Environmental DNA (eDNA) analysis is a revolutionary method to monitor marine biodiversity from animal DNA traces. Examining the capacity of eDNA to provide accurate biodiversity measures in species‐rich ecosystems such as coral reefs is a prerequisite for their application in long‐term monitoring. Here, we surveyed two Colombian tropical marine reefs, the island of Providencia and Gayraca Bay near Santa Marta, using eDNA and underwater visual census (UVC) methods. We collected a large quantity of surface water (30 L per filter) above the reefs and applied a metabarcoding protocol using three different primer sets targeting the 12S mitochondrial DNA, which are specific to the vertebrates Actinopterygii and Elasmobranchii. By assigning eDNA sequences to species using a public reference database, we detected the presence of 107 and 85 fish species, 106 and 92 genera, and 73 and 57 families in Providencia and Gayraca Bay, respectively. Of the species identified using eDNA, 32.7% (Providencia) and 18.8% (Gayraca) were also found in the UVCs. We further found congruence in genus and species richness and abundance between eDNA and UVC approaches in Providencia but not in Gayraca Bay. Mismatches between eDNA and UVC had a phylogenetic and ecological signal, with eDNA detecting a broader phylogenetic diversity and more effectively detecting smaller species, pelagic species and those in deeper habitats. Altogether, eDNA can be used for fast and broad biodiversity surveys and is applicable to species‐rich ecosystems in the tropics, but improved coverage of the reference database is required before this new method could serve as an effective complement to traditional census methods.
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.
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 modelling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatio-temporally dynamic landscapes. Modelled processes can include environmental filtering, 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 a case study, we examined alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth's Cenozoic era. We found that a carrying capacity linked with energy was the only model variant that could simultaneously produce a realistic LDG, species range size frequencies, and phylogenetic tree balance. 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 step towards a numeric and mechanistic understanding of the physical and biological processes that shape Earth's biodiversity.
Aim Tropical America, including the Tropical Eastern Pacific and the Caribbean Sea, presents a high level of marine biodiversity, but its fish fauna has been poorly documented. In early studies marine species distributions were interpreted based on tectonic activity during the late Cenozoic, while more recent studies have highlighted a link with the present‐day environment. Here, we described the assemblage richness and composition of fishes in Tropical America and related these properties to both the past evolution of marine environmental conditions and current environmental gradients. Location Tropical America. Taxon Demersal and benthic fishes. Methods We mapped the distribution of 2,216 demersal and benthic fish species of Tropical America using existing occurrence data. We computed three assemblage indicators: species richness, composition and nestedness, which we explained by environmental gradients. We linked compositional distance to environmental differences using distance‐based redundancy analysis, species richness and nestedness using a generalized linear model. We ran simulations of a mechanistic model in which three processes determine the spatial dynamics of biodiversity: speciation, dispersal and extinction. This model yielded estimates for species assemblage properties following palaeogeographic changes in the region that shaped the current coastal habitat configuration. Results Fish species richness in Tropical America peaks around the Florida Peninsula, Bahamas and Greater Antilles. Fish composition varies along a depth gradient, between the Tropical Eastern Pacific and the Caribbean Sea, and forms distinct domains within the Caribbean region. The nestedness component of β‐diversity is lower in shallower assemblages, especially those along the outer section of the Greater Caribbean. Species richness and nestedness are partly explained by current environmental conditions, but model simulations illustrate how this may be further explained by the tectonic history of the region. Main conclusions Species richness peaks in the Greater Caribbean, coinciding with generally favourable current environmental conditions for demersal and benthic fishes. The high species richness and the low nestedness of fish assemblages in the Cuba region are compatible with the results of palaeo‐environmental changes that have occurred in that area. Effects of the plate tectonic history might still be present in the organization of fish fauna in this region.
Summary The documentation of biodiversity distribution through species range identification is crucial for macroecology, biogeography, conservation, and restoration. However, for plants, species range maps remain scarce and often inaccurate. We present a novel approach to map species ranges at a global scale, integrating polygon mapping and species distribution modelling (SDM). We develop a polygon mapping algorithm by considering distances and nestedness of occurrences. We further apply an SDM approach considering multiple modelling algorithms, complexity levels, and pseudo‐absence selections to map the species at a high spatial resolution and intersect it with the generated polygons. We use this approach to construct range maps for all 1957 species of Fagales and Pinales with data compilated from multiple sources. We construct high‐resolution global species richness maps of these important plant clades, and document diversity hotspots for both clades in southern and south‐western China, Central America, and Borneo. We validate the approach with two representative genera, Quercus and Pinus, using previously published coarser range maps, and find good agreement. By efficiently producing high‐resolution range maps, our mapping approach offers a new tool in the field of macroecology for studying global species distribution patterns and supporting ongoing conservation efforts.
Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithmspecific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github. com/N-SDM/N-SDM.
<p>Explaining the origin of large-scale biodiversity gradients has been a key aspiration of early naturalists such as Wegener, Darwin and Humboldt; who looked at natural processes in an integrated way. Early on, these naturalists acknowledged the role of plate tectonics and climate variations in shaping modern day biodiversity patterns.<span>&#160;</span></p><p>As science advanced, the complexity of ecological, evolutionary, geological and climatological processes became evident while research became increasingly fragmented across different disciplines. Nevertheless, recent development in mechanistic modeling approaches now enable bringing disciplines back together, opening a new interdisciplinary scientific pathway.</p><p>Here, we present GEN3SIS, the GENeral Engine for Eco-Evolutionary SImulationS. It is the first spatially explicit eco-evolutionary model that incorporates deep-time Earth history, including plate tectonics, as well as climate variations in a modular way. The modular design allows exploring the consequences of user-defined biological processes that act across &#8220;real world&#8221; spatio-temporal landscapes. Emerging from the model are specie&#8217;s ranges, alpha and beta diversity patterns, ecological traits as well as phylogenies. Subsequently, these patterns can be compared to empirical data. Furthermore, GEN3SIS allows assessing paleoclimatic and paleogeographic hypotheses by using different Earth history scenarios and comparing simulation outputs with empirical biological data.</p><p>As a case study, we explore the cold-adapted plant biodiversity dynamics throughout the Earth&#8217;s Cenozoic history, based on a deep-time tectonic and climate reconstruction. The Cenozoic India-Asia collision formed the Himalayan mountain range. In this highly elevated region, the first cold niches of the Cenozoic appeared, demanding adaptation from the local living flora. We hindcast diversification of cold-adapted species with GEN3SIS, for which we use a topo-climatic reconstruction for the last 55 Myr. The model predicts the emergence of current cold-species richness patterns. Moreover, simulations indicate that cold-adapted flora emerged in the Oligocene, first in the Himalayas, followed by a spread to the Arctic. This agrees with observed low species richness and high nestedness of Arctic assemblages compared to those of the Himalayan mountain ranges. Under ongoing climate change a major loss of cold-adapted plant diversity is expected by the end of the century, particularly in lower latitude mountain ranges. Hindcasting and forecasting dynamics of cold-adapted lineages highlights the transient fate of cold organisms in a warming world.</p><p>GEN3SIS is made available as an R package, which allows customizing (i) the simulated landscape including environmental variables and (ii) all the processes interacting under different spatial and temporal scales. Consequently, GEN3SIS fosters collaborations between different natural disciplines and therefore contributes to an interdisciplinary understanding of the processes that shaped Earth&#8217;s history.</p>
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