Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,543 bioRxiv papers from 307,989 authors.
Most tweeted bioRxiv papers, last 24 hours
512 results found. For more information, click each entry to expand.
23 tweets immunology
Angel K. Kongsomboonvech, Felipe Rodriguez, Anh L. Diep, Brandon M. Justice, Brayan E. Castallanos, Ana Camejo, Debanjan Mukhopadhyay, Gregory A. Taylor, Masahiro Yamamoto, Jeroen P.J. Saeij, Michael L. Reese, Kirk Jensen
Host resistance to Toxoplasma gondii relies on CD8 T cell IFNγ responses, which if modulated by the host or parasite could influence chronic infection and parasite transmission between hosts. Since host-parasite interactions that govern this response are not fully elucidated, we investigated requirements for eliciting naïve CD8 T cell IFNγ responses to a vacuolar resident antigen of T. gondii , TGD057. Naïve TGD057 antigen-specific CD8 T cells (T57) were isolated from transnuclear mice and responded to parasite-infected bone marrow-derived macrophages (BMDMs) in an antigen-dependent manner, first by producing IL-2 and then IFNγ. T57 IFNγ responses to TGD057 were independent of the parasite's protein export machinery ASP5 and MYR1. Instead, host immunity pathways downstream of the regulatory Immunity-Related GTPases (IRG), including partial dependence on Guanylate-Binding Proteins, are required. Multiple T. gondii ROP5 isoforms and allele types, including ‘avirulent’ ROP5A from clade A and D parasite strains, were able to suppress CD8 T cell IFNγ responses to parasite-infected BMDMs. Phenotypic variance between clades B, C, D, F, and A strains suggest T57 IFNγ differentiation occurs independently of parasite virulence or any known IRG-ROP5 interaction. Consistent with this, removal of ROP5 is not enough to elicit maximal CD8 T cell IFNγ production to parasite-infected cells. Instead, macrophage expression of the pathogen sensors, NLRP3 and to a large extent NLRP1, were absolute requirements. Other members of the conventional inflammasome cascade are only partially required, as revealed by decreased but not abrogated T57 IFNγ responses to parasite-infected ASC, caspase-1/11, and gasdermin D deficient cells. Moreover, IFNγ production was only partially reduced in the absence of IL-12, IL-18 or IL-1R signaling. In summary, T. gondii effectors and host machinery that modulate parasitophorous vacuolar membranes, as well as NLR-dependent but inflammasome-independent pathways, determine the full commitment of CD8 T cells IFNγ responses to a vacuolar antigen.
23 tweets evolutionary biology
The mating-type switching endonuclease HO plays a central role in the natural life cycle of Saccharomyces cerevisiae, but its evolutionary origin is unknown. HO is a recent addition to yeast genomes, present in only a few genera. It resembles a degenerated intein fused to a zinc finger domain. Here we show that HO is structurally and phylogenetically related to a family of unorthodox homing genetic elements found in Torulaspora and Lachancea yeasts. These WHO elements integrate into the aldolase gene FBA1, replacing its 3' end each time. Their structural organization is different from all known classes of homing elements. We show that a WHO protein cleaves Torulaspora delbrueckii FBA1 efficiently and in an allele-specific manner, leading to DNA repair by gene conversion or NHEJ. The DNA rearrangement steps during WHO element homing are very similar to those during mating-type switching, and indicate that HO is a domesticated WHO-like element.
22 tweets synthetic biology
Living cells segregate molecules and reactions in various subcellular compartments and locations. Spatial organization is likely essential for expanding the biochemical functions of synthetic reaction systems, including artificial cells. Here we describe programmable synthetic organelles based on highly stable lipid sponge phase droplets that spontaneously assemble from a galactose-derived single-chain lipid and non-ionic detergents. Fluorescent dyes and biologically relevant molecules partition into droplets based on their size, polarity, and specific binding motifs. The sequestration of macromolecules can be further programmed by the addition of suitably functionalized amphiphiles to the droplets. We demonstrate that droplets can harbor functional soluble and transmembrane proteins, allowing for the co-localization and concentration of enzymes and substrates to enhance reaction rates. Droplets protect bound proteins from proteases, and these interactions can be engineered to be reversible and optically controlled. Lipid sponge droplets permit the facile introduction of membrane environments and self-assembling spatial organization into biochemical reaction systems.
