Biology subjects

Hui, C.

Publications and source records attributed to Hui, C..

18 recordsLinked to original sources

bioRxiv2026

A strong-to-weak interaction shift during microbiome succession is coupled to colonizer-dependent antimicrobial resistance

The outcome of ecological succession is often attributed to the characteristics of the invader or the resident community, but rarely to how the community's interaction network reorganizes during assembly. Here, we track intraspecific lineage dynamics and infer time-resolved community interaction networks using Dynamic Covariance Mapping during ecological invasion of the mouse gut by a chromosomally barcoded, spectinomycin-resistant Escherichia coli K12 colonizer. The network is initially dominated by strong, predominantly inhibitory interactions, but as community diversity recovers, the distribution of interaction strengths contracts toward zero, producing a community increasingly dominated by weak and near-neutral interactions. The dominant eigenvalue of the DCM-inferred interaction matrix moves toward marginal stability predicted for dynamically assembling ecological networks. This pattern replicates across eight independent mice in two experimental cohorts, at both inter- and intra-species resolution. The ecological transition coincides with the reproducible resurgence of Paenibacillaceae to high relative abundance and persistent coexistence with E. coli under continued spectinomycin pressure. Whole-genome sequencing of recovered Paenibacillus macerans isolates identifies recurrent mutations in ribosomal protein S5 region associated with spectinomycin binding and strongly implicating this variant in resistance. Strikingly, under antibiotic pressure but without E. coli K12 invasion, resident Paenibacillaceae never blooms, indicating that expansion of the resistant population depends on the ecological context established by the colonizer. These findings show that gut microbiome succession is accompanied by a reproducible transition from strong toward weak interactions and link this network reorganization to the colonizer-dependent ecological benefit of antimicrobial resistance.

ecology↗

bioRxiv2026

Inferring unobserved vector dynamics for dengue forecasting using physics-informed neural networks and mechanistic transmission models

Accurately characterising mosquito infection dynamics is essential for effective dengue prevention and control, yet these dynamics are rarely observable through routine surveillance. Here, we integrate a reduced SI-SIR transmission model with monthly dengue incidence data using physics-informed neural networks (PINNs) to develop a Dengue-Informed Neural Network (DINN). The DINN simultaneously fits reported dengue cases from 15 countries between January 2014 to April 2025 and reconstructs the unobserved time series of infected mosquitoes. Using the inferred mosquito infection dynamics together with five-month-lagged climatic variables, we subsequently train recurrent neural networks (RNN, GRU and LSTM) to forecast mosquito infection levels one month ahead. For each country, the optimal forecasting model is selected based on out-of-sample performance, and model behaviour is interpreted using SHAP analysis. We show that the DINN captures multi-wave outbreak dynamics across diverse epidemic regions and enables the development of country-specific vector forecasting models. When integrated with the transmission model, the predicted mosquito trajectories support two-year projections of dengue incidence, providing a quantitative framework for early warning and evidence-based control strategies. Author summaryMechanistic models of dengue transmission often require assumptions about mosquito population sizes and temperature-dependent biological traits that are difficult to specify accurately and rarely observed in practice. These limitations can reduce the reliability of outbreak prediction and hinder the evaluation of control strategies. Here, we introduce a Dengue-Informed Neural Network (DINN) that integrates deep learning with a reduced SI-SIR transmission model to address these challenges. Without requiring prior specification of mosquito initial conditions or biological trait functions, DINN infers latent time series of infectious mosquitoes directly from reported human case data and reconstructs complex, multi-wave transmission dynamics. Applying this approach to dengue surveillance data from 15 countries, we uncover substantial regional variation in inferred mosquito infection patterns, reflecting differences in environmental variability and vector control. By combining the inferred mosquito dynamics with climatic information, we further develop interpretable forecasting models and generate two-year projections of dengue incidence. Our results demonstrate that DINN provides a flexible and interpretable framework for inferring unobserved vector dynamics and offers practical support for early warning and evidence-based control of mosquito-borne diseases.

