Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

Through the development of environmental DNA (eDNA) metabarcoding, in situ monitoring of organisms is becoming easier and promises a revolution in our approaches to detect changes in biodiversity over space and time. A cornerstone of eDNA approach is the development of primer pairs that allow amplifying the DNA of specific taxonomic groups, which is then used to link the DNA sequence to taxonomic identification. Here, we propose a framework for comparing primer pairs regarding (a) their capacity to bind and amplify a broad coverage of species within the target clade using in silico PCR, (b) their capacity to not only discriminate between species but also genera or families, and (c) their in situ specificity and efficiency across a variety of environments. As a case study, we focus on two mitochondrial 12S primer pairs, MiFish‐U and teleo, which were designed to amplify fishes. We found that the performance of in silico PCRs were high for both primer pairs, but teleo amplified more genera across Actinopterygii, Chondrichthyes, and Petromyzontomorphi than MiFish‐U. In contrast, the discriminatory power for species, genera, and families were higher for MiFish‐U than teleo, likely associated with the greater length of the amplified DNA fragments. The evaluation of their in situ efficiency showed a higher recovered species richness of teleo compared to MiFish‐U in tropical and temperate freshwater environments, but that generally both teleo and MiFish‐U primers pairs perform well to monitor fish species. Since more species were detected when used together, those primer pairs are best used in combination to increase the ability of species detection.

High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes. We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA.