Biodiversity increases and decreases ecosystem stability.

Losses and gains in species diversity affect ecological stability1-7 and the sustainability of ecosystem functions and services8-13. Experiments and models have revealed positive, negative and no effects of diversity on individual components of stability, such as temporal variability, resistance and resilience2,3,6,11,12,14. How these stability components covary remains poorly understood15. Similarly, the effects of diversity on overall ecosystem stability16, which is conceptually akin to ecosystem multifunctionality17,18, remain unknown. Here we studied communities of aquatic ciliates to understand how temporal variability, resistance and overall ecosystem stability responded to diversity (that is, species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12,939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components covaried negatively along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of the negative effects of diversity on individual stability components3. Integrating our findings with the ecosystem multifunctionality concept revealed hump- and U-shaped effects of diversity on overall ecosystem stability. That is, biodiversity can increase overall ecosystem stability when biodiversity is low, and decrease it when biodiversity is high, or the opposite with a U-shaped relationship. The effects of diversity on ecosystem multifunctionality would also be hump- or U-shaped if diversity had positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform the perceived effects of diversity on ecological stability and may help to translate this science into policy-relevant information.

Environmental DNA metabarcoding: Transforming how we survey animal and plant communities.

The genomic revolution has fundamentally changed how we survey biodiversity on earth. High-throughput sequencing ("HTS") platforms now enable the rapid sequencing of DNA from diverse kinds of environmental samples (termed "environmental DNA" or "eDNA"). Coupling HTS with our ability to associate sequences from eDNA with a taxonomic name is called "eDNA metabarcoding" and offers a powerful molecular tool capable of noninvasively surveying species richness from many ecosystems. Here, we review the use of eDNA metabarcoding for surveying animal and plant richness, and the challenges in using eDNA approaches to estimate relative abundance. We highlight eDNA applications in freshwater, marine and terrestrial environments, and in this broad context, we distill what is known about the ability of different eDNA sample types to approximate richness in space and across time. We provide guiding questions for study design and discuss the eDNA metabarcoding workflow with a focus on primers and library preparation methods. We additionally discuss important criteria for consideration of bioinformatic filtering of data sets, with recommendations for increasing transparency. Finally, looking to the future, we discuss emerging applications of eDNA metabarcoding in ecology, conservation, invasion biology, biomonitoring, and how eDNA metabarcoding can empower citizen science and biodiversity education.

Experimental evidence for strong stabilizing forces at high functional diversity of aquatic microbial communities.

Unveiling the mechanisms that promote coexistence in biological communities is a fundamental problem in ecology. Stable coexistence of many species is commonly observed in natural communities. Most of these natural communities, however, are composed of species from multiple trophic and functional groups, while theory and experiments on coexistence have been focusing on functionally similar species. Here, we investigated how functional diversity affects the stability of species coexistence and productivity in multispecies communities by characterizing experimentally all pairwise species interactions in a pool of 11 species of eukaryotes (10 protists and one rotifer) belonging to three different functional groups. Species within the same functional group showed stronger competitive interactions compared to among-functional group interactions. This often led to competitive exclusion between species that had higher functional relatedness, but only at low levels of species richness. Communities with higher functional diversity resulted in increased species coexistence and community biomass production. Our experimental findings and the results of a stochastic model tailored to the experimental interaction matrix suggest the emergence of strong stabilizing forces when species from different functional groups interact in a homogeneous environment. By combining theoretical analysis with experiments we could also disentangle the relationship between species richness and functional diversity, showing that functional diversity per se is a crucial driver of productivity and stability in multispecies community.

An integrated spatio-temporal view of riverine biodiversity using environmental DNA metabarcoding.