20 tweets evolutionary biology
For the past decades, simulation-based likelihood-free inference methods have enabled to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics). Here we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history. First, we provide guidelines to construct artificial neural networks that comply with the intrinsic properties of SNP data such as invariance to permutation of haplotypes, long scale interactions between SNPs and variable genomic length. Thanks to a Bayesian hyperparameter optimization procedure, we evaluate the performances of multiple networks and compare them to well established methods like Approximate Bayesian Computation (ABC). Even without the expert knowledge of summary statistics, our approach compares fairly well to an ABC based on handcrafted features. Furthermore we show that combining deep learning and ABC can improve performances while taking advantage of both frameworks. Finally, we apply our approach to reconstruct the effective population size history of cattle breed populations.
20 tweets neuroscience
Auditory prediction error responses elicited by surprising sounds can be reliably recorded with musical stimuli that are more complex and realistic than those typically employed in EEG or MEG oddball paradigms. However, these responses are reduced as the predictive uncertainty of the stimuli increases. In this study, we investigate whether this effect is modulated by musical expertise. Magnetic mismatch negativity (MMNm) responses were recorded from 26 musicians and 24 non-musicians while they listened to low- and high-uncertainty melodic sequences in a musical multi-feature paradigm that included pitch, slide, intensity, and timbre deviants. When compared to non-musicians, musically trained participants had significantly larger pitch and slide MMNm responses. However, both groups showed comparable reductions of pitch and slide MMNm amplitudes in the high-uncertainty condition compared to the low-uncertainty condition. In a separate, behavioral deviance detection experiment, musicians were more accurate and confident about their responses than non-musicians, but deviance detection in both groups was similarly affected by the uncertainty of the melodies. In both experiments, the interaction between uncertainty and expertise was not significant, suggesting that the effect is comparable in both groups. Consequently, our results replicate the modulatory effect of predictive uncertainty on prediction error; show that it is present across different types of listeners; and suggest that expertise-related and stimulus-driven modulations of predictive precision are dissociable and independent.
20 tweets systems biology
As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia of unknown etiology by Chinese authorities on 7 January, 2020. In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probable be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from a seafood market (reservoir) to people, we simplified the model as Reservoir-People transmission network model. The basic reproduction number (R0) was calculated from the RP model to assess the transmissibility of the 2019-nCoV.
19 tweets bioengineering
Kurt G Schilling, Justin Blaber, Colin Hansen, Baxter Rogers, Adam W Anderson, Seth Smith, Praitayini Kanakaraj, Tonia Rex, Susan M Resnick, Andrea T. Shafer, Laurie Cutting, Neil Woodward, David Zald, Bennett A. Landman
Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U- nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.
18 tweets bioinformatics
Over the past decade, studies of the human genome and microbiome have deepened our understanding of the connections between human genes, environments, microbes, and disease. For example, the sheer number of indicators of the microbiome and human genetic common variants associated with disease has been immense, but clinical utility has been elusive. Here, we compared the predictive capabilities of the human microbiome versus human genomic common variants across 13 common diseases. We concluded that microbiomic indicators outperform human genetics in predicting host phenotype (overall Microbiome-Association-Study [MAS] area under the curve [AUC] = 0.79 [SE = 0.03], overall Genome-Wide-Association-Study [GWAS] AUC = 0.67 [SE = 0.02]). Our results, while preliminary and focused on a subset of the totality of disease, demonstrate the relative predictive ability of the microbiome, indicating that it may outperform human genetics in discriminating human disease cases and controls. They additionally motivate the need for population-level microbiome sequencing resources, akin to the UK Biobank, to further improve and reproduce metagenomic models of disease.
18 tweets molecular biology
Tandem repeat elements such as the highly diverse class of satellite repeats occupy large parts of eukaryotic chromosomes. Most occur at (peri)centromeric and (sub)telomeric regions and have been implicated in chromosome organization, stabilization, and segregation. Others are located more dispersed throughout the genome, but their functions remained largely enigmatic. Satellite repeats in euchromatic regions were hypothesized to regulate gene expression in cis by modulation of the local heterochromatin, or in trans via repeat-derived transcripts. Yet, due to a lack of experimental models, gene regulatory potential of satellite repeats remains largely unexplored. Here we show that, in the vector mosquito Aedes aegypti, a satellite repeat promotes sequence-specific gene silencing via the expression of two abundant PIWI-interacting RNAs (piRNAs). Strikingly, whereas satellite repeats and piRNA sequences generally evolve extremely fast, this locus was conserved for approximately 200 million years, suggesting a central function in mosquito biology. Tandem repeat-derived piRNA production commenced shortly after egg-laying and inactivation of the most abundant of the two piRNAs in early embryos resulted in an arrest of embryonic development. Transcriptional profiling in these embryos revealed the failure to degrade maternally provided transcripts that are normally cleared during maternal-to-zygotic transition. Our results reveal a novel mechanism in which satellite repeats regulate global gene expression in trans via piRNA-mediated gene silencing, which is fundamental to embryonic development. These findings highlight the regulatory potential of this enigmatic class of repeats.
