. L1 and L2. Data presented available in S3 Data. - Figure_S9.pdf: Normalized richness of (A) selected simulation, (B) terrestrial mammals, (C) birds, (D) amphibians and (E) reptiles, with Pearson correlation values for comparisons between simulated and empirical data. - Figure_S10.pdf: Mean absolute evolutionary events (i.e. speciation and extinction) for every 1 myr for the top seven best matching current spatial alpha-biodiversity simulations for each model with and without environmental dynamics. Data presented available in S6 Data. - Figure_S11.pdf: Standardized speciation events for every 1 myr of the top seven best matching current spatial alpha-biodiversity simulations for each model with and without environmental dynamics. Data presented available in S6 Data. - Figure_S12.pdf: Standardized extinction events for every 1 myr of the top seven best matching current spatial alpha-biodiversity simulations for each model with and without environmental dynamics. Data presented available in S6 Data. - Figure_S13.pdf: Correlation of model parameters and emerging patterns for all models and landscapes without deep-time environmental dynamics (A) M0 L1.0, (B) M0 L2.0, (C) M1 L1.0, (D) M1 L2.0, (E) M2 L1.0 and (F) M2 L2.0. Emerging patterns: (i) phylogeny beta is the phylogenetic tree imbalance statistic measured as the value that maximizes the likelihood in the β-splitting model; (ii) range quant 0.95% is the value of the 95% quantile of the species range area distribution; (iii) LDG % loss is the slope of the linear regression of species richness; (iv) richness r is the highest Pearson correlation between simulated and empirical -diversity; (v) nLTT diff is the lowest difference between simulated and empirical normalized lineage though time curves; and (vi) LDG curve r is the highest Pearson correlation between simulated and empirical standardized mean species number per latitude. Data presented available in S3 Data. - Figure_S14.pdf: Correlation of model parameters and three emerging patterns for all models and landscapes considering deep-time environmental dynamics (A) M0 L1, (B) M0 L2, (C) M1 L1, (D) M1 L2, (E) M2 L1 and (F) M2 L2. Emerging patterns: (i) phylogeny beta is the phylogenetic tree imbalance statistic measured as the value that maximizes the likelihood in the β-splitting model; (ii) range quant 0.95% is the value of the 95% quantile of the species range area distribution; (iii) LDG % loss is the slope of the linear regression of species richness; (iv) richness r is the highest Pearson correlation between simulated and empirical -diversity; (v) nLTT diff is the lowest difference between simulated and empirical normalized lineage though time curves; and (vi) LDG curve r is the highest Pearson correlation between simulated and empirical standardized mean species number per latitude. Data presented available in S3 Data. - Figure_S15.pdf: Results of the island case study showing (A) landscape size and environmental dynamics and (B) results of three experiments (i.e. lower, equal and higher trait evolution compared with the temporal environmental variation). The time series in (B) shows  richness (log10 scale) on theoretical oceanic islands, following the geomorphological dynamics of islands. Thick lines indicate the average of the replicates, whereas thin lines indicate SD envelopes (n=30 for each trait evolutionary rate scenario). The dashed grey vertical bar crossing the entire plot indicates the period in which the island reaches its maximum size. ### **Animations**: - Animation_S1.mp4: Reconstructed dynamic landscape L1 (i.e. world 65 Ma) with the environmental values used for the main case study. - Animation_S2.mp4: Reconstructed dynamic landscape L2 (i.e. world 65 Ma) with the environmental values used for the main case study. - Animation_S3.mp4: Theoretical dynamic landscape (i.e. theoretical island) with the environmental values used for the supplementary case study. - Animation_S4.mp4: Dynamic simulated biodiversity patterns (i.e. M5 L1 world from 65 Ma to the present). The map shows the  diversity and the top and right graphs indicate the richness profile of longitude and latitude, respectively. ### **Data**: - config: Contains the gen3sis configurations objects for models M0, M1 and M2. - landscape: Contains the gen3sis landscape objects for L1 and L2. Subfolder are named according to the paleo-topographic reconstructions used (i.e. L1 for Scotese and L2 for Straume). We provide a 1° and 4° landscape but omit the large distances files for 1° landscapes. These can be reconstructed using the function create_input_landscape from gen3sis R-package (e.g. Scripts / landscape / compile_gen3sis_landscape.R ). ### **Scripts**: - run_gen3sis.R: Main call of first of gen3sis at wrapper level. Useful for launching multiple simulations in a remote cluster and saving output data at desired location. - config: Contains the config_creator.R script that generates the config files of M0, M1 and M2 (available under Data) and a config parameters reference in a semi-column separated file (i.e. m0_config_parameters.tx, m1_config_parameters.tx and m2_config_parameters.tx). Config_creator.R uses the files config_template_m0.R, config_template_m1.R and config_template_m2.R as templates and create automatically the folders contain the config files (i.e. /configs_m0/, /configs_m1/ and /configs_m2/) for all three models. - landscape: Contains the convenience function compile_gen3sis_landscape.R to compile input landscapes from an rds file. Subfolders for L1 and L2 with scripts to guide landscape creation (i.e. create_L1.R and create_L2.R) as well as support data and scripts. Final data is provided at the ( Data / landscape ) folder. ## **Reference**: Oskar Hagen, Benjamin Flück, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loïc Pellissier. (2021) gen3sis: a general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity. PLOS Biology. **Contact**: Oskar Hagen (oskar@hagen.bio), {"references":["Hagen, Oskar et al (2021) gen3sis: the general engine for eco-evolutionary simulations on the origins of biodiversity. bioRxiv. https://doi.org/10.1101/2021.03.24.436109"]}