Anthropogenically forced changes in global freshwater biodiversity demand more efficient monitoring approaches. Consequently, environmental DNA (eDNA) analysis is enabling ecosystem-scale biodiversity assessment, yet the appropriate spatio-temporal resolution of robust biodiversity assessment remains ambiguous. Here, using intensive, spatio-temporal eDNA sampling across space (five rivers in Europe and North America, with an upper range of 20-35 km between samples), time (19 timepoints between 2017 and 2018) and environmental conditions (river flow, pH, conductivity, temperature and rainfall), we characterise the resolution at which information on diversity across the animal kingdom can be gathered from rivers using eDNA. In space, beta diversity was mainly dictated by turnover, on a scale of tens of kilometres, highlighting that diversity measures are not confounded by eDNA from upstream. Fish communities showed nested assemblages along some rivers, coinciding with habitat use. Across time, seasonal life history events, including salmon and eel migration, were detected. Finally, effects of environmental conditions were taxon-specific, reflecting habitat filtering of communities rather than effects on DNA molecules. We conclude that riverine eDNA metabarcoding can measure biodiversity at spatio-temporal scales relevant to species and community ecology, demonstrating its utility in delivering insights into river community ecology during a time of environmental change.

Global arthropod beta-diversity is spatially and temporally structured by latitude.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

Strategies for sample labelling and library preparation in DNA metabarcoding studies.

Metabarcoding of DNA extracted from environmental or bulk specimen samples is increasingly used to profile biota in basic and applied biodiversity research because of its targeted nature that allows sequencing of genetic markers from many samples in parallel. To achieve this, PCR amplification is carried out with primers designed to target a taxonomically informative marker within a taxonomic group, and sample-specific nucleotide identifiers are added to the amplicons prior to sequencing. The latter enables assignment of the sequences back to the samples they originated from. Nucleotide identifiers can be added during the metabarcoding PCR and during "library preparation", that is, when amplicons are prepared for sequencing. Different strategies to achieve this labelling exist. All have advantages, challenges and limitations, some of which can lead to misleading results, and in the worst case compromise the fidelity of the metabarcoding data. Given the range of questions addressed using metabarcoding, ensuring that data generation is robust and fit for the chosen purpose is critically important for practitioners seeking to employ metabarcoding for biodiversity assessments. Here, we present an overview of the three main workflows for sample-specific labelling and library preparation in metabarcoding studies on Illumina sequencing platforms; one-step PCR, two-step PCR, and tagged PCR. Further, we distill the key considerations for researchers seeking to select an appropriate metabarcoding strategy for their specific study. Ultimately, by gaining insights into the consequences of different metabarcoding workflows, we hope to further consolidate the power of metabarcoding as a tool to assess biodiversity across a range of applications.

Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning.

Rapidly assessing biodiversity is essential for environmental monitoring; however, traditional approaches are limited in the scope needed for most ecological systems. Environmental DNA (eDNA) based assessment offers enhanced scope for assessing biodiversity, while also increasing sampling efficiency and reducing processing time, compared to traditional methods. Here we investigated the effects of landuse and seasonality on headwater community richness and functional diversity, via spatio-temporal dynamics, using both eDNA and traditional sampling. We found that eDNA provided greater resolution in assessing biodiversity dynamics in time and space, compared to traditional sampling. Community richness was seasonally linked, peaking in spring and summer, with temporal turnover having a greater effect on community composition compared to localized nestedness. Overall, our assessment of ecosystem function shows that community formation is driven by regional resource availability, implying regional management requirements should be considered. Our findings show that eDNA based ecological assessment is a powerful, rapid and effective assessment strategy that enables complex spatio-temporal studies of community diversity and ecosystem function, previously infeasible using traditional methods.

Executing multi-taxa eDNA ecological assessment via traditional metrics and interactive networks.

Current approaches to ecological assessment are limited by the traditional morpho-taxonomic methods presently employed and the inability to meet increasing demands for rapid assessments. Advancements in high throughput sequencing now enable rapid high-resolution ecological assessment using environmental DNA (eDNA). Here we test the ability of using eDNA-based ecological assessment methods against traditional assessment of two key indicator groups (diatoms and macroinvertebrates) and show how eDNA across multiple gene regions (COI, rbcL, 12S and 18S) can be used to infer interactive networks that link to ecological assessment criteria. We compared results between taxonomic and eDNA based assessments and found significant positive associations between macroinvertebrate (p < 0.001 R2 = 0.645) and diatom (p = 0.015, R2 = 0.222) assessment metrics. We further assessed the ability of eDNA based assessment to identify environmentally sensitive genera and found an order of magnitude greater potential for 18S, versus COI or rbcL, to determine environmental filtering of ecologically assessed communities. Lastly, we compared the ability of traditional metrics against co-occurrence network properties of our combined 18S, COI and rbcL indicator genera to infer habitat quality measures currently used by managers. We found that transitivity (network connectivity), linkage density and cohesion were significantly associated with habitat modification scores (HMS), whereas network properties were inconsistent with linking to the habitat quality score (HQS) metric. The incorporation of multi-marker eDNA network assessment opens up a means for finer scale ecological assessment, currently limited using traditional methods. While utilization of eDNA-based assessment is recommended, direct comparisons with traditional approaches are difficult as the methods are intrinsically different and should be treated as such with regards to future research. Overall, our findings show that eDNA can be used for effective ecological assessment while offering a wider range of scope and application compared to traditional assessment methods.

