Estimates Of Relative Abundance Research Articles

Environmental DNA metabarcoding in the Cape Fold aquatic ecoregion: Opportunities and challenges for eDNA uptake in an endemism hotspot

Abstract Environmental DNA (eDNA) metabarcoding has the potential to significantly improve surveys of biodiversity in freshwater systems. However, this methodology is still infrequently used in global hotspots of endemic species, in part because of two major barriers to the success of eDNA metabarcoding: (1) insufficient regional taxonomic representation in public reference sequence databases; and (2) inconsistent species incidence and abundance estimates when compared to conventional surveys. We sampled eDNA and conducted visual surveys in the headwaters of two rivers in the Cape Fold aquatic ecoregion, South Africa. A reference sequence database was generated for the regional diversity of fishes to improve taxonomic classification of endemic species. We also compared the consistency of incidence data and relative abundance estimates of fishes from eDNA metabarcoding sequencing results and visual surveys (snorkel and underwater cameras) at each site sampled. Only 1% of eDNA metabarcoding reads could be classified to fish species without the supplementation of reference sequence databases for local endemic species. Once regional reference sequences were added, a total of nine species were detected and >99% reads classified. A strong positive relationship (Φ = 0.75) was observed between the patterns of detections using eDNA and visual approaches. However, eDNA metabarcoding detected more species than visual methods. In addition, the relative read frequency and abundance observed in visual surveys was significantly correlated (R2 = 0.85–0.88, p < 0.001) in the majority of the small pools surveyed. Incomplete public reference sequence databases hinder the use of eDNA metabarcoding in regions of high endemicity. Local reference sequence libraries can overcome these challenges. When appropriately implemented, eDNA metabarcoding shows promise, producing results consistent or better than the more frequently used and labour‐intensive visual surveys. Efficient survey methods like eDNA metabarcoding are urgently needed to improve aquatic monitoring efforts globally. Understanding how eDNA metabarcoding sequencing results relate to conventional survey methods is a key step in its implementation in ecoregions with high endemicity.

Enriching barcoding markers in environmental samples utilizing a phylogenetic probe design: Insights from mock communities

AbstractHybridization capture is an emerging method making use of short oligonucleotide baits to enrich DNA libraries for genomic fragments of specific organisms thus enabling detection of their presence in environmental samples. Although it offers a primer‐independent alternative to metabarcoding, little empirical work has been dedicated to characterizing the underlying biases and coupled implications for biological interpretation. Moreover, few published bioinformatic pipelines are available for designing polynucleotide capture baits from a reference sequence collection. We designed RNA‐baits specifically targeting two chloroplast barcoding genes matK and rbcL to reveal the plant taxonomic diversity present in a given environmental sample. Our approach leverages the sensitivity of hybridization capture and the capacity of high‐throughput DNA sequencing instruments. It builds on a new and universal method based on ancestral sequence reconstruction, ultimately limiting the number of bait‐probes required and reducing experimental costs, while accessing high levels of taxonomic diversity. Our bait‐set selectively targets four main plant orders (Fagales, Pinales, Asterales, and Poales), representing ~18% of all described vascular plants. This is achieved through the use of only 4084 baits, each 80 nucleotides in length (80‐mer), capturing ~1.0–1.6 k nucleotide sequences from each taxon. Tests on mock communities revealed important factors influencing capture efficiency and relative abundance estimates, including GC‐content, the overall target length per taxa, and the bait density and mean number of mismatches to the bait sequence. Our results show that hybridization capture, like metabarcoding, requires caution when interpreting results quantitatively within (paleo)‐ecological studies. Biases detected in this work have the potential to be mitigated with bait designs that avoid extreme base compositional biases and balancing bait targets across taxa. However, we strongly recommend the use of mock communities and read simulations to quantify the accuracy of taxonomic representation when using new bait designs.

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data.

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

Improving Bacterial Metagenomic Research through Long-Read Sequencing.

