Environmental DNA Time Series in Ecology

Barcoding Brazilian mammals to monitor biological diversity and threats: Trends, perspectives, and knowledge gaps

DNA barcoding and environmental DNA (eDNA) represent significant advances for biomonitoring the world's biodiversity and its threats. However, these methods are highly dependent on the presence of species sequences on molecular databases. Brazil is one of the world's largest and most biologically diverse countries. However, many knowledge gaps still exist for describing, identifying, and monitoring of mammalian biodiversity using molecular methods. We aimed to unravel the patterns of the presence of Brazilian mammal species on molecular databases to improve our understanding of how effectively it would be to monitor them using DNA barcoding and environmental DNA, and contribute to mammalian conservation. We foundt many gaps in molecular databases, with many taxa being poorly represented, particularly from Amazonia, the order Lagomorpha, and arboreal, gomivorous, near extinct, and illegally traded species. Moreover, our analyses revealed that species description year was the most important factor determining the probability of a species to being sequenced. Primates are the group with the highest number of species considered a priority for sequencing due to their high level of combined threats. We highlight where investments are needed to fill knowledge gaps and increase the representativity of species on molecular databases to enable a better monitoring ability of Brazilian mammals encompassing different traits using DNA barcoding and environmental DNA.

Harnessing eDNA metabarcoding to investigate fish community composition and its seasonal changes in the Oslo fjord

In the face of global ecosystem changes driven by anthropogenic activities, effective biomonitoring strategies are crucial for mitigating impacts on vulnerable aquatic habitats. Time series analysis underscores a great significance in understanding the dynamic nature of marine ecosystems, especially amidst climate change disrupting established seasonal patterns. Focusing on Norway's Oslo fjord, our research utilises eDNA-based monitoring for temporal analysis of aquatic biodiversity during a one year period, with bi-monthly sampling along a transect. To increase the robustness of the study, a taxonomic assignment comparing BLAST+ and SINTAX approaches was done. Utilising MiFish and Elas02 primer sets, our study detected 63 unique fish species, including several commercially important species. Our findings reveal a substantial increase in read abundance during specific migratory cycles, highlighting the efficacy of eDNA metabarcoding for fish composition characterization. Seasonal dynamics for certain species exhibit clear patterns, emphasising the method's utility in unravelling ecological complexities. eDNA metabarcoding emerges as a cost-effective tool with considerable potential for fish community monitoring for conservation purposes in dynamic marine environments like the Oslo fjord, contributing valuable insights for informed management strategies.

Species distribution modeling combined with environmental DNA analysis to explore distribution of invasive alien mosquitofish (Gambusia affinis) in China

China has become one of the most serious countries suffering from biological invasions in the world. In the context of global climate change, invasive alien species (IAS) are likely to invade a wider area, posing greater ecological and economic threats in China. Western mosquitofish (Gambusia affinis), which is known as one of the 100 most invasive alien species, has distributed widely in southern China and is gradually spreading to the north, causing serious ecological damage and economic losses. However, its distribution in China is still unclear. Hence, there is an urgent need for a more convenient way to detect and monitor the distribution of G. affinis to put forward specific management. Therefore, we detected the distribution of G. affinis in China under current and future climate change by combing Maxent modeling prediction and eDNA verification, which is a more time-saving and reliable method to estimate the distribution of species. The Maxent modeling showed that G. affinis has a broad habitat suitability in China (especially in southern China) and would continue to spread in the future with ongoing climate change. However, eDNA monitoring showed that occurrences can already be detected in regions that Maxent still categorized as unsuitable. Besides temperature, precipitation and human influence were the most important environmental factors affecting the distribution of G. affinis in China. In addition, by environmental DNA analysis, we verified the presence of G. affinis predicted by Maxent in the Qinling Mountains where the presence of G. affinis had not been previously recorded.

Genomics for monitoring and understanding species responses to global climate change

All life forms across the globe are experiencing drastic changes in environmental conditions as a result of global climate change. These environmental changes are happening rapidly, incur substantial socioeconomic costs, pose threats to biodiversity and diminish a species' potential to adapt to future environments. Understanding and monitoring how organisms respond to human-driven climate change is therefore a major priority for the conservation of biodiversity in a rapidly changing environment. Recent developments in genomic, transcriptomic and epigenomic technologies are enabling unprecedented insights into the evolutionary processes and molecular bases of adaptation. This Review summarizes methods that apply and integrate omics tools to experimentally investigate, monitor and predict how species and communities in the wild cope with global climate change, which is by genetically adapting to new environmental conditions, through range shifts or through phenotypic plasticity. We identify advantages and limitations of each method and discuss future research avenues that would improve our understanding of species' evolutionary responses to global climate change, highlighting the need for holistic, multi-omics approaches to ecosystem monitoring during global climate change.

Novel community data in ecology-properties and prospects

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

The MetaInvert soil invertebrate genome resource provides insights into below-ground biodiversity and evolution

Soil invertebrates are among the least understood metazoans on Earth. Thus far, the lack of taxonomically broad and dense genomic resources has made it hard to thoroughly investigate their evolution and ecology. With MetaInvert we provide draft genome assemblies for 232 soil invertebrate species, representing 14 common groups and 94 families. We show that this data substantially extends the taxonomic scope of DNA- or RNA-based taxonomic identification. Moreover, we confirm that theories of genome evolution cannot be generalised across evolutionarily distinct invertebrate groups. The soil invertebrate genomes presented here will support the management of soil biodiversity through molecular monitoring of community composition and function, and the discovery of evolutionary adaptations to the challenges of soil conditions.