ecology↗

bioRxiv2025

Thermodynamic principles govern evolutionary tradeoffs by regulating allostery

Allostery governs biological activities by signaling environmental cues at distal sites, however, the molecular basis for the orchestration and evolution of "the second secret of life" is evanescent. Seminal work sheds light embracing structure- and function-centric approaches, overlooking the widely accepted ensemble allosteric model based on a proteins free-energy landscape and the thermodynamic nature of allostery. Here, we unraveled allosteric regulation and its evolvability by examining energetic funnels of proteins harboring a highly evolvable ancient scaffold. We uncover intricate allosteric connectivities and their coordinated cross-talk to enable the statistical thermodynamic coupling. We decipher universal molecular determinants for the emergence of functions and environmental adaptability. Our integrative biophysical/statistical/evolutionary analysis ties the evolutionary forces via thermodynamic principles and decrypts how tradeoffs are settled at the molecular level. One-Sentence SummaryWe reveal how physical and evolutionary laws shaped the biophysical properties of proteins

biophysics↗

bioRxiv2025

Bayesian inference captures metabolite-bacteria interactions in a microbial community

Macro-ecosystems, including the human gut, host a vast and diverse set of microbes that indirectly interact with each other through consuming and producing metabolites. Disruptions in this microbial network can affect macro-ecosystem functioning and, in the human gut, contribute to the onset and progression of various disorders, including diabetes, rheumatoid arthritis and Parkinsons disease. A theoretical foundation for understanding the intricate and dynamic interactions between microbes and metabolites is essential for developing microbiota-targeted interventions to improve macro-ecosystem functioning and health. To this end a precise mathematical framework is crucial to capture and quantify the complex dynamics of the microbial system. Here, we develop a dynamic network model of coupled ordinary differential equations and present a computational workflow that integrates a generative model with Bayesian inference for model identification. Our approach infers interaction rates, quantifying metabolite consumption and production from simulated time-series data within a Bayesian framework, incorporating prior knowledge and uncertainty quantification. We show that our approach is accurate and reliable in communities of various sizes, sparsity and with different levels of observational noise. This workflow enables in-silico predictions of system behaviour under perturbations and offers a robust method to integrate high-dimensional biological data with dynamic network models. By refining our understanding of microbial dynamics, this framework is capable of assessing microbiota-targeted interventions and their potential to improve the health of the macro-ecosystem.

microbiology↗

bioRxiv2025

Synergistic latency reversal by the SMAC mimetic AZD5582 and iBET JQ1, combined with Nef ablation, facilitates immune-mediated elimination of latently HIV-1-infected T-cells

The shock-and-kill strategy can reactivate latent HIV-1 in vivo but has failed to meaningfully reduce the reactivatable reservoir, suggesting insufficient immunological visibility of infected cells. To investigate latency reversal agent (LRA)-specific shortcomings, we developed a novel HIV-1 latency model - Jurkat E6.1 subclonal T-cell lines, each harbouring a single, full-length, NL4.3-based provirus with a GFPOPT reporter in the Env V5 loop - and quantified HIV-1 reactivation, Env surface presentation across LRAs and synergistically acting combinations. Bryostatin-1 induced GBP5-mediated restriction of surface Env and resistance to apoptosis. Bryostatin-1/JQ1 synergised for HIV-1 reactivation and boosted levels of cell-surface Env, but failed to reduce resistance to apoptosis. AZD5582/JQ1 boosted cell-surface Env levels, without co-induction of GBP5, T-cell activation or apoptotic resistance. HIV-1 Nef antagonised cytotoxic killing of HIV-1-reactivating T-cells - its knockout, combined with AZD5582/JQ1, achieved the highest immune-mediated elimination of latently infected T-cells, representing a promising HIV cure approach.

