Undiscovered bird extinctions obscure the true magnitude of human-driven extinction waves.

Birds are among the best-studied animal groups, but their prehistoric diversity is poorly known due to low fossilization potential. Hence, while many human-driven bird extinctions (i.e., extinctions caused directly by human activities such as hunting, as well as indirectly through human-associated impacts such as land use change, fire, and the introduction of invasive species) have been recorded, the true number is likely much larger. Here, by combining recorded extinctions with model estimates based on the completeness of the fossil record, we suggest that at least ~1300-1500 bird species (~12% of the total) have gone extinct since the Late Pleistocene, with 55% of these extinctions undiscovered (not yet discovered or left no trace). We estimate that the Pacific accounts for 61% of total bird extinctions. Bird extinction rate varied through time with an intense episode ~1300 CE, which likely represents the largest human-driven vertebrate extinction wave ever, and a rate 80 (60-95) times the background extinction rate. Thus, humans have already driven more than one in nine bird species to extinction, with likely severe, and potentially irreversible, ecological and evolutionary consequences.

Madagascar's extraordinary biodiversity: Evolution, distribution, and use.

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.

Madagascar's extraordinary biodiversity: Threats and opportunities.

Madagascar's unique biota is heavily affected by human activity and is under intense threat. Here, we review the current state of knowledge on the conservation status of Madagascar's terrestrial and freshwater biodiversity by presenting data and analyses on documented and predicted species-level conservation statuses, the most prevalent and relevant threats, ex situ collections and programs, and the coverage and comprehensiveness of protected areas. The existing terrestrial protected area network in Madagascar covers 10.4% of its land area and includes at least part of the range of the majority of described native species of vertebrates with known distributions (97.1% of freshwater fishes, amphibians, reptiles, birds, and mammals combined) and plants (67.7%). The overall figures are higher for threatened species (97.7% of threatened vertebrates and 79.6% of threatened plants occurring within at least one protected area). International Union for Conservation of Nature (IUCN) Red List assessments and Bayesian neural network analyses for plants identify overexploitation of biological resources and unsustainable agriculture as the most prominent threats to biodiversity. We highlight five opportunities for action at multiple levels to ensure that conservation and ecological restoration objectives, programs, and activities take account of complex underlying and interacting factors and produce tangible benefits for the biodiversity and people of Madagascar.

The origin and evolution of open habitats in North America inferred by Bayesian deep learning models.

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.

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

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.

Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence.

Trees are fundamental for Earth's biodiversity as primary producers and ecosystem engineers and are responsible for many of nature's contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is necessary to quantify the current biodiversity crisis and to prioritize conservation efforts. However, the most comprehensive dataset of tree species extinction risk-the Red List of the International Union for the Conservation of Nature (IUCN RL)-lacks assessments for a substantial number of known tree species. The RL is based on a time-consuming expert-based assessment process, which hampers the inclusion of less-known species and the continued updating of extinction risk assessments. In this study, we used a computational pipeline to approximate RL extinction risk assessments for more than 21,000 tree species (leading to an overall assessment of 89% of all known tree species) using a supervised learning approach trained based on available IUCN RL assessments. We harvested the occurrence data for tree species worldwide from online databases, which we used with other publicly available data to design features characterizing the species' geographic range, biome and climatic affinities, and exposure to human footprint. We trained deep neural network models to predict their conservation status, based on these features. We estimated 43% of the assessed tree species to be threatened with extinction and found taxonomic and geographic heterogeneities in the distribution of threatened species. The results are consistent with the recent estimates by the Global Tree Assessment initiative, indicating that our approach provides robust and time-efficient approximations of species' IUCN RL extinction risk assessments.

Target sequence capture of Barnadesioideae (Compositae) demonstrates the utility of low coverage loci in phylogenomic analyses.

Target sequence capture has emerged as a powerful method to sequence hundreds or thousands of genomic regions in a cost- and time-efficient approach. In most cases, however, targeted regions lack full sequence information for certain samples, due to taxonomic, laboratory, or stochastic factors. Loci lacking molecular data for a large number of samples are commonly excluded from downstream analyses, even though they may still contain valuable information. On the other hand, including data-poor loci may bias phylogenetic analyses. Here we use a target sequence capture dataset of an ecologically and taxonomically diverse group of spiny sunflowers (Asteraceae, or Compositae: Barnadesioideae) to test how the inclusion or exclusion of such data-poor loci affects phylogenetic inference. We investigate the sensitivity of concatenation and coalescent approaches to missing data with matrices of varying taxonomic completeness by filtering loci with different proportions of missing samples prior to data analysis. We find that missing data affect both the topology and branch support of the resulting phylogenies. The matrix containing all loci yielded the overall highest node support values, independently of the amount of missing nucleotides. These results provide empirical support to earlier suggestions based on single genes and data simulations that taxa with high amounts of missing data should not be readily dismissed as they can provide essential information for phylogenomic reconstruction.

