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The transition from a greenhouse to an icehouse world at the Eocene-Oligocene Transition (EOT) coincided with a large decrease of pollen from the steppe-adapted genus Nitraria. This genus, now common along the Mediterranean coast, Asia and Australia, has a proposed coastal origin and a geographically widespread fossil record. Here we investigated the evolution, taxonomic diversity and morphological disparity of Nitraria throughout the Cenozoic by integrating extant taxa and fossil palynological morphotypes into a unified phylogenetic framework based on both DNA sequences and pollen morphological data. We present the oldest fossil pollen grain of Nitraria, at least 53 Myr old. This fossil was found in Central Asian deposits, providing new evidence for its origin in this area. We found that the EOT is an evolutionary bottleneck for Nitraria, coinciding with retreat of the proto-Paratethys Sea, a major global cooling event and a turnover in Central Asian steppe vegetation. We infer the crown age of modern Nitraria spp. to be significantly younger (Miocene) than previously estimated (Palaeocene). The diversity trajectory of Nitraria inferred from extant-only taxa differs markedly from one that also considers extinct taxa. Our study demonstrates it is therefore critical to apply an integrative approach to fully understand the plant evolutionary history of Nitrariaceae.

Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, to quantify species extinction risks, or to evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance due to incomplete sampling limited data and resource availabilities. Several imputation methods exist to predict missing values and have been successfully evaluated and used to fill the gaps in datasets of quantitative traits. Here we explore the performance of different imputation methods on discrete biological traits i.e. qualitative or categorical traits such as diet or habitat. We develop a bioinformatics pipeline to impute trait data combining phylogenetic, machine learning, and deep learning methods while integrating a simulation framework to evaluate their performance on synthetic datasets. Using this pipeline we run a wide range of simulations under different missing rates, mechanisms, and biases and different evolutionary models. Our results indicate that a new ensemble approach, where we combined the imputation results of a selection of imputation methods provides the most robust and accurate prediction of missing discrete traits. We apply our pipeline to an incomplete trait dataset of 1015 elasmobranch species (including sharks and rays) and found a high imputation accuracy of the predictions based on an expert-based assessment of the missing traits. Our bioinformatic pipeline, implemented in an open-source R package, facilitates the application and comparison of multiple imputation methods to make robust predictions of missing trait values in biological datasets.

Popular comparative phylogenetic models such as Brownian Motion, Ornstein-Ulhenbeck, and their extensions, assume that, at speciation, a trait value is inherited identically by the two descendant species. This assumption contrasts with models of speciation at the micro-evolutionary scale where phenotypic distributions of the descendants are sub-samples of the ancestral distribution. Various described mechanisms of speciation can lead to a displacement of the ancestral phenotypic mean among descendants and an asymmetric inheritance of the ancestral phenotypic variance. In contrast, even macro-evolutionary models that account for intraspecific variance assume symmetrically conserved inheritance of the ancestral phenotypic distribution at speciation. Here we develop an Asymmetric Brownian Motion model (ABM) that relaxes the hypothesis of symmetric and conserved inheritance of the ancestral distribution at the time of speciation. The ABM jointly models the evolution of both intra- and inter-specific phenotypic variation. It also allows the mode of phenotypic inheritance at speciation to be inferred, ranging from a symmetric and conserved inheritance, where descendants inherit the ancestral distribution, to an asymmetric and displaced inheritance, where descendants inherit divergent phenotypic means and variances. To demonstrate this model, we analyze the evolution of beak morphology in Darwin finches, finding evidence of character displacement at speciation. The ABM model helps to bridge micro- and macro-evolutionary models of trait evolution by providing a more robust framework for testing the effects of ecological speciation, character displacement, and niche partitioning on trait evolution at the macro-evolutionary scale.

AimThe global Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time- consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for understudied areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here we present IUCNN, an R- package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other traits if available).InnovationWe implement a user-friendly workflow to train and validate neural network models, and subsequently use them to predict species RL status. IUCNN contains functions to address specific issues related to the RL framework, including a regression-based approach to account for the ordinal nature of RL categories and class imbalance in the training data, a Bayesian approach for improved uncertainty quantification, and a target accuracy threshold approach that limits predictions to only those species whose RL status can be predicted with high confidence. Most analyses can be run with few lines of code, without prior knowledge of neural network models. We demonstrate the use of IUCNN on an empirical dataset of ∼14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods.Main conclusionsIUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large-scale analyses on the extinction risk of species so far not well represented on the official RL.

Madagascar’s biota is hyperdiverse and includes exceptional levels of endemicity. We review the current state of knowledge on Madagascar’s past and current terrestrial and freshwater biodiversity by compiling and presenting comprehensive data on species diversity, endemism, and rates of species description and human uses, in addition to presenting an updated and simplified map of vegetation types. We report a substantial increase of records and species new to science in recent years; however, the diversity and evolution of many groups remain practically unknown (e.g., fungi and most invertebrates). Digitization efforts are increasing the resolution of species richness patterns and we highlight the crucial role of field- and collections-based research for advancing biodiversity knowledge and identifying gaps in our understanding, particularly as species richness corresponds closely to collection effort. Phylogenetic diversity patterns mirror that of species richness and endemism in most of the analyzed groups. We highlight humid forests as centers of diversity and endemism because of their role as refugia and centers of recent and rapid radiations. However, the distinct endemism of other areas, such as the grassland-woodland mosaic of the Central Highlands and the spiny forest of the southwest, is also biologically important despite lower species richness. The documented uses of Malagasy biodiversity are manifold, with much potential for the uncovering of new useful traits for food, medicine, and climate mitigation. The data presented here showcase Madagascar as a unique “living laboratory” for our understanding of evolution and the complex interactions between people and nature. The gathering and analysis of biodiversity data must continue and accelerate if we are to fully understand and safeguard this unique subset of Earth’s biodiversity.

Aim: Geo-referenced species occurrence records are a prime example of biodiversity data and a cornerstone of biodiversity research. Yet, their availability depends on non-biological factors, including the political framework in the region of collection. Here we present Bio-Dem (www.bio-dem.surge.sh), an open-source software to explore the relationship between the availability of species occurrence records and the political framework in countries worldwide and through time. Location: Global. Taxon: Any. Methods: Bio-Dem accesses the number of occurrence records available from countries worldwide from the Global Biodiversity Information Facility (www.gbif.org) and socio-political information from these countries from the Varieties of Democracy database (www.v-dem.net) as well as information on colonial history from the Issue Correlates of War Project. Results: Bio-Dem is available as a free graphical user interface web application at www.bio-dem.surge.sh. Through three interactive graphs, Bio-Dem enables users to explore the relationship between species occurrence records and countries’ regime type, freedom of movement, freedom of association, gross domestic product, education length, political corruption, armed conflict and colonial history. We describe possible links of these specific political indicators with the collection and mobilization of biodiversity data. Illustrative examples and video tutorials explaining the use of Bio-Dem are available at www.bio-dem.surge.sh. Main conclusions: Bio-Dem provides a user-friendly way to explore the link between socio-political variables and the availability of species occurrence records in user-selected taxa and geographic regions, and to generate hypotheses on their connection. We envision Bio-Dem as a useful tool for teachers communicating the caveats of available biodiversity data, for biodiversity scientists exploring potential spatial and temporal bias in their data, and for social scientists exploring the impact of political systems on scientific data collection and biodiversity conservation.

Behavioral innovations can be ecologically transformative for lineages that perform them and for their associated communities. Many ecologically dominant, superorganismal, and speciose ant lineages use a mouth-to-mouth fluid exchange behavior – trophallaxis – to share both exogenously sourced and endogenously produced materials across their colonies, while lineages that are less abundant, less cooperative and less speciose tend not to perform this behavior. How and why this behavior evolved and fixed in only some ant lineages remains unclear and whether this trait enables ants’ ecological dominance is not yet understood. Here we show that trophallaxis evolved in two major events ~110 Ma in lineages that today encompass 36% of ants, and in numerous smaller and more recent events. We found that trophallaxis evolved early only in ant lineages that had reduced intra-colonial conflict by losing workers ability to reproduce. Our causal models indicate that this signature behavior of superorganismal ants required social cooperation and ecological opportunism, and likely contributed to the large colony sizes and speciation patterns of the ants that use it and dominate our landscapes today. We hypothesize that the early evolution of trophallaxis was brought about by a major shift in terrestrial ecosystems through the origin and diversification of flowering plants and the consequent opportunistic inclusion of nectar and sap-sucker honeydew in the ant diet.

What controls species diversity and diversification is one of the major questions in evolutionary biology and paleontology. Previous studies have addressed this issue based on various plant and animal groups, geographic regions, and time intervals. However, as most previous research focused on terrestrial or marine ecosystems, our understanding of the controls on diversification of biota (and particularly invertebrates) in freshwater environments in deep time is still limited. Here, we infer diversification rates of North American freshwater gastropods from the Late Triassic to the Pleistocene and explore potential links between shifts in speciation and extinction and major changes in paleogeography, climate, and biotic interactions. We found that variation in the speciation rate is best explained by changes in continental fragmentation, with rate shifts coinciding with major paleogeographic reorganizations in the Mesozoic, in particular the retreat of the Sundance Sea and subsequent development of the Bighorn wetland and the advance of the Western Interior Seaway. Climatic events in the Cenozoic (Middle Eocene Climate Optimum, Miocene Climate Optimum) variably coincide with shifts in speciation and extinction as well, but no significant long-term association could be detected. Similarly, no influence of diversity dependence was found across the entire time frame of ~ 214 Myr. Our results indicate that short-term climatic events and paleogeographic changes are relevant to the diversification of continental freshwater biota, while long-term trends have limited effect.

