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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

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. 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 alpha, beta, and gamma 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., PLoS Biology, 19 (7), ISSN:1544-9173, ISSN:1545-7885

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., New Phytologist, 235 (2), ISSN:0028-646X, ISSN:1469-8137

Environmental DNA (eDNA) analysis is a revolutionary method to monitor marine biodiversity from animal DNA traces. Examining the capacity of eDNA to provide accurate biodiversity measures in species‐rich ecosystems such as coral reefs is a prerequisite for their application in long‐term monitoring. Here, we surveyed two Colombian tropical marine reefs, the island of Providencia and Gayraca Bay near Santa Marta, using eDNA and underwater visual census (UVC) methods. We collected a large quantity of surface water (30 L per filter) above the reefs and applied a metabarcoding protocol using three different primer sets targeting the 12S mitochondrial DNA, which are specific to the vertebrates Actinopterygii and Elasmobranchii. By assigning eDNA sequences to species using a public reference database, we detected the presence of 107 and 85 fish species, 106 and 92 genera, and 73 and 57 families in Providencia and Gayraca Bay, respectively. Of the species identified using eDNA, 32.7% (Providencia) and 18.8% (Gayraca) were also found in the UVCs. We further found congruence in genus and species richness and abundance between eDNA and UVC approaches in Providencia but not in Gayraca Bay. Mismatches between eDNA and UVC had a phylogenetic and ecological signal, with eDNA detecting a broader phylogenetic diversity and more effectively detecting smaller species, pelagic species and those in deeper habitats. Altogether, eDNA can be used for fast and broad biodiversity surveys and is applicable to species‐rich ecosystems in the tropics, but improved coverage of the reference database is required before this new method could serve as an effective complement to traditional census methods., Environmental DNA, 3 (1), ISSN:2637-4943