Finer Taxonomic Resolution Research Articles

Predicting plants in the wild: Mapping arctic and boreal plants with UAS-based visible and near infrared reflectance spectra

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data (<10 cm pixels) is often necessary to resolve patches of many Arctic and boreal plant groups, such as bryophytes and lichens, which are significant components of terrestrial vegetation cover. Separation of co-occurring small vegetation patches in images also requires high spectral resolution. Our goal here was to test the capabilities of UAS-based imaging spectroscopy for mapping plant functional types (PFT) using high spatial and spectral resolution data over Arctic and boreal vegetation at four sites in central Alaska. We then tested several Machine and Deep learning models of PFT cover using the reflectance spectra. The best models were very simple, balancing both bias (overfitting caused by imbalance sample sizes) and variance (fit to the independent validation data), explaining > 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

VESPA: an optimized protocol for accurate metabarcoding-based characterization of vertebrate eukaryotic endosymbiont and parasite assemblages

Protocols for characterizing taxonomic assemblages by deep sequencing of short DNA barcode regions (metabarcoding) have revolutionized our understanding of microbial communities and are standardized for bacteria, archaea, and fungi. Unfortunately, comparable methods for host-associated eukaryotes have lagged due to technical challenges. Despite 54 published studies, issues remain with primer complementarity, off-target amplification, and lack of external validation. Here, we present VESPA (Vertebrate Eukaryotic endoSymbiont and Parasite Analysis) primers and optimized metabarcoding protocol for host-associated eukaryotic community analysis. Using in silico prediction, panel PCR, engineered mock community standards, and clinical samples, we demonstrate VESPA to be more effective at resolving host-associated eukaryotic assemblages than previously published methods and to minimize off-target amplification. When applied to human and non-human primate samples, VESPA enables reconstruction of host-associated eukaryotic endosymbiont communities more accurately and at finer taxonomic resolution than microscopy. VESPA has the potential to advance basic and translational science on vertebrate eukaryotic endosymbiont communities, similar to achievements made for bacterial, archaeal, and fungal microbiomes.

Global Distribution and Diversity of Marine Parmales.

Parmales (Bolidophyceae) is a minor eukaryotic phytoplankton group, sister to diatoms, which exists as two distinct forms of unicellular organisms: silicified cells and naked flagellates. Since their discovery, many field studies on Parmales have been performed; however, their global distribution has not yet been examined in detail. We herein compiled more than 3,000 marine DNA metabarcoding datasets targeting the V4 region of the 18S rRNA gene from the EukBank database. By linking this large dataset with the latest morphological and genetic information, we provide updated estimates on the diversity and distribution of Parmales in the global ocean at a fine taxonomic resolution. Parmalean amplicon sequence variants (ASVs) were detected in nearly 90% of the samples analyzed. However, the relative abundance of parmaleans in the eukaryotic community was less than 0.2% on average, and the estimated true richness of parmalean ASVs was approximately 316 ASVs, confirming their low abundance and diversity. A phylogenetic ana-lysis divided these algae into four clades, and three known morphotypes of silicified cells were classified into three different clades. The abundance of Parmales is generally high in the poles and decreases towards the tropics, and individual clades/subclades show further distinctions in their distribution. Collectively, the present results suggest clade/subclade-specific adaptation to different ecological niches.

Genome-resolved metagenomics provides insights into the microbial-mediated sulfur and nitrogen cycling in temperate seagrass meadows