The birth of modern rainforestsThe origin of modern rainforests can be traced to the aftermath of the bolide impact at the end of the Cretaceous. Carvalhoet al.used fossilized pollen and leaves to characterize the changes that took place in northern South American forests at this time (see the Perspective by Jacobs and Currano). They not only found changes in species composition but were also able to infer changes in forest structure. Extinctions were widespread, especially among gymnosperms. Angiosperm taxa came to dominate the forests over the 6 million years of recovery, when the flora began to resemble that of modern lowland neotropical forest. The leaf data also imply that the forest canopy evolved from relatively open to closed and layered, leading to increased vertical stratification and a greater diversity of plant growth forms.Science, this issue p.63; see also p.28

Some of the most extensive terrestrial biomes today consist of open vegetation, including temperate grasslands and tropical savannas. These biomes originated relatively recently in Earth’s history, likely replacing forested habitats in the second half of the Cenozoic. However, the timing of their origination and expansion remains disputed. Here, we present a Bayesian deep learning model that utilizes information from fossil evidence, geologic models, and paleoclimatic proxies to reconstruct paleovegetation, placing the emergence of open habitats in North America at around 23 million years ago. By the time of the onset of the Quaternary glacial cycles, open habitats were covering more than 30% of North America and were expanding at peak rates, to eventually become the most prominent natural vegetation type today. Our entirely data-driven approach demonstrates how deep learning can harness unexplored signals from complex data sets to provide insights into the evolution of Earth’s biomes in time and space.

We live in a time of highly accelerated extinction, which has the potential to mirror past mass extinction events. However, the rarity of these events and the restructuring of diversity that they cause complicate direct comparisons between the current extinction crisis and earlier mass extinctions. Among animals, turtles (Testudinata) are one of few groups which both have a sufficient fossil record and a sufficiently stable ecological importance to enable meaningful comparisons between the end Cretaceous mass extinction and the ongoing extinction event. In this paper we analyze the fossil record of turtles and recover three significant peaks in extinction rate. Two of these are in the Cretaceous, the second of these took place at the Cretaceous–Paleogene transition (K-Pg), reflecting the overall patterns previously reported for many other taxa. The third major extinction event started in the Pliocene and continues until now. This peak only affected terrestrial turtles and started much earlier in Eurasia and Africa lineages than elsewhere. This suggests that it may be linked to co-occurring hominins rather than having been caused by global climate change.

The ongoing environmental crisis poses an urgent need to forecast the who, where and when of future species extinctions, as such information is crucial for targeting conservation efforts. Commonly, such forecasts are made based on conservation status assessments produced by the International Union for Conservation of Nature (IUCN). However, when researchers apply these IUCN conservation status data for predicting future extinctions, important information is often omitted, which can impact the accuracy of these predictions. Here we present a new approach and a software for simulating future extinctions based on IUCN conservation status information, which incorporates generation length information of individual species when modeling extinction risks. Additionally, we explicitly model future changes in conservation status for each species, based on status transition rates that we estimate from the IUCN assessment history of the last decades. Finally, we apply a Markov chain Monte Carlo algorithm to estimate extinction rates for each species, based on the simulated future extinctions. These estimates inherently incorporate the chances of conservation status changes and the generation length for each given species and are specific to the simulated time frame. We demonstrate the utility of our approach by estimating future extinction rates for all bird species. Our average extinction rate estimate for the next 100 yr across all birds is 6.98 x 10(-4) extinctions per species-year, and we predict an expected biodiversity loss of between 669 and 738 bird species within that time frame. Further, the rate estimates between species sharing the same IUCN status show larger variation than the rates estimated with alternative approaches, which reflects expected differences in extinction risk among taxa of the same conservation status. Our method demonstrates the utility of applying species-specific information to the estimation of extinction rates, rather than assuming equal extinction risks for species assigned to the same conservation status.