Metagenomic sequencing analysis is central to investigating microbial communities in clinical and environmental studies. Short-read sequencing remains the primary approach for metagenomic research; however, long-read sequencing may offer advantages of improved metagenomic assembly and resolved taxonomic identification. To compare the relative performance for metagenomic studies, we simulated short- and long-read datasets using increasingly complex metagenomes comprising 10, 20, and 50 microbial taxa. Additionally, we used an empirical dataset of paired short- and long-read data generated from mouse fecal pellets to assess real-world performance. We compared metagenomic assembly quality, taxonomic classification, and metagenome-assembled genome (MAG) recovery rates. We show that long-read sequencing data significantly improve taxonomic classification and assembly quality. Metagenomic assemblies using simulated long reads were more complete and more contiguous with higher rates of MAG recovery. This resulted in more precise taxonomic classifications. Principal component analysis of empirical data demonstrated that sequencing technology affects compositional results as samples clustered by sequence type, not sample type. Overall, we highlight strengths of long-read metagenomic sequencing for microbiome studies, including improving the accuracy of classification and relative abundance estimates. These results will aid researchers when considering which sequencing approaches to use for metagenomic projects.

Determining abundance predictors of the European Hare (Lepus europaeus), a global invader in southeastern Brazil

Abstract European Hare (Lepus europaeus), like many invasive species, have declined in much of their native range but flourished in non-native regions (e.g. South America). Previous studies suggested that loss of farmland heterogeneity due to agricultural intensification is the main driver of the species decline in its native range in Europe. Yet, little is known about the role of spatial and temporal heterogeneity and land cover types as predictors of European Hare local abundance in Neotropical agricultural landscapes. We hypothesized that spatial and temporal heterogeneity, rather than land cover types, would be the most influential predictors of hare local abundance in intensively managed Neotropical agricultural landscapes. We sampled 55 sites embedded within agricultural-dominated landscapes from southeastern Brazil with camera traps and transect surveys. Sites were selected along an uncorrelated gradient of native vegetation cover and compositional heterogeneity. We estimated the relative abundance of European hares using an occupancy model that accounts for imperfect detection of species induced primarily by variation in local abundance. We found that land cover diversity, sugarcane, and savanna cover were the best predictors of European Hare relative abundance. Hare relative abundance estimates were low and did not vary dramatically among sites, suggesting that this invader has not yet attained high local density in our study region. European Hare attained the highest relative abundance in agricultural landscapes that locally combine higher compositional heterogeneity, including sugarcane crops, and little to no native savanna. Areas with these combined features may represent the most important nascent foci, fostering ongoing northward spread of this invasive species in the Neotropics.

Recovery of a Population of Slimy Sculpin (Uranidea cognata) after an Autumn Fish Kill in the Headwaters of a Minnesota Trout Stream

Toxic runoff from heavy rains on 26 September 2019 caused a complete fish kill on the 2 km long headwater reach of Garvin Brook, Winona County, Minnesota, USA. This project examined the recovery of the slimy sculpin (Uranidea cognata) population within the lower 900 m of the kill zone, comparing relative abundance estimates and size structures between the kill zone and a downstream, unimpacted reference section. Electrofishing surveys were conducted at 24 sites (12 within both kill and reference zones) at 6, 11, 18, 28, and 42 months post-kill to assess relative abundance (catch-per-effort (CPE), fish/min) and population age structures (based on total length). At six months post-kill, sculpin were present throughout the kill zone. However, adult CPE declined significantly with upstream distance, which was suggestive of the ongoing immigration of adult sculpin from downstream. Age structures were similar in kill and reference zones with all size/age groups present in both zones after six months, while CPE was twice as high in the reference (6.3 fish/min) versus the kill zone (3.0 fish/min). After 11 months, CPE did not differ between zones (14 fish/min) and remained that way for the remainder of the study. However, age structures differed dramatically between zones at both 11 and 18 months post-kill; adult sculpin were significantly more abundant in the reference zone, whereas juvenile fish dominated the kill zone. By 28 months post-kill, both sculpin abundance and age structure within the kill zone had fully recovered from the kill. Sculpin recovery was accomplished mostly through enhanced reproduction within the kill zone following initial post-kill movements of both adult and juvenile fish into the kill zone from downstream. Low predation on juvenile sculpin due to a reduced abundance of trout and adult sculpin post-kill likely allowed more rapid recovery of the sculpin population within the kill zone.

