Insights into Ecological & Evolutionary Processes via community metabarcoding

Abstract

This Special Issue brings together papers that highlight the power of high-throughput sequencing (HTS) data to address classic questions in ecology and evolution, particularly focused on metabarcoding (amplicon) datasets in conjunction with complementary -omics data types and/or models/theory to infer overall ecosystem processes. We highlight key papers that show the power of the new technology to address questions related to: (i) dynamics of community assembly and how these may change across environmental conditions, successional processes and extended evolutionary time; (ii) interaction networks, and how these can show predictable patterns over spatial and temporal gradients, providing insights into questions of biotic resilience. Studies also examined (iii) cross-scale interactions and host-microbiome associations, with critical developments demonstrating the ease of comparison and integration across scales of organismic complexity that allow insights at one scale to inform the other. These approaches are also amenable to (iv) studies of invasive species and biotic homogenization, providing insights on shifts in alpha- and beta-diversity across a wide range of spatial scales. Biodiversity—the multiplicity of life, from microbes to macro-organisms and from genes to ecosystems—is in crisis, yet we have little understanding of factors that can sustain biodiversity and enhance its resilience to perturbations (IPBES, 2019; Oliver et al., 2015). Key questions that remain include the interplay between niche and neutral processes in shaping the assembly of communities (Mittelbach & McGill, 2019) and the associated role of stochastic and deterministic processes governing assembly (Menéndez-Serra et al., 2023); the complexity-stability paradox (Domínguez-García et al., 2019); metacommunity dynamics and the connection between local and regional diversity (Thompson et al., 2020); the extent to which a given community can exist in equilibrium or steady state (Qian & Akçay, 2020) and concepts of alternative stable states (Van Nes et al., 2016), among others. These questions have been the focus of much theoretical development in the past, but the ability to generate the data needed to validate these theories has been limited by the difficulty of sampling biological communities at the needed scale. However, without answers to these fundamental questions, we are left with major gaps in our understanding of biodiversity dynamics and questions of biotic resilience, ecosystem sustainability and strategies for restoration, which are all so critical for effective conservation and management of ecosystems. The advance of molecular profiling methods (e.g. metabarcoding-marker gene amplicon-based community profiling metagenomics, and metatranscriptomics) has recently provided a remarkably effective toolkit for measuring biodiversity and presents the opportunity to answer the outstanding questions mentioned above. Moreover, because these approaches harness common tools across both macro- and micro-organisms, we have the ability to answer macroecological questions of shifts in community composition across scales (e.g. the work of Brown et al., 2020). These technological developments have initiated a dramatic shift in the ability to measure ecological metrics within entire macro- and micro-organismal communities, and how they change over space and time. In this Special Issue, as we outline below, authors use high throughput technologies to address classic questions in ecology and evolution and/or use models/theory to infer key ecological and evolutionary processes and make predictions. Describing the composition and structure of communities and their responses to perturbations and stressors has been a primary objective of ecological research since its inception. We still struggle to understand and predict the mechanisms shaping the dynamics of biological communities and how these accommodate or collapse in the face of change (Urban et al., 2016). Community profiling methods, by providing data on the diversity and abundance of the entire community of taxa across sites of different age, nutrient availability and so forth, are providing unprecedented insights into the processes of assembly. New modelling approaches (Overcast et al., 2019, 2021) are now being applied to these data to provide insights into the temporal and spatial components that govern the assembly process, and hence the factors that might dictate resilience. In this issue, Overcast et al. (2023) describe an eco-evolutionary simulation model that uses community-scale genetic data to study community assembly dynamics and show that there are detectable signatures of neutral and non-neutral processes in simulated community profiles. Applying the model to soil microarthropod metabarcoding data from Cyprus, they show that widespread low-elevation communities are structured by neutral processes, while isolated high-elevation habitats are shaped by non-neutral processes. Studies in this category included terrestrial and marine systems, macro- and micro-organism assembly, and comparisons of community assembly processes across scales of organismic complexity. For terrestrial communities, several papers focused on the respective roles of environmental filtering, niche conservatism/lability and spatial isolation in shaping animal species diversity at a given site. Noguerales et al. (2023) use whole organism bulk community DNA (Creedy et al., 2022) metabarcoding at both operational taxonomic unit (OTU) level and amplicon sequence variant (ASV) level to tease apart the role of environmental filtering and spatial isolation in metacommunity dynamics of soil microarthropods. The study showed that OTU (species) richness follows an altitudinal gradient, presumably associated with filtering and niche-based processes; the ASV diversity showed a contrasting pattern of decline in genetic diversity associated with anthropogenic disturbance. The paper by Andujar et al. (2022) uses the soil mesofauna in the Canary Islands to