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.Evaluación Automatizada de la Conservación de la Familia Orchidaceae mediante Aprendizaje Profundo Resumen Los análisis de la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza (UICN) son esenciales para la priorización de las necesidades de conservación, pero requieren de muchos recursos y por lo tanto están disponibles sólo para una fracción de la riqueza mundial de especies. Las evaluaciones automatizadas de la conservación basadas en los registros disponibles de presencia geográfica pueden ser una alternativa rápida pero no está claro cuán confiables son estas evaluaciones. Realizamos evaluaciones automatizadas de la conservación para 13,910 especies (47.3% de las especies conocidas de la familia) de la diversa y mundialmente distribuida familia de las orquídeas (Orchidaceae), en la cual la mayoría de las especies (13,049) no tenían una valoración previa por parte de la UICN. Usamos un método novedoso basado en una red neural profunda (IUC-NN). Identificamos 4,342 especies de orquídeas (31.2% de las especies evaluadas) como posiblemente amenazadas por la extinción (equivalente a las categorías de la UICN en peligro crítico [CR], en peligro [EN] o vulnerable [VU]) y a Madagascar, África Occidental, el sudeste de Asia y varias islas oceánicas como áreas prioritarias para la conservación de orquídeas. La familia Orchidaceae proporcionó un modelo con el cual probar la sensibilidad de los métodos de evaluación automatizada ante problemas con la disponibilidad de datos, la calidad de los datos y los sesgos de muestreo geográfico. La IUC-NN identificó posibles especies amenazadas con una certeza de 84.3% con un sesgo de evaluación geográfica significativamente más bajo en relación con la Lista Roja de la UICN y mostró solidez incluso cuando la disponibilidad de datos fue baja y hubo errores geográficos en los datos de entrada. En general, nuestros resultados demostraron que las evaluaciones automatizadas tienen un papel importante que desempeñar en la identificación de especies con mayor riesgo de extinción.

Significance The biological interchange between North and South America associated with the formation of the Isthmus of Panama is key to defining current gradients of species diversity. A major gap in our understanding of the interchange is its asymmetry, where mammals of North American origin attained higher diversity in South America than vice versa. The prevailing view is that this asymmetry resulted from higher origination of immigrant mammals in South America. In contrast, we find that asymmetry results from high extinction of native mammals in South America, which reduced the diversity of native mammals available to disperse northwards. These results shed light on the legacy of the biotic interchange to understand the current patterns of species diversity across the Americas., The interchange between the previously disconnected faunas of North and South America was a massive experiment in biological invasion. A major gap in our understanding of this invasion is why there was a drastic increase in the proportion of mammals of North American origin found in South America. Four nonmutually exclusive mechanisms may explain this asymmetry: 1) Higher dispersal rate of North American mammals toward the south, 2) higher origination of North American immigrants in South America, 3) higher extinction of mammals with South American origin, and 4) similar dispersal rate but a larger pool of native taxa in North versus South America. We test among these mechanisms by analyzing ∼20,000 fossil occurrences with Bayesian methods to infer dispersal and diversification rates and taxonomic selectivity of immigrants. We find no differences in the dispersal and origination rates of immigrants. In contrast, native South American mammals show higher extinction. We also find that two clades with North American origin (Carnivora and Artiodactyla) had significantly more immigrants in South America than other clades. Altogether, the asymmetry of the interchange was not due to higher origination of immigrants in South America as previously suggested, but resulted from higher extinction of native taxa in southern South America. These results from one of the greatest biological invasions highlight how biogeographic processes and biotic interactions can shape continental diversity.

Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, substantially improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resourcelimited world.

Research data are available on the Mendeley Data platform (https://doi.org/10.17632/xfx2yhw949.2), which include datasets, input files and R scripts of the analyses., Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi. org/10.1016/j.ympev.2022.107635, Most of the unique and diverse vertebrate fauna that inhabits Madagascar derives from in situ diversification from colonisers that reached this continental island through overseas dispersal. The endemic Malagasy Scincinae lizards are amongst the most species-rich squamate groups on the island. They colonised all bioclimatic zones and display many ecomorphological adaptations to a fossorial (burrowing) lifestyle. Here we propose a new phylogenetic hypothesis for their diversification based on the largest taxon sampling so far compiled for this group. We estimated divergence times and investigated several aspects of their diversification (diversification rate, body size and fossorial lifestyle evolution, and biogeography). We found that diversification rate was constant throughout most of the evolutionary history of the group, but decreased over the last 6–4 million years and independently from body size and fossorial lifestyle evolution. Fossoriality has evolved from fully quadrupedal ancestors at least five times independently, which demonstrates that even complex morphological syndromes – in this case involving traits such as limb regression, body elongation, modification of cephalic scalation, depigmentation, and eyes and ear-opening regression – can evolve repeatedly and independently given enough time and eco-evolutionary advantages. Initial diversification of the group likely occurred in forests, and the divergence of sand-swimmer genera around 20 Ma appears linked to a period of aridification. Our results show that the large phenotypic variability of Malagasy Scincinae has not influenced diversification rate and that their rich species diversity results from a constant accumulation of lineages through time. By compiling large geographic and trait-related datasets together with the computation of a new time tree for the group, our study contributes important insights on the diversification of Malagasy vertebrates., Portuguese National Funds through FCT (Fundação para a Ciência e a Tecnologia) supported this work with the projects UIDP/50027/2020 and PTDC/BIA-EVL/31254/2017, Contract grant to AC [2020.00823.CEECIND/CP1601/CT0003, PD/BD/128493/2017, SFRH/BD/102495/2014, SFRH/BPD/111015/2015, Agencia Española de Investigación, with a Juan de la Cierva-Formación grant [FCI2019-039443-I/AEI/10.13039/501100011033], Swedish Research Council [2019-05191], Swiss National Science Foundation [PCEFP3_187012], Swedish Research Council [VR: 2019-04739]

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.