A CRISPR-based strategy for targeted sequencing in biodiversity science.

Many applications in molecular ecology require the ability to match specific DNA sequences from single- or mixed-species samples with a diagnostic reference library. Widely used methods for DNA barcoding and metabarcoding employ PCR and amplicon sequencing to identify taxa based on target sequences, but the target-specific enrichment capabilities of CRISPR-Cas systems may offer advantages in some applications. We identified 54,837 CRISPR-Cas guide RNAs that may be useful for enriching chloroplast DNA across phylogenetically diverse plant species. We tested a subset of 17 guide RNAs invitro to enrich plant DNA strands ranging in size from diagnostic DNA barcodes of 1,428 bp to entire chloroplast genomes of 121,284 bp. We used an Oxford Nanopore sequencer to evaluate sequencing success based on both single- and mixed-species samples, which yielded mean chloroplast sequence lengths of 2,530-11,367 bp, depending on the experiment. In comparison to mixed-species experiments, single-species experiments yielded more on-target sequence reads and greater mean pairwise identity between contigs and the plant species' reference genomes. But nevertheless, these mixed-species experiments yielded sufficient data to provide ≥48-fold increase in sequence length and better estimates of relative abundance for a commercially prepared mixture of plant species compared to DNA metabarcoding based on the chloroplast trnL-P6 marker. Prior work developed CRISPR-based enrichment protocols for long-read sequencing and our experiments pioneered its use for plant DNA barcoding and chloroplast assemblies that may have advantages over workflows that require PCR and short-read sequencing. Future work would benefit from continuing to develop invitro and in silico methods for CRISPR-based analyses of mixed-species samples, especially when the appropriate reference genomes for contig assembly cannot be known a priori.

Association of Root Biofilm Bacteriome with Root Caries Lesion Severity and Activity

Introduction: This research aimed to assess the association of root biofilm bacteriome with root caries lesion severity and activity in institutionalised Colombian elderlies and was conducted to gather data on the root caries bacteriome in this population. Methods: A bacteriome evaluation of biofilm samples from sound and carious root surfaces was performed. Root caries was categorised (ICDAS Root criteria) based on severity (sound surfaces, initial: non-cavitated, moderate/extensive combined: cavitated) and activity status (active and inactive). DNA was extracted and the V4 region of the 16S rRNA gene was sequenced; afterwards the classification of features was conducted employing amplicon sequence variants and taxonomic assignment via the Human Oral Microbiome Database (HOMD). Bacterial richness, diversity (Simpson’s and Shannon’s indices), and relative abundance estimation were assessed and compared based on root caries severity and activity status (including Sound surfaces). Results: A total of 130 biofilm samples were examined: sound (n = 45) and with root caries lesions (n = 85; by severity: initial: n = 41; moderate/extensive: n = 44; by activity: active: n = 60; inactive: n = 25). Species richness was significantly lower in biofilms from moderate/extensive and active groups compared to sound sites. There was a higher relative abundance of species like Lechtotricia wadei, Capnocytophaga granulosa, Cardiobacterium valvarum, Porphyromonas pasteri – in sound sites; Dialister invisus, Streptococcus mutans, Pseudoramibacter alactolyticus and Bacteroidetes (G-5) bacterium 511 – in moderate/extensive lesions, and Fusobacterium nucleatum subsp. animalis, Prevotella denticola, Lactobacillus fermentum, Saccharibacteria (TM7) (G-5)bacterium HMT 356 – in active lesions. Conclusion: Root caries bacteriome exhibited differences in species proportions between the compared groups. Specifically, cavitated caries lesions and active caries lesions showed higher relative abundance of acidogenic bacteria.

