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.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Freshwaters worldwide face serious threats, making their protection increasingly important. Freshwater monitoring has historically produced valuable data and continues to develop. Rapid improvements to biomolecular techniques are revolutionizing the way scientists describe biological communities and are bringing about major changes in biomonitoring. Combined with high‐throughput sequencing, DNA metabarcoding is fast and cost‐effective, generating massive amounts of data. In a world with numerous ecological threats, “big data” constitute a tremendous opportunity to improve the efficiency of biological monitoring. These fundamental changes in biomonitoring will require freshwater ecologists and environmental managers to reconsider how they handle large amounts of data.

DNA metabarcoding is increasingly used for the assessment of aquatic communities, and numerous studies have investigated the consistency of this technique with traditional morpho‐taxonomic approaches. These individual studies have used DNA metabarcoding to assess diversity and community structure of aquatic organisms both in marine and freshwater systems globally over the last decade. However, a systematic analysis of the comparability and effectiveness of DNA‐based community assessment across all of these studies has hitherto been lacking. Here, we performed the first meta‐analysis of available studies comparing traditional methods and DNA metabarcoding to measure and assess biological diversity of key aquatic groups, including plankton, microphytobentos, macroinvertebrates, and fish. Across 215 data sets, we found that DNA metabarcoding provides richness estimates that are globally consistent to those obtained using traditional methods, both at local and regional scale. DNA metabarcoding also generates species inventories that are highly congruent with traditional methods for fish. Contrastingly, species inventories of plankton, microphytobenthos and macroinvertebrates obtained by DNA metabarcoding showed pronounced differences to traditional methods, missing some taxa but at the same time detecting otherwise overseen diversity. The method is generally sufficiently advanced to study the composition of fish communities and replace more invasive traditional methods. For smaller organisms, like macroinvertebrates, plankton and microphytobenthos, DNA metabarcoding may continue to give complementary rather than identical estimates compared to traditional approaches. Systematic and comparable data collection will increase the understanding of different aspects of this complementarity, and increase the effectiveness of the method and adequate interpretation of the results.

Diatoms are micro-algal indicators of freshwater pollution. Current standardized methodologies are based on microscopic determinations, which is time consuming and prone to identification uncertainties. The use of DNA-barcoding has been proposed as a way to avoid these flaws. Combining barcoding with next-generation sequencing enables collection of a large quantity of barcodes from natural samples. These barcodes are identified as certain diatom taxa by comparing the sequences to a reference barcoding library using algorithms. Proof of concept was recently demonstrated for synthetic and natural communities and underlined the importance of the quality of this reference library. We present an open-access and curated reference barcoding database for diatoms, called R-Syst::diatom, developed in the framework of R-Syst, the network of systematic supported by INRA (French National Institute for Agricultural Research), see http://www.rsyst.inra.fr/en. R-Syst::diatom links DNA-barcodes to their taxonomical identifications, and is dedicated to identify barcodes from natural samples. The data come from two sources, a culture collection of freshwater algae maintained in INRA in which new strains are regularly deposited and barcoded and from the NCBI (National Center for Biotechnology Information) nucleotide database. Two kinds of barcodes were chosen to support the database: 18S (18S ribosomal RNA) and rbcL (Ribulose-1,5-bisphosphate carboxylase/oxygenase), because of their efficiency. Data are curated using innovative (Declic) and classical bioinformatic tools (Blast, classical phylogenies) and up-to-date taxonomy (Catalogues and peer reviewed papers). Every 6 months R-Syst::diatom is updated. The database is available through the R-Syst microalgae website (http://www.rsyst.inra.fr/) and a platform dedicated to next-generation sequencing data analysis, virtual_BiodiversityL@b (https://galaxy-pgtp.pierroton.inra.fr/). We present here the content of the library regarding the number of barcodes and diatom taxa. In addition to these information, morphological features (e.g. biovolumes, chloroplasts…), life-forms (mobility, colony-type) or ecological features (taxa preferenda to pollution) are indicated in R-Syst::diatom.Database URL: http://www.rsyst.inra.fr/

Summary 1. Anthropogenic impacts on the biogeochemical cycles of nitrogen (N) and phosphorus (P) affect natural ecosystems worldwide. Modelling is required to predict where and when these key nutrients limit primary production in freshwaters. 2. We reviewed 382 nutrient‐enrichment experiments to examine which factors promote limitation of microphytobenthos biomass by N or P in streams and rivers. Using regression models, we examined whether the response of microphytobenthos biomass to N and P additions could be predicted by the absolute N and P concentrations in the water, the water N:P ratio or a combination of the two. 3. The absolute N concentration in the water was the best predictor of the magnitude of the response of microphytobenthos biomass to N additions. In comparison, the N:P ratio was the best predictor of whether or not N was limiting. However, predictions were uncertain except at extreme N:P ratios <1 : 1 and >100 : 1. 4. The absolute P concentration in the water was the best predictor of the magnitude of the response of microphytobenthos biomass to P additions. Neither the absolute nor the relative N and P concentrations predicted whether or not P was limiting. 5. The absolute and the relative N and P water concentrations contribute significant and complementary insights into the responses of microphytobenthos biomass to nutrient enrichment in running waters. However, ability to predict nutrient limitation from these concentrations is constrained by substantial error in the models. In the future, the prediction ability of models of nutrient limitation might be improved by focussing on regional scales and accounting for additional factors such as light and disturbance.

Classical biomonitoring techniques have focused primarily on measures linked to various biodiversity metrics and indicator species. Next-generation biomonitoring (NGB) describes a suite of tools and approaches that allow the examination of a broader spectrum of organizational levels-from genes to entire ecosystems. Here, we frame 10 key questions that we envisage will drive the field of NGB over the next decade. While Makiola et al. Questions for Next-Generation Biomonitoring not exhaustive, this list covers most of the key challenges facing NGB, and provides the basis of the next steps for research and implementation in this field. These questions have been grouped into current-and outlook-related categories, corresponding to the organization of this paper.

DNA metabarcoding has been introduced as a revolutionary way to identify organisms and monitor ecosystems. However, the potential of this approach for biomonitoring remains partially unfulfilled because a significant part of the sampled DNA cannot be affiliated to species due to incomplete reference libraries. Thus, biotic indices, which are based on the estimated abundances of species in a community and their ecological profiles, can be inaccurate. We propose to compute biotic indices using phylogenetic imputation of operational taxonomic units (OTUs') ecological profiles (OTU-PITI approach). First, OTUs sequences are inserted within a reference phylogeny. Second, OTUs' ecological profiles are estimated on the basis of their phylogenetic relationships with reference species whose ecology is known. Based on these ecological profiles, biotic indices can be computed using all available OTUs. Using freshwater diatoms as a case study, we show that short DNA barcodes can be placed accurately within a phylogeny and their ecological preferences estimated with a satisfactory level of precision. In the light of these results, we tested the approach with a data set of 139 environmental samples of benthic river diatoms for which the same biotic index (specific sensitivity index) was calculated using (a) traditional microscopy, (b) OTUs with taxonomic assignment approach, (c) OTUs with phylogenetic estimation of ecological profiles (OTU-PITI) and (d) OTU with taxonomic assignment completed by the phylogenetic approach (OTU-PITI) for unclassified OTUs. Using traditional microscopy as a reference, we found that the combination of the OTUs' taxonomic assignment completed by the phylogenetic method performed satisfactorily and substantially better than the other methods tested.