The presence of seagrasses facilitates numerous microbial-mediated biogeochemical cycles, with sulfur- and nitrogen-cycling microorganisms playing crucial roles as regulators. Despite efforts to comprehend the diversity of microbes in seagrass ecosystems, the metabolic functions of these benthic microorganisms in seagrass sediments remain largely unknown. Using metagenomics, we provide insights into the sulfur- and nitrogen-cycling pathways and key metabolic capacities of microorganisms in both Z. japonica-colonized and unvegetated sediments over a seasonal period. Taxonomic analysis of N and S cycling genes revealed that δ- and γ- proteobacteria dominated the benthic sulfate-reducing bacteria, while α- and γ-proteobacteria played a significant role in the sulfur-oxidation processes. The proteobacterial lineages were also major contributors to the benthic nitrogen cycling. However, at a finer taxonomic resolution, microbial participants in different processes were observed to be highly diverse and mainly driven by environmental factors such as temperature and salinity. The gene pools of sulfur and nitrogen cycles in the seagrass sediments were dominated by genes involved in sulfide oxidation (fccA) and hydroxylamine oxidation (hao), respectively. Seagrass colonization elevated the relative abundance of genes responsible for sulfite production (phsC), hydroxylamine oxidation (hao), and nitrogen fixation (nifK), but suppressed sulfur oxidation (soxXYZ) and denitrification (nosZ and nirS). The prevalence of proteobacterial lineages functioned with versatile capabilities in both sulfur and nitrogen cycles in seagrass ecosystems, highlighting tight couplings between these processes, which was further supported by the recovery of 83 metagenome-assembled genomes (MAGs). These findings broaden our understanding of the biogeochemical processes that are mediated by microorganisms in seagrass ecosystems.

Replicate DNA metabarcoding can discriminate seasonal and spatial abundance shifts in river macroinvertebrate assemblages.

The delivery of consistent and accurate fine-resolution data on biodiversity using metabarcoding promises to improve environmental assessment and research. Whilst this approach is a substantial improvement upon traditional techniques, critics note that metabarcoding data are suitable for establishing taxon occurrence, but not abundance. We propose a novel hierarchical approach to recovering abundance information from metabarcoding, and demonstrate this technique using benthic macroinvertebrates. To sample a range of abundance structures without introducing additional changes in composition, we combined seasonal surveys with fish-exclusion experiments at Catamaran Brook in northern New Brunswick, Canada. Five monthly surveys collected 31 benthic samples for DNA metabarcoding divided between caged and control treatments. A further six samples per survey were processed using traditional morphological identification for comparison. By estimating the probability of detecting a single individual, multispecies abundance models infer changes in abundance based on changes in detection frequency. Using replicate detections of 184 genera (and 318 species) from metabarcoding samples, our analysis identified changes in abundance arising from both seasonal dynamics and the exclusion of fish predators. Counts obtained from morphological samples were highly variable, a feature that limited the opportunity for more robust comparison, and emphasizing the difficulty standard methods also face to detect changes in abundance. Our approach is the first to demonstrate how quantitative estimates of abundance can be made using metabarcoding, both among species within sites as well as within species among sites. Many samples are required to capture true abundance patterns, particularly in streams where counts are highly variable, but few studies can afford to process entire samples. Our approach allows study of responses across whole communities, and at fine taxonomic resolution. We discuss how ecological studies can use additional sampling to capture changes in abundance at fine resolution, and how this can complement broad-scale biomonitoring using DNA metabarcoding.

Glucose addition improves the culture performance of Pacific white shrimp by regulating the assembly of Rhodobacteraceae taxa in gut bacterial community

The family Rhodobacteraceae is a major core bacterial group in the gut of Pacific white shrimp (Penaeus vannamei) and its abundance can be stimulated by the carbohydrates (e.g., glucose) addition in biofloc-based culture systems. However, how glucose addition regulates Rhodobacteraceae assembly at a fine taxonomic resolution and its relationship with shrimp culture performance are unclear. Here, using full-length 16S rRNA gene PacBio sequencing, we explored fine-scale assembly mechanism of gut Rhodobacteraceae regulated by glucose addition. Glucose addition significantly enhanced the growth, survival rate, and feed efficiency of shrimps, and induced Rhodobacteraceae assembly consequently to create a more stable gut bacterial community, reflected by enhanced convergence and network stability as compared to those of the control. Five Ruegeria taxa were identified as potential probiotic candidates for improving shrimp culture performance. Meanwhile, the neutral model showed that positive host selection of gut Ruegeria taxa (primarily R. conchae and R. arenilitoris) from both water and gut metacommunities was enhanced by glucose addition, which stimulated deterministic assembly of gut Rhodobacteraceae. Moreover, differences in assembly processes of Rhodobacteraceae taxa at presumed strain-level within the same species (i.e., R. conchae) were observed, suggesting the necessity of revealing aquaculture bacterial assembly at the finer taxonomic resolution. Collectively, structural equation modeling demonstrated glucose addition improved shrimp culture performance largely by regulating the gut Rhodobacteraceae assembly. Our study provides insights into the assembly of gut Rhodobacteraceae stimulated by a basic regulatory measure in biofloc-based culture systems, holding the practical potential in managing a beneficial microbiota for shrimp culture.