microbiology↗

bioRxiv2025

Fluctuating selection breaks Hamilton's rule for the evolution of altruism

Altruistic behaviours, those that benefit recipients at a cost to donors, have long posed a puzzle in evolutionary biology and sociobiology. Established theories, such as kin selection, group selection, and reciprocal altruism, explain altruism via assortment mechanisms that depend on preferential interactions among altruists to ensure a higher average payoff than selfish individuals. Hamiltons rule defines the minimum level of such assortative interactions required for altruism to evolve. However, in populations with limited growth, increased competition can erode the selective advantage of altruism and thus require more stringent Hamiltons rule. Here, we propose a fundamentally different mechanism: fluctuating selection driven by increased competition due to added social benefits of altruism in populations with limited carrying capacity. Under fluctuating selection, altruists can invade a selfish population without the need of assortment mechanisms and outcompete selfish individuals through a transient phase, even when the cost of altruism exceeds its direct benefit. Classical invasion analysis, which compares the long-term growth rates of rare altruistic mutants and resident selfish individuals, fails in our model because both strategies exhibit zero long-term growth. Instead, we show that altruism can invade when its short-term, time-averaging growth rate exceeds that of selfish individuals, which necessitates a prolonged transient phase. This invasion is enabled by payoff-regulated density dependence, which captures the dynamic interplay between payoff and density on fitness under fluctuating selection. Our findings challenge the necessity of Hamiltons rule and show that genuine altruism, with less average payoff than selfish individuals, even extremely self-sacrificial, can emerge under natural selection. This suggests that altruism may arise not from preferential interaction, but as an adaptive response to population fluctuations in constrained environments, providing an alternative paradigm for understanding the evolution of altruistic behaviour.

evolutionary biology↗

bioRxiv2024

Charge Scaling in Potassium Channel Simulations: Conductance, Ion Occupancy, Voltage Response, and Selectivity

Potassium (K+) channels are widely distributed in many types of organisms. They combine high efficiency ([~]100 pS) and K+/Na+ selectivity by a conserved selectivity filter (SF). Molecular Dynamics (MD) simulations can provide detailed, atomistic mechanisms of this sophisticated ion permeation. However, currently there are clear inconsistencies between computational predictions and experimental results. Firstly, the ion occupancy of the SF in simulations is lower than expected ([~]2.5 in MD compared to [~]4 in X-ray crystallography). Secondly, in many reported MD simulations of K+ channels, K+ conductance is typically an order of magnitude lower than experimental values. This discrepancy is in part because the force fields used in MD simulations of potassium channels do not account for polarization. One of the proposed solutions is the Electronic Continuum Correction (ECC), a force field modification that scales down formal charges, to introduce the polarization in a mean-field way. When the ECC is used in conjunction with the Charmm36m force field, the simulated K+ conductance increases 13-fold. Following the analysis of ion occupancy states using Hamiltonian Replica Exchange (HRE) simulations, we propose a new parameter set for Amber14sb, that also leads to a similar increase in conductance. These two force fields are then used to compute, for the first time, the full current-voltage (I-V) curves from MD simulations, approaching quantitative agreement with experiments at all voltages. In general, the ECC-enabled simulations are in excellent agreement with experiment, in terms of ion occupancy, conductance, current-voltage response, and K+/Na+ selectivity. Significance StatementPotassium (K+) channels are essential membrane proteins that facilitate the selective conduction of K+ ions while excluding Na+ ions. Incorporation of electronic polarization effects through the Electronic Continuum Correction (ECC) method enables molecular dynamics simulations to reproduce rapid K+ permeation under physiological voltage conditions. In this study, we demonstrate that employing ECC approximated polarization in molecular dynamics simulations allows for the prediction of multiple channel properties: conductance, ion occupancy, voltage response, and selectivity, with unprecedented accuracy. Furthermore, our simulations provide atomistic insights into the asymmetrical current-voltage (I-V) relationship observed in the MthK channel.

biophysics↗

bioRxiv2023

Computational exploration of the global microbiome for antibiotic discovery

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

microbiology↗

bioRxiv2023

The Genetic Architecture of Dietary Iron Overload and Associated Pathology in Mice