Accounting for spatiotemporal sampling variation in joint species distribution models

Abstract Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

Cisco population characteristics in Wisconsin lakes in relation to lake‐ and landscape‐level factors

AbstractObjectiveDeclines in Cisco Coregonus artedi populations in some inland lakes have prompted assessments of Cisco occurrence and extirpation risk in relation to various stressors to identify refuge lakes and factors that promote Cisco persistence. However, most previous assessments have focused on presence–absence of Cisco rather than examining how population characteristics, such as relative abundance or growth, might change in relation to lake‐ and landscape‐level environmental factors. Consequently, our specific objectives were to identify important environmental factors explaining variation in Cisco relative abundance and growth and to determine whether population metrics describing size and age distributions were related to relative abundance in Wisconsin inland lakes.MethodsCisco were collected from 48 inland Wisconsin lakes during 2011–2015 using vertical monofilament gill nets and population‐specific relative abundance estimates (catch per unit effort [CPUE]) were quantified as the number of individuals per gill‐net night. Sagittal otoliths were removed from a subsample of Cisco for age estimation and growth was indexed as mean total length (TL; mm) at age 2. Length and age data were used to develop a suite of metrics describing size and age distributions of each population. Random forest models were used to evaluate relationships between 10 biologically relevant predictor variables representing variation in physical, climatic, catchment, and limnological characteristics and Cisco CPUE and growth. Pearson correlations were used to determine whether population characteristics were related to CPUE.ResultCisco populations exhibited large variation in relative abundance, growth, and size and age distributions. Best‐fit random forest models explained approximately 25% of the variation in Cisco CPUE and 46% of the variation in growth. Growing degree‐days and variables associated with availability, quality, and quantity of suitable oxythermal conditions were identified as important predictors of both Cisco CPUE and growth; CPUE was also identified as an important predictor of growth. Mean TL and mean TL at age 2 were negatively related to Cisco CPUE, whereas mean age, number of age‐classes present, and maximum observed age were positively related to CPUE.ConclusionOur results suggest that maintenance of suitable oxythermal habitat conditions may be critical to conserving abundant Cisco populations. Our assessment also provides insights on how Cisco populations may respond to environmental and anthropogenic stressors, which could aid ongoing and future conservation and management efforts in Wisconsin and elsewhere.

Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size

Abstract Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent. We present a case study demonstrating how data from the eBird citizen science programme can be combined with regional monitoring efforts by the US Fish and Wildlife Service to produce high‐resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high‐resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information with those based on aerial survey data alone. Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations. We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the US Fish and Wildlife Service and inform permitting regulations (e.g. mortality/harm associated with wind energy development). Policy implications: Our results demonstrate the ability of citizen science data to complement targeted monitoring programmes and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey‐based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.

Biomass burning in the Neotropics is exposing migrating birds to elevated fine particulate matter concentrations

AbstractAimA unique risk faced by nocturnally migrating birds is the disorienting influence of artificial light at night (ALAN). ALAN originates from anthropogenic activities that can generate other forms of environmental pollution, including the emission of fine particulate matter (PM2.5). PM2.5 concentrations can display strong seasonal variation whose origin can be natural or anthropogenic. How this variation affects seasonal associations with ALAN and PM2.5 for nocturnally migrating bird populations has not been explored.LocationWestern Hemisphere.Time Period2021Major Taxa StudiedNocturnally migrating passerine (NMP) bird species.MethodsWe combined monthly estimates of PM2.5 and ALAN with weekly estimates of relative abundance for 164 NMP species derived using observations from eBird. We identified groups of species with similar associations with monthly PM2.5. We summarized their shared environmental, geographical, and ecological attributes.ResultsPM2.5 was lowest in North America, especially at higher latitudes during the boreal winter. PM2.5 was highest in the Amazon Basin, especially during the dry season (August–October). ALAN was highest within eastern North America, especially during the boreal winter. For NMP species, PM2.5 associations reached their lowest levels during the breeding season (<10 μg/m3) and highest levels during the nonbreeding season, especially for long‐distance migrants that winter in Central and South America (~20 μg/m3). Species that migrate through Central America in the spring encountered similarly high PM2.5 concentrations. ALAN associations reached their highest levels for species that migrate (~12 nW/cm2/sr) or spend the nonbreeding season (~15 nW/cm2/sr) in eastern North America.Main ConclusionsWe did not find evidence that the disorienting influence of ALAN enhances PM2.5 exposure during stopover in the spring and autumn for NMP species. Rather, our findings suggest biomass burning in the Neotropics is exposing NMP species to consistently elevated PM2.5 concentrations for an extended period of their annual life cycles.

Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay

AbstractObjectiveThis paper applies a catch multiple survey analysis (CMSA) to Atlantic horseshoe crabs Limulus polyphemus in the Delaware Bay to generate robust population estimates for harvest management. Currently, horseshoe crabs along the U.S. Atlantic coast are harvested as bait for other fisheries and collected for their blood, which is used in a biomedical industry. The Delaware Bay is home to the largest population of horseshoe crabs and is a significant stopover for shorebirds to rebuild energy by consuming horseshoe crab eggs prior to completing their northward migration. To address this interrelationship, the Adaptive Resource Management (ARM) Framework has been used since 2013 to ensure that horseshoe crab harvest within the region takes into account the forage needs of migratory birds. Since its inception, the ARM Framework has used a single trawl survey's swept area‐based population estimates of horseshoe crab relative abundance and a theoretical population model developed primarily from literature‐derived values. With more data collected in the region in recent years and other sources of mortality that can now be quantified, a catch survey model can provide horseshoe crab population estimates going forward.MethodsA CMSA was used to estimate male and female horseshoe crab population size for 2003–2021 using all quantifiable sources of mortality and three fishery‐independent indices of abundance.ResultThe CMSA results indicated that adult abundance of male and female horseshoe crabs was stable from 2003 to 2013 and then began to increase through 2017, a result that is consistent with stock rebuilding following a period of harvest restrictions as recommended by the ARM Framework. Population estimates were lower in recent years but remained above the levels estimated before implementation of the ARM Framework. In 2021, the CMSA estimated that there were over 6 million mature females and nearly 16 million mature male horseshoe crabs in the region.ConclusionThe CMSA provides the best and most comprehensive population estimates of horseshoe crabs in Delaware Bay and will improve modeling efforts within the ARM Framework going forward.

Taxonomy, Sequence Variance and Functional Profiling of the Microbial Community of Long-Ripened Cheddar Cheese Using Shotgun Metagenomics.

Shotgun metagenomic sequencing was used to investigate the diversity of the microbial community of Cheddar cheese ripened over 32 months. The changes in taxa abundance were compared from assembly-based, non-assembly-based, and mOTUs2 sequencing pipelines to delineate the community profile for each age group. Metagenomic assembled genomes (MAGs) passing the quality threshold were obtained for 11 species from 58 samples. Although Lactococcus cremoris and Lacticaseibacillus paracasei were dominant across the shotgun samples, other species were identified using MG-RAST. NMDS analysis of the beta diversity of the microbial community revealed the similarity of the cheeses in older age groups (7 months to 32 months). As expected, the abundance of Lactococcus cremoris consistently decreased over ripening, while the proportion of permeable cells increased. Over the ripening period, the relative abundance of viable Lacticaseibacillus paracasei progressively increased, but at a variable rate among trials. Reads attributed to Siphoviridae and Ascomycota remained below 1% relative abundance. The functional profiles of PMA-treated cheeses differed from those of non-PMA-treated cheeses. Starter rotation was reflected in the single nucleotide variant profiles of Lactococcus cremoris (SNVs of this species using mOTUs2), while the incoming milk was the leading factor in discriminating Lacticaseibacillus paracasei/casei SNV profiles. The relative abundance estimates from Kraken2, non-assembly-based (MG-RAST) and marker gene clusters (mOTUs2) were consistent across age groups for the two dominant taxa. Metagenomics enabled sequence variant analysis below the bacterial species level and functional profiling that may affect the metabolic interactions between subpopulations in cheese during ripening, which could help explain the overall flavour development of cheese. Future work will integrate microbial variants with volatile profiles to associate the development of compounds related to cheese flavour at each ripening stage.