A continental-scale chironomid training set for reconstructing Arctic temperatures

We present chironomid species assemblage data from 402 lakes across northern North America, Greenland, Iceland, and Svalbard to inform interpretations of Holocene subfossil chironomid assemblages used in paleolimnological reconstruction. This calibration-set was developed by re-identifying and taxonomically harmonizing chironomids in previously described surface sediment samples, with identifications made at finer taxonomic resolution than in original publications. The large geographic coverage of this dataset is intended to provide climatic analogues for a wide range of Holocene climates in the northwest North Atlantic region and North American Arctic, including Greenland. For many of these regions, modern calibration data are sparse despite keen interest in paleoclimate reconstructions from high latitudes. A suite of chironomid-based temperature models based upon this training set are evaluated here and the best statistical model is used to reconstruct late glacial (Allerød and Younger Dryas) and Holocene paleotemperatures at five non-glacial lakes representing a wide range of climate zones across Greenland. The new continent-scale training set offers more analogues for the majority of Greenland subfossil assemblages than existing smaller training sets, with many in Iceland and northern Canada. We find strong agreement between chironomid-based reconstructions derived from the new model and independent glacier-based evidence for multi-millennial Holocene temperature trends. Some of the new Holocene reconstructions are very similar to published data, but at a subset of sites and time periods we find improved paleotemperature reconstructions attributable both to the new model's finer taxonomic resolution and to its expanded geographic/climatic coverage, which resulted in improved characterization of species optima. In the late glacial, the new model's finer taxonomic resolution yields a unique ability to resolve temperatures of the Allerød from colder temperatures of the Younger Dryas, although the magnitude of that temperature difference may be underestimated. This study demonstrates the value of geographically and climatically broad paleoecological training sets. The large, taxonomically harmonized dataset presented here should be useful for a wide range of future investigations, including but not limited to paleotemperature reconstructions across the Arctic.

Amplicon Sequencing of Single-Copy Protein-Coding Genes Reveals Accurate Diversity for Sequence-Discrete Microbiome Populations.

ABSTRACTAn in-depth understanding of microbial function and the division of ecological niches requires accurate delineation and identification of microbes at a fine taxonomic resolution. Microbial phylotypes are typically defined using a 97% small subunit (16S) rRNA threshold. However, increasing evidence has demonstrated the ubiquitous presence of taxonomic units of distinct functions within phylotypes. These so-called sequence-discrete populations (SDPs) have used to be mainly delineated by disjunct sequence similarity at the whole-genome level. However, gene markers that could accurately identify and quantify SDPs are lacking in microbial community studies. Here, we developed a pipeline to screen single-copy protein-coding genes that could accurately characterize SDP diversity via amplicon sequencing of microbial communities. Fifteen candidate marker genes were evaluated using three criteria (extent of sequence divergence, phylogenetic accuracy, and conservation of primer regions) and the selected genes were subject to test the efficiency in differentiating SDPs within Gilliamella, a core honeybee gut microbial phylotype, as a proof-of-concept. The results showed that the 16S V4 region failed to report accurate SDP diversities due to low taxonomic resolution and changing copy numbers. In contrast, the single-copy genes recommended by our pipeline were able to successfully quantify Gilliamella SDPs for both mock samples and honeybee guts, with results highly consistent with those of metagenomics. The pipeline developed in this study is expected to identify single-copy protein coding genes capable of accurately quantifying diverse bacterial communities at the SDP level.IMPORTANCE Microbial communities can be distinguished by discrete genetic and ecological characteristics. These sequence-discrete populations are foundational for investigating the composition and functional structures of microbial communities at high resolution. In this study, we screened for reliable single-copy protein-coding marker genes to identify sequence-discrete populations through our pipeline. Using marker gene amplicon sequencing, we could accurately and efficiently delineate the population diversity in microbial communities. These results suggest that single copy protein-coding genes can be an accurate, quantitative, and economical alternative for characterizing population diversity. Moreover, the feasibility of a gene as marker for any bacterial population identification can be quickly evaluated by the pipeline proposed here.