Rapid progression and future of environmental DNA research.

Environmental DNA based research is a new field within molecular ecology that is seeing an amazing increase in research activity. In our Communications Biology article, we studied the degradation of eDNA in variable systems. Presented here is a short overview of eDNA science and current research activities underway in North Wales.

Drift versus selection as drivers of phenotypic divergence at small spatial scales: The case of Belgjarskógur threespine stickleback.

Divergence in phenotypic traits is facilitated by a combination of natural selection, phenotypic plasticity, gene flow, and genetic drift, whereby the role of drift is expected to be particularly important in small and isolated populations. Separating the components of phenotypic divergence is notoriously difficult, particularly for multivariate phenotypes. Here, we assessed phenotypic divergence of threespine stickleback (Gasterosteus aculeatus) across 19 semi-interconnected ponds within a small geographic region (~7.5 km2) using comparisons of multivariate phenotypic divergence (PST), neutral genetic (FST), and environmental (EST) variation. We found phenotypic divergence across the ponds in a suite of functionally relevant phenotypic traits, including feeding, defense, and swimming traits, and body shape (geometric morphometric). Comparisons of PSTs with FSTs suggest that phenotypic divergence is predominantly driven by neutral processes or stabilizing selection, whereas phenotypic divergence in defensive traits is in accordance with divergent selection. Comparisons of population pairwise PSTs with ESTs suggest that phenotypic divergence in swimming traits is correlated with prey availability, whereas there were no clear associations between phenotypic divergence and environmental difference in the other phenotypic groups. Overall, our results suggest that phenotypic divergence of these small populations at small geographic scales is largely driven by neutral processes (gene flow, drift), although environmental determinants (natural selection or phenotypic plasticity) may play a role.

Environmental DNA size sorting and degradation experiment indicates the state of Daphnia magna mitochondrial and nuclear eDNA is subcellular.

Environmental DNA analysis has emerged as a key component of biodiversity and environmental monitoring. However, the state and fate of eDNA in natural environments is still poorly understood for many ecological systems. Here we assess the state and fate of eDNA derived from the water flea, Daphnia magna, using a full factorial mesocosm experiment. We measured the quantity and degradation of eDNA over a two month period across a range of filters differing in pore size (0, 0.2, 1 and 10 µm), which spans the range of eDNA source material including subcellular, cellular and tissue. We also used two primer sets targeting mitochondrial (COI) and nuclear (18S) genomic regions. Our findings demonstrated that eDNA was most prevalent in the effluent water, but also reliably detected on the 0.2 µm filter, suggesting subcellular material is the predominate state of eDNA. Temporal eDNA quantity dynamics followed an exponential decay function over the course of 6-17 days, demonstrating a predictable decline in eDNA concentration. Nuclear eDNA was more abundant than mitochondrial eDNA, which may be a result of greater primer affinity, or indicate greater availability of nuclear eDNA gene targets in the environment. In contrast to two previous size-sorting experiments, which utilizing fish eDNA, our findings suggest that the state of invertebrate eDNA is much smaller than previously suspected. Overall, our data suggest that the detection of eDNA greatly depends on our knowledge of the state and fate of eDNA, which differ among species, and likely across environmental conditions.

Performance of amplicon and shotgun sequencing for accurate biomass estimation in invertebrate community samples.