highlight the importance of environmental filtering and niche conservatism as a driver of insular community assembly, showing little evidence of niche lability, and strong geographic structure. Likewise, the paper by Arjona et al. (2022) focuses on soil arthropod communities at different depths, highlighting the diversity of species (many new species records), with the results supporting the hypothesis that deeper soil beetle communities are much more dispersal limited compared to those closer to the surface. Focusing on biodiversity loss in beetle communities in Gaoligongshan National Park in southwestern China, Li et al. (2022) use high-throughput community barcoding to compare scenarios of climate-change-induced biodiversity loss, by simulating local extinction of communities clustered by season, elevation or latitude. The expectation was that close relatives (as inferred from phylogenetic affinities) would be buffered against loss of evolutionary history; that is, if one species went extinct, the clade would still be represented by other members. However, they find that regional biodiversity was not adequately buffered by the shared evolutionary history remaining after extinction. The overall promise of whole community metabarcoding is presented in Emerson et al. (2022) who highlight the potential to complement such high throughput barcode sequencing with deep learning image recognition workflows to advance the way we study terrestrial arthropod biodiversity as a whole. Considering marine systems, Macheriotou et al. (2023) use a community phylogenetics approach with metabarcoding data to assess the dynamics of nematode diversity across an ocean depth gradient. They showed that nematode ASV richness increases with depth up to the bathyal zone (200–4000 m), then decreases; moreover, strong phylogenetic clustering of ASVs suggests that communities have been assembled through environmental filtering. Kiemel et al. (2022) again use DNA metabarcoding (cytochrome oxidase, COI, and 18S ribosomal RNA) to ask (i) how zooplankton communities are spatially and temporally connected, (ii) what are the environmental factors influencing local communities, and (iii) what are the underlying metacommunity dynamics in this system. There was no difference between ephemeral and permanent kettle holes (ponds formed by retreating glaciers) and overall the results suggest that communities are mainly structured by environmental filtering based on pH, water temperature, kettle hole size and hydroperiod. Species sorting is a dominant driver in community assembly in the studied kettle hole zooplankton metacommunity. Likewise Govender et al. (2022) use a metabarcoding approach to highlight the point that, while sheltered marine bights around South Africa have lower pelagic zooplankton diversity due to structural homogeneity, they actually represent important fish spawning grounds (with key ramifications for fisheries and higher-level consumers). In this case, diversity measures could thus not be used as a proxy for ecological importance. Finally, Ip, Chang, Oh, et al. (2022) combine standardised sampling using Autonomous Reef Monitoring Structures (ARMS) and high-throughput sequencing to test whether coral cover shapes diversity patterns among organisms inhabiting hidden spaces within the reef matrix (the “cryptobiome”). They showed that, while marine fungi, bacteria, phytoplankton and other planktonic organisms were impacted primarily by abiotic factors (depth, temperature, level of particles in the water column and distance from the mainland), diversity patterns in larger-sized metazoans were associated with coral cover. A number of studies focused explicitly on microbial communities. For example, Pino et al. (2023) use 16S rRNA and ITS metabarcoding of soil microbiomes (bacteria and fungi) across large scale edaphic and climatic gradients in Australia to ask classic questions in soil science and macroecology: Are broad soil classifications sufficient to capture biological soil function, and what large-scale factors determine turnover in community composition? The authors find that soil classes are predictive of bacterial and fungal community composition regardless of spatial proximity, natural and cultivated soils are reliably distinct in their microbiomes, and the primary drivers of these microbiome community differences are soil pH and temperature cycles. Van der Loos et al. (2022) explore the interplay between environment and host genotype in shaping the stability and variability of microbial composition. Using seaweed-associated bacterial communities along a salinity gradient, they were able to identify a small group of core microbes possibly involved in salinity adaptation of the host. The experimental study by Nappi et al. (2022) tested the effects of two bacterial strains on the assembly and succession of microbial communities associated with the green macroalga bacterial strains a with one strong but in the taxonomic of the microbial and the but that were and included taxa that may the host. effects not to be a of but in distinct differences in the potential of the the for community this work insights on the development of new (e.g. for or Finally, there are several studies in which the authors processes across scales and et al. (2022) compare community assembly processes across scales of organismic complexity showing that (i) small soil fungi) were by stochastic processes while the community assembly of soil organisms was more (ii) the effects of soil and and its interaction with for community structure with and (iii) the spatial of which their on the assembly of the soil communities. suggest that the assembly of soil communities can be to extent by in et al. (2022) use metabarcoding to explore the development of successional communities in recently on and and how soil communities change through and how this change between different soil They were able to show diversity but also biotic soil with since The shifts were associated with the development of communities major of study examined interaction networks, and how the of the might the and of