Spatial- and size-related shifts in feeding habits of the common dolphinfish (Coryphaena hippurus) in the Southeast Pacific Ocean

Feeding habits of common dolphinfish (Coryphaena hippurus) were investigated in the Southeast Pacific Ocean using stomach content analysis. Stomachs were collected from 1506 individuals ranging between 18 and 187 cm in fork length. The fish were caught by longliner boats in 2009–2017. Based on percentage of wet weight (%Wi), percentage of number (%Ni), and frequency of occurrence (%Oi), flyingfishes were the predominant prey, followed by the cephalopods jumbo squid (Dosidicus gigas) and argonaut (Argonauta spp.). Permutational multivariate analysis of variance (PERMANOVA) detected significant differences between zones (p < 0.001). PERMANOVA also revealed two size-related shifts in feeding habits to occur at FL ~ 80 cm and FL ~ 110 cm (p < 0.001). Cumulative prey curves did not reach an asymptote in any of the zones and size classes, which may be attributed to the fine taxonomic resolution used. Spatial variations in daily meal ranged between 296.3 and 438.9 g day−1, corresponding to daily ration of 6.1–8.2% body mass BM day−1. Otherwise, size-related variations in both DM and DR were observed. Thus, while daily food intake increased with size from 177.3 for the smallest fish (LF < 80 cm) to 496.7 g day−1 for the largest ones (LF ≥ 110 cm), DR decreased from 13.0 ± 22.1 to 3.7 ± 0.7 BM day−1. The present results contribute to improve the understanding of dolphinfish feeding habits in the Southeast Pacific Ocean.

Plant biodiversity assessment through pollen DNA metabarcoding in Natura 2000 habitats (Italian Alps)

Monitoring biodiversity is of increasing importance in natural ecosystems. Metabarcoding can be used as a powerful molecular tool to complement traditional biodiversity monitoring, as total environmental DNA can be analyzed from complex samples containing DNA of different origin. The aim of this research was to demonstrate the potential of pollen DNA metabarcoding using the chloroplast trnL partial gene sequencing to characterize plant biodiversity. Collecting airborne biological particles with gravimetric Tauber traps in four Natura 2000 habitats within the Natural Park of Paneveggio Pale di San Martino (Italian Alps), at three-time intervals in 1 year, metabarcoding identified 68 taxa belonging to 32 local plant families. Metabarcoding could identify with finer taxonomic resolution almost all non-rare families found by conventional light microscopy concurrently applied. However, compared to microscopy quantitative results, Poaceae, Betulaceae, and Oleaceae were found to contribute to a lesser extent to the plant biodiversity and Pinaceae were more represented. Temporal changes detected by metabarcoding matched the features of each pollen season, as defined by aerobiological studies running in parallel, and spatial heterogeneity was revealed between sites. Our results showcase that pollen metabarcoding is a promising approach in detecting plant species composition which could provide support to continuous monitoring required in Natura 2000 habitats for biodiversity conservation.

Beyond Taxonomic Identification: Integration of Ecological Responses to a Soil Bacterial 16S rRNA Gene Database.

High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pH-abundance responses, and pH optima (pH at which OTU abundance is maximal). We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER/), and flat files are made available for use in bioinformatic pipelines. The further development of advanced informatics infrastructures incorporating modeled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

Exploring the Microdiversity Within Marine Bacterial Taxa: Toward an Integrated Biogeography in the Southern Ocean.

