ABSTRACT
Environmental DNA (eDNA) workflows contain many familiar molecular-lab techniques, but also employ several unique methodologies. When working with eDNA, it is essential to avoid contamination from the point of collection through preservation and select a meaningful negative control. As eDNA can be obtained from a variety of samples and habitats (e.g., soil, water, air, or tissue), protocols will vary depending on usage. Samples may require additional steps to dilute, block, or remove inhibitors or physically break up samples or filters. Thereafter, standard DNA isolation techniques (kit-based or phenol:chloroform:isoamyl [PCI]) are employed. Once DNA is extracted, it is typically quantified using a fluorometer. Yields vary greatly, but are important to know prior to amplification of the gene(s) of interest. Long-term storage of both the sampled material and the extracted DNA is encouraged, as it provides a backup for spilled/contaminated samples, lost data, reanalysis, and future studies using newer technology. Storage in a freezer is often ideal; however, some storage buffers (e.g., Longmires) require that filters or swabs are kept at room temperature to prevent precipitation of buffer-related solutes. These baseline methods for eDNA isolation, validation, and preservation are detailed in this protocol chapter. In addition, we outline a cost-effective, homebrew extraction protocol optimized to extract eDNA.
Subject(s)
DNA, Environmental , DNA, Environmental/isolation & purification , DNA, Environmental/analysis , DNA, Environmental/genetics , Preservation, Biological/methods , Specimen Handling/methodsABSTRACT
Outgroup selection has been a major challenge since the rise of phylogenetics, and it has remained so in the phylogenomic era. Our goal here is to use large phylogenomic animal datasets to examine the impact of outgroup selection on the final topology. The results of our analyses further solidify the fact that distant outgroups can cause random rooting, and that this holds for concatenated and coalescent-based methods. The results also indicate that the standard practice of using multiple outgroups often causes random rooting. Most researchers go out of their way to get multiple outgroups, as this has been standard practice for decades. Based on our findings, this practice should stop. Instead, our results suggest that a single (most closely) related relative should be selected as the outgroup, unless all outgroups are roughly equally closely related to the ingroup.
Subject(s)
Phylogeny , AnimalsABSTRACT
Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.
ABSTRACT
Lawns are a ubiquitous, human-made environment created for human enjoyment, leisure, and aesthetics. While net positive for carbon storage, lawns can have negative environmental impacts. Lawns require frequent mowing, which produces high levels of CO2 pollution and kills off native plants. Lawn fertilizing creates its own environmental pollution. One (presumed) ecologically-friendly alternative to lawns is restoration, or rewilding, of these spaces as meadows, which need less maintenance (e.g., infrequent mowing). However, little work has compared lawns against small-scale meadows for biodiversity outside of pollinator studies. Here, we tested the hypotheses that compared to lawns, meadows have (1) unique and higher levels of soil microbial biodiversity and (2) different soil physical and chemical characteristics. We conducted bacterial (16S) and fungal (ITS2) metabarcoding, and found that both bacteria and fungi are indeed more diverse in meadows (significantly so for bacteria). Species composition between meadows and lawns was significantly different for both types of microbes, including higher levels of mycorrhizal fungi in meadows. We also found that chemistry (e.g., potassium and metrics relating to pH) differed significantly between lawns and meadows and was more optimal for plant growth in the meadows. We believe these differences are caused by the different organisms dwelling in these habitats. In summary, these findings point to notable-positive-shifts in microbial and chemical compositions within meadows, further indicating that meadow restoration benefits biodiversity and soil health.
Subject(s)
Grassland , Soil , Humans , Soil/chemistry , Soil Microbiology , Biodiversity , Ecosystem , Bacteria/geneticsABSTRACT
Environmental DNA (eDNA) sequencing-DNA collected from the environment from living cells or shed DNA-was first developed for working with microbes and has greatly benefitted microbial ecologists for decades since. These tools have only become increasingly powerful with the advent of metabarcoding and metagenomics. Most new studies that examine diverse assemblages of bacteria, archaea, protists, fungi, and viruses lean heavily into eDNA using these newer technologies, as the necessary sequencing technology and bioinformatic tools have become increasingly affordable and user friendly. However, eDNA methods are rapidly evolving, and sometimes it can feel overwhelming to simply keep up with the basics. In this review, we provide a starting point for microbial ecologists who are new to DNA-based methods by detailing the eDNA methods that are most pertinent, including study design, sample collection and storage, selecting the right sequencing technology, lab protocols, equipment, and a few bioinformatic tools. Furthermore, we focus on how eDNA work can benefit restoration and what modifications are needed when working in this subfield.