18 tweets scientific communication and education
The reproducibility crisis in science is a multifaceted problem involving practices and incentives, both in the laboratory and in publication. Fortunately, some of the root causes are known and can be addressed by scientists and authors alike. After careful consideration of the available literature, the National Institutes of Health identified several key problems with the way that scientists conduct and report their research and introduced guidelines to improve the rigor and reproducibility of pre-clinical studies. Many journals have implemented policies addressing these same criteria. We currently have, however, no comprehensive data on how these guidelines are impacting the reporting of research. Using SciScore, an automated tool developed to review the methods sections of manuscripts for the presence of criteria associated with the NIH and other reporting guidelines, e.g., ARRIVE, RRIDs, we have analyzed ~1.6 million PubMed Central papers to determine the degree to which articles were addressing these criteria. The tool scores each paper on a ten point scale identifying sentences that are associated with compliance with criteria associated with increased rigor (5 pts) and those associated with key resource identification and authentication (5 pts). From these data, we have built the Rigor and Transparency Index, which is the average score for analyzed papers in a particular journal. Our analyses show that the average score over all journals has increased since 1997, but remains below five, indicating that less than half of the rigor and reproducibility criteria are routinely addressed by authors. To analyze the data further, we examined the prevalence of individual criteria across the literature, e.g., the reporting of a subject's sex (21-37% of studies between 1997 and 2019), the inclusion of sample size calculations (2-10%), whether the study addressed blinding (3-9%), or the identifiability of key biological resources such as antibodies (11-43%), transgenic organisms (14-22%), and cell lines (33-39%). The greatest increase in prevalence for rigor criteria was seen in the use of randomization of subjects (10-30%), while software tool identifiability improved the most among key resource types (42-87%). We further analyzed individual journals over time that had implemented specific author guidelines covering rigor criteria, and found that in some journals, they had a big impact, whereas in others they did not. We speculate that unless they are enforced, author guidelines alone do little to improve the number of criteria addressed by authors. Our Rigor and Transparency Index did not correlate with the impact factors of journals.
18 tweets biochemistry
De novo emergence, and emergence of the earliest proteins specifically, demands a transition from disordered polypeptides into structured proteins with well-defined functions. However, can peptides confer evolutionary relevant functions, let alone with minimal abiotic amino acid alphabets? How can such polypeptides evolve into mature proteins? Specifically, while nucleic acids binding is presumed a primordial function, it demands basic amino acids that do not readily form abiotically. To address these questions, we describe an experimentally-validated trajectory from a phase-separating polypeptide to a dsDNA-binding protein. The intermediates comprise sequence-duplicated, functional proteins made of only 10 amino acid types, with ornithine, which can form abiotically, as the only basic amino acid. Statistical, chemical modification of ornithine sidechains to arginine promoted structure and function. The function concomitantly evolved – from phase separation with RNA (coacervates) to avid and specific dsDNA binding – thereby demonstrating a smooth, gradual peptide-to-protein transition with respect to sequence, structure, and function.
17 tweets microbiology
Live H. Hagen, Charles G. Brooke, Claire Shaw, Angela D. Norbeck, Hailan Piao, Magnus Ø. Arntzen, Heather Brewer, Alex Copeland, Nancy Isern, Anil Shukla, Simon Roux, Vincent Lombard, Bernard Henrissat, Michelle A. O’Malley, Igor V. Grigoriev, Susannah Tringe, Roderick Mackie, Ljiljana Pasa-Tolic, Phillip B. Pope, Matthias Hess
The rumen harbors a complex microbial mixture of archaea, bacteria, protozoa and fungi that efficiently breakdown plant biomass and its complex dietary carbohydrates into soluble sugars that can be fermented and subsequently converted into metabolites and nutrients utilized by the host animal. While rumen bacterial populations have been well documented, only a fraction of the rumen eukarya are taxonomically and functionally characterized, despite the recognition that they contribute to the cellulolytic phenotype of the rumen microbiota. To investigate how anaerobic fungi actively engage in digestion of recalcitrant fiber that is resistant to degradation, we resolved genome-centric metaproteome and metatranscriptome datasets generated from switchgrass samples incubated for 48 hours in nylon bags within the rumen of cannulated dairy cows. Across a gene catalogue covering anaerobic rumen bacteria, fungi and viruses, a significant portion of the detected proteins originated from fungal populations. Intriguingly, the carbohydrate-active enzyme (CAZyme) profile suggested a domain-specific functional specialization, with bacterial populations primarily engaged in the degradation of polysaccharides such as hemicellulose, whereas fungi were inferred to target recalcitrant cellulose structures via the detection of a number of endo- and exo-acting enzymes belonging to the glycoside hydrolase (GH) family 5, 6, 8 and 48. Notably, members of the GH48 family were amongst the highest abundant CAZymes and detected representatives from this family also included dockerin domains that are associated with fungal cellulosomes. A eukaryote-selected metatranscriptome further reinforced the contribution of uncultured fungi in the ruminal degradation of recalcitrant fibers. These findings elucidate the intricate networks of in situ recalcitrant fiber deconstruction, and importantly, suggests that the anaerobic rumen fungi contribute a specific set of CAZymes that complement the enzyme repertoire provided by the specialized plant cell wall degrading rumen bacteria.