Most of the microbial biogeographic patterns in the oceans have been depicted at the whole community level, leaving out finer taxonomic resolution (i.e., microdiversity) that is crucial to conduct intra-population phylogeographic study, as commonly done for macroorganisms. Here, we present a new approach to unravel the bacterial phylogeographic patterns combining community-wide survey by 16S rRNA gene metabarcoding and intra-species resolution through the oligotyping method, allowing robust estimations of genetic and phylogeographic indices, and migration parameters. As a proof-of-concept, we focused on the bacterial genus Spirochaeta across three distant biogeographic provinces of the Southern Ocean; maritime Antarctica, sub-Antarctic Islands, and Patagonia. Each targeted Spirochaeta operational taxonomic units were characterized by a substantial intrapopulation microdiversity, and significant genetic differentiation and phylogeographic structure among the three provinces. Gene flow estimations among Spirochaeta populations support the role of the Antarctic Polar Front as a biogeographic barrier to bacterial dispersal between Antarctic and sub-Antarctic provinces. Conversely, the Antarctic Circumpolar Current appears as the main driver of gene flow, connecting sub-Antarctic Islands with Patagonia and maritime Antarctica. Additionally, historical processes (drift and dispersal limitation) govern up to 86% of the spatial turnover among Spirochaeta populations. Overall, our approach bridges the gap between microbial and macrobial ecology by revealing strong congruency with macroorganisms distribution patterns at the populational level, shaped by the same oceanographic structures and ecological processes.

Strategies for the bioinformatic treatment of high-throughput sequencing data for diatom studies and bioassessment

Diatom biomonitoring and ecological studies can greatly benefit from DNA metabarcoding compared to conventional microscopical analysis by potentially providing more reliable and accurate data in a cost- and time-efficient way. A conventional strategy for the bioinformatic treatment of sequencing data involves the clustering of quality filtered sequences into Operational Taxonomic Units (OTUs) based on a global sequence similarity, and their assignment to taxonomy using a reference library. Then, the obtained species lists of the successfully assigned taxa are used for subsequent analyses or quality index calculation. However, the high diversity of bioinformatic methods and parameters make inter-studies comparison difficult, especially because OTUs are specific to a given study. Clustering sequences into OTUs aims to reduce the biasing effect of sequencing artefacts and to reach an approximate species level delimitation at the price of potentially grouping together sequences with different ecology. A similar bias occurs when sequences that differ from each other by their ecological preference are assigned to the same taxa. The incompleteness of reference libraries can further introduce a bias by not taking into account unassigned sequences, thus losing the ecological information they possess. In order to overcome these biases, our studies tested new approaches on de novo developed diatom indices based on periphytic samples collected from streams in France and Hungary. Index development was performed with the leave-one-out cross validation (LOOCV) technique by building a model on a training dataset containing n-1 samples and testing it on the remaining test sample. Test values were correlated with a reference environmental gradient. The model was based on the calculation of optimum and tolerance of taxonomic units along the reference gradient and a modified Zelinka-Marvan diatom index equation. Taxonomic units tested in the studies were morphospecies, OTUs (95% similarity threshold), Individual Sequence Units (ISUs, via minimal bioinformatic quality filtering) and Exact Sequence Variants (ESVs, via DADA2 denoising algorithm). The “clustering-free” approach (ISU- and ESV-based indices) performed better than the OTU-based one, providing a fine taxonomic resolution where the ecological difference on genetically close sequence variants could be detected. Thus, these indices are more adapted to a standardized and comparable routine bioassessment. The “taxonomy-free” approach revealed the ecological preferences for those molecular taxonomic units (ISUs/ESVs) that otherwise either (i) would have been assigned to the same taxa due to genetic similarity, or (ii) would not have been recognized because of their absence from the reference libraries. However, we also found that taxonomic information cannot be neglected in ecological studies when the presence of organisms under particular environmental conditions is to be explained or interpreted e.g. via the traits they possess. New types of clustering methods are welcome in the future of biomonitoring where the delimitation of taxonomic units should be refined based on a higher emphasis on their ecology rather than on morphological or genetical criteria.

Manual Sampling and Video Observations: An Integrated Approach to Studying Flower-Visiting Arthropods in High-Mountain Environments.

