Biology subjects

Griffiths, R.

Publications and source records attributed to Griffiths, R..

6 recordsLinked to original sources

bioRxiv2026

Environmental drivers of soil virus diversity across UK habitats

Soil viruses are critical members of the soil microbiome, shaping terrestrial ecosystem processes and microbial dynamics. However, the environmental factors governing their landscape-scale biogeography remain poorly characterised. Here, we analysed bulk soil metagenomic data from the UKCEH Countryside Survey across broad habitat classes to identify the key drivers of viral diversity and community assembly in UK soils. We show that soil pH is the primary predictor of both viral species richness and overall community composition, alongside significant contributions from the co-related soil organic matter and broad habitat type. Viral species diversity exhibited a strongly non-linear relationship with pH, peaking in neutral to slightly acidic soils. Viral community dissimilarity strongly correlated with 16S rRNA bacterial community structure across all taxonomic ranks, with the strongest association at ASV-level resolution. Despite high spatial turnover among viral contigs, and significant habitat-specific enrichment of particular anti-phage defence systems, viral lifestyle proxies like integrase gene frequencies showed no habitat-specific variation. Together, our results demonstrate that UK soil viral communities are primarily structured by soil pH and habitat parameters and closely coupled to patterns of bacterial host distribution, providing a baseline for future investigations and monitoring of change.

microbiology↗

bioRxiv2026

Mapping tsetse fly connectivity in Uganda with machine learning landscape genetics

Introduction - Tsetse flies (genus Glossina) are biting insects that transmit human and animal trypanosomiases across sub-Saharan Africa, and sustainable vector control depends on understanding dispersal barriers and reinvasion routes. Despite major progress toward elimination, Uganda remains at risk for both human forms of the disease (Trypanosoma brucei gambiense and T. b. rhodesiense) and planners still lack reliable maps of tsetse movement and reinvasion risk. Methods and Results - We address this gap with machine-learning landscape genetics and species distribution models, integrating estimates of population genetic distance and geospatial environmental data to predict and map Glossina fuscipes fuscipes connectivity across Uganda and western Kenya. Inputs included microsatellite genotypes from 11 loci genotyped in 2,736 flies sampled from 87 localities and remotely sensed environmental predictors summarized along least-cost paths. Random forest models predicted patterns of genetic differentiation better than distance-only models, supporting the use of a machine-learning framework for connectivity inference across complex heterogeneous landscapes, and identified variables related to temperature and water availability as the strongest predictors of genetic connectivity. Conclusions - Combining landscape genetics predictions of connectivity with a species distribution model revealed regions with high habitat suitability but low connectivity that represent priority zones for area-wide integrated pest management strategies, including established riverine control tools such as tiny targets and other targeted interventions aimed at reducing reinvasion risk. These results provide biologically interpretable maps and quantitative uncertainty metrics that can guide targeted tsetse control, while providing a transferable analytical pipeline for modeling and mapping genetic connectivity across other species and landscapes.

evolutionary biology↗

bioRxiv2024

Soil properties in agricultural systems affect microbial genomic traits

Understanding the relationships between bacterial taxa, their ecological and genomic traits, and their environment, is important for elucidating the mechanisms that drive microbial community dynamics and their roles in ecosystem functioning. This is especially true for soils, where dramatic shifts in resource input or physicochemical properties occur through land use and agricultural practices. Here, we examined the relationships between soil properties and bacterial traits within highly managed agricultural soil systems subjected to arable crop rotations or management as permanent pasture. We assessed the bacterial communities within these soils using amplicon sequencing and assigned each amplicon trait scores for rRNA copy number, genome size, and GC content, which are classically associated with potential growth rates and specialisation. We also calculated the niche breadth trait of each amplicon as a measure of social ubiquity within the examined samples. Within this soil system, we demonstrated that pH was the primary driver of bacterial traits. The weighted mean trait scores of the samples revealed that bacterial communities associated with soils at lower pH (<7) tended to have larger genomes (possess more potential plasticity), have more rRNA (higher growth rate potential), and are more ubiquitous (have less niche specialisation) than the bacterial communities from higher pH soils. Our findings highlight not only the association between pH and bacterial community composition but also the importance of pH in driving community functionality by directly influencing genomic and niche traits.