Ecological network structure in response to community assembly processes over evolutionary time

The dynamic structure of ecological communities results from interactions among taxa that change with shifts in species composition in space and time. However, our ability to study the interplay of ecological and evolutionary processes on community assembly remains relatively unexplored due to the difficulty of measuring community structure over long temporal scales. Here, we made use of a geological chronosequence across the Hawaiian Islands, representing 50 years to 4.15 million years of ecosystem development, to sample 11 communities of arthropods and their associated plant taxa using semiquantitative DNA metabarcoding. We then examined how ecological communities changed with community age by calculating quantitative network statistics for bipartite networks of arthropod-plant associations. The average number of interactions per species (linkage density), ratio of plant to arthropod species (vulnerability) and uniformity of energy flow (interaction evenness) increased significantly in concert with community age. The index of specializationH 2 ' has a curvilinear relationship with community age. Our analyses suggest that younger communities are characterized by fewer but stronger interactions, while biotic associations become more even and diverse as communities mature. These shifts in structure became especially prominent on East Maui (~0.5 million years old) and older volcanos, after enough time had elapsed for adaptation and specialization to act on populations in situ. Such natural progression of specialization during community assembly is probably impeded by the rapid infiltration of non-native species, with special risk to younger or more recently disturbed communities that are composed of fewer specialized relationships.

Dissent in the sediment? Lake sediments as archives of short- and long-range impact of anthropogenic activities in northeastern Germany

The suitability of lake sediment cores to reconstruct past inputs, regional pollution, and usage patterns of pesticides has been shown previously. Until now, no such data exist for lakes in eastern Germany. Therefore, 10 sediment cores (length 1 m) of 10 lakes in eastern Germany, the territory of the former German Democratic Republic (GDR), were collected and cut into 5-10-mm layers. In each layer, concentrations of trace elements (TEs) As, Cd, Cr, Cu, Ni, Pb, S, and Zn, as well as of organochlorine pesticides (OCPs), i.e., dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH), were analyzed. A miniaturized solid-liquid extraction technique in conjunction with headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) was used for the latter. The progression of TE concentrations over time is uniform. It follows a trans-regional pattern and is indicative of activity and policy making in West Germany before 1990 instead of those in the GDR. Of OCPs, only transformation products of DDT were found. Congener ratios indicate a mainly aerial input. In the lakes' profiles, several regional features and responses to national policies and measures are visible. Dichlorodiphenyldichloroethane (DDD) concentrations reflect the history of DDT use in the GDR. Lake sediments proved to be suitable to archive short- and long-range impacts of anthropogenic activity. Our data can be used to complement and validate other forms of environmental pollution long-term monitoring and to check for the efficiency of pollution countermeasures in the past.

Modelling environmental DNA transport in rivers reveals highly resolved spatio-temporal biodiversity patterns

The ever-increasing threats to riverine ecosystems call for novel approaches for highly resolved biodiversity assessments across taxonomic groups and spatio-temporal scales. Recent advances in the joint use of environmental DNA (eDNA) data and eDNA transport models in rivers (e.g., eDITH) allow uncovering the full structure of riverine biodiversity, hence elucidating ecosystem processes and supporting conservation measures. We applied eDITH to a metabarcoding dataset covering three taxonomic groups (fish, invertebrates, bacteria) and three seasons for a catchment sampled for eDNA at 73 sites. We upscaled eDNA-based biodiversity predictions to approximately 1900 reaches, and assessed α- and β-diversity patterns across seasons and taxonomic groups over the whole network. Genus richness predicted by eDITH was generally higher than values from direct eDNA analysis. Both predicted α- and β-diversity varied depending on season and taxonomic group. Predicted fish α-diversity increased downstream in all seasons, while invertebrate and bacteria α-diversity either decreased downstream or were unrelated to network position. Spatial β-diversity mostly decreased downstream, especially for bacteria. The eDITH model yielded a more refined assessment of freshwater biodiversity as compared to raw eDNA data, both in terms of spatial coverage, diversity patterns and effect of covariates, thus providing a more complete picture of freshwater biodiversity.

Getting the bugs out of AI: Advancing ecological research on arthropods through computer vision

Deep learning for computer vision has shown promising results in the field of entomology, however, there still remains untapped potential. Deep learning performance is enabled primarily by large quantities of annotated data which, outside of rare circumstances, are limited in ecological studies. Currently, to utilize deep learning systems, ecologists undergo extensive data collection efforts, or limit their problem to niche tasks. These solutions do not scale to region agnostic models. However, there are solutions that employ data augmentation, simulators, generative models, and self-supervised learning that can supplement limited labelled data. Here, we highlight the success of deep learning for computer vision within entomology, discuss data collection efforts, provide methodologies for optimizing learning from limited annotations, and conclude with practical guidelines for how to achieve a foundation model for entomology capable of accessible automated ecological monitoring on a global scale.

Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimizing sampling strategies therefore is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to undersampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna derived from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained), and number of sampling points. SSDMs were remade from different sampling simulations and compared with the "perfect knowledge" SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimize potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally for eDNA and other biodiversity survey techniques. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.

Sedimentary DNA for tracking the long-term changes in biodiversity

Understanding long-term dynamics is vitally important for explaining current biodiversity patterns and setting conservation goals in a changing world. However, the changes in biodiversity in time and space, particularly the dynamics at the centuries or even longer time scales, are poorly documented because of a lack of continuous monitoring data. The sedimentary DNA (sedDNA) has a great potential for paleo-community reconstruction, and it has recently been used as a powerful tool to characterize past dynamics in terms of biodiversity over geological timescales. In particular, it is useful for prokaryotes and eukaryotes that do not fossilize; hence, it is revolutionizing the scope of paleoecological research. Here, a "Research Weaving" method was performed with systematic maps and bibliometric webs based on the Web of Science for Science Citation Index Expanded, presenting a comprehensive landscape of the sedDNA that traces biological dynamics. We identified that most sedDNA-based studies have focused on microbial dynamics and on using samples from multitypes of sediments. This review summarized the advantages and common applications of sedDNA, focused on the biodiversity in microbial communities, and provided an outlook for the future of sedDNA research.