Simple SummaryOur study compared two different methods to identify the arthropods visiting the flowers of the vulnerable endemic alpine species Androsace brevis (Primulaceae) and investigate their behavior. Using the traditional method of manual sampling we could taxonomically identify visiting arthropods on a fine scale and determine which taxa carry pollen. Conversely, video observations provided information on arthropod behavior and activity. By integrating the results obtained from these two approaches, we estimated the diversity of A. brevis flower-visiting arthropods and evaluated which taxa could be involved in its pollination. Our results, in addition to providing new insights on flowering plant–arthropod interactions in early season in the Alps, might be useful in developing effective methods of studying the ecological relationships in high-mountain ecosystems.Despite the rising interest in biotic interactions in mountain ecosystems, little is known about high-altitude flower-visiting arthropods. In particular, since the research in these environment can be limited or undermined by harsh conditions and logistical difficulties, it is mandatory to develop effective approaches that maximize possibilities to gather high-quality data. Here we compared two different methods, manual sampling and video observations, to investigate the interactions between the high-mountain arthropod community and flowers of Androsace brevis (Primulaceae), a vulnerable endemic alpine species with a short flowering period occurring in early season. We manually sampled flower-visiting arthropods according to the timed-observations method and recorded their activity on video. We assessed differences and effectiveness of the two approaches to estimate flower-visiting arthropod diversity and to identify potential taxa involved in A. brevis pollination. Both methods proved to be effective and comparable in describing the diversity of flower visitors at a high taxonomic level. However, with manual sampling we were able to obtain a fine taxonomic resolution for sampled arthropods and to evaluate which taxa actually carry A. brevis pollen, while video observations were less invasive and allowed us to assess arthropod behavior and to spot rare taxa. By combining the data obtained with these two approaches we could accurately identify flower-visiting arthropods, characterize their behavior, and hypothesize a role of Hymenoptera Apoidea and Diptera Brachycera in A. brevis pollination. Therefore, we propose integrating the two approaches as a powerful instrument to unravel interactions between flowering plants and associated fauna that can provide crucial information for the conservation of vulnerable environments such as high-mountain ecosystems.

Ground-Dwelling Invertebrate Abundance Positively Related to Volume of Logging Residues in the Southern Appalachians, USA

Invertebrates, especially those dependent on woody debris for a portion of their life cycle, may be greatly impacted by the amount of downed wood retained following timber harvests. To document relationships between invertebrates and logging residues, we sampled invertebrates with pitfall traps placed near or far from woody debris in 10 recently (2013–2015) harvested sites in western North Carolina with varying levels of woody debris retention. We measured the groundcover and microclimate at each trap and estimated site-level woody debris volume. We modeled predictors (e.g., site-level woody debris volume, percent woody debris cover at the trap site, site type) of captures of spiders (Araneae), harvestmen (Opiliones), centipedes/millipedes (Chilopoda/Diplopoda), ground beetles (Carabidae), rove beetles (Staphylinidae), other beetles, ants (Formicidae), grasshoppers (Acrididae/Tetrigidae), crickets (Gryllidae), and cave crickets (Rhaphidophoridae). In addition, we modeled ant occurrence at a finer taxonomic resolution, including red imported fire ants (Solenopsis invicta Buren) and 13 other genera/species. Forest type, whether hardwood or white pine (Pinus strobus L.) overstory preharvest, was a predictor of invertebrate response for 21 of 24 taxonomic analyses. Invertebrate captures or the occurrence probability of ants increased with increasing site-level woody debris volume for 13 of the 24 taxa examined and increased with increasing coarse woody debris (CWD; diameter ≥ 10 cm) cover at the trap level for seven of 24 taxa examined. Our results indicate that woody debris in harvested sites is important for the conservation of a majority of the taxa we studied, which is likely because of the unique microclimate offered near/under woody debris. Stand-scale factors typically were more important predictors of invertebrate response than trap-level cover of woody debris. We recommend implementing sustainability strategies (e.g., Biomass Harvesting Guidelines) to retain woody debris scattered across harvested sites to aid in the conservation of invertebrates.

Soil Bacterial Communities Exhibit Strong Biogeographic Patterns at Fine Taxonomic Resolution.