Subject(s)
DNA, Environmental , DNA, Environmental/genetics , DNA Primers , DNA Barcoding, Taxonomic/methods , Ecology , DNA/genetics , Environmental Monitoring , BiodiversityABSTRACT
Human communication has been described as a contextual social inference process. Research into great ape communication has been inspired by this view to look for the evolutionary roots of the social, cognitive and interactional processes involved in human communication. This approach has been highly productive, yet it is partly compromised by the widespread focus on how great apes use and understand individual signals. This paper introduces a computational model that formalizes great ape communication as a multi-faceted social inference process that integrates (a) information contained in the signals that make up an utterance, (b) the relationship between communicative partners and (c) the social context. This model makes accurate qualitative and quantitative predictions about real-world communicative interactions between semi-wild-living chimpanzees. When enriched with a pragmatic reasoning process, the model explains repeatedly reported differences between humans and great apes in the interpretation of ambiguous signals (e.g. pointing or iconic gestures). This approach has direct implications for observational and experimental studies of great ape communication and provides a new tool for theorizing about the evolution of uniquely human communication. This article is part of the theme issue 'Revisiting the human 'interaction engine': comparative approaches to social action coordination'.
Subject(s)
Animal Communication , Hominidae , Animals , Communication , Computer Simulation , Gestures , Humans , Pan troglodytesABSTRACT
Language is learned in complex social settings where listeners must reconstruct speakers' intended meanings from context. To navigate this challenge, children can use pragmatic reasoning to learn the meaning of unfamiliar words. A critical challenge for pragmatic reasoning is that it requires integrating multiple information sources, which have typically been studied separately. Here we study this integration process. First, we experimentally isolate two sources of pragmatic information: expectations about informative communication and common ground. Next, we use a probabilistic model of conversational reasoning to formalize how these information sources should be combined and how this process might develop. We use this model to generate quantitative predictions, which we test against new experimental data from 3- to 5-year-old children (N = 243) and adults (N = 694). Results show close alignment between model predictions and data. Furthermore, the model provided a better explanation of the data compared with simpler alternative models assuming that participants selectively ignore one information source. This work integrates distinct sets of findings regarding information sources for early language learning and suggests that pragmatic reasoning models can provide a quantitative framework for understanding developmental changes in language learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Subject(s)
Cues , Language Development , Adult , Child, Preschool , Communication , Humans , Language , LearningABSTRACT
The meanings of natural language utterances depend heavily on context. Yet, what counts as context is often only implicit in conversation. The utterance it's warm outside signals that the temperature outside is relatively high, but the temperature could be high relative to a number of different comparison classes: other days of the year, other weeks, other seasons, etc. Theories of context sensitivity in language agree that the comparison class is a crucial variable for understanding meaning, but little is known about how a listener decides upon the comparison class. Using the case study of gradable adjectives (e.g., warm), we extend a Bayesian model of pragmatic inference to reason flexibly about the comparison class and test its qualitative predictions in a large-scale free-production experiment. We find that human listeners infer the comparison class by reasoning about the kinds of observations that would be remarkable enough for a speaker to mention, given the speaker and listener's shared knowledge of the world. Further, we quantitatively synthesize the model and data using Bayesian data analysis, which reveals that usage frequency and a preference for basic-level categories are two main factors in comparison class inference. This work presents new data and reveals the mechanisms by which human listeners recover the relevant aspects of context when understanding language.
