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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.

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.

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.

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.

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.

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.

Summary The evolution of continuous traits is the central component of comparative analyses in phylogenetics, and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models, and we explore the effects of random errors in trait measurement on the accuracy of model selection. We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein–Uhlenbeck (OU) and accelerating/decelerating rates (ACDC). Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favour of more parameter-rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection. Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support. Importantly, as measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.

Summary Despite the advancement of phylogenetic methods to estimate speciation and extinction rates, their power can be limited under variable rates, in particular for clades with high extinction rates and small number of extant species. Fossil data can provide a powerful alternative source of information to investigate diversification processes. 2. Here, we present PyRate, a computer program to estimate speciation and extinction rates and their temporal dynamics from fossil occurrence data. The rates are inferred in a Bayesian framework and are comparable to those estimated from phylogenetic trees. 3. We describe how PyRate can be used to explore different models of diversification. In addition to the diversification rates, it provides estimates of the parameters of the preservation process (fossilization and sampling) and the times of speciation and extinction of each species in the data set. Moreover, we develop a new birth–death model to correlate the variation of speciation/extinction rates with changes of a continuous trait. 4. Finally, we demonstrate the use of Bayes factors for model selection and show how the posterior estimates of a PyRate analysis can be used to generate calibration densities for Bayesian molecular clock analysis. PyRate is an open-source command-line Python program available at http://sourceforge.net/projects/pyrate/.

The evolution of key innovations, novel traits that promote diversification, is often seen as major driver for the unequal distribution of species richness within the tree of life. In this study, we aim to determine the factors underlying the extraordinary radiation of the subfamily Bromelioideae, one of the most diverse clades among the neotropical plant family Bromeliaceae. Based on an extended molecular phylogenetic data set, we examine the effect of two putative key innovations, that is, the Crassulacean acid metabolism (CAM) and the water-impounding tank, on speciation and extinction rates. To this aim, we develop a novel Bayesian implementation of the phylogenetic comparative method, binary state speciation and extinction, which enables hypotheses testing by Bayes factors and accommodates the uncertainty on model selection by Bayesian model averaging. Both CAM and tank habit were found to correlate with increased net diversification, thus fulfilling the criteria for key innovations. Our analyses further revealed that CAM photosynthesis is correlated with a twofold increase in speciation rate, whereas the evolution of the tank had primarily an effect on extinction rates that were found five times lower in tank-forming lineages compared to tank-less clades. These differences are discussed in the light of biogeography, ecology, and past climate change.

Aim: To reconstruct the spatio-temporal evolution of Fosterella (Bromeliaceae), a genus characterized by a high degree of endemism in the Central Andes, and to account for contemporary patterns of diversity and distribution within the genus. Location: Fosterella has its centre of diversity in the Central Andes (24 species), where it occurs in two major biomes: the Yungas and seasonally dry tropical forests (SDTF). The genus displays three major disjunctions: Amazonia (one species), Central America (one species), and the Brazilian Shield (five species). Methods: Phylogenetic relationships within Fosterella were inferred based on six plastid DNA regions. Parsimony and likelihood methods, a Bayesian relaxed molecular clock, ancestral area reconstructions, and diversification rate analyses were used to infer the spatio-temporal evolution of Fosterella. Results: The origin of extant lineages of Fosterella was placed in the late Miocene (c. 9.6Ma) during the last rapid Andean uplift. SDTF and azonal lowland sites were inferred as the most likely ancestral habitats. The Yungas were colonized several times independently from c. 4.7Ma onwards. Only one clade diversified in the Yungas, indicative of an ecological shift to moister and cooler conditions. Two recent long-distance dispersals to Central America and to Amazonia were inferred. Diversification rates within Fosterella were found to be constant through time and comparatively low (0.4 species Myr⁻¹). Main conclusions: Allopatric speciation is the main mode of diversification in Fosterella. The isolated distribution of suitable habitats fostered the evolution of a high degree of endemism. The low speciation rates in Fosterella contrast with high diversification rates of Andean high-elevation taxa but are similar to other Andean low- to mid-elevation taxa. The last rapid Andean uplift did not leave a detectable signature in the diversification rates of Fosterella.