Bacteria have been inferred to exhibit relatively weak biogeographic patterns. To what extent such findings reflect true biological phenomena or methodological artifacts remains unclear. Here, we addressed this question by analyzing the turnover of soil bacterial communities from three data sets. We applied three methodological innovations: (i) design of a hierarchical sampling scheme to disentangle environmental from spatial factors driving turnover; (ii) resolution of 16S rRNA gene amplicon sequence variants to enable higher-resolution community profiling; and (iii) application of the new metric zeta diversity to analyze multisite turnover and drivers. At fine taxonomic resolution, rapid compositional turnover was observed across multiple spatial scales. Turnover was overwhelmingly driven by deterministic processes and influenced by the rare biosphere. The communities also exhibited strong distance decay patterns and taxon-area relationships, with z values within the interquartile range reported for macroorganisms. These biogeographical patterns were weakened upon applying two standard approaches to process community sequencing data: clustering sequences at 97% identity threshold and/or filtering the rare biosphere (sequences lower than 0.05% relative abundance). Comparable findings were made across local, regional, and global data sets and when using shotgun metagenomic markers. Altogether, these findings suggest that bacteria exhibit strong biogeographic patterns, but these signals can be obscured by methodological limitations. We advocate various innovations, including using zeta diversity, to advance the study of microbial biogeography.IMPORTANCE It is commonly thought that bacterial distributions show lower spatial variation than for multicellular organisms. In this article, we present evidence that these inferences are artifacts caused by methodological limitations. Through leveraging innovations in sampling design, sequence processing, and diversity analysis, we provide multifaceted evidence that bacterial communities in fact exhibit strong distribution patterns. This is driven by selection due to factors such as local soil characteristics. Altogether, these findings suggest that the processes underpinning diversity patterns are more unified across all domains of life than previously thought, which has broad implications for the understanding and management of soil biodiversity.

Maize Bran Particle Size Governs the Community Composition and Metabolic Output of Human Gut Microbiota in in vitro Fermentations.

Differences in the chemical and physical properties of dietary fibers are increasingly known to exert effects on their fermentation by gut microbiota. Here, we demonstrate that maize bran particle size fractions show metabolic output and microbial community differences similar to those we previously observed for wheat brans. As for wheat brans, maize bran particles varied in starch and protein content and in sugar composition with respect to size. We fermented maize bran particles varying in size in vitro with human fecal microbiota as inocula, measuring their metabolic fate [i.e., short-chain fatty acids (SCFAs)] and resulting community structure (via 16S rRNA gene amplicon sequencing). Metabolically, acetate, propionate and butyrate productions were size-dependent. 16S rRNA sequencing revealed that the size-dependent SCFA production was linked to divergent microbial community structures, which exerted effects at fine taxonomic resolution (the genus and species level). These results further suggest that the physical properties of bran particles, such as size, are important variables governing microbial community compositional and metabolic responses.

Fine Resolution Analysis of Microbial Communities Provides Insights Into the Variability of Cocoa Bean Fermentation.

Cocoa bean fermentation is an important microbial process, where most metabolites that affect chocolate quality and aroma are generated. Production of reproducible high-quality beans is a major challenge because most fermentations occur in open containers with a lack of variable control. Here we present a study that aims to identify the effect of farm protocols, climate, and bean mass exposure, in the dynamics and composition of microbial communities. Using high-throughput sequencing of molecular markers for bacteria and yeasts, complemented with culture-based methods, we evaluated the microbial diversity and dynamics associated to spontaneous cocoa fermentation in two distinct agro-ecological zones in Colombia. The bacterial communities were classified at two levels of evolutionary relationship, at a coarse resolution (OTU-level) and at a finer resolution (oligotype-level). A total of six bacterial OTUs were present in both farms, following a microbial succession that starts with the Enterobacteraceae family (one OTU), transitioning to the Lactobacillaceae family (three OTUs), and finishing with Acetobacteraceae family (two OTUs). When undesirable practices were done, OTUs were observed at unexpected moments during the fermentation. At a finer taxonomic resolution, 48 oligotypes were identified, with 46 present in both farms. These oligotypes have different patterns of prevalence. In the case of Lactobacillaceae a high evenness was observed among oligotypes. In contrast, for Enterobacteraceae and Acetobacteraceae a high dominance of one or two oligotypes was observed, these oligotypes were the same for both farms, despite geographic location and season of sampling. When the overall fermentations were compared using correlations matrices of oligotypes abundance, they show a clear clustering by farm, suggesting that farm protocols generate a unique fingerprint in the dynamics and interactions of the microbial communities. The comparison between the upper and middle layers of the bean mass showed that environmental exposure affects the paces at which ecological successions occur, and therefore, is an important source of cocoa quality heterogeneity. In conclusion, the results presented here showed that the dynamics of microbial fermentation can be used to identify the sources of variability and evidence the need for better fermentation technologies that favor the production of reproducible high-quality cocoa beans.