Subject(s)
Communication , Comprehension , Bayes Theorem , Humans , Language , SeasonsABSTRACT
Syllogistic reasoning lies at the intriguing intersection of natural and formal reasoning of language and logic. Syllogisms comprise a formal system of reasoning yet make use of natural language quantifiers (e.g., all, some) and invite natural language conclusions. The conclusions people tend to draw from syllogisms, however, deviate substantially from the purely logical system. Are principles of natural language understanding to blame? We introduce a probabilistic pragmatic perspective on syllogistic reasoning: We decompose reasoning with natural language arguments into two subproblems: language comprehension and language production. We formalize models of these processes within the Rational Speech Act framework and explore the pressures that pragmatic reasoning places on the production of conclusions. We test our models on a recent, large data set of syllogistic reasoning and find that the selection process of conclusions from syllogisms are best modeled as a pragmatic speaker who has the goal of aligning the beliefs of a naive listener with those of their own. We compare our model to previously published models that implement two alternative theories-Mental Models and Probability Heuristics-finding that our model quantitatively predicts the full distributions of responses as well as or better than previous accounts, but with far fewer parameters. Our results suggest that human syllogistic reasoning may be best understood not as a poor approximation to ideal logical reasoning, but rather as rational probabilistic inference in support of natural communication.
Subject(s)
Logic , Problem Solving , Heuristics , Humans , Models, Psychological , ProbabilityABSTRACT
Pragmatics is foundational to language use and learning. Computational cognitive models have been successfully used to predict pragmatic phenomena in adults and children - on an aggregate level. It is unclear if they can be used to predict behavior on an individual level. We address this question in children (N = 60, 3- to 5-year-olds), taking advantage of recent work on pragmatic cue integration. In Part 1, we use data from four independent tasks to estimate child-specific sensitivity parameters to three information sources: semantic knowledge, expectations about speaker informativeness, and sensitivity to common ground. In Part 2, we use these parameters to generate participant-specific trial-by-trial predictions for a new task that jointly manipulated all three information sources. The model accurately predicted children's behavior in the majority of trials. This work advances a substantive theory of individual differences in which the primary locus of developmental variation is sensitivity to individual information sources.
ABSTRACT
Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. How is this information integrated? We specify a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This model accurately predicted 2-5-year-old children's word learning across a range of experimental conditions in which they had to integrate three information sources. Model comparison suggests that the central locus of development is an increased sensitivity to individual information sources, rather than changes in integration ability. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
Subject(s)
Language Development , Learning , Vocabulary , Bayes Theorem , Child Development , Child, Preschool , Female , Humans , Male , Models, TheoreticalABSTRACT
Bloodfeeding is employed by many parasitic animals and requires specific innovations for efficient feeding. Some of these innovations are molecular features that are related to the inhibition of hemostasis. For example, bloodfeeding insects, bats, and leeches release proteins with anticoagulatory activity through their salivary secretions. The antistasin-like protein family, composed of serine protease inhibitors with one or more antistasin-like domains, is tightly linked to inhibition of hemostasis in leeches. However, this protein family has been recorded also in non-bloodfeeding invertebrates, such as cnidarians, mollusks, polychaetes, and oligochaetes. The present study aims to 1) root the antistasin-like gene tree and delimit the major orthologous groups, 2) identify potential independent origins of salivary proteins secreted by leeches, and 3) identify major changes in domain and/or motif structure within each orthologous group. Five clades containing leech antistasin-like proteins are distinguishable through rigorous phylogenetic analyses based on nine new transcriptomes and a diverse set of comparative data: the trypsin + leukocyte elastase inhibitors clade, the antistasin clade, the therostasin clade, and two additional, unnamed clades. The antistasin-like gene tree supports multiple origins of leech antistasin-like proteins due to the presence of both leech and non-leech sequences in one of the unnamed clades, but a single origin of factor Xa and trypsin + leukocyte elastase inhibitors. This is further supported by three sequence motifs that are exclusive to antistasins, the trypsin + leukocyte elastase inhibitor clade, and the therostasin clade, respectively. We discuss the implications of our findings for the evolution of this diverse family of leech anticoagulants.