Pooling of intra-site measurements inflates variability of the correlation between environmental DNA concentration and organism abundance.

Environmental DNA (eDNA) analysis can promote efficient ecosystem monitoring and resource management. However, limited knowledge of the factors affecting the relationship between eDNA concentration and organism abundance causes uncertainty in relative abundance estimates based on eDNA concentration. Pooling of data points obtained from multiple locations within a site has been used to mitigate intra-site variation in eDNA and abundance estimates, but decreases the sample size used for estimating the relationship. I here assessed how the pooling of intra-site measurements of eDNA concentration and organism abundance impacted the reliability of the correlative relationship between eDNA concentration and organism abundance. Mathematical models were developed to simulate measurements of eDNA concentrations and organism abundances from multiple locations in a given survey site, and the CVs (coefficient of variability) of the correlations were compared depending on whether data points from different locations were individually treated or pooled. Although the mean and median values of the correlation coefficients were similar between the scenarios, the CVs of the simulated correlations were substantially higher under the pooled scenario than the individual scenario. Additionally, I re-analyzed two empirical studies conducted in lakes, both showing higher CVs of the correlations by pooling intra-site measurements. This study suggests that it would make eDNA-based abundance estimation more reliable and reproducible to individually analyze target eDNA concentrations and organism abundance estimates.

RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes.

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either (i) require powerful computational resources that may not be available for portable sequencers or (ii) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: (i) read mapping, (ii) relative abundance estimation, and (iii) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides (i) 25.8× and 3.4× better average throughput and (ii) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.

Seasonal habitat use of yellow perch Perca flavescens in a north‐temperate lake

AbstractKnowledge of species‐specific fish biology, ecology, and habitat use is critical for informing science‐based management. Yellow perch Perca flavescens are an ecologically and recreationally important fish species. Yet, for many north‐temperate systems (e.g. inland lakes of Wisconsin), little is known about yellow perch seasonal habitat use and ecology. We characterised yellow perch seasonal spatial distribution, size–structure, and condition between littoral and pelagic habitats in Crystal Lake (Vilas Co., WI) during the open water period of 2021. Seasonal spatial distribution and population size–structure were characterised by contemporaneous sampling in littoral and pelagic habitats using mini‐fyke nets and vertical gill nets, respectively. Relative abundance estimates for yellow perch in the littoral zone were greatest during early spring and declined throughout the open water season. Yellow perch pelagic relative abundance estimates were lowest in spring, peaked at the onset of stratification, and then declined and remained relatively consistent throughout the open water season. In general, yellow perch population size–structure and condition were greatest in the pelagic zone. Our findings suggest that after spring spawning, larger yellow perch prefer/select pelagic habitats, while sub‐adults and juveniles prefer/select littoral habitats, potentially resulting from increased predator refugia and(or) resource availability. Our results emphasise the importance of evaluating gear and fishery assessment bias, particularly when the target species may use a range of habitat types within a system.

Improved species level bacterial characterization from rhizosphere soil of wilt infected Punica granatum