Decomposing predictability to identify dominant causal drivers in complex ecosystems

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

Fishing for fish environmental DNA: Ecological applications, methodological considerations, surveying designs, and ways forward

Vast global declines of freshwater and marine fish diversity and population abundance pose serious threats to both ecosystem sustainability and human livelihoods. Environmental DNA (eDNA)-based biomonitoring provides robust, efficient, and cost-effective assessment of species occurrences and population trends in diverse aquatic environments. Thus, it holds great potential for improving conventional surveillance frameworks to facilitate fish conservation and fisheries management. However, the many technical considerations and rapid developments underway in the eDNA arena can overwhelm researchers and practitioners new to the field. Here, we systematically analysed 416 fish eDNA studies to summarize research trends in terms of investigated targets, research aims, and study systems, and reviewed the applications, rationales, methodological considerations, and limitations of eDNA methods with an emphasis on fish and fisheries research. We highlighted how eDNA technology may advance our knowledge of fish behaviour, species distributions, population genetics, community structures, and ecological interactions. We also synthesized the current knowledge of several important methodological concerns, including the qualitative and quantitative power eDNA has to recover fish biodiversity and abundance, and the spatial and temporal representations of eDNA with respect to its sources. To facilitate ecological applications implementing fish eDNA techniques, recent literature was summarized to generate guidelines for effective sampling in lentic, lotic, and marine habitats. Finally, we identified current gaps and limitations, and pointed out newly emerging research avenues for fish eDNA. As methodological optimization and standardization improve, eDNA technology should revolutionize fish monitoring and promote biodiversity conservation and fisheries management that transcends geographic and temporal boundaries.

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

How does genetic architecture affect eco-evolutionary dynamics? A theoretical perspective

Recent studies have revealed the importance of feedbacks between contemporary rapid evolution (i.e. evolution that occurs through changes in allele frequencies) and ecological dynamics. Despite its inherent interdisciplinary nature, however, studies on eco-evolutionary feedbacks have been mostly ecological and tended to focus on adaptation at the phenotypic level without considering the genetic architecture of evolutionary processes. In empirical studies, researchers have often compared ecological dynamics when the focal species under selection has a single genotype with dynamics when it has multiple genotypes. In theoretical studies, common approaches are models of quantitative traits where mean trait values change adaptively along the fitness gradient and Mendelian traits with two alleles at a single locus. On the other hand, it is well known that genetic architecture can affect short-term evolutionary dynamics in population genetics. Indeed, recent theoretical studies have demonstrated that genetic architecture (e.g. the number of loci, linkage disequilibrium and ploidy) matters in eco-evolutionary dynamics (e.g. evolutionary rescue where rapid evolution prevents extinction and population cycles driven by (co)evolution). I propose that theoretical approaches will promote the synthesis of functional genomics and eco-evolutionary dynamics through models that combine population genetics and ecology as well as nonlinear time-series analyses using emerging big data.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Ecohydrology 2.0

This paper aims at a definition of the domain of ecohydrology, a relatively new discipline borne out of an intrusion-as advertised by this Topical Collection of the Rendiconti Lincei-of hydrology and geomorphology into ecology (or vice-versa, depending on the reader's background). The study of hydrologic controls on the biota proves, in our view, significantly broader than envisioned by its original focus that was centered on the critical zone where much of the action of soil, climate and vegetation interactions takes place. In this review of related topics and contributions, we propose a reasoned broadening of perspective, in particular by firmly centering ecohydrology on the fluvial catchment as its fundamental control volume. A substantial unity of materials and methods suggests that our advocacy may be considered legitimate.

Salinity Is a Key Determinant for the Microeukaryotic Community in Lake Ecosystems of the Inner Mongolia Plateau, China

The arid and semiarid areas experienced remarkable lake shrinkage during recent decades due to intensive human activities and climate change, which would result in unprecedented changes of microeukaryotic communities. However, little is known about how climate change affects the structure and ecological mechanisms of microeukaryotic communities in this area. Here, we used an 18S rRNA gene-based high-throughput sequencing approach to explore the structure, interspecies interaction, and assembly processes of the microeukaryotic community in lake ecosystems of the Inner Mongolia Plateau. As a direct result of climate change, salinity has become the key determinant of the lacustrine microeukaryotic community in this region. The microeukaryotic community in this ecosystem can be divided into three groups: salt (Lake Daihai), brackish (Lake Dalinuoer) and freshwater lakes. Co-occurrence network analysis revealed that salinity shapes the interspecies interactions of the microeukaryotic community. This causes interspecies interactions to change from antagonistic to cooperative with an increase in salinity. Phylogenetic-based β-nearest taxon distance analyses revealed that stochastic processes mainly dominated the microeukaryotic community assembly in lake ecosystems of the Inner Mongolia Plateau, and salinity stress drove the assembly processes of the microeukaryotic community from stochastic to deterministic. Overall, these findings expand the current understanding of interspecies interactions and assembly processes of microeukaryotic communities during climate change in lake ecosystems of the Inner Mongolia Plateau.

Best practices in metabarcoding of fungi: From experimental design to results

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

DNA in extracellular vesicles: from evolution to its current application in health and disease

Extracellular vesicle (EV) secretion is a highly conserved evolutionary trait in all organisms in the three domains of life. The packaging and release of EVs appears to be a bulk-flow process which takes place mainly under extreme conditions. EVs participate in horizontal gene transfer, which supports the survival of prokaryotic and eukaryotic microbes. In higher eukaryotes, almost all cells secrete a heterogeneous population of EVs loaded with various biomolecules. EV secretion is typically higher in cancer microenvironments, promoting tumor progression and metastasis. EVs are now recognized as additional mediators of autocrine and paracrine communication in health and disease. In this context, proteins and RNAs have been studied the most, but extracellular vesicle DNA (EV-DNA) has started to gain in importance in the last few years. In this review, we summarize new findings related to the loading mechanism(s), localization, and post-shedding function of EV-DNA. We also discuss the feasibility of using EV-DNA as a biomarker when performing a liquid biopsy, at the same time emphasizing the lack of data from clinical trials in this regard. Finally, we outline the potential of EV-DNA uptake and its interaction with the host genome as a promising tool for understanding the mechanisms of cancer evolution.

Protecting DNA in membrane vesicles seems to be a conserved phenomenon for the horizontal genetic flux between prokaryotes and lower eukaryotes.

Capturing and analyzing this vesicular DNA enables quick and non-invasive monitoring of natural ecosystems.