Subject(s)
Annelida/genetics , Annelida/metabolism , Evolution, Molecular , Invertebrate Hormones/genetics , Invertebrate Hormones/metabolism , Leeches/genetics , Animals , Anticoagulants/chemistry , Factor Xa/genetics , Hemostasis , Phylogeny , Salivary Proteins and Peptides/genetics , Serine Proteinase Inhibitors/genetics , TranscriptomeABSTRACT
Understanding the genetic causes of evolutionary diversification is challenging because differences across species are complex, often involving many genes. However, cases where single or few genetic loci affect a trait that varies dramatically across a radiation of species provide tractable opportunities to understand the genetics of diversification. Here, we begin to explore how diversification of bioluminescent signals across species of cypridinid ostracods ("sea fireflies") was influenced by evolution of a single gene, cypridinid-luciferase. In addition to emission spectra ("colour") of bioluminescence from 21 cypridinid species, we report 13 new c-luciferase genes from de novo transcriptomes, including in vitro assays to confirm function of four of those genes. Our comparative analyses suggest some amino acid sites in c-luciferase evolved under episodic diversifying selection and may be associated with changes in both enzyme kinetics and colour, two enzymatic functions that directly impact the phenotype of bioluminescent signals. The analyses also suggest multiple other amino acid positions in c-luciferase evolved neutrally or under purifying selection, and may have impacted the variation of colour of bioluminescent signals across genera. Previous mutagenesis studies at candidate sites show epistatic interactions, which could constrain the evolution of c-luciferase function. This work provides important steps toward understanding the genetic basis of diversification of behavioural signals across multiple species, suggesting different evolutionary processes act at different times during a radiation of species. These results set the stage for additional mutagenesis studies that could explicitly link selection, drift, and constraint to the evolution of phenotypic diversification.
Subject(s)
Crustacea , Fireflies , Animals , Fireflies/genetics , Luciferases/genetics , PhenotypeABSTRACT
There are considerable phylogenetic incongruencies between morphological and phylogenomic data for the deep evolution of animals. This has contributed to a heated debate over the earliest-branching lineage of the animal kingdom: the sister to all other Metazoa (SOM). Here, we use published phylogenomic data sets ($\sim $45,000-400,000 characters in size with $\sim $15-100 taxa) that focus on early metazoan phylogeny to evaluate the impact of incorporating morphological data sets ($\sim $15-275 characters). We additionally use small exemplar data sets to quantify how increased taxon sampling can help stabilize phylogenetic inferences. We apply a plethora of common methods, that is, likelihood models and their "equivalent" under parsimony: character weighting schemes. Our results are at odds with the typical view of phylogenomics, that is, that genomic-scale data sets will swamp out inferences from morphological data. Instead, weighting morphological data 2-10$\times $ in both likelihood and parsimony can in some cases "flip" which phylum is inferred to be the SOM. This typically results in the molecular hypothesis of Ctenophora as the SOM flipping to Porifera (or occasionally Placozoa). However, greater taxon sampling improves phylogenetic stability, with some of the larger molecular data sets ($>$200,000 characters and up to $\sim $100 taxa) showing node stability even with $\geqq100\times $ upweighting of morphological data. Accordingly, our analyses have three strong messages. 1) The assumption that genomic data will automatically "swamp out" morphological data is not always true for the SOM question. Morphological data have a strong influence in our analyses of combined data sets, even when outnumbered thousands of times by molecular data. Morphology therefore should not be counted out a priori. 2) We here quantify for the first time how the stability of the SOM node improves for several genomic data sets when the taxon sampling is increased. 3) The patterns of "flipping points" (i.e., the weighting of morphological data it takes to change the inferred SOM) carry information about the phylogenetic stability of matrices. The weighting space is an innovative way to assess comparability of data sets that could be developed into a new sensitivity analysis tool. [Metazoa; Morphology; Phylogenomics; Weighting.].
Subject(s)
Genome , Genomics , Animals , Genome/genetics , PhylogenyABSTRACT
Language is a remarkably efficient tool for transmitting information. Yet human speakers make statements that are inefficient, imprecise, or even contrary to their own beliefs, all in the service of being polite. What rational machinery underlies polite language use? Here, we show that polite speech emerges from the competition of three communicative goals: to convey information, to be kind, and to present oneself in a good light. We formalize this goal tradeoff using a probabilistic model of utterance production, which predicts human utterance choices in socially sensitive situations with high quantitative accuracy, and we show that our full model is superior to its variants with subsets of the three goals. This utility-theoretic approach to speech acts takes a step toward explaining the richness and subtlety of social language use.
ABSTRACT
The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.