A bioinformatic platform to integrate target capture and whole genome sequences of various read depths for phylogenomics.

The increasing availability of short-read whole genome sequencing (WGS) provides unprecedented opportunities to study ecological and evolutionary processes. Although loci of interest can be extracted from WGS data and combined with target sequence data, this requires suitable bioinformatic workflows. Here, we test different assembly and locus extraction strategies and implement them into secapr, a pipeline that processes short-read data into multilocus alignments for phylogenetics and molecular ecology analyses. We integrate the processing of data from low-coverage WGS (<30×) and target sequence capture into a flexible framework, while optimizing de novo contig assembly and loci extraction. Specifically, we test different assembly strategies by contrasting their ability to recover loci from targeted butterfly protein-coding genes, using four data sets: a WGS data set across different average coverages (10×, 5× and 2×) and a data set for which these loci were enriched prior to sequencing via target sequence capture. Using the resulting de novo contigs, we account for potential errors within contigs and infer phylogenetic trees to evaluate the ability of each assembly strategy to recover species relationships. We demonstrate that choosing multiple sizes of kmer simultaneously for assembly results in the highest yield of extracted loci from de novo assembled contigs, while data sets derived from sequencing read depths as low as 5× recovers the expected species relationships in phylogenetic trees. By making the tested assembly approaches available in the secapr pipeline, we hope to inspire future studies to incorporate complementary data and make an informed choice on the optimal assembly strategy.

The past and future human impact on mammalian diversity.

To understand the current biodiversity crisis, it is crucial to determine how humans have affected biodiversity in the past. However, the extent of human involvement in species extinctions from the Late Pleistocene onward remains contentious. Here, we apply Bayesian models to the fossil record to estimate how mammalian extinction rates have changed over the past 126,000 years, inferring specific times of rate increases. We specifically test the hypothesis of human-caused extinctions by using posterior predictive methods. We find that human population size is able to predict past extinctions with 96% accuracy. Predictors based on past climate, in contrast, perform no better than expected by chance, suggesting that climate had a negligible impact on global mammal extinctions. Based on current trends, we predict for the near future a rate escalation of unprecedented magnitude. Our results provide a comprehensive assessment of the human impact on past and predicted future extinctions of mammals.

Phylogenomics and biogeography of the world's thrushes (Aves, Turdus): new evidence for a more parsimonious evolutionary history.

To elucidate the relationships and spatial range evolution across the world of the bird genus Turdus (Aves), we produced a large genomic dataset comprising ca 2 million nucleotides for ca 100 samples representing 53 species, including over 2000 loci. We estimated time-calibrated maximum-likelihood and multispecies coalescent phylogenies and carried out biogeographic analyses. Our results indicate that there have been considerably fewer trans-oceanic dispersals within the genus Turdus than previously suggested, such that the Palaearctic clade did not originate in America and the African clade was not involved in the colonization of the Americas. Instead, our findings suggest that dispersal from the Western Palaearctic via the Antilles to the Neotropics might have occurred in a single event, giving rise to the rich Neotropical diversity of Turdus observed today, with no reverse dispersals to the Palaearctic or Africa. Our large multilocus dataset, combined with dense species-level sampling and analysed under probabilistic methods, brings important insights into historical biogeography and systematics, even in a scenario of fast and spatially complex diversification.

Allele Phasing Greatly Improves the Phylogenetic Utility of Ultraconserved Elements.