Open vegetation today constitutes one of the most extensive biomes on earth, including temperate grasslands and tropical savannas. Yet these biomes originated relatively recently in earth history, likely replacing forested habitats as recently as the second half of the Cenozoic, although the timing of their origination and the dynamics of their expansion remain uncertain. Here, we present a new hypothesis of paleovegetation change in North America, showing that open habitats originated between 25 and 20 Ma in the center of the continent, and expanded rapidly starting 8 Ma to eventually become the most prominent vegetation type today. To obtain space-time predictions of paleovegetation, we developed a new Bayesian deep learning model that utilizes available information from fossil evidence, geologic models, and paleoclimate proxies. We compiled a large dataset of paleovegetation reconstructions from the peer-reviewed literature, which we used in combination with current vegetation data to train the model. The model learns to predict vegetation based on the learned associations between the vegetation at a given site and multiple biotic and abiotic predictors: fossil mammal occurrences, plant macrofossils, estimates of temperature and precipitation, latitude, and the effects of spatial and temporal autocorrelation. Our results provide a new, spatially detailed reconstruction of habitat evolution in North America and our deep learning model paves the way for a new quantitative approach to estimating paleovegetation changes.

The insect order Hymenoptera originated during the Permian nearly 300 Mya. Ancestrally herbivorous hymenopteran lineages today make up the paraphyletic suborder ‘Symphyta’, which encompasses c. 8200 species with very diverse host-plant associations. We use phylogeny-based statistical analyses to explore the drivers of diversity dynamics within the ‘Symphyta’, with a particular focus on the hypothesis that diversification of herbivorous insects has been driven by the explosive radiation of angiosperms during and after the Cretaceous. Our ancestral-state estimates reveal that the first symphytans fed on gymnosperms, and that shifts onto angiosperms and pteridophytes – and back – have occurred at different time intervals in different groups. Trait-dependent analyses indicate that average net diversification rates do not differ between symphytan lineages feeding on angiosperms, gymnosperms or pteridophytes, but trait-independent models show that the highest diversification rates are found in a few angiosperm-feeding lineages that may have been favoured by the radiations of their host taxa during the Cenozoic. Intriguingly, lineages-through-time plots show signs of an early Cretaceous mass extinction, with a recovery starting first in angiosperm-associated clades. Hence, the oft-invoked assumption of herbivore diversification driven by the rise of flowering plants may overlook a Cretaceous global turnover in insect herbivore communities during the rapid displacement of gymnosperm- and pteridophyte-dominated floras by angiosperms.

This folder contains the data and analysis scripts necessary to reproduce the example analysis presented in Zizka et al (2021) Bio-Dem, a tool to explore the relationship between biodiversity data availability and socio-political conditions in time and space.

Over a million species face extinction, carrying with them untold options for food, medicine, fibre, shelter, ecological resilience, aesthetic and cultural values. There is therefore an urgent need to design conservation policies that maximise the protection of biodiversity and its contributions to people, within the constraints of limited budgets. Here we present a novel framework for spatial conservation prioritisation that combines simulation models, reinforcement learning and ground validation to identify optimal policies. Our methodology, CAPTAIN (Conservation Area Prioritisation Through Artificial Intelligence Networks), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a fixed budget, our model protects substantially more species from extinction than the random or naively targeted protection of areas. CAPTAIN also outperforms the most widely used software for spatial conservation prioritisation (Marxan) in 97% of cases and reduces species loss by an average of 40% under simulations, besides yielding prioritisation maps at substantially higher spatial resolution using empirical data. We find that regular biodiversity monitoring, even if simple and with a degree of inaccuracy – characteristic of citizen science surveys – substantially improves biodiversity outcomes. Given the complexity of people–nature interactions and wealth of associated data, artificial intelligence holds great promise for improving the conservation of biological and ecosystem values in a rapidly changing and resource-limited world.

In a recent paper1 we presented a new model, the Bayesian Brownian Bridge (BBB), to infer clade age based on fossil evidence and modern diversity. We benchmarked the method with extensive simulations, including a wide range of diversification histories and sampling heterogeneities that go well beyond the necessarily simplistic model assumptions. Applying BBB to 198 angiosperm families, we found that their fossil record is compatible with clade origins earlier than most contemporary palaeobotanical interpretations. In particular, we estimated with high probability that crown-angiosperms originated before the Cretaceous (> 145 Ma). Budd and colleagues2 critique our study, arguing that the BBB model is biased towards older estimates when fossil data are scarce or absent, that our underlying fossil dataset is unsound, that our clade age estimates are therefore biased by early diverging lineages that are underrepresented in the fossil record, and that pooling of fossil data for analysis at higher taxonomic ranks overcomes these biases. Here, we explore their points and perform new simulations to show that their critique has no merit.