ABSTRACT
Pyrosomes are tunicates in the phylum Chordata, which also contains vertebrates. Their gigantic blooms play important ecological and biogeochemical roles in oceans. Pyrosoma, meaning "fire-body", derives from their brilliant bioluminescence. The biochemistry of this light production is unknown, but has been hypothesized to be bacterial in origin. We found that mixing coelenterazine-a eukaryote-specific luciferin-with Pyrosoma atlanticum homogenate produced light. To identify the bioluminescent machinery, we sequenced P. atlanticum transcriptomes and found a sequence match to a cnidarian luciferase (RLuc). We expressed this novel luciferase (PyroLuc) and, combined with coelenterazine, it produced light. A similar gene was recently predicted from a bioluminescent brittle star, indicating that RLuc-like luciferases may have evolved convergently from homologous dehalogenases across phyla (Cnidaria, Echinodermata, and Chordata). This report indicates that a widespread gene may be able to functionally converge, resulting in bioluminescence across animal phyla, and describes and characterizes the first putative chordate luciferase.
Subject(s)
Luciferases/genetics , Urochordata , Animals , Biological Evolution , Chordata , Computational Biology , Evolution, Molecular , Gene Expression Profiling , Imidazoles , Luminescence , Luminescent Measurements , Models, Molecular , Phylogeny , Pyrazines , Sequence Alignment , Sequence Analysis, DNA , Species SpecificityABSTRACT
Fossil rodent middens are powerful tools in paleoecology. In arid parts of western North America, packrat (Neotoma spp.) middens preserve plant and animal remains for tens of thousands of years. Midden contents are so well preserved that fragments of endogenous ancient DNA (aDNA) can be extracted and analyzed across millennia. Here, we explore the use of shotgun metagenomics to study the aDNA obtained from packrat middens up to 32,000 C14 years old. Eleven Illumina HiSeq 2500 libraries were successfully sequenced, and between 0.11% and 6.7% of reads were classified using Centrifuge against the NCBI "nt" database. Eukaryotic taxa identified belonged primarily to vascular plants with smaller proportions mapping to ascomycete fungi, arthropods, chordates, and nematodes. Plant taxonomic diversity in the middens is shown to change through time and tracks changes in assemblages determined by morphological examination of the plant remains. Amplicon sequencing of ITS2 and rbcL provided minimal data for some middens, but failed at amplifying the highly fragmented DNA present in others. With repeated sampling and deep sequencing, analysis of packrat midden aDNA from well-preserved midden material can provide highly detailed characterizations of past communities of plants, animals, bacteria, and fungi present as trace DNA fossils. The prospects for gaining more paleoecological insights from aDNA for rodent middens will continue to improve with optimization of laboratory methods, decreasing sequencing costs, and increasing computational power.
ABSTRACT
Tessler et al. demonstrate that a 'soft' robot causes less stress to a jellyfish while handling compared to a traditional 'hard' robot.
Subject(s)
Marine Biology/methods , Robotics , Scyphozoa/physiology , Specimen Handling/methods , Transcriptome , Animals , Robotics/methods , Scyphozoa/genetics , Stress, PhysiologicalABSTRACT
Leech-derived invertebrate DNA (iDNA) has been successfully leveraged to conduct surveys of vertebrate host biodiversity across the Indo Pacific. However, this technique has been limited methodologically, typically only targeting mammalian 16S rDNA, or both 16S and vertebrate 12S rDNA for leech host determination. To improve the taxonomic richness of vertebrate host species in iDNA surveys, we re-analyze datasets from Bangladesh, Cambodia, China, and Madagascar through metabarcoding via next generation sequencing (NGS) of 12S, 16S (2 types, one designed to target mammals and the other, residual eDNA), nicotinamide adenine dinucleotide hydride dehydrogenase 2 (ND2), and cytochrome c oxidase subunit 1 (COI). With our 5 primer sets, we identify 41 unique vertebrate hosts to the species level, among 1,200 leeches analyzed, along with an additional 13 taxa to the family rank. Within our 41 taxa, we note that adding ND2 and COI loci increased species richness detection by 25%. NGS has emerged as more efficient than Sanger sequencing for large scale metabarcoding applications and, with the decline in cost of NGS, our pooled sample multilocus protocol is an attractive option for iDNA biodiversity surveys.