both et al., 2022) and micro-organismal et al., 2022) communities. an opportunity to questions to interaction and can provide of resilience to the promise and importance of metabarcoding for a understanding of entire of different and (2023) approaches that can biodiversity to approaches provide not on species but also on species with new approaches using species phylogenetic and learning to infer and Moreover, metabarcoding can provide a for hidden diversity (e.g. et al., 2022) and associated interactions (e.g. et al., Using metabarcoding et al. (2022) on diversity by in and soil they that, while soils have the the has the number of fungal by marine developments in high throughput approaches have insights into et al. (2022) the provided by these approaches to how interactions change as a of in They how the approach can be applied to understanding key questions in change in how interactions change through space and the of and other anthropogenic studies have how environmental DNA from can be used to identify the community of and has the potential to et al., The paper by et al. (2022) an in which they used DNA metabarcoding of in the to in to the of is to resilience, the study in the paper by et al. (2022) focuses on factors that might diversity in communities. they showed that metabarcoding data that were more but much compared to The results their hypothesis that niche of taxa to spatial turnover of phylogenetic species and of compared to low-elevation ecosystems. Finally, et al. 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(2022) uses DNA metabarcoding of to test factors that might dictate interactions in along elevation The results showed that the structure of the ecological was by both (i) the phylogenetic of the on based on their taxonomic and (ii) on the species in to the cover of the The results also other of the environment that in in and and in et al. (2022) the of on diversity and interactions in arthropod communities at two metabarcoding from and arthropod from they contrasting responses to of and the is impacted for both the effects of are for due to their for The approach can also be used to address applied questions of biological interactions as in et al. (2022) who showed that can allow not of biological interactions but also evidence of or which can biological by data can also be used to infer in the overall of interactions in a given biological Ip, Chang, et al. (2022) use metabarcoding in coral to shifts in community composition and structure of fish A key was that of the in was a common to coral spawning due to large of and fish associated with the high on coral by evolutionary the study by et al. (2022) uses the sequence to show how interactions among arthropod communities more over the Using of associations, they showed that the number of interactions species of to arthropod species and of with community age, that the communities show a natural over extended time. The widespread of molecular profiling methods has provided unprecedented for processes across with the approaches used for metabarcoding of whole communities of or the overall methods and being amenable to the tools used for microbial community applied to the environmental this of deep of communities, from community structure and ecosystem to associated with we now have the opportunity to of macro- and community structure across biological communities and assess the interplay between biotic and abiotic components of entire ecosystems. these et al. (2022) test the importance of stochastic and deterministic processes in shaping bacterial community dynamics associated with a widespread and important phytoplankton a of and experimental approaches to assess bacterial community assembly over they that deterministic processes microbial communities within conditions, stochastic processes were more of studies examined questions involved in the interaction between and their at the effects of the microbiome on and et al. (2022) use metabarcoding methods to the interplay between and microbiome in several isolated and of the They showed differences in the composition not richness or of the and microbiome of associated with and geographic et al. (2022) test the hypothesis that the of their measuring microbial community turnover they showed that beta-diversity and hence turnover is much in compared to The microbial and turnover of could ecological and the evolutionary of approaches the importance of high-throughput sequencing approaches for understanding how microbial communities can and Considering microbes and their et al. (2022) tested the hypothesis that the of and across an gradient. They used along an in the to show that of with but of highlighting a in our understanding of microbial and associated to and The paper by et al. (2022) examined the microbiome of a marine and the potential role of the microbiomes in pelagic and nutrient They showed that the and of the may the structure of pelagic and and The paper by et al. (2022) communities across results that the diversity is shaped by the composition of and which in are shaped by the of the The work of the mechanisms that may interactions and among microbial communities at The interactions between microbiomes and their host species can change across gradients, scale understand these et al. (2022) tested the role of and host in fungal communities across a of and factors in the The results their hypothesis that host and were the major drivers of fungal community structure. insights are now showing the between and the different components of their et al. (2022) in the of the different components of a across environmental Using a of whole and metabarcoding in and bacterial components of along elevation gradients they showed that, while and these turnover with The turnover in a for the taxonomic highlighting the importance of of different components of in evolutionary The paper by et al. (2022) examined the of and environmental and on the microbiome of The results showed that a to salinity to lower community richness and host but there was an in bacterial in the et al. (2022) tested the interplay between