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists such as Whewell, Lyell, Humboldt, Darwin and Wallace. These pioneers already acknowledged interactions between ecological and evolutionary processes, as well as the roles of continental movements, orogeny and climate variations in shaping biodiversity patterns. As science advanced, the complexity of ecological, evolutionary, geological and climatological processes became evident in an increasingly fragmented scientific landscape. Recent developments in computer modelling now enable the strengthening of interdisciplinary fields, opening unprecedented scientific pathways. In this thesis, a novel general engine for eco-evolutionary simulations (gen3sis) is presented. The engine consists of a spatially-explicit modelling framework that enables modular implementation of multiple macroecological and macroevolutionary processes interacting across representative spatio-temporally dynamic landscapes. Applications of gen3sis shed light into long-standing enigmas of global biodiversity patterns, such as: the latitudinal diversity gradient (LDG), the pantropical diversity gradient (PDG) and the life history of cold-adapted floras. Multiple reconstructed paleolandscapes and processes (e.g. environmental filtering, biotic interactions, energetic carrying capacities, dispersal, allopatric speciation, and the evolution of stress tolerance and competitive ability) were used to simulate emergent biodiversity patterns (e.g. ,  and  diversity, past and current species ranges, and phylogenies). Conclusions are based on comparisons of simulated patterns with literature reviews and empirical data (i.e. species ranges, phylogenies and fossils) of multiple faunas and floras. Bridging ecological and evolutionary mechanisms with paleo-environments shaped by plate-tectonic movements, mountain uplifts and deep-time climate changes using gen3sis is shown to be indispensable for reconstructing the formation of many global biodiversity patterns. Energetic carrying capacity was a significant process when concurrently simulating a realistic LDG, species range size frequencies, and phylogenetic tree balance of major tetrapod groups. Differences in paleo-environmental dynamics between continents (e.g. mountain and island formation and habitat fragmentation), combined with weak niche evolution, can explain the PDG by shaping spatial and temporal patterns of species origination and extinction. Simulations matched observed distribution and phylogenetic patterns of tropical plants and animals. Geological and climatological events, combined with species interactions and the evolution of competitive and temperature tolerance traits, provide a remarkable match with observed distributions, fossil records and the phylogenetic nestedness of cold-adapted plants. This thesis moves beyond correlational approaches and provides a novel framework for formalizing and exploring multiple hypotheses and reconstructions associated with the origin of biodiversity. Model comparison with empirical data serves hindcast, which might inform biodiversity trajectories. By advancing our numeric understanding of the physical and biological processes that shape biodiversity, new and interdisciplinary tools such as gen3sis support scientists to piece together key puzzles of the Earth’s astonishing biodiversity.

Generating genomic data for 19 tropical reef fish species of the Western Indian Ocean, we investigate how species ecology influences genetic diversity patterns from local to regional scales. We distinguish between the α, β and γ components of genetic diversity, which we subsequently link to six ecological traits. We find that the α and γ components of genetic diversity are strongly correlated so that species with a high total regional genetic diversity display systematically high local diversity. The α and γ diversity components are negatively associated with species abundance recorded using underwater visual surveys and positively with body size. Pelagic larval duration is found to be negatively related to genetic β diversity supporting its role as a dispersal trait in marine fishes. Deviation from the neutral theory of molecular evolution motivates further effort to understand the processes shaping genetic diversity and ultimately the diversification of the exceptional diversity of tropical reef fishes.

Spatially explicit simulations of gene flow within complex landscapes could help forecast the responses of populations to global and anthropological changes. Simulating how past climate change shaped intraspecific genetic variation can provide a validation of models in anticipation of their use to predict future changes. We review simulation models that provide inferences on population genetic structure. Existing simulation models generally integrate complex demographic and genetic processes but are less focused on the landscape dynamics. In contrast to previous approaches integrating detailed demographic and genetic processes and only secondarily landscape dynamics, we present a model based on parsimonious biological mechanisms combining habitat suitability and cellular processes, applicable to complex landscapes. The simulation model takes as input (a) the species dispersal capacities as the main biological parameter, (b) the species habitat suitability, and (c) the landscape structure, modulating dispersal. Our model emphasizes the role of landscape features and their temporal dynamics in generating genetic differentiation among populations within species. We illustrate our model on caribou/reindeer populations sampled across the entire species distribution range in the Northern Hemisphere. We show that simulations over the past 21 kyr predict a population genetic structure that matches empirical data. This approach looking at the impact of historical landscape dynamics on intraspecific structure can be used to forecast population structure under climate change scenarios and evaluate how species range shifts might induce erosion of genetic variation within species., Evolutionary Applications, 13 (6), ISSN:1752-4571, ISSN:1752-4563