Depth gradients drive changes in early successional community composition and associated coral larvae settlement interactions

Depth gradients are known to drive patterns of adult and juvenile coral distribution, but the influence on early successional communities and subsequent interactions with coral larvae settlement are poorly understood. We studied how early successional communities changed across a 40 m depth gradient to test whether patterns of coral larvae settlement could be explained by changes in preferred settlement substrates. Light decreased 83-fold from 3 to 40 m, and coral settlement orientation switched from tile undersides at 3 and 8 m to tile topsides at 25 and 40 m as expected. Regardless of the reduction in light, community composition on the topsides of settlement tiles did not change across depths, being dominated by turf algae (49%), crustose coralline algae (CCA; 29%), and biofilmed tile (12%). In contrast, settlement tile undersides were dominated by coralline and fleshy algae at 3 m, transitioning to ascidians and bryozoans at 8 m and 25 m, and biofilmed tile (80%) at 40 m. Following multiple regression analysis from an initial 21 bio-physical variables (R2 = 0.34), the optimal reduced model for predicting coral settlement included just five variables and explained most of the variability (R2 = 0.29). CCA contributed an R2 of 0.14 to the model and was positively correlated with Acroporidae settlement, biofilmed tile contributed an R2 of 0.09 to the model and was negatively correlated with Acroporidae settlement, while the other predictors explained nominal model variance. As assessed using electivity indices, Acroporidae switched settlement preference from CCA at shallower depths to biofilmed tile in deeper environments. In addition to the influence of light on larval settlement behaviour as described in previous research, our results show that CCA also provide an important facilitator of coral settlement behaviour across depth gradients. However, as in shallow reefs environments, further work is required to elucidate whether changes occurring at a finer taxonomic resolution within the CCA community can help to further explain switches in coral larval settlement behaviour in light-limited environments.

DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness

The complexity and natural variability of ecosystems present a challenge for reliable detection of change due to anthropogenic influences. This issue is exacerbated by necessary trade-offs that reduce the quality and resolution of survey data for assessments at large scales. The Peace-Athabasca Delta (PAD) is a large inland wetland complex in northern Alberta, Canada. Despite its geographic isolation, the PAD is threatened by encroachment of oil sands mining in the Athabasca watershed and hydroelectric dams in the Peace watershed. Methods capable of reliably detecting changes in ecosystem health are needed to evaluate and manage risks. Between 2011 and 2016, aquatic macroinvertebrates were sampled across a gradient of wetland flood frequency, applying both microscope-based morphological identification and DNA metabarcoding. By using multispecies occupancy models, we demonstrate that DNA metabarcoding detected a much broader range of taxa and more taxa per sample compared to traditional morphological identification and was essential to identifying significant responses to flood and thermal regimes. We show that family-level occupancy masks high variation among genera and quantify the bias of barcoding primers on the probability of detection in a natural community. Interestingly, patterns of community assembly were nearly random, suggesting a strong role of stochasticity in the dynamics of the metacommunity. This variability seriously compromises effective monitoring at local scales but also reflects resilience to hydrological and thermal variability. Nevertheless, simulations showed the greater efficiency of metabarcoding, particularly at a finer taxonomic resolution, provided the statistical power needed to detect change at the landscape scale.