Cancer-derived extracellular vesicles containing DNA open up novel directions in cell-to-cell communication and therefore disease monitoring.

Complex and fluctuating conditions of the tumor microenvironment, mimicking natural ecosystems, could favor EV-DNA release, mediating tumor multi-clonal evolution and providing survival benefits.

Long-term abundance trends of insect taxa are only weakly correlated

Changes in the abundances of animals, such as with the ongoing concern about insect declines, are often assumed to be general across taxa. However, this assumption is largely untested. Here, we used a database of assemblage-wide long-term insect and arachnid monitoring to compare abundance trends among co-occurring pairs of taxa. We show that 60% of co-occurring taxa qualitatively showed long-term trends in the same direction-either both increasing or both decreasing. However, in terms of magnitude, temporal trends were only weakly correlated (mean freshwater r = 0.05 (±0.03), mean terrestrial r = 0.12 (±0.09)). The strongest correlation was between trends of beetles and those of moths/butterflies (r = 0.26). Overall, even though there is some support for directional similarity in temporal trends, we find that changes in the abundance of one taxon provide little information on the changes of other taxa. No clear candidate for umbrella or indicator taxa emerged from our analysis. We conclude that obtaining a better picture of changes in insect abundances will require monitoring of multiple taxa, which remains uncommon, especially in the terrestrial realm.

Why sequence all eukaryotes?

Life on Earth has evolved from initial simplicity to the astounding complexity we experience today. Bacteria and archaea have largely excelled in metabolic diversification, but eukaryotes additionally display abundant morphological innovation. How have these innovations come about and what constraints are there on the origins of novelty and the continuing maintenance of biodiversity on Earth? The history of life and the code for the working parts of cells and systems are written in the genome. The Earth BioGenome Project has proposed that the genomes of all extant, named eukaryotes-about 2 million species-should be sequenced to high quality to produce a digital library of life on Earth, beginning with strategic phylogenetic, ecological, and high-impact priorities. Here we discuss why we should sequence all eukaryotic species, not just a representative few scattered across the many branches of the tree of life. We suggest that many questions of evolutionary and ecological significance will only be addressable when whole-genome data representing divergences at all of the branchings in the tree of life or all species in natural ecosystems are available. We envisage that a genomic tree of life will foster understanding of the ongoing processes of speciation, adaptation, and organismal dependencies within entire ecosystems. These explorations will resolve long-standing problems in phylogenetics, evolution, ecology, conservation, agriculture, bioindustry, and medicine.

Plant Sedimentary Ancient DNA From Far East Russia Covering the Last 28,000 Years Reveals Different Assembly Rules in Cold and Warm Climates

Woody plants are expanding into the Arctic in response to the warming climate. The impact on arctic plant communities is not well understood due to the limited knowledge about plant assembly rules. Records of past plant diversity over long time series are rare. Here, we applied sedimentary ancient DNA metabarcoding targeting the P6 loop of the chloroplast trnL gene to a sediment record from Lake Ilirney (central Chukotka, Far Eastern Russia) covering the last 28 thousand years. Our results show that forb-rich steppe-tundra and dwarf-shrub tundra dominated during the cold climate before 14 ka, while deciduous erect-shrub tundra was abundant during the warm period since 14 ka. Larix invasion during the late Holocene substantially lagged behind the likely warmest period between 10 and 6 ka, where the vegetation biomass could be highest. We reveal highest richness during 28–23 ka and a second richness peak during 13–9 ka, with both periods being accompanied by low relative abundance of shrubs. During the cold period before 14 ka, rich plant assemblages were phylogenetically clustered, suggesting low genetic divergence in the assemblages despite the great number of species. This probably originates from environmental filtering along with niche differentiation due to limited resources under harsh environmental conditions. In contrast, during the warmer period after 14 ka, rich plant assemblages were phylogenetically overdispersed. This results from a high number of species which were found to harbor high genetic divergence, likely originating from an erratic recruitment process in the course of warming. Some of our evidence may be of relevance for inferring future arctic plant assembly rules and diversity changes. By analogy to the past, we expect a lagged response of tree invasion. Plant richness might overshoot in the short term; in the long-term, however, the ongoing expansion of deciduous shrubs will eventually result in a phylogenetically more diverse community.

Pathogens and disease vectors/hosts monitoring in aquatic environments: Potential of using eDNA/eRNA based approach

Infectious diseases are spreading in to previously unreported geographical regions, and are reappeared in regions 75 or 100 years after their last reported case, as a result of environmental changes caused by anthropogenic activities. A pathogen, vector/host monitoring methodology is therefore indispensable in identifying potential transmission sites, providing early warnings and evaluating the human health risks of these infectious diseases in a given area. Recently, environmental DNA (eDNA) and environmental RNA approach (eRNA) have become widespread in monitoring organisms in the environment due to advantages like lower cost, time, and labour requirements. However, eDNA/eRNA based monitoring of pathogens and vectors/hosts using aquatic samples is limited to very few studies. In this review, we summarized the currently available eDNA/eRNA based human and non-human pathogens and vectors/hosts detection studies in aquatic samples. Species-specific shedding, transport, and decay of eDNA/eRNA in aquatic environments which is essential in estimating the abundance of pathogen, vectors/host in focus is also summarized. We also suggest the usage of eDNA/eRNA approach in urban aquatic samples like runoff in identifying the disease vectors/hosts inhabiting in locations which are not accessible easily.