Tissue iron overload is a frequent pathologic finding in multiple disease states including non-alcoholic fatty liver disease (NAFLD), neurodegenerative disorders, cardiomyopathy, diabetes, and some forms of cancer. The role of iron, as a cause or consequence of disease progression and observed phenotypic manifestations, remains controversial. In addition, the impact of genetic variation on iron overload related phenotypes is unclear, and the identification of genetic modifiers is incomplete. Here, we used the Hybrid Mouse Diversity Panel (HMDP), consisting of over 100 genetically distinct mouse strains optimized for genome-wide association studies and systems genetics, to characterize the genetic architecture of dietary iron overload and pathology. Dietary iron overload was induced by feeding male mice (114 strains, 6-7 mice per strain on average) a high iron diet for six weeks, and then tissues were collected at 10-11 weeks of age. Liver metal levels and gene expression were measured by ICP-MS/ICP-AES and RNASeq, and lipids were measured by colorimetric assays. FaST-LMM was used for genetic mapping, and Metascape, WGCNA, and Mergeomics were used for pathway, module, and key driver bioinformatics analyses. Mice on the high iron diet accumulated iron in the liver, with a 6.5 fold difference across strain means. The iron loaded diet also led to a spectrum of copper deficiency and anemia, with liver copper levels highly positively correlated with red blood cell count, hemoglobin, and hematocrit. Hepatic steatosis of various severity was observed histologically, with 52.5 fold variation in triglyceride levels across the strains. Liver triglyceride and iron mapped most significantly to an overlapping locus on chromosome 7 that has not been previously associated with either trait. Based on network modeling, significant key drivers for both iron and triglyceride accumulation are involved in cholesterol biosynthesis and oxidative stress management. To make the full data set accessible and useable by others, we have made our data and analyses available on a resource website. Author summaryThe response to a high iron diet is determined in part by genetic factors. We now report the responses to such a diet in a diverse set of inbred strains of mice, known as the Hybrid Mouse Diversity Panel, that enables high resolution genetic mapping and systems genetics analyses. The levels of iron in the liver varied about >5 fold across the strains, with genetic variation explaining up to 74% of the variation in liver iron. Pathologies included copper deficiency, anemia, and fatty liver, with liver triglycerides varying over 50 fold among the strains. Genetic mapping and network modeling identified significant genetic loci and pathways underlying the response to diet.

genetics↗

bioRxiv2023

A kernel integral method to remove biases in estimating trait turnover

O_LITrait diversity, including trait turnover, that differentiates the roles of species and communities according to their functions, is a fundamental component of biodiversity. Accurately capturing trait diversity is crucial to better understand and predict community assembly, as well as the consequences of global change on community resilience. Existing methods to compute trait turnover have limitations. Trait space approaches based on minimum convex polygons only consider species with extreme trait values. Tree-based approaches using dendrograms consider all species but distort trait distance between species. More recent trait space methods using complex polytopes try to harmonise the advantages of both methods, but their current implementation have mathematical flaws. C_LIO_LIWe propose a new kernel integral method (KIM) to compute trait turnover, based on the integration of kernel density estimators (KDEs) rather than using polytopes. We explore how this difference and the computational aspects of the KDE computation can influence the estimates of trait turnover. We compare our novel method to existing ones using justified theoretical expectations for a large number of simulations in which we control the number of species and the distribution of their traits. We illustrate the practical application of KIM using plant species introduced to the Pacific Islands of French Polynesia. C_LIO_LIAnalyses on simulated data show that KIM generates results better aligned with theoretical expectations than other methods and is less sensitive to the total number of species. Analyses for French Polynesia data also show that different methods can lead to different conclusions about trait turnover, and that the choice of method should be carefully considered based on the research question. C_LIO_LIMathematical aspects for computing trait turnover are crucial as they can have important effects on the results and therefore lead to different conclusions. Our novel kernel integral method generates values that better reflect the distribution of species in the trait space than other existing methods. We therefore recommend using KIM in future studies on trait turnover. In contrast, tree-based approaches should be kept for phylogenetic diversity, as phylogenetic trees will then reflect the constrained speciation process. C_LI

ecology↗

bioRxiv2022

Intra- and inter-species interactions drive early phases of invasion in mice gut microbiota