microbiology↗

bioRxiv2022

Unique Effects of Sedatives, Dissociatives, Psychedelics, Stimulants, and Cannabinoids on Episodic Memory: A Review and Reanalysis of Acute Drug Effects on Recollection, Familiarity, and Metamemory

Despite distinct classes of psychoactive drugs producing putatively unique states of consciousness, there is surprising overlap in terms of their effects on episodic memory and cognition more generally. Episodic memory is supported by multiple subprocesses that have been mostly overlooked in psychopharmacology and could differentiate drug classes. Here, we reanalyzed episodic memory confidence data from 10 previously published datasets (28 drug conditions total) using signal detection models to estimate 2 conscious states involved in episodic memory and 1 consciously-controlled metacognitive process of memory: the retrieval of specific details from ones past (recollection), noetic recognition in the absence of retrieved details (familiarity), and accurate introspection of memory decisions (metamemory). We observed that sedatives, dissociatives, psychedelics, stimulants, and cannabinoids had unique patterns of effects on these mnemonic processes dependent on which phase of memory (encoding, consolidation, or retrieval) was targeted. All drugs at encoding except stimulants impaired recollection, and sedatives, dissociatives, and cannabinoids at encoding impaired familiarity. The effects of sedatives on metamemory were mixed, whereas dissociatives and cannabinoids at encoding tended to enhance metamemory. Surprisingly, psychedelics at encoding tended to enhance familiarity and did not impact metamemory. Stimulants at encoding and retrieval enhanced metamemory, but at consolidation, they impaired metamemory. Together, these findings may have relevance to mechanisms underlying unique subjective phenomena under different drug classes, such as blackouts from sedatives or deja vu from psychedelics. This study provides a framework for interrogating drug effects within a domain of cognition beyond the global impairments on task performance typically reported in psychopharmacology. Public significance statementThis systematic review and reanalysis of several datasets indicate that sedatives (alcohol, zolpidem, triazolam), dissociatives (ketamine, dextromethorphan), psychedelics (psilocybin, MDMA), stimulants (dextroamphetamine, dextromethamphetamine), and cannabinoids (THC) can each have idiosyncratic effects on episodic memory, differentially impairing certain mnemonic processes while sparing or even facilitating others. Such findings inform how different drugs can produce unique subjective phenomena and provide a framework for future work to differentiate the effects of psychoactive drugs within a domain of cognition.

neuroscience↗

bioRxiv2020

An Rshiny app for modelling environmental DNA data: accounting for false positive and false negative observation error.

O_LIEnvironmental DNA (eDNA) surveys have become a popular tool for assessing the distribution of species. However, it is known that false positive and false negative observation error can occur at both stages of eDNA surveys, namely the field sampling stage and laboratory analysis stage. C_LIO_LIWe present an RShiny app that implements the Griffin et al. (2019) statistical method, which accounts for false positive and false negative errors in both stages of eDNA surveys. Following Griffin et al. (2019), we employ a Bayesian approach and perform efficient Bayesian variable selection to identify important predictors for the probability of species presence as well as the probabilities of observation error at either stage. C_LIO_LIWe demonstrate the RShiny app using a data set on great crested newts collected by Natural England in 2018 and we identify water quality, pond area, fish presence, macrophyte cover, frequency of drying as important predictors for species presence at a site. C_LIO_LIThe state-of-the-art statistical method that we have implemented is the only one that has specifically been developed for the purposes of modelling false negatives and false positives in eDNA data. Our RShiny app is user-friendly, requires no prior knowledge of R and fits the models very efficiently. Therefore, it should be part of the tool-kit of any researcher or practitioner who is collecting or analysing eDNA data. C_LI

ecology↗

bioRxiv2019

Beyond taxonomic identification: integration of ecological responses to a soil bacterial 16S rRNA gene database.

High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalising syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterise taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth "CS") to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from the survey metadata. Specifically, we modelled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on putative landscape scale pH-abundance responses. We identify that most of the soil OTUs examined exhibit predictable abundance responses across soil pH gradients, though with the exception of known acidophilic lineages, the pH optima of OTU relative abundance was variable and could not be generalised by broad taxonomy. This highlights the need for tools and databases to predict ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER), and as a DADA2 database for use in bioinformatics pipelines. The further development of advanced informatics infrastructures incorporating modelled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

microbiology↗