Revisiting the effect of PCR replication and sequencing depth on biodiversity metrics in environmental DNA metabarcoding

Environmental DNA (eDNA) metabarcoding is an increasingly popular tool for measuring and cataloguing biodiversity. Because the environments and substrates in which DNA is preserved differ considerably, eDNA research often requires bespoke approaches to generating eDNA data. Here, we explore how two experimental choices in eDNA study design-the number of PCR replicates and the depth of sequencing of PCR replicates-influence the composition and consistency of taxa recovered from eDNA extracts. We perform 24 PCR replicates from each of six soil samples using two of the most common metabarcodes for Fungi and Viridiplantae (ITS1 and ITS2), and sequence each replicate to an average depth of ~84,000 reads. We find that PCR replicates are broadly consistent in composition and relative abundance of dominant taxa, but that low abundance taxa are often unique to one or a few PCR replicates. Taxa observed in one out of 24 PCR replicates make up 21-29% of the total taxa detected. We also observe that sequencing depth or rarefaction influences alpha diversity and beta diversity estimates. Read sampling depth influences local contribution to beta diversity, placement in ordinations, and beta dispersion in ordinations. Our results suggest that, because common taxa drive some alpha diversity estimates, few PCR replicates and low read sampling depths may be sufficient for many biological applications of eDNA metabarcoding. However, because rare taxa are recovered stochastically, eDNA metabarcoding may never fully recover the true amplifiable alpha diversity in an eDNA extract. Rare taxa drive PCR replicate outliers of alpha and beta diversity and lead to dispersion differences at different read sampling depths. We conclude that researchers should consider the complexity and unevenness of a community when choosing analytical approaches, read sampling depths, and filtering thresholds to arrive at stable estimates.

The Time Machine framework: monitoring and prediction of biodiversity loss

Transdisciplinary solutions are needed to achieve the sustainability of ecosystem services for future generations. We propose a framework to identify the causes of ecosystem function loss and to forecast the future of ecosystem services under different climate and pollution scenarios. The framework (i) applies an artificial intelligence (AI) time-series analysis to identify relationships among environmental change, biodiversity dynamics and ecosystem functions; (ii) validates relationships between loss of biodiversity and environmental change in fabricated ecosystems; and (iii) forecasts the likely future of ecosystem services and their socioeconomic impact under different pollution and climate scenarios. We illustrate the framework by applying it to watersheds, and provide system-level approaches that enable natural capital restoration by associating multidecadal biodiversity changes to chemical pollution.

A Multi-Gene Region Targeted Capture Approach to Detect Plant DNA in Environmental Samples: A Case Study From Coastal Environments

Metabarcoding of plant DNA recovered from environmental samples, termed environmental DNA (eDNA), has been used to detect invasive species, track biodiversity changes, and reconstruct past ecosystems. The P6 loop of the trnL intron is the most widely utilised gene region for metabarcoding plants due to the short fragment length and subsequent ease of recovery from degraded DNA, which is characteristic of environmental samples. However, the taxonomic resolution for this gene region is limited, often precluding species level identification. Additionally, targeting gene regions using universal primers can bias results as some taxa will amplify more effectively than others. To increase the ability of DNA metabarcoding to better resolve flowering plant species (angiosperms) within environmental samples, and reduce bias in amplification, we developed a multi-gene targeted capture method that simultaneously targets 20 chloroplast gene regions in a single assay across all flowering plant species. Using this approach, we effectively recovered multiple chloroplast gene regions for three species within artificial DNA mixtures down to 0.001 ng/μL of DNA. We tested the detection level of this approach, successfully recovering target genes for 10 flowering plant species. Finally, we applied this approach to sediment samples containing unknown compositions of eDNA and confidently detected plant species that were later verified with observation data. Targeting multiple chloroplast gene regions in environmental samples, enabled species-level information to be recovered from complex DNA mixtures. Thus, the method developed here, confers an improved level of data on community composition, which can be used to better understand flowering plant assemblages in environmental samples.

Lake microbial communities are not resistant or resilient to repeated large-scale natural pulse disturbances

Opportunities to study community level responses to extreme natural pulse disturbances in unaltered ecosystems are rare. Lake sediment records that span thousands of years can contain well resolved sediment pulses, triggered by earthquakes. These paleo-records provide a means to study repeated pulse disturbance and processes of resistance (insensitivity to disturbance) and ecological resilience (capacity to regain structure, function and process). In this study, sedimentary DNA was extracted from a sediment core from Lake Paringa (New Zealand) that is situated in a near natural catchment. Metabarcoding and inferred functions were used to assess the lake microbial community over the past 1,100 years - a period that included four major earthquakes. Microbial community composition and function differed significantly between highly perturbed (postseismic, c. 50 yrs) phases directly after the earthquakes and more stable (interseismic, c. 250 yr) phases, indicating a lack of community resistance. Although community structure differed significantly in successive postseismic phases, function did not, suggesting potential functional redundancy. Significant differences in composition and function in successive interseismic phases demonstrates communities are not resilient to large-scale natural pulse disturbances. The clear difference in structure and function, and high number of indicator taxa (responsible for driving differences in communities between phases) in the fourth interseismic phase likely represents a regime shift, possibly due to the two-fold increase in sediment and terrestrial biospheric organic carbon fluxes recorded following the fourth earthquake. Large pulse disturbances that enhance sediment inputs into lake systems may produce an underappreciated mechanism that destabilises lake ecosystem processes.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Sediment archives reveal irreversible shifts in plankton communities after World War II and agricultural pollution

To evaluate the stability and resilience¹ of coastal ecosystem communities to perturbations that occurred during the Anthropocene,² pre-industrial biodiversity baselines inferred from paleoarchives are needed.^3,4 The study of ancient DNA (aDNA) from sediments (sedaDNA)⁵ has provided valuable information about past dynamics of microbial species^6-8 and communities^9-18 in relation to ecosystem variations. Shifts in planktonic protist communities might significantly affect marine ecosystems through cascading effects,^19-21 and therefore the analysis of this compartment is essential for the assessment of ecosystem variations. Here, sediment cores collected from different sites of the Bay of Brest (northeast Atlantic, France) allowed ca. 1,400 years of retrospective analyses of the effects of human pollution on marine protists. Comparison of sedaDNA extractions and metabarcoding analyses with different barcode regions (V4 and V7 18S rDNA) revealed that protist assemblages in ancient sediments are mainly composed of species known to produce resting stages. Heavy-metal pollution traces in sediments were ascribed to the World War II period and coincided with community shifts within dinoflagellates and stramenopiles. After the war and especially from the 1980s to 1990s, protist genera shifts followed chronic contaminations of agricultural origin. Community composition reconstruction over time showed that there was no recovery to a Middle Ages baseline composition. This demonstrates the irreversibility of the observed shifts after the cumulative effect of war and agricultural pollutions. Developing a paleoecological approach, this study highlights how human contaminations irreversibly affect marine microbial compartments, which contributes to the debate on coastal ecosystem preservation and restoration.