Advances in high-throughput sequencing techniques now allow relatively easy and affordable sequencing of large portions of the genome, even for nonmodel organisms. Many phylogenetic studies reduce costs by focusing their sequencing efforts on a selected set of targeted loci, commonly enriched using sequence capture. The advantage of this approach is that it recovers a consistent set of loci, each with high sequencing depth, which leads to more confidence in the assembly of target sequences. High sequencing depth can also be used to identify phylogenetically informative allelic variation within sequenced individuals, but allele sequences are infrequently assembled in phylogenetic studies. Instead, many scientists perform their phylogenetic analyses using contig sequences which result from the de novo assembly of sequencing reads into contigs containing only canonical nucleobases, and this may reduce both statistical power and phylogenetic accuracy. Here, we develop an easy-to-use pipeline to recover allele sequences from sequence capture data, and we use simulated and empirical data to demonstrate the utility of integrating these allele sequences to analyses performed under the multispecies coalescent model. Our empirical analyses of ultraconserved element locus data collected from the South American hummingbird genus Topaza demonstrate that phased allele sequences carry sufficient phylogenetic information to infer the genetic structure, lineage divergence, and biogeographic history of a genus that diversified during the last 3 myr. The phylogenetic results support the recognition of two species and suggest a high rate of gene flow across large distances of rainforest habitats but rare admixture across the Amazon River. Our simulations provide evidence that analyzing allele sequences leads to more accurate estimates of tree topology and divergence times than the more common approach of using contig sequences.

A Guide to Carrying Out a Phylogenomic Target Sequence Capture Project.

High-throughput DNA sequencing techniques enable time- and cost-effective sequencing of large portions of the genome. Instead of sequencing and annotating whole genomes, many phylogenetic studies focus sequencing effort on large sets of pre-selected loci, which further reduces costs and bioinformatic challenges while increasing coverage. One common approach that enriches loci before sequencing is often referred to as target sequence capture. This technique has been shown to be applicable to phylogenetic studies of greatly varying evolutionary depth. Moreover, it has proven to produce powerful, large multi-locus DNA sequence datasets suitable for phylogenetic analyses. However, target capture requires careful considerations, which may greatly affect the success of experiments. Here we provide a simple flowchart for designing phylogenomic target capture experiments. We discuss necessary decisions from the identification of target loci to the final bioinformatic processing of sequence data. We outline challenges and solutions related to the taxonomic scope, sample quality, and available genomic resources of target capture projects. We hope this review will serve as a useful roadmap for designing and carrying out successful phylogenetic target capture studies.

Conceptual and empirical advances in Neotropical biodiversity research.

The unparalleled biodiversity found in the American tropics (the Neotropics) has attracted the attention of naturalists for centuries. Despite major advances in recent years in our understanding of the origin and diversification of many Neotropical taxa and biotic regions, many questions remain to be answered. Additional biological and geological data are still needed, as well as methodological advances that are capable of bridging these research fields. In this review, aimed primarily at advanced students and early-career scientists, we introduce the concept of "trans-disciplinary biogeography," which refers to the integration of data from multiple areas of research in biology (e.g., community ecology, phylogeography, systematics, historical biogeography) and Earth and the physical sciences (e.g., geology, climatology, palaeontology), as a means to reconstruct the giant puzzle of Neotropical biodiversity and evolution in space and time. We caution against extrapolating results derived from the study of one or a few taxa to convey general scenarios of Neotropical evolution and landscape formation. We urge more coordination and integration of data and ideas among disciplines, transcending their traditional boundaries, as a basis for advancing tomorrow's ground-breaking research. Our review highlights the great opportunities for studying the Neotropical biota to understand the evolution of life.

SECAPR-a bioinformatics pipeline for the rapid and user-friendly processing of targeted enriched Illumina sequences, from raw reads to alignments.

Evolutionary biology has entered an era of unprecedented amounts of DNA sequence data, as new sequencing technologies such as Massive Parallel Sequencing (MPS) can generate billions of nucleotides within less than a day. The current bottleneck is how to efficiently handle, process, and analyze such large amounts of data in an automated and reproducible way. To tackle these challenges we introduce the Sequence Capture Processor (SECAPR) pipeline for processing raw sequencing data into multiple sequence alignments for downstream phylogenetic and phylogeographic analyses. SECAPR is user-friendly and we provide an exhaustive empirical data tutorial intended for users with no prior experience with analyzing MPS output. SECAPR is particularly useful for the processing of sequence capture (synonyms: target or hybrid enrichment) datasets for non-model organisms, as we demonstrate using an empirical sequence capture dataset of the palm genus Geonoma (Arecaceae). Various quality control and plotting functions help the user to decide on the most suitable settings for even challenging datasets. SECAPR is an easy-to-use, free, and versatile pipeline, aimed to enable efficient and reproducible processing of MPS data for many samples in parallel.

Estimating Age-Dependent Extinction: Contrasting Evidence from Fossils and Phylogenies.

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.