The pleiotropic drug resistance (PDR) transporter Pdr11p is expressed under anaerobic growth conditions at the plasma membrane of the yeast Saccharomyces cerevisiae, where it facilitates the uptake of exogenous sterols. Members of the fungal PDR family contain six conserved cysteines in their extracellular loops (ECL). For the functional analysis of these cysteine residues in Pdr11p, we generated a series of single cysteine-to-serine mutants. All mutant proteins expressed well and displayed robust ATPase activity upon purification. Mass-spectrometry analysis identified two cysteine residues (C582 and C603) in ECL3 forming a disulfide bond. Further characterization by cell-based assays showed that all mutants are compromised in facilitating sterol uptake, protein stability, and trafficking to the plasma membrane. Our data highlight the fundamental importance of all six extracellular cysteine residues for the functional integrity of Pdr11p and provide new structural insights into the PDR family of transporters.

The extinction of species can destabilize ecological processes. A way to assess the ecological consequences of species loss is by examining changes in functional diversity. The preservation of functional diversity depends on the range of ecological roles performed by species, or functional richness, and the number of species per role, or functional redundancy. However, current knowledge is based on short timescales and an understanding of how functional diversity responds to long-term biodiversity dynamics has been limited by the availability of deep-time, trait-based data. Here, we compile an exceptional trait dataset of fossil molluscs from a 23-million-year interval in the Caribbean Sea (34 011 records, 4422 species) and develop a novel Bayesian model of multi-trait-dependent diversification to reconstruct mollusc (i) diversity dynamics, (ii) changes in functional diversity, and (iii) extinction selectivity over the last 23 Myr. Our results identify high diversification between 23–5 Mya, leading to increases in both functional richness and redundancy. Conversely, over the last three million years, a period of high extinction rates resulted in the loss of 49% of species but only 3% of functional richness. Extinction rates were significantly higher in small, functionally redundant species suggesting that competition mediated the response of species to environmental change. Taken together, our results identify long-term diversification and selective extinction against redundant species that allowed functional diversity to grow over time, ultimately buffering the ecological functions of biological communities against extinction.

IUCN Red List assessments are essential for prioritizing conservation needs but are resource-intensive and therefore only available for a fraction of global species richness. Tropical plant species are particularly under-represented on the IUCN Red List. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. Here, we present automated conservation assessments for 13,910 species of the diverse and globally distributed Orchid family (Orchidaceae), based on a novel method using a deep neural network (IUC-NN), most of which (13,049) were previously unassessed by the IUCN Red List. We identified 4,342 (31.2 % of the evaluated orchid species) asPossibly Threatenedwith extinction (equivalent to the IUCN categories CR, EN, or VU) and point to Madagascar, East Africa, south-east Asia, and several oceanic islands as priority areas for orchid conservation. Furthermore, the Orchid family provides a model, to test the sensitivity of automated assessment methods to issues with data availability, data quality and geographic sampling bias. IUC-NN identified threat-ened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias compared to the IUCN Red List, and was robust against low data availability and geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in achieving goals of identifying the species that are at greatest risk of extinction.

Patterns of molecular coevolution can reveal structural and functional constraints within or among organic molecules. These patterns are better understood when considering the underlying evolutionary process, which enables us to disentangle the signal of the dependent evolution of sites (coevolution) from the effects of shared ancestry of genes. Conversely, disregarding the dependent evolution of sites when studying the history of genes negatively impacts the accuracy of the inferred phylogenetic trees. Although molecular coevolution and phylogenetic history are interdependent, analyses of the two processes are conducted separately, a choice dictated by computational convenience, but at the expense of accuracy. We present a Bayesian method and associated software to infer how many and which sites of an alignment evolve according to an independent or a pairwise dependent evolutionary process, and to simultaneously estimate the phylogenetic relationships among sequences. We validate our method on synthetic datasets and challenge our predictions of coevolution on the 16S rRNA molecule by comparing them with its known molecular structure. Finally, we assess the accuracy of phylogenetic trees inferred under the assumption of independence among sites using synthetic datasets, the 16S rRNA molecule and 10 additional alignments of protein-coding genes of eukaryotes. Our results demonstrate that inferring phylogenetic trees while accounting for dependent site evolution significantly impacts the estimates of the phylogeny and the evolutionary process.