Seagrass (Halophila stipulacea) invasion enhances carbon sequestration in the Mediterranean Sea

The introduction and establishment of exotic species often result in significant changes in recipient communities and their associated ecosystem services. However, usually the magnitude and direction of the changes are difficult to quantify because there is no pre-introduction data. Specifically, little is known about the effect of marine exotic macrophytes on organic carbon sequestration and storage. Here, we combine dating sediment cores (²¹⁰ Pb) with sediment eDNA fingerprinting to reconstruct the chronology of pre- and post-arrival of the Red Sea seagrass Halophila stipulacea spreading into the Eastern Mediterranean native seagrass meadows. We then compare sediment organic carbon storage and burial rates before and after the arrival of H. stipulacea and between exotic (H. stipulacea) and native (C. nodosa and P. oceanica) meadows since the time of arrival following a Before-After-Control-Impact (BACI) approach. This analysis revealed that H. stipulacea arrived at the areas of study in Limassol (Cyprus) and West Crete (Greece) in the 1930s and 1970s, respectively. Average sediment organic carbon after the arrival of H. stipulacea to the sites increased in the exotic meadows twofold, from 8.4 ± 2.5 g C_org  m^-2  year^-1 to 14.7 ± 3.6 g C_org  m^-2  year^-1 , and, since then, burial rates in the exotic seagrass meadows were higher than in native ones of Cymodocea nodosa and Posidonia oceanica. Carbon isotopic data indicated a 50% increase of the seagrass contribution to the total sediment C_org pool since the arrival of H. stipulacea. Our results demonstrate that the invasion of H. stipulacea may play an important role in maintaining the blue carbon sink capacity in the future warmer Mediterranean Sea, by developing new carbon sinks in bare sediments and colonizing areas previously occupied by the colder thermal affinity P. oceanica.

Chronological distribution of dinoflagellate-infecting RNA virus in marine sediment core

A bivalve-killing marine dinoflagellate, Heterocapsa circularisquama, is susceptible to the infectious single-stranded RNA virus, Heterocapsa circularisquama RNA virus (HcRNAV). The ecological relationship between H. circularisquama and HcRNAV was intensively studied from 2001 through 2005; however, only limited data are available for the ecological dynamics of HcRNAV before 2001. In this study, we applied radiometric dating and reverse transcription PCR (RT-PCR) to determine the chronological distribution of HcRNAV in a marine sediment core sampled from the Uranouchi Inlet, Kochi, Japan, where H. circularisquama was first discovered. Our results show that HcRNAV had existed in the inlet long before its first bloom in 1988. Furthermore, five HcRNAV variants, phylogenetically distinguishable based on the nucleotide sequence of the major capsid protein (MCP) gene, were identified. These variants were found to be distributed throughout the core over time, suggesting that the HcRNAV sequences registered in the NCBI database are only a portion of the variants that have emerged in the history of HcRNAV diversification. Herein, we have verified the applicability of the retrospective approach for speculating the distribution of algal RNA viruses over time in aquatic environments.

Vegetation Reconstruction From Siberia and the Tibetan Plateau Using Modern Analogue Technique–Comparing Sedimentary (Ancient) DNA and Pollen Data

To reconstruct past vegetation from pollen or, more recently, lake sedimentary DNA (sedDNA) data is a common goal in palaeoecology. To overcome the bias of a researcher’s subjective assessment and to assign past assemblages to modern vegetation types quantitatively, the modern analogue technique (MAT) is often used for vegetation reconstruction. However, a rigorous comparison of MAT-derived pollen-based and sedDNA-based vegetation reconstruction is lacking. Here, we assess the dissimilarity between modern taxa assemblages from lake surface-sediments and fossil taxa assemblages from four lake sediment cores from the south-eastern Tibetan Plateau and northern Siberia using receiver operating characteristic (ROC) curves, ordination methods, and Procrustes analyses. Modern sedDNA samples from 190 lakes and pollen samples from 136 lakes were collected from a variety of vegetation types. Our results show that more modern analogues are found with sedDNA than pollen when applying similarly derived thresholds. In particular, there are few modern pollen analogues for open vegetation such as alpine or arctic tundra, limiting the ability of treeline shifts to be clearly reconstructed. In contrast, the shifts in the main vegetation communities are well captured by sedimentary ancient DNA (sedaDNA). For example, pronounced shifts from late-glacial alpine meadow/steppe to early–mid-Holocene coniferous forests to late Holocene Tibetan shrubland vegetation types are reconstructed for Lake Naleng on the south-eastern Tibetan Plateau. Procrustes and PROTEST analyses reveal that intertaxa relationships inferred from modern sedaDNA datasets align with past relationships generally, while intertaxa relationships derived from modern pollen spectra are mostly significantly different from fossil pollen relationships. Overall, we conclude that a quantitative sedaDNA-based vegetation reconstruction using MAT is more reliable than a pollen-based reconstruction, probably because of the more straightforward taphonomy that can relate sedDNA assemblages to the vegetation surrounding the lake.

Seasonality, DNA degradation and spatial heterogeneity as drivers of eDNA detection dynamics

In recent years, eDNA-based assessments have evolved as valuable tools for research and conservation. Most eDNA-based applications rely on comparisons across time or space. However, temporal, and spatial dynamics of eDNA concentrations are shaped by various drivers that can affect the reliability of such comparative approaches. Here, we assessed (i) seasonal variability, (ii) degradation rates and (iii) micro-habitat heterogeneity of eDNA concentrations as key factors likely to inflict uncertainty in across site and time comparisons. In a controlled mesocosm experiment, using the white-clawed crayfish as a model organism, we found detection probabilities of technical replicates to vary substantially and range from as little as 20 to upwards of 80% between seasons. Further, degradation rates of crayfish eDNA were low and target eDNA was still detectable 14-21 days after the removal of crayfish. Finally, we recorded substantial small-scale in-situ heterogeneity and large variability among sampling sites in a single pond of merely 1000m² in size. Consequently, all three tested drivers of spatial and temporal variation have the potential to severely impact the reliability of eDNA-based site comparisons and need to be accounted for in sampling design and data analysis of field-based applications.

