Phylogenetic signal is the tendency for closely related species to display similar trait values as a consequence of their phylogenetic proximity. Ecologists and evolutionary biologists are becoming increasingly interested in studying the phylogenetic signal and the processes which drive patterns of trait values in the phylogeny. Here, we present a new R package, which provides a collection of tools to explore the phylogenetic signal for continuous biological traits. These tools are mainly based on the concept of autocorrelation and have been first developed in the field of spatial statistics. To illustrate the use of the package, we analyze the phylogenetic signal in pollution sensitivity for 17 species of diatoms.
The recent emergence of barcoding approaches coupled to those of next-generation sequencing (NGS) has raised new perspectives for studying environmental communities. In this framework, we tested the possibility to derive accurate inventories of diatom communities from pyrosequencing outputs with an available DNA reference library. We used three molecular markers targeting the nuclear, chloroplast and mitochondrial genomes (SSU rDNA, rbcL and cox1) and three samples of a mock community composed of 30 known diatom strains belonging to 21 species. In the goal to detect methodological biases, one sample was constituted directly from pooled cultures, whereas the others consisted of pooled PCR products. The NGS reads obtained by pyrosequencing (Roche 454) were compared first to a DNA reference library including the sequences of all the species used to constitute the mock community, and second to a complete DNA reference library with a larger taxonomic coverage. A stringent taxonomic assignation gave inventories that were compared to the real one. We detected biases due to DNA extraction and PCR amplification that resulted in false-negative detection. Conversely, pyrosequencing errors appeared to generate false positives, especially in case of closely allied species. The taxonomic coverage of DNA reference libraries appears to be the most crucial factor, together with marker polymorphism which is essential to identify taxa at the species level. RbcL offers a high resolving power together with a large DNA reference library. Although needing further optimization, pyrosequencing is suitable for identifying diatom assemblages and may find applications in the field of freshwater biomonitoring.
Robust critical systems are characterized by power laws which occur over a broad range of conditions. Their robust behaviour has been explained by local interactions. While such systems could be widespread in nature, their properties are not well understood. Here, we study three robust critical ecosystem models and a null model that lacks spatial interactions. In all these models, individuals aggregate in patches whose size distributions follow power laws which melt down under increasing external stress. We propose that this power-law decay associated with the connectivity of the system can be used to evaluate the level of stress exerted on the ecosystem. We identify several indicators along the transition to extinction. These indicators give us a relative measure of the distance to extinction, and have therefore potential application to conservation biology, especially for ecosystems with self-organization and critical transitions.
Aquatic biomonitoring has become an essential task in Europe and many other regions as a consequence of strong anthropogenic pressures affecting the health of lakes, rivers, oceans and groundwater. A typical assessment of the environmental quality status, such as it is required by European but also North American and other legislation, relies on matching the composition of assemblages of organisms identified using morphological criteria present in aquatic ecosystems to those expected in the absence of anthropogenic pressures. Through decade-long and difficult intercalibration exercises among networks of regulators and scientists in European countries, a pragmatic biomonitoring approach was developed and adopted, which now produces invaluable information. Nonetheless, this approach is based on several hundred different protocols, making Next-Generation Biomonitoring of Aquatic Ecosystems
The species structure of an ectomycorrhizal (ECM) community was assessed monthly for 15 months in the two horizons (A1 and A2) of an oak temperate forest in northeastern France. Ectomycorrhizal species were identified each month by internal transcribed spacer sequencing. Seventy-five fungal symbionts were identified. The community was dominated by Tomentellaceae, Russulaceae, Cortinariaceae, and Boletales. Four species are abundant in the study site: Lactarius quietus, Tomentella sublilacina, Cenococcum geophilum, and Russula sp1. The relative abundance of each species varied depending on the soil horizon and over time. Some species, such as L. quietus, were present in the A1 and A2 horizons. C. geophilum was located particularly in the A2 horizon, whereas T. sublilacina was more abundant in A1. Some species, such as Clavulina sp., were detected in winter, while T. sublilacina and L. quietus were present all year long. Our results support the hypothesis that a rapid turnover of species composition of the ECM community occurs over the course of a month. The spatial and temporal unequal distribution of ECM species could be explained by their ecological preferences, driven by such factors as root longevity, competition for resources, and resistance to environmental variability.The fine roots of social tree species in temperate and boreal forests are symbiotically associated with fungi (52), forming composite organs called ectomycorrhizas (ECM). The ECM fungi play a crucial role in tree health by enhancing the nutrient acquisition, drought tolerance, and pathogen resistance of their hosts. ECMs efficiently take up water and organic and inorganic nutrients from the soil via the extramatricial mycelium and translocate these to colonized tree roots, receiving carbohydrates from the host in return (52). Most of the ectomycorrhizal roots are located in the top 20 centimeters of the soil, an area which is enriched in organic matter and where nutrients are concentrated (50). The ECM fungal community is species rich at the forest stand level, where hundreds of different fungal symbionts can be identified by morphotyping and DNA-based molecular methods (13,17,36,56).Beside its species composition, the structure is an important characteristic of the ECM community. Differences in ECM community structure on different scales are well documented: on the ecosystem scale (postdisturbance or postplanting successions) and along forest dynamics (4, 28, 56, 65), on the seasonal scale (6,8,20,33,54), and along spatial dimensions (vertical scale [13,14,24,49] and horizontal scale [36,58]). In a microsite or on a forest strand scale, species are distributed neither uniformly nor randomly but rather are aggregated in patches or distributed along gradients (9,12,19,24,44). The spatial heterogeneity of communities is important in terms of succession, adaptation, maintenance of species diversity, interspecific competition, and community stability (38). A spatial niche differentiation of ECM species and of ECM exploration types (1) could be du...
Diatoms are main bioindicators used to assess the ecological quality of rivers, but their identification is difficult and time-consuming. Next Generation Sequencing (NGS) can be used to study communities of microorganisms, so we carried out a test of the reliability of 454 pyrosequencing for estimating diatom inventories in environmental samples. We used small subunit ribosomal deoxyribonucleic acid (SSU rDNA), ribulose-1, 5-bisphosphate carboxylase (rbcL), and cytochrome oxidase I (COI) markers and examined reference libraries to define thresholds between the intra-and interspecific and intra-and intergeneric genetic distances. Based on tests of 1 mock community, we used a threshold of 99% identity for SSU rDNA and rbcL sequences to study freshwater diatoms at the species level. We applied 454 pyrosequencing to 4 contrasting environmental samples (with one in duplicate), assigned taxon names to environmental sequences, and compared the qualitative and quantitative molecular inventories to those obtained by microscopy. Species richness detected by microscopy was always higher than that detected by pyrosequencing. Some morphologically detected taxa may have been persistent frustules from dead cells. Some taxa detected by molecular analysis were not detected by morphology and vice versa. The main source of divergence appears to be inadequate taxonomic coverage in DNA reference libraries. Only a small percentage of species (but almost all genera) in morphological inventories were included in DNA reference libraries. DNA reference libraries contained a smaller percentage of species from tropical (27.1-38.1%) than from temperate samples (53.7-77.8%). Agreement between morphological and molecular inventories was better for species with relative abundance >1% than for rare species. The rbcL marker appeared to provide more reproducible results (94.9% species similarity between the 2 duplicates) and was very useful for molecular identification, but procedural standardization is needed. The water-quality ranking assigned to a site via the Pollution Sensitivity diatom index was the same whether calculated with molecular or morphological data. Pyrosequencing is a promising approach for detecting all species, even rare ones, once reference libraries have been developed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.