Species occurrence records from online databases are an indispensable resource in ecological, biogeographical and palaeontological research. However, issues with data quality, especially incorrect geo-referencing or dating, can diminish their usefulness. Manual cleaning is time-consuming, error prone, difficult to reproduce and limited to known geographical areas and taxonomic groups, making it impractical for datasets with thousands or millions of records. Here, we present CoordinateCleaner, an r-package to scan datasets of species occurrence records for geo-referencing and dating imprecisions and data entry errors in a standardized and reproducible way. CoordinateCleaner is tailored to problems common in biological and palaeontological databases and can handle datasets with millions of records. The software includes (a) functions to flag potentially problematic coordinate records based on geographical gazetteers, (b) a global database of 9,691 geo-referenced biodiversity institutions to identify records that are likely from horticulture or captivity, (c) novel algorithms to identify datasets with rasterized data, conversion errors and strong decimal rounding and (d) spatio-temporal tests for fossils. We describe the individual functions available in CoordinateCleaner and demonstrate them on more than 90 million occurrences of flowering plants from the Global Biodiversity Information Facility (GBIF) and 19,000 fossil occurrences from the Palaeobiology Database (PBDB). We find that in GBIF more than 3.4 million records (3.7%) are potentially problematic and that 179 of the tested contributing datasets (18.5%) might be biased by rasterized coordinates. In PBDB, 1205 records (6.3%) are potentially problematic. All cleaning functions and the biodiversity institution database are open-source and available within the CoordinateCleaner r-package.

Identifying species with disproportionate contributions to biodiversity can lead to effective conservation prioritisation. Despite well-established methods for identifying endangered species adding inordinately toevolutionarydiversity, in this contextfunctionaldiversity has been overlooked. Here, we compare different metrics designed to identify threatened species that contribute strongly to functional diversity. We use the diverse and threatened global marine megafauna as a case study. We found that functional contributions of species are not fully captured in a single metric. Although we found a very strong correlation between functional specialisation and distinctiveness, functional uniqueness was only moderately correlated with the other two metrics and identified a different set of top-10 species. These functional contributions were then integrated and combined with extinction risk to identify species that are both important contributors to functional diversityandendangered. For instance, the top-10FunctionallyUniqueSpecialized andEndangered (FUSE) species contains three critically endangered, five endangered and two vulnerable species which - despite comprising only 3% of species - are among the top 10% most functionally unique and hold 15% of the global functional richness. The FUSE index was remarkably robust to different mathematical formulations. Combining one or more facets of a species contribution to functional diversity with endangerment, such as with the FUSE index, adds to the toolbox for conservation prioritisation. Nevertheless, we discuss how these new tools must be handled with care alongside other metrics and information.

Saber-toothed cats were armed with formidable weapons. They evolved a number of highly derived morphological features, most notably a pair of extremely long upper canines, which makes them unique within the felid family. Although the sabertooth character evolved several times among carnivorous mammals, sabertooth clades mostly had disjunctive occurrences both in space and time, and no sabertooth is alive today. We studied the rates of phenotypic and taxonomic diversification in the mandible of sabertooths, as compared to the rates calculated for both extinct and extant conical toothed cats. We found that the mandible's shape and physical properties in sabertooth clades evolved at distinctly higher rates than the rest of the felid tree. In addition, sabertooths had similar speciation rate to conical toothed cats, but statistically higher extinction rate. The wealth of morphological specializations required to be a sabertooth, and their tendency to focus on large-sized species as prey, was likely responsible for such high extinction rate, and for the peculiar, disjunctive patterns of sabertooth clade occurrence in the fossil record. © 2018 Elsevier B.V.

Linking pattern to process across spatial and temporal scales has been a key goal of the field of biogeography. In January 2017, the 8th biennial conference of the International Biogeography Society sponsored a symposium on Building up biogeography—process to pattern that aimed to review progress towards this goal. Here we present a summary of the symposium, in which we identified promising areas of current research and suggested future research directions. We focus on (1) emerging types of data such as behavioural observations and ancient DNA, (2) how to better incorporate historical data (such as fossils) to move beyond what we term “footprint measures” of past dynamics and (3) the role that novel modelling approaches (e.g. maximum entropy theory of ecology and approximate Bayesian computation) and conceptual frameworks can play in the unification of disciplines. We suggest that the gaps separating pattern and process are shrinking, and that we can better bridge these aspects by considering the dimensions of space and time simultaneously.

The Cocinetas Basin in the Guajira Peninsula, the northernmost tip of South America, today has a dry climate with low rainfall ( ten months) and no year-long rivers or permanent standing bodies of fresh water. In contrast, the fossil and geological record indicate that the Cocinetas Basin was much wetter during the Miocene-Pliocene (∼17–2.8 Ma). Water needed to sustain the paleofauna could either have originated from local sources or been brought by a larger river system (e.g. proto Magdalena/Orinoco river) with headwaters either in Andean ranges or the Guyana shield. We present a provenance study of the Pliocene Ware Formation, using petrographic analysis of conglomerate clasts and heavy minerals, and U-Pb dating of 140 detrital zircons. Clasts and heavy minerals are typical of ensialic metamorphic and igneous sources. The detrital zircon age distribution indicates the Guajira ranges as the most probable sediment source. The overall results indicate that the fluvial system of the Ware Formation drained the surrounding ranges. The water was probably derived by local precipitation onto the Guajira peninsula.