New applications of DNA and RNA sequencing are expanding the field of biodiversity discovery and ecological monitoring, yet questions remain regarding precision and efficiency. Due to primer bias, the ability of metabarcoding to accurately depict biomass of different taxa from bulk communities remains unclear, while PCR-free whole mitochondrial genome (mitogenome) sequencing may provide a more reliable alternative. Here, we used a set of documented mock communities comprising 13 species of freshwater macroinvertebrates of estimated individual biomass, to compare the detection efficiency of COI metabarcoding (three different amplicons) and shotgun mitogenome sequencing. Additionally, we used individual COI barcoding and de novo mitochondrial genome sequencing, to provide reference sequences for OTU assignment and metagenome mapping (mitogenome skimming), respectively. We found that, even though both methods occasionally failed to recover very low abundance species, metabarcoding was less consistent, by failing to recover some species with higher abundances, probably due to primer bias. Shotgun sequencing results provided highly significant correlations between read number and biomass in all but one species. Conversely, the read-biomass relationships obtained from metabarcoding varied across amplicons. Specifically, we found significant relationships for eight of 13 (amplicons B1FR-450 bp, FF130R-130 bp) or four of 13 (amplicon FFFR, 658 bp) species. Combining the results of all three COI amplicons (multiamplicon approach) improved the read-biomass correlations for some of the species. Overall, mitogenomic sequencing yielded more informative predictions of biomass content from bulk macroinvertebrate communities than metabarcoding. However, for large-scale ecological studies, metabarcoding currently remains the most commonly used approach for diversity assessment.

Acidity promotes degradation of multi-species environmental DNA in lotic mesocosms.

Accurate quantification of biodiversity is fundamental to understanding ecosystem function and for environmental assessment. Molecular methods using environmental DNA (eDNA) offer a non-invasive, rapid, and cost-effective alternative to traditional biodiversity assessments, which require high levels of expertise. While eDNA analyses are increasingly being utilized, there remains considerable uncertainty regarding the dynamics of multispecies eDNA, especially in variable systems such as rivers. Here, we utilize four sets of upland stream mesocosms, across an acid-base gradient, to assess the temporal and environmental degradation of multispecies eDNA. Sampling included water column and biofilm sampling over time with eDNA quantified using qPCR. Our findings show that the persistence of lotic multispecies eDNA, sampled from water and biofilm, decays to non-detectable levels within 2 days and that acidic environments accelerate the degradation process. Collectively, the results provide the basis for a predictive framework for the relationship between lotic eDNA degradation dynamics in spatio-temporally dynamic river ecosystems.

Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity.

The use of environmental DNA (eDNA) in biodiversity assessments offers a step-change in sensitivity, throughput and simultaneous measures of ecosystem diversity and function. There remains, however, a need to examine eDNA persistence in the wild through simultaneous temporal measures of eDNA and biota. Here, we use metabarcoding of two markers of different lengths, derived from an annual time series of aqueous lake eDNA to examine temporal shifts in ecosystem biodiversity and in an ecologically important group of macroinvertebrates (Diptera: Chironomidae). The analyses allow different levels of detection and validation of taxon richness and community composition (ß-diversity) through time, with shorter eDNA fragments dominating the eDNA community. Comparisons between eDNA, community DNA, taxonomy and UK species abundance data further show significant relationships between diversity estimates derived across the disparate methodologies. Our results reveal the temporal dynamics of eDNA and validate the utility of eDNA metabarcoding for tracking seasonal diversity at the ecosystem scale.

Dynamic species classification of microorganisms across time, abiotic and biotic environments-A sliding window approach.

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology.

Peripheral genetic structure of Helicoverpa zea indicates asymmetrical panmixia.

