Biology subjects

Davies, T. J.

Publications and source records attributed to Davies, T. J..

11 recordsLinked to original sources

bioRxiv2023

Abnormal weather drives disease outbreaks in wild and agricultural plants

Predicting effects of climate change on plant disease is critical for protecting ecosystems and food production. Climate change could exacerbate plant disease because parasites may be quicker to acclimate and adapt to novel climatic conditions than their hosts due to their smaller body sizes and faster generation times. Here we show how disease pressure responds to the anomalous weather that will increasingly occur with climate change by compiling a global database (5380 plant populations; 437 unique plant-disease combinations; 2,858,795 individual plant-disease samples) of disease incidence in both agricultural and wild plant systems. Because wild plant populations are assumed to be adapted to local climates, we hypothesized that large deviations from historical conditions would increase disease incidence. By contrast, since agricultural plants have been transported globally, we did not expect the historical climate where they are currently grown to be as predictive of disease incidence. Supporting these hypotheses, we found that disease outbreaks tended to occur during periods of warm temperatures in agricultural and cool-climate wild plant systems, but also occurred in warm-adapted wild (but not agricultural) plant systems experiencing anomalously cool weather. Outbreaks were additionally associated with higher rainfall in wild systems, especially those with historically wet climates. Our results suggest that historical climate affects susceptibility to disease for wild plant-disease systems, while warming drives risks for agricultural plant disease outbreaks regardless of historical climate.

ecology↗

bioRxiv2023

The evolutionary history of spines: a Cenozoic arms race with mammals

The role of mammal herbivory in plant evolution is largely unrecognised. Spines on stems are a common and important feature found in [~]9% of eudicot woody plant species worldwide. Spines evolved independently multiple times during the Cenozoic. The timing and extent of spiny plant diversification varied among continents, pointing towards continental rather than global drivers. Spine evolution is closely related to radiation of extant ungulates and extinct ground sloths, rather than climate variation. Diversification began in the Paleogene in herbivore species-rich Eurasia and North America, emerging later in the Neogene in species-poorer South America, Africa and Australia. Spiny lineages expanded their ecological footprint over non-spiny plants, mainly through intercontinental migrations, indicating that spines likely provided a competitive advantage with increasing, and novel, mammal herbivory pressure.

evolutionary biology↗

bioRxiv2021

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli

2.Several bioinformatics genotyping algorithms are now commonly used to characterise antimicrobial resistance (AMR) gene profiles in whole genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli, is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance, and SRST2, run with the ResFinder database) and their outputs compared. For simulations tests where 3,079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3,076 (99.9%) simulations, ABRicate for 3,054 (99.2%), ARIBA for 2,783 (90.4%) and SRST2 for 2,108 (68.5%). For simulations tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance the correct alleles in 35,338/46,318 (76.3%) ABRicate identified in 11,842/46,318 (25.6%) of simulations, ARIBA in 1679/46,318 (3.6%), and SRST2 in 2000/46,318 (4.3%). In real data, across all methods, 1392/1818 (76%) isolates had discrepant allele calls for at least one gene. Our evaluations revealed poor performance in scenarios that would be expected to be challenging (e.g. identification of AMR genes at <10x coverage, discriminating between closely related AMR gene sequences), but also identified systematic sequence classification (i.e. naming) errors even in straightforward circumstances, which contributed to 1081/3092 (35%) errors in our most simple simulations and at least 2530/4321 (59%) discrepancies in real data. Further, many of the remaining discrepancies were likely "artefactual" with reporting cut-off differences accounting for at least 1430/4321 (33%) discrepants. Comparing outputs generated by running multiple algorithms on the same dataset can help identify and resolve these artefacts, but ideally new and more robust genotyping algorithms are needed. 3. Impact statementWhole-genome sequencing is widely used for studying the epidemiology of antimicrobial resistance (AMR) genes in bacteria; however, there is some concern that outputs are highly dependent on the bioinformatics methods used. This work evaluates these concerns in detail by comparing four different, commonly used AMR gene typing methods using large simulated and real datasets. The results highlight performance issues for most methods in at least one of several simulated and real-life scenarios. However most discrepancies between methods were due to differential labelling of the same sequences related to the assumptions made regarding the underlying structure of the reference resistance gene database used (i.e. that resistance genes can be easily classified in well-defined groups). This study represents a major advance in quantifying and evaluating the nature of discrepancies between outputs of different AMR typing algorithms, with relevance for historic and future work using these algorithms. Some of the discrepancies can be resolved by choosing methods with fewer assumptions about the reference AMR gene database and manually resolving outputs generated using multiple programs. However, ideally new and better methods are needed.