All together now: Limitations and recommendations for the simultaneous analysis of all eukaryotic soil sequences

The soil environment contains a large, but historically underexplored, reservoir of biodiversity. Sequencing prokaryotic marker genes has become commonplace for the discovery and characterization of soil bacteria and archaea. Increasingly, this approach is also applied to eukaryotic marker genes to characterize the diversity and distribution of soil eukaryotes. However, understanding the properties and limitations of eukaryotic marker sequences is essential for correctly analysing, interpreting, and synthesizing the resulting data. Here, we illustrate several biases from sequencing data that affect measurements of biodiversity that arise from variation in morphology, taxonomy and phylogeny between organisms, as well as from sampling designs. We recommend analytical approaches to overcome these limitations, and outline how the benchmarking and standardization of sequencing protocols may improve the comparability of the data.

Investigating variability in microbial community composition in replicate environmental DNA samples down lake sediment cores

Lake sediments are natural archives that accumulate information on biological communities and their surrounding catchments. Paleolimnology has traditionally focussed on identifying fossilized organisms to reconstruct past environments. In the last decade, the application of molecular methodologies has increased in paleolimnological studies, but further research investigating factors such as sample heterogeneity and DNA degradation are required. In the present study we investigated bacterial community heterogeneity (16S rRNA metabarcoding) within depth slices (1-cm width). Sediment cores were collected from three lakes with differing sediment compositions. Samples were collected from a variety of depths which represent a period of time of approximately 1,200 years. Triplicate samples were collected from each depth slice and bacterial 16S rRNA metabarcoding was undertaken on each sample. Accumulation curves demonstrated that except for the deepest (oldest) slices, the combination of three replicate samples were insufficient to characterise the entire bacterial diversity. However, shared Amplicon Sequence Variants (ASVs) accounted for the majority of the reads in each depth slice (max. shared proportional read abundance 96%, 86%, 65% in the three lakes). Replicates within a depth slice generally clustered together in the Non-metric multidimensional scaling analysis. There was high community dissimilarity in older sediment in one of the cores, which was likely due to the laminae in the sediment core not being horizontal. Given that most paleolimnology studies explore broad scale shifts in community structure rather than seeking to identify rare species, this study demonstrates that a single sample is adequate to characterise shifts in dominant bacterial ASVs.

Animals, protists and bacteria share marine biogeographic patterns

Over millennia, ecological and evolutionary mechanisms have shaped macroecological patterns across the tree of life. Research describing these patterns at both regional and global scales has traditionally focused on the study of metazoan species. Consequently, there is a limited understanding of cross-phylum biogeographic structuring and an escalating need to understand the macroecology of both microscopic and macroscopic organisms. Here we used environmental DNA (eDNA) metabarcoding to explore the biodiversity of marine metazoans, protists and bacteria along an extensive and highly heterogeneous coastline. Our results showed remarkably consistent biogeographic structure across the kingdoms of life despite billions of years of evolution. Analyses investigating the drivers of these patterns for each taxonomic kingdom found that environmental conditions (such as temperature) and, to a lesser extent, anthropogenic stressors (such as fishing pressure and pollution) explained some of the observed variation. Additionally, metazoans displayed biogeographic patterns that suggested regional biotic homogenization. Against the backdrop of global pervasive anthropogenic environmental change, our work highlights the importance of considering multiple domains of life to understand the maintenance and drivers of biodiversity patterns across broad taxonomic, ecological and geographical scales.

Connecting high-throughput biodiversity inventories: Opportunities for a site-based genomic framework for global integration and synthesis

High-throughput sequencing (HTS) is increasingly being used for the characterization and monitoring of biodiversity. If applied in a structured way, across broad geographical scales, it offers the potential for a much deeper understanding of global biodiversity through the integration of massive quantities of molecular inventory data generated independently at local, regional and global scales. The universality, reliability and efficiency of HTS data can potentially facilitate the seamless linking of data among species assemblages from different sites, at different hierarchical levels of diversity, for any taxonomic group and regardless of prior taxonomic knowledge. However, collective international efforts are required to optimally exploit the potential of site-based HTS data for global integration and synthesis, efforts that at present are limited to the microbial domain. To contribute to the development of an analogous strategy for the nonmicrobial terrestrial domain, an international symposium entitled "Next Generation Biodiversity Monitoring" was held in November 2019 in Nicosia (Cyprus). The symposium brought together evolutionary geneticists, ecologists and biodiversity scientists involved in diverse regional and global initiatives using HTS as a core tool for biodiversity assessment. In this review, we summarize the consensus that emerged from the 3-day symposium. We converged on the opinion that an effective terrestrial Genomic Observatories network for global biodiversity integration and synthesis should be spatially led and strategically united under the umbrella of the metabarcoding approach. Subsequently, we outline an HTS-based strategy to collectively build an integrative framework for site-based biodiversity data generation.

Environmental DNA bioassays corroborate field data for detection of overwintering species at risk Blanding's turtles (Emydoidea blandingii)

Environmental DNA (eDNA) is gaining traction in conservation ecology as a powerful tool for detecting species at risk. We developed a quantitative polymerase chain reaction assay to detect a DNA amplicon fragment of the mitochondrial nicotinamide adenine dinucleotide locus of the Blanding's turtle (Emydoidea blandingii) for detecting overwintering individuals. Seventy-eight water samples were collected from 17 wetland sites in Ontario, Canada. We used traditional field data to identify a priori positive and negative control sites. Fifty percent of positive control sites amplified. Detection was related to the number of individuals estimated from field observations in at least one region surveyed. Positive control sites had lower total dissolved solids and electrical conductivity in relation to negative control sites. Shedding rates were within the same order of magnitude for brumating and active turtles. We recommend collecting additional samples at a larger number of locations to maximize detection. Recommended sampling design changes may overshadow the additional effects of water chemistry and low eDNA shedding rates. eDNA offers tremendous potential to practitioners conducting species at risk assessments in environmental consulting by providing a faster, more efficient method of detection compared with traditional surveys.