Seasonal climatic shifts create peripheral habitats that alternate between habitable and uninhabitable for migratory species. Such dynamic peripheral habitats are potential sites where migratory species could evolve high genetic diversity resulting from convergence of immigrants from multiple regionally distant areas. Migrant populations of Helicoverpa zea (Boddie) captured during two different seasons were assessed for genetic structure using microsatellite markers and for host plant type using stable carbon isotope analysis. Individuals (N = 568) were genotyped and divided into 13 putative populations based on collection site and time. Fixation indices (F-statistics), analysis of molecular variance (AMOVA), and discriminant analysis of principal components (DAPC) were used to examine within and among population genetic variation. Mean number of alleles per locus was 10.25 (± 3.2 SD), and allelic richness ranged from 2.38 to 5.13 (± 3.2 SD). The observed and expected heterozygosity ranged from 0.07 to 0.48 and 0.08 to 0.62, respectively. Low F ST (0.01 to 0.02) and high F IS (0.08 to 0.33) values suggest captured migrants originated from breeding populations with different allele frequencies. We postulate that high genetic diversity within migrant populations and low genetic differentiation among migrant populations of H. zea are the result of asymmetrical immigration due to the high dispersal and reproductive behavior of H. zea, which may hinder the adaptation and establishment of H. zea to peripheral habitat. These findings highlight the importance of assessing peripheral population structure in relation to ecological and evolutionary dynamics of this and other highly reproductive and dispersive species.

Active colonization dynamics and diversity patterns are influenced by dendritic network connectivity and species interactions.

Habitat network connectivity influences colonization dynamics, species invasions, and biodiversity patterns. Recent theoretical work suggests dendritic networks, such as those found in rivers, alter expectations regarding colonization and dispersal dynamics compared with other network types. As many native and non-native species are spreading along river networks, this may have important ecological implications. However, experimental studies testing the effects of network structure on colonization and diversity patterns are scarce. Up to now, experimental studies have only considered networks where sites are connected with small corridors, or dispersal was experimentally controlled, which eliminates possible effects of species interactions on colonization dynamics. Here, we tested the effect of network connectivity and species interactions on colonization dynamics using continuous linear and dendritic (i.e., river-like) networks, which allow for active dispersal. We used a set of six protist species and one rotifer species in linear and dendritic microcosm networks. At the start of the experiment, we introduced species, either singularly or as a community within the networks. Species subsequently actively colonized the networks. We periodically measured densities of species throughout the networks over 2 weeks to track community dynamics, colonization, and diversity patterns. We found that colonization of dendritic networks was faster compared with colonization of linear networks, which resulted in higher local mean species richness in dendritic networks. Initially, community similarity was also greater in dendritic networks compared with linear networks, but this effect vanished over time. The presence of species interactions increased community evenness over time, compared with extrapolations from single-species setups. Our experimental findings confirm previous theoretical work and show that network connectivity, species-specific dispersal ability, and species interactions greatly influence the dispersal and colonization of dendritic networks. We argue that these factors need to be considered in empirical studies, where effects of network connectivity on colonization patterns have been largely underestimated.

Connectivity in a pond system influences migration and genetic structure in threespine stickleback.

Neutral genetic structure of natural populations is primarily influenced by migration (the movement of individuals and, subsequently, their genes) and drift (the statistical chance of losing genetic diversity over time). Migration between populations is influenced by several factors, including individual behavior, physical barriers, and environmental heterogeneity among populations. However, drift is expected to be stronger in populations with low immigration rate and small effective population size. With the technological advancement in geological information systems and spatial analysis tools, landscape genetics now allows the development of realistic migration models and increased insight to important processes influencing diversity of natural populations. In this study, we investigated the relationship between landscape connectivity and genetic distance of threespine stickleback (Gasterosteus aculeatus) inhabiting a pond complex in Belgjarskógur, Northeast Iceland. We used two landscape genetic approaches (i.e., least-cost-path and isolation-by-resistance) and asked whether gene flow, as measured by genetic distance, was more strongly associated with Euclidean distance (isolation-by-distance) or with landscape connectivity provided by areas prone to flooding (as indicated by Carex sp. cover)? We found substantial genetic structure across the study area, with pairwise genetic distances among populations (DPS) ranging from 0.118 to 0.488. Genetic distances among populations were more strongly correlated with least-cost-path and isolation-by-resistance than with Euclidean distance, whereas the relative contribution of isolation-by-resistance and Euclidian distance could not be disentangled. These results indicate that migration among stickleback populations occurs via periodically flooded areas. Overall, this study highlights the importance of transient landscape elements influencing migration and genetic structure of populations at small spatial scales.