microbiology↗

bioRxiv2021

Herbarium specimens reveal herbivory patterns across the genus Cucurbita

PREMISEQuantifying how closely related plant species differ in susceptibility to insect herbivory is important for our understanding of variation in plant-insect ecological interactions and evolutionary pressures on plant functional traits. However, empirically measuring in situ variation in herbivory over the entire geographic range where a plant-insect complex occurs is logistically difficult. Recently, new methods have been developed to use herbarium specimens to investigate patterns in plant-insect interactions across geographic areas, and during periods of accelerating anthropogenic change. Such investigations can provide insights into changes in herbivory intensity and phenology in plants that are of ecological and agricultural importance. METHODSHere, we analyze 274 pressed herbarium samples from all 14 species in the economically important plant genus Cucurbita (Cucurbitaceae) to investigate variation in herbivory damage. This collection is comprised of specimens of wild, undomesticated Cucurbita that were collected from across their native range in the Neotropics and subtropics, and Cucurbita cultivars that were collected from both within their native range and from locations where they have been introduced for agriculture in temperate Eastern North America. RESULTSWe find that herbivory is common on individuals of all Cucurbita species collected from throughout their geographic ranges; however, estimates of herbivory varied considerably among individuals, with greater damage observed in specimens collected from unmanaged habitat. We also find evidence that mesophytic species accrue more insect damage than xerophytic species. CONCLUSIONSOur study demonstrates that herbarium specimens are a useful resource for understanding ecological interactions between domesticated crop plants and co-evolved insect herbivores.

ecology↗

bioRxiv2020

Environmental DNA for tracking waterhole visitation in savanna ecosystems

Game parks are the last preserve of many large mammals, and in savanna ecosystems, management of surface waters poses a conservation challenge. In arid and semi-arid regions, water can be a scarce resource during dry seasons and drought. Artificial waterholes are common in parks and reserves across Africa, but can alter mammal community composition by favoring drought intolerant species, with consequences for disease dynamics, and population viability of drought-tolerant species. Analysis of waterborne environmental DNA (eDNA) is increasingly used to inform conservation of rare and invasive species, and conduct large-scale biodiversity assessments. To explore the reliability of eDNA as an indicator of mammal waterhole use in savannas, we compare eDNA metabarcoding and camera traps for documenting artificial waterhole use in the Kruger National Park, South Africa, a global hotspot for mammal diversity. We show that eDNA metabarcoding can recover the majority of mammal species detected by camera traps, including a number of endangered species, but DNA signatures of mammal visitation are temporally limited, with best performance when tracking water-dependent large bodied mammals visiting within two days of sampling. Our results highlight limitation of eDNA based monitoring in these systems, including the lack of long-term eDNA persistence in small and highly utilized waterholes, and variability in detection rates among species. However, we demonstrate that eDNA-based approaches can be used to track mammals of conservation concern, and reflect patterns of recent waterhole use and co-occurrence across water-dependent species, both of which are crucial for making evidence-based decisions regarding water management and provisioning.

ecology↗

bioRxiv2020

Response to Kozlov et al.: Inaccurate estimation of biases in herbarium specimen data