host environmental and the of an on the bacterial community of an host. They the structure of a host and the spatial turnover in its with high among microbial communities in and host structure in throughput approaches have also provided insights into the role of microbiomes in resilience. et al. (2022) tested the of microbiome communities on to by two species in one one microbial diversity with but microbes showed with highlighting for to to in A critical in microbiome studies is to tease apart the importance of the host and the environment in shaping that can be and (2022) several used methods for host-microbiome processes that to between and They used to measure power and and find that there are between and among the They that no one is and make for the scenarios which different methods are of and through the of species to loss of resilience, with of the role of biodiversity in ecosystem et al., and against and shifts et al., However, species and from can be a for et al., because of this have that species be into conservation et al., the is to and there is a that the of species in a given is critical to its resilience et al., throughput approaches are now providing for the study of the use of can provide unprecedented of both in and terrestrial et al., 2020). In an new uses the genetic from metabarcoding studies to from species et al., 2019); this was in several studies in this to provide insights into the of species and the associated biotic et al., et al., The modelling approaches in the of community assembly can be applied to understanding and the of on species Overcast et al. (2023) how metabarcoding data can be used to identify the importance of neutral processes in communities. This study the way for high throughput data to measure of and its resilience to anthropogenic The paper by et al. (2022) showed that the of microbial biodiversity and a predictable core microbiome in their up to from the showed around shifts in these et al. (2023) used environmental DNA to the interplay between environmental factors in the effects of with of species showing that environmental and their interaction biological among in the metabarcoding across gradients insights into processes of in the of biotic or the in by the community et al., the paper by et al. (2022) used the of the Islands in which sites of high elevation show diversity of species over the from metabarcoding of entire arthropod communities that, species diversity is the of species is Likewise, et al. (2022) used DNA metabarcoding and modelling to arthropod the of and and the of into primary habitats on in the Focusing on one from of the that with to age, and of there were alternative by fundamental eco-evolutionary processes with associated that were detectable from the high-throughput metabarcoding The study showed that and taxonomic richness was associated with to and that not to of arthropod across the different on the the insights in of the major there were several that from a key role in metabarcoding while insights can be from molecular the availability of a molecular for unprecedented to the The availability of a to identify the and of in a its as or and its overall and Moreover, is critical that the of the has been as can to role of natural history in such the data that have been through molecular profiling approaches have the fundamental importance of from reliably species and et al., The importance of a is by et al. (2022) who describe the importance of a and for biological Likewise, et al. (2022) the of without a and a fungal rRNA with to A role of in these approaches is that they can provide of For example, metabarcoding of from has provided key insights into environmental change over both in the availability of and in interaction et al., et al., This work to the of research that how metabarcoding of can provide on in interactions through and microbiome et al., and interactions et al., metabarcoding studies used clustering approaches and ASVs into to to The of this was to the in the while also taxa into However, new approaches have presented the opportunity for ASVs and hence insights into patterns et al., The important of the ability to at different of genetic clustering is that the comparison can be into the processes that govern species assembly. scale metabarcoding studies generate a of not DNA sequence but also phylogenetic and as as environmental data and to understand biodiversity processes, using such datasets more modelling approaches and learning methods, such as of the in this Special Issue have For example, learning approaches may be applied to image recognition to study arthropod biodiversity as in Emerson et al. learning was used to make of to based on of microbial taxa et al., learning methods with eco-evolutionary simulation can identify the ecological and evolutionary processes that to generate biodiversity as by Overcast et al. The of papers in this Special Issue the critical insights that can be using high-throughput particularly in to biodiversity We now have a for understanding how overall species composition across (i) spatial gradients, gradients of or associated Moreover, we can also through (ii) whether using or to show how and other interactions have and or ecological that provide insights into how entire communities change over extended The of papers a of with on and the other on The critical point is that we have a that comparison of processes across scales. concepts for understanding biodiversity in macro-organisms can be tested in using and dynamics that have been from microbial can provide insights into factors shaping communities of macro-organisms and their interaction with entire ecosystems. the approaches more be to the potential of high-throughput to answer of the questions in biodiversity authors together to the Special Issue and as of the a of the which was then by with all authors to the The Special Issue was through a group The & by the of the for with insights through a by the National from and for and A to The authors no of is not to this as no new data were or in this

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