Space-time dynamics in monitoring neotropical fish communities using eDNA metabarcoding

The biodiverse Neotropical ecoregion remains insufficiently assessed, poorly managed, and threatened by unregulated human activities. Novel, rapid and cost-effective DNA-based approaches are valuable to improve understanding of the biological communities and for biomonitoring in remote areas. Here, we evaluate the potential of environmental DNA (eDNA) metabarcoding for assessing the structure and distribution of fish communities by analysing water and sediment from 11 locations along the Jequitinhonha River catchment (Brazil). Each site was sampled twice, before and after a major rain event in a five-week period and fish diversity was estimated using high-throughput sequencing of 12S rRNA amplicons. In total, 252 Molecular Operational Taxonomic Units (MOTUs) and 34 fish species were recovered, including endemic, introduced, and previously unrecorded species for this basin. Spatio-temporal variation of eDNA from fish assemblages was observed and species richness was nearly twice as high before the major rain event compared to afterwards. Yet, peaks of diversity were primarily associated with only four of the locations. No correlation between β-diversity and longitudinal distance or presence of dams was detected, but low species richness observed at sites located near dams might that these anthropogenic barriers may have an impact on local fish diversity. Unexpectedly high α-diversity levels recorded at the river mouth suggest that these sections should be further evaluated as putative "eDNA reservoirs" for rapid monitoring. By uncovering spatio-temporal changes, unrecorded biodiversity components, and putative anthropogenic impacts on fish assemblages, we further strengthen the potential of eDNA metabarcoding as a biomonitoring tool, especially in regions often neglected or difficult to access.

Examining Natural History through the Lens of Palaeogenomics

The many high-resolution tools that are uniquely applicable to specimens from the Quaternary period (the past ~2.5 Ma) provide an opportunity to cross-validate data and test hypotheses based on the morphology and distribution of fossils. Among these tools is palaeogenomics - the genome-scale sequencing of genetic material from ancient specimens - that can provide direct insight into ecology and evolution, potentially improving the accuracy of inferences about past ecological communities over longer timescales. Palaeogenomics has revealed instances of over- and underestimation of extinct diversity, detected cryptic faunal migration and turnover, allowed quantification of widespread sex biases and sexual dimorphism in the fossil record, revealed past hybridisation events and hybrid individuals, and has highlighted previously unrecognised routes of zoonotic disease transfer.

A pilot study of eDNA metabarcoding to estimate plant biodiversity by an alpine glacier core (Adamello glacier, North Italy)

Current biodiversity loss is a major concern and thus biodiversity assessment of modern ecosystems is compelling and needs to be contextualized on a longer timescale. High Throughput Sequencing (HTS) is progressively becoming a major source of data on biodiversity time series. In this multi proxy study, we tested, for the first time, the potential of HTS to estimate plant biodiversity archived in the surface layers of a temperate alpine glacier, amplifying the trnL barcode for vascular plants from eDNA of firn samples. A 573 cm long core was drilled by the Adamello glacier and cut into sections; produced samples were analyzed for physical properties, stable isotope ratio, and plant biodiversity by eDNA metabarcoding and conventional light microscopy analysis. Results highlighted the presence of pollen and plant remains within the distinct layers of snow, firn and ice. While stable isotope ratio showed a scarcely informative pattern, DNA metabarcoding described distinct plant species composition among the different samples, with a broad taxonomic representation of the biodiversity of the catchment area and a high-ranking resolution. New knowledge on climate and plant biodiversity changes of large catchment areas can be obtained by this novel approach, relevant for future estimates of climate change effects.

Evaluating Impact Using Time-Series Data

Humanity's impact on the environment is increasing, as are strategies to conserve biodiversity, but a lack of understanding about how interventions affect ecological and conservation outcomes hampers decision-making. Time series are often used to assess impacts, but ecologists tend to compare average values from before to after an impact; overlooking the potential for the intervention to elicit a change in trend. Without methods that allow for a range of responses, erroneous conclusions can be drawn, especially for large, multi-time-series datasets, which are increasingly available. Drawing on literature in other disciplines and pioneering work in ecology, we present a standardised framework to robustly assesses how interventions, like natural disasters or conservation policies, affect ecological time series.

Uncovering ecological state dynamics with hidden Markov models

Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

Sertraline inhibits top-down forces (predation) in microbial food web and promotes nitrification in sediment

Sertraline is a widely used antidepressant that becomes an aquatic pollutant through metabolic excretion and improper disposal. Determining the impact of sertraline on benthic microbial ecosystems is important for the transformation of river biogenic elements. However, the molecular initiating event induced by sertraline is more readily observed at higher levels, such as the individual or population level of larger organisms, and the effect is not pronounced in benthic organisms, which are directly involved in nitrogen transformation. Therefore, this study used DNA metabarcoding to analyze the effect of sertraline on the microbial ecosystem and material cycles in river sediment through the lens of a microbial food web. The presence of sertraline in the river sediment enhanced the mineralization capacity of nitrogen and increased the accumulation of nitrate in the sediment. Sertraline affected the structure of the microbial food web by stimulating different successions of bacteria and eukaryotes. A structural equation model revealed that sertraline affected the microbial food web model through top-down forces (predation) by reducing the trophic transfer efficiency from metazoans to protozoans. This effect resulted in decreases in the trophic transfer efficiency from protozoans to bacteria and increases in nitrogen mineralization capacity. This was followed by a gradual increase in the nitrification reaction under the action of nitrifying bacteria, increasing the threat to the ecological health of rivers. The results show that sertraline affects the material cycle of river ecosystems and emphasizes that the assessment of the ecological risks of sertraline needs to be considered from the perspective of the material cycle of ecosystems.