Kozlov and colleagues1 call into question the application of herbarium specimens to quantify historical patterns of herbivory2-5. It is already widely appreciated that collectors of herbarium specimens may tend to avoid insect damage, thus making herbivory estimates from herbarium specimens potentially down-biased2. However, Kozlov et al. additionally suggest that variation in sampling selectivity among collectors and curators may lead herbarium specimens to misrepresent patterns of herbivory in nature. The authors sought to quantify these biases by collecting and contrasting insect herbivory data across 17 plant species from herbarium versus standard field ecological sampling procedures, and then assessed the selection of these specimens by curators. They concluded that herbivory estimates from herbarium specimens are highly variable, rendering them an inaccurate representation of herbivory in nature. Our re-analysis of Kozlov et al.s data suggests that, in contrast with their results, herbarium specimens indeed provide a useful record of herbivory as long as sample sizes are appropriate. In addition, we assert that by arguing that herbarium specimens are "distorting mirrors", Kozlov et al.s conclusions fundamentally overstep their data, which narrowly assesses biases across species. Kozlov et al. argue that herbarium specimens are inaccurate data sources, but fail to characterize the specific circumstances under which assumed biases would apply. Thus, Kozlov et al.s data do not support their main premise, and the authors extrapolate beyond the specific biases investigated in their study; we believe their contribution does a disservice to researchers interested in exploring the potential value of herbarium specimens for studying herbivory through time.

ecology↗

bioRxiv2020

Predicting missing links in global host-parasite networks

O_LIParasites that infect multiple species cause major health burdens globally, but for many, the full suite of susceptible hosts is unknown. Predicting undocumented host-parasite associations will help expand knowledge of parasite host specificities, promote the development of theory in disease ecology and evolution, and support surveillance of multi-host infectious diseases. Analysis of global species interaction networks allows for leveraging of information across taxa, but link prediction at this scale is often limited by extreme network sparsity, and lack of comparable trait data across species. C_LIO_LIHere we use recently developed methods to predict missing links in global mammal-parasite networks using readily available data: network properties and evolutionary relationships among hosts. We demonstrate how these link predictions can efficiently guide the collection of species interaction data and increase the completeness of global species interaction networks. C_LIO_LIWe amalgamate a global mammal host-parasite interaction network (>29,000 interactions) and apply a hierarchical Bayesian approach for link prediction that leverages information on network structure and scaled phylogenetic distances among hosts. We use these predictions to guide targeted literature searches of the most likely yet undocumented interactions, and identify empirical evidence supporting many of the top "missing" links. C_LIO_LIWe find that link prediction in global host-parasite networks can accurately predict parasites of humans, domesticated animals, and endangered wildlife, representing a combination of published interactions missing from existing global databases, and potential but currently undocumented associations. C_LIO_LIOur study provides further insight into the use of phylogenies for predicting host-parasite interactions, and highlights the utility of iterated prediction and targeted search to efficiently guide the collection of host-parasite interaction. These data are critical for understanding the evolution of host specificity, and may be used to support disease surveillance through a process of predicting missing links, and targeting research towards the most likely undocumented interactions. C_LI

ecology↗

bioRxiv2019

The realised velocity of climate change reveals remarkable idiosyncrasy of species’ distributional shifts

To date, our understanding of how species have shifted in response to recent climate warming has been based on a few studies with a limited number of species. Here we present a comprehensive, global overview of species distributional responses to changing climate across a broad variety of taxa (animals, plants, and fungi). We characterise species responses using a metric that describes the realised velocity of climate change: how closely species responses have tracked changing climate through time. In contrast to existing climate velocity metrics that have focused on space, we focus on species and index their responses to a null expectation of change in order to examine drivers of inter-specific variation. Here we show that species are tracking climate on average, but not sufficiently to keep up with the pace of climate change. Further, species responses are highly idiosyncratic, and thus highlight that projections assuming uniform responses may be misleading. This is in stark contrast to species present-day and historical climate niches, which show strong evidence of the imprint of evolutionary history and functional traits. Our analyses are a first step in exploring the vast wealth of empirical data on species historic responses to recent climate change.