Pomegranate crops are prone to wilt complex disease, which is known to severely hamper the crop yield. There have been limited studies that have explored bacteria–plant–host associations in wilt complex disease affecting pomegranate crops. In the present study, wilt infected rhizosphere soil samples (ISI, ASI) in pomegranate were studied in comparison to a healthy control (HSC). The 16S metagenomics sequencing approach using the MinION platform was employed for screening of bacterial communities and predictive functional pathways. Altered physicochemical properties in the soil samples were recorded showing a comparatively acidic pH in the ISI (6.35) and ASI (6.63) soil samples to the HSC soil (7.66), along with higher electrical conductivity in the ISI (139.5 µS/cm), ASI soil (180 µS/cm), HSC soil sample (123.33 µS/cm). While concentration of micronutrients such as Cl and B were significantly higher in the ISI and ASI soil as compared to the HSC, Cu and Zn were significantly higher in the ASI soil. The effectiveness and accuracy of 16S metagenomics studies in identifying beneficial and pathogenic bacterial communities in multi-pathogen–host systems depend on the completeness and consistency of the available 16S rRNA sequence repositories. Enhancing these repositories could significantly improve the exploratory potential of such studies. Thus, multiple 16S rRNA data repositories (RDP, GTDB, EzBioCloud, SILVA, and GreenGenes) were benchmarked, and the findings indicated that SILVA yields the most reliable matches. Consequently, SILVA was chosen for further analysis at the species level. Relative abundance estimates of bacterial species showed variations of growth promoting bacteria, namely, Staphylococcus epidermidis, Bacillus subtilis, Bacillus megatarium, Pseudomonas aeruginosa, Pseudomonas putida, Pseudomonas stutzeri and Micrococcus luteus. Functional profiling predictions employing PICRUSt2 revealed a number of enriched pathways such as transporter protein families involved in signalling and cellular processes, iron complex transport system substrate binding protein, peptidoglycan biosynthesis II (staphylococci) and TCA cycle VII (acetate-producers). In line with past reports, results suggest that an acidic pH along with the bioavailability of micronutrients such as Fe and Mn could be facilitating the prevalence and virulence of Fusarium oxysporum, a known causative pathogen, against the host and beneficial bacterial communities. This study identifies bacterial communities taking into account the physicochemical and other abiotic soil parameters in wilt-affected pomegranate crops. The insights obtained could be instrumental in developing effective management strategies to enhance crop yield and mitigate the impact of wilt complex disease on pomegranate crops.

Trends in dolphin abundance estimated from fisheries data: A cautionary note

The previously published index of relative abundance of the northeastern offshore stock of spotted dolphins, the species most affected by the purse-seine fishery for tunas in the eastern Pacific Ocean, shows a decreasing trend in the last two decades despite dramatic reductions in incidental mortality since the early 1970s. To better understand the behaviour of this index, the effects of changes in data quality and methods of searching on estimation of relative abundance using current methodologies have been studied here. Changes in data quality since the late 1980s have led to a dramatic reduction in the proportion of sightings that are reported on or near the trackline. The decreasing trend in the index in the late 1970s and through the 1980s is strongly influenced by the fit of the detection function to the high proportion of sightings near the trackline that was present in the data during that time period. If this excess of sightings near the trackline is spurious, then much of the decreasing trend in the index over this time period is likely spurious. In addition, part of the decrease in the index in the late 1980s to mid-1990s is probably due to changes in data-collection biases that result from a dramatic increase in the amount of searching that is currently being carried out using helicopters as compared to high-powered binoculars. The results suggest that trends in bias associated with changes in data quality and fishery operations may have contributed to a trend in the index on the order of 1.0-1.5% per year, or approximately 25-33% of the maximum growth rate of the northeastern stock of offshore spotted dolphin. The pervasive nature of these sources of bias, and their potential magnitude relative to the maximum growth rates of the dolphin species involved, make use of this index in population growth models ill-advised. Fishery-derived indices such as these may be most useful for comparing trends in relative abundance between species, when the sources of biases are unlikely to be species-specific.

Using baited remote underwater videos to survey freshwater turtles

AbstractNew and innovative techniques utilizing underwater cameras have the potential to track changes in turtle populations whilst being less invasive and time intensive than traditional trapping surveys. In this study, we trialled the use of Baited Remote Underwater Videos (BRUVs) in five upland streams to test if BRUVs could be used for abundance, size and sex estimates of freshwater turtles. We also compared BRUVs to cathedral traps and identified key considerations for future use of this method, such as whether the time of day influenced results. Our study successfully detected 83 turtles from 52 h of footage and identified all species known to occur in the study area. The deployment time did not significantly influence relative abundance. BRUVs also produced statistically similar catch per unit effort estimates to cathedral trapping techniques. No small turtles (i.e. juveniles) were detected, and difficulties with visibility, sex accuracy and carapace measurements are discussed for future BRUV use. Our study shows that BRUVs are a useful, time effective, non‐invasive technique to collect relative abundance and species richness estimates for freshwater turtles.