16 tweets neuroscience
While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome (the complete set of structural brain connections) that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state fMRI functional connectivity. The contributions of network features to the dynamic fluctuations were examined by constructing randomly rewired surrogate connectome data in which network features of interest were selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from spatial geometry and network topology of the connectome, as well as from localized structural connections involving visual areas. By providing a structural account of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.
15 tweets microbiology
Fungi are the leading cause of insect disease, contributing to the decline of wild and managed populations1,2. For ecologically and economically critical species, such as the European honey bee (Apis mellifera), the presence and prevalence of fungal pathogens can have far reaching consequences, endangering other species and threatening food security3,4,5. Our ability to address fungal epidemics and opportunistic infections is currently hampered by the limited number of antifungal therapies6,7. Novel antifungal treatments are frequently of bacterial origin and produced by defensive symbionts (bacteria that associate with an animal/plant host and protect against natural enemies 89. Here we examined the capacity of a honey bee-associated bacterium, Bombella apis, to suppress the growth of fungal pathogens and ultimately protect bee brood (larvae and pupae) from infection. Our results showed that strains of B. apis inhibit the growth of two insect fungal pathogens, Beauveria bassiana and Aspergillus flavus, in vitro. This phenotype was recapitulated in vivo; bee brood supplemented with B. apis were significantly less likely to be infected by A. flavus. Additionally, the presence of B. apis reduced sporulation of A. flavus in the few bees that were infected. Analyses of biosynthetic gene clusters across B. apis strains suggest antifungal production via a Type I polyketide synthase. Secreted metabolites from B. apis alone were sufficient to suppress fungal growth, supporting this hypothesis. Together, these data suggest that B. apis protects bee brood from fungal infection by the secretion of an antifungal metabolite. On the basis of this discovery, new antifungal treatments could be developed to mitigate honey bee colony losses, and, in the future, could address fungal epidemics in other species.
15 tweets systems biology
Predicting phenotype from genotype is the holy grail of quantitative systems biology. Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli . We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance and applicability to various scenarios. This study can help to guide further development of kinetic models, and to demonstrate how to apply such models in real-world setting, ultimately enabling the design of efficient cell factories.
14 tweets biochemistry
Members of the divalent anion sodium symporter (DASS) family (SLC13 in humans) play critical roles in metabolic homeostasis, influencing many processes including fatty acid synthesis, insulin resistance, adiposity, and lifespan determination. DASS transporters catalyse the Na+-driven concentrative uptake of Krebs cycle intermediates and sulfate into cells; disrupting their function can protect against age-related metabolic diseases and can extend lifespan. An inward-facing crystal structure and an outward-facing model of a bacterial DASS family member, VcINDY from Vibrio cholerae, predict an elevator-like transport mechanism involving a large rigid body movement of the substrate binding site. How substrate binding influences the conformational state of VcINDY is currently unknown. Here, we probe the interaction between substrate binding and VcINDY conformation using a site-specific alkylation strategy to probe the solvent accessibility of several broadly distributed positions in VcINDY in the presence and absence of substrates (Na+ and succinate). Our findings reveal that accessibility to all positions tested can be modulated by the presence of substrates, with the majority becoming less accessible in the presence of Na+. Mapping these solvent accessibility changes onto the known structures of VcINDY suggests that Na+ binding drives the transporter into an as-yet-unidentified intermediate state. We also observe substantial, separable effects of Na+ and succinate binding at several amino acid positions suggesting distinct effects of the two substrates. Furthermore, analysis of a solely succinate-sensitive residue indicates that VcINDY binds its substrate with a low affinity and proceeds via an ordered process in which one or more Na+ ions must bind prior to succinate. These findings provide insight into the mechanism of VcINDY, which is currently the only structural-characterised representative of the entire DASS family.