ecology↗

bioRxiv2019

Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in Escherichia coli

Resistance to amoxicillin-clavulanate, a widely used beta-lactam/beta-lactamase inhibitor combination antibiotic, is rising globally, yet susceptibility testing remains challenging. To test whether whole-genome sequencing (WGS) could provide a more reliable assessment of susceptibility than traditional methods, we predicted resistance from WGS for 976 E. coli bloodstream infection isolates from Oxfordshire, UK, comparing against phenotypes from the BD Phoenix (calibrated against EUCAST guidelines). 339/976 (35%) isolates were amoxicillin-clavulanate resistant. Predictions based solely on beta-lactamase presence/absence performed poorly (sensitivity 23% (78/339)) but improved when genetic features associated with penicillinase hyper-production (e.g. promoter mutations, copy number estimates) were considered (sensitivity 82% (277/339); p<0.0001). Most discrepancies occurred in isolates with peri-breakpoint MICs. We investigated two potential causes; the phenotypic reference and the binary resistant/susceptible classification. We performed reference standard, replicated phenotyping in a random stratified subsample of 261/976 (27%) isolates using agar dilution, following both EUCAST and CLSI guidelines, which use different clavulanate concentrations. As well as disagreeing with each other, neither agar dilution phenotype aligned perfectly with genetic features. A random-effects model investigating associations between genetic features and MICs showed that some genetic features had small, variable and additive effects, resulting in variable resistance classification. Using model fixed-effects to predict MICs for the non-agar dilution isolates, predicted MICs were in essential agreement ({+/-}1 doubling dilution) with observed (BD Phoenix) MICs for 691/715 (97%) isolates. This suggests amoxicillin-clavulanate resistance in E. coli is quantitative, rather than qualitative, explaining the poorly reproducible binary (resistant/susceptible) phenotypes and suboptimal concordance between different phenotypic methods and with WGS-based predictions.

microbiology↗

bioRxiv2018

Validation of a 14-drug microtitre plate that includes delamanid and bedaquiline for susceptibility testing of M. tuberculosis

UKMYC5 is a 96-well microtitre plate designed by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to enable the measurement of minimum inhibitory concentrations (MICs) of 14 different anti-TB compounds for >30,000 clinical tuberculosis isolates. Unlike the MYCOTB plate, on which UKMYC5 is based, the plate included two new (bedaquiline and delamanid) and two repurposed (clofazimine and linezolid) compounds. UKMYC5 plates were tested by seven laboratories on four continents using a panel of 19 external quality assessment (EQA) strains, including H37Rv. To assess the optimal combination of reading method and incubation time, MICs were measured from each plate by two readers using three methods (mirrored-box, microscope and Vizion Digital viewing system) after 7, 10, 14 and 21 days incubation. In addition, all EQA strains were whole-genome sequenced and phenotypically characterized by 7H10/7H11 agar proportion method (APM) and MGIT960. We conclude that the UKMYC5 plate is optimally read using the Vizion system after 14 days incubation, achieving an inter-reader agreement of 97.9% and intra- and inter-laboratory reproducibilities of 95.6% and 93.1%, respectively. The mirrored-box had similar reproducibilities. Strains classified as resistant by APM, MGIT960 or the presence of mutations known to confer resistance consistently record elevated MICs compared with those strains classified as susceptible. Finally, the UKMYC5 plate records intermediate MICs for one strain which the APM measured MICs close the applied critical concentration, providing early evidence that the UKMYC5 plate can quantitatively measure the magnitude of resistance to anti-TB compounds due to specific genetic variation.

microbiology↗

bioRxiv2017

The unrealized potential of herbaria in global change biology

Plant and fungal specimens in herbaria are becoming primary resources for investigating how plant phenology and geographic distributions shift with climate change, greatly expanding inferences across spatial, temporal, and phylogenetic dimensions. However, these specimens contain a wealth of additional data--including nutrients, defensive compounds, herbivore damage, disease lesions, and signatures of physiological processes--that capture ecological and evolutionary responses to the Anthropocene but which are less frequently utilized. Here, we outline the diversity of herbarium data, global change topics to which they have been applied, and new hypotheses they could inform. We find that herbarium data have been used extensively to study impacts of climate change and invasive species, but that such data are less commonly used to address other drivers of biodiversity loss, including habitat conversion, pollution, and overexploitation. In addition, we note that fungal specimens are under-explored relative to vascular plants. To facilitate broader application of plant and fungal specimens in global change research, we outline the limitations of these data and modern sampling and statistical tools that may be applied to surmount challenges they present. Using a case study of insect herbivory, we illustrate how novel herbarium data may be employed to test hypotheses for which few data exist, despite potentially large biases. With the goal of positioning herbaria as hubs for global change research, we suggest future research directions and curation priorities.

plant biology↗