A longstanding debate in evolutionary biology and paleontology is whether ecological interactions such as competition impose diversity dependence on speciation and extinction rates. Here, we analyze the fossil record of terrestrial mammalian carnivores in North America and Eurasia using a Bayesian framework to assess whether their diversity dynamics were affected by diversity dependence within and between families (12 in Eurasia, 10 in North America). We found eight instances of within-clade diversity dependence suppressing speciation rates and detected between-clade effects increasing extinction rates in six instances. Diversity dependence often involved lineages that migrated between continents and we found that speciation was more responsive to diversity changes within the clade, whereas extinction responded to diversity of taxa in other clades. The analysis of the fossil record of Carnivora suggests that interactions within and between clades are associated with different speciation and extinction regimes, opening room for a broader theory of diversity dependence.

Background: Understanding why species go extinct has become a major goal of evolutionary biology. Recent studies have suggested that both species traits and rates of evolution might predict extinction probability in a changing world. Here, we tested whether species conservation status correlates with recent rates of niche evolution within their lineages across 11,465 species of terrestrial vertebrates.Results: We find no consistent association between rates of niche evolution and current IUCN status in birds, mammals, amphibians and squamates. Our results suggest that rates of niche evolution estimated over evolutionary time are a poor predictor of species extinction probability at present time.Conclusions: Our results are consistent with previous studies showing that past rates of evolution are unrelated to how species will adapt to climate change in the future. This mismatch might be explained by the different time scales involved, difficulties in accurately estimating evolutionary rates and extinction risks, or simply the fact that the selective pressures affecting biodiversity are different today than in the past.

National governments are the main actors responsible for mapping and protecting their biodiversity, but countries differ in their capacity, willingness, and effectiveness to do so. We quantify the global biodiversity managed by different regime types and developed a tool to explore the links between level of democracy and other key socio-economic variables with the number of natural history specimens registered within country boundaries. Using this tool, distinct and previously unknown patterns emerge around the world, that urge for increased collaboration between the natural and social sciences to further explore these patterns and their underlying processes.

The ongoing environmental crisis poses an urgent need to forecast the who, where, and when of future species extinctions, as such information is crucial for targeting conservation efforts. Commonly, such forecasts are made based on conservation status assessments produced by the International Union for Conservation of Nature (IUCN). However, when researchers apply these IUCN conservation status data for predicting future extinctions, important information is often omitted, which can impact the accuracy of these predictions.Here we present a new approach and a software for simulating future extinctions based on IUCN conservation status information, which incorporates generation length information of individual species when modeling extinction risks. Additionally, we explicitly model future changes in conservation status for each species, based on status transition rates that we estimate from the IUCN assessment history of the last decades. Finally, we apply a Markov chain Monte Carlo algorithm to estimate extinction rates for each species, based on the simulated future extinctions. These estimates inherently incorporate the chances of conservation status changes and the generation length for each given species and are specific to the simulated time frame.We demonstrate the utility of our approach by estimating extinction rates for all bird species. Our average extinction risk estimate for the next 100 years across all birds is 6.98 × 10−4 extinctions per species-year, and we predict an expected biodiversity loss of between 669 to 738 bird species within that time frame. Further, the rate estimates between species sharing the same IUCN status show larger variation than the rates estimated with alternative approaches, which reflects expected differences in extinction risk among taxa of the same conservation status. Our method demonstrates the utility of applying species-specific information to the estimation of extinction rates, rather than assuming equal extinction risks for species assigned to the same conservation status.

One of the most remarkable aspects of our species is that while we show surprisingly little genetic diversity, we demonstrate astonishing amounts of cultural diversity. Perhaps most impressive is the diversity of our technologies, broadly defined as all the physical objects we produce and the skills we use to produce them. Despite considerable focus on the evolution of technology by social scientists and philosophers, there have been few attempts to systematically quantify technological diversity, and therefore the dynamics of technological change remain poorly understood. Here we show a novel Bayesian model for examining technological diversification adopted from palaeontological analysis of occurrence data. We use this framework to estimate the tempo of diversification in American car and truck models produced between 1896 and 2014, and to test the relative importance of competition and extrinsic factors in shaping changes in macro-evolutionary rates. Our results identify a four-fold decrease in the origination and extinction rates of car models, and a negative net diversification rate over the last 30 years. We also demonstrate that competition played a more significant role in car model diversification than either changes in oil prices or gross domestic product. Together our analyses provide a set of tools that can enhance current research on technological and cultural evolution by providing a flexible and quantitative framework for exploring the dynamics of diversification.