14 tweets genomics
Despite the wealth of genomic and transcriptomic data of pivotal angiosperm species, the phylogenetic relationships of flowering plants are still not fully resolved. Microsynteny, or the conservation of relative gene order, has been recognized as a valuable and alternative phylogenetic character to sequence-based characters (nucleotides or amino acids). Here, we present a novel approach for phylogenetic tree reconstruction based on genome-wide synteny network data. We generated and analyzed synteny networks from 123 species from 52 families across 31 orders of flowering plants, including several lineages for which phylogenetic relationships are ambiguous. We obtained a stable and highly resolved phylogeny that is largely congruent with sequence-based phylogenies. However, our results unveiled several novel relationships for some key clades, such as magnoliids sister to monocots, Vitales as sister to core-eudicots, and Saxifragales sister to Santalales, in turn both sister to Caryophyllales. Our results highlight that phylogenies based on genome structure and organization are complementary to sequence-based phylogenies and provide alternative hypotheses of angiosperm relationships to be further tested.
14 tweets systems biology
Edward L Huttlin, Raphael J. Bruckner, José Navarrete-Perea, Joe R. Cannon, Kurt Baltier, Fana Gebreab, Melanie P. Gygi, Alexandra Thornock, Gabriela Zárraga, Stanley Tam, John Szpyt, Alexandra Panov, Hannah Parzen, Sipei Fu, Arvene Golbazi, Eila Maenpaa, Keegan Stricker, Sanjukta Guha Thakurta, Ramin Rad, Joshua Pan, David P. Nusinow, Joao A Paulo, Devin K. Schweppe, Laura Pontano Vaites, Wade Harper, Steven Gygi
Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins - half the proteome - in 293T cells and includes 118,162 interactions among 14,586 proteins; the second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome at unprecedented scale, encoding protein function, localization, and complex membership. Their comparison validates thousands of interactions and reveals extensive customization of each network. While shared interactions reside in core complexes and involve essential proteins, cell-specific interactions bridge conserved complexes, likely 'rewiring' each cell's interactome. Interactions are gained and lost in tandem among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.
13 tweets neuroscience
Learning to perform feedback control is critical for learning many real-world tasks that involve continuous control such as juggling or bike riding. However, most motor learning studies to date have investigated how humans learn feedforward but not feedback control, making it unclear whether people can learn new continuous feedback control policies. Using a manual tracking task, we explicitly examined whether people could learn to counter either a 90˚ visuomotor rotation or mirror-reversal using feedback control. We analyzed participants' performance using a frequency domain system identification approach which revealed two distinct components of learning: 1) adaptation of baseline control, which was present only under the rotation, and 2) de novo learning of a continuous feedback control policy, which was present under both rotation and mirror reversal. Our results demonstrate for the first time that people are capable of acquiring a new, continuous feedback controller via de novo learning.
13 tweets ecology
Organism abundance is a critical parameter in ecology, but its estimation is often challenging. Approaches utilizing eDNA to indirectly estimate abundance have recently generated substantial interest. However, preliminary correlations observed between eDNA concentration and abundance in nature are typically moderate in strength with significant unexplained variation. Here we apply a novel approach to integrate allometric scaling coefficients into models of eDNA concentration and organism abundance. We hypothesize that eDNA particle production scales non-linearly with mass, with scaling coefficients < 1. Wild populations often exhibit substantial variation in individual body size distributions; we therefore predict that the distribution of mass across individuals within a population will influence population-level eDNA production rates. To test our hypothesis, we collected standardized body size distribution and mark-recapture abundance data using whole-lake experiments involving nine populations of brook trout. We correlated eDNA concentration with three metrics of abundance: density (individuals/ha), biomass (kg/ha), and allometrically scaled mass (ASM) (∑(individual mass0.73)/ha). Density and biomass were both significantly positively correlated with eDNA concentration (adj. R2 = 0.59 and 0.63, respectively), but ASM exhibited improved model fit (adj. R2 = 0.78). We also demonstrate how estimates of ASM derived from eDNA samples in systems lacking abundance data can be converted to biomass or density estimates with additional size structure data. Future experiments should empirically validate allometric scaling coefficients for eDNA production, particularly where substantial intraspecific size distribution variation exists. Incorporating allometric scaling may improve predictive models to the extent that eDNA concentration may become a reliable indicator of abundance in nature.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
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