The stability and dynamics of ecological communities are dictated by interaction networks typically quantified at the level of species.1-10 But how such networks are influenced by intra-species variation (ISV) is poorly understood.11-14 Here, we use ~500,000 chromosomal barcodes to track high-resolution intra-species clonal lineages of Escherichia coli invading mice gut with the increasing complexity of gut microbiome: germ-free, antibiotic-perturbed, and innate microbiota. By co-clustering the dynamics of intra-species clonal lineages and those of gut bacteria from 16S rRNA profiling, we show the emergence of complex time-dependent interactions between E. coli clones and resident gut bacteria. With a new approach, dynamic covariance mapping (DCM), we differentiate three phases of invasion in susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli coexisting with resident bacteria in a quasi-steady state. Comparison of the dynamics, stability and fitness from experimental replicates and different cohorts suggest that phase 1 is driven by mutations in E. coli before colonization, while phase 3 is by de novo mutations. Our results highlight the transient nature of interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. One-Sentence SummaryHigh-resolution lineage tracking and dynamic covariance mapping (DCM) define three distinct phases during early gut microbiome invasion.

evolutionary biology↗

bioRxiv2022

Reconstitution and Expression of mcy Gene Cluster in The Model Cyanobacterium Synechococcus 7942 Reveals a Roll of MC-LR in Cell Division

Cyanobacterial blooms pose a serious threat to public health due to the presence of cyanotoxins. The most common cyanotoxins, microcystins (MCs), can cause acute poisoning at high concentrations and hepatocellular carcinoma following chronic exposure. Among all MC variants, MC-LR produced by Microcystis aeruginosa PCC 7806 is the most common toxic MC. Although the biosynthetic pathway for MC-LR has been proposed, experimental support of this pathway is lacking. In an effort to experimentally validate this pathway, we expressed the 55 kb microcystin biosynthetic gene cluster (mcy cluster) (mcyA-J) and produced MC-LR in the model cyanobacterium Synechococcus 7942. We designed and constructed the strong bidirectional promoter biPpsbA2 between mcyA and mcyD, reassembled the mcy cluster in yeast by transformation-associated recombination (TAR cloning), transformed the gene cluster into the NSII site of Synechococcus 7942, and successfully expressed MC-LR at a level of 0.006-0.018 fg cell-1 day-1. The expression of MC-LR led to abnormal cell division and the filamentation of Synechococcus 7942 cells, further analysis proved a role of MC-LR in functional assembly of the cell division protein FtsZ, by competing its GTP binding site. These results represent the first synthetic biological expression of the mcy cluster and the autotrophic production of MC-LR in a photosynthetic model organism, which lays the foundation for resolving the MC biosynthesis pathway. The suggested role of MC-LR in cell division reveals a mechanism of how blooming cyanobacteria gain a competitive edge over their non-blooming counterparts. HighlightsO_LIWe expressed the 55 kb mcy cluster and produced MC-LR in the model cyanobacterium Synechococcus 7942. C_LIO_LIThis is the first realized production of MC in the model non-toxin-production cyanobacteria from CO2 by photosynthesis. C_LIO_LICompared with the self-replicating plasmid, the recombination of the mcy cluster into the genome of Synechococcus 7942 is more suitable for the heterologous production of microcystin. C_LIO_LIMC-LR inhibits cell division by irreversibly competing the GTP binding domain of the cell division protein FtsZ. C_LIO_LIThe newly discovered effect of MC-LR on cell division reveals a mechanism of how blooming cyanobacteria gain competitive edge over their non-blooming counterparts. C_LI

synthetic biology↗

bioRxiv2021

A multi-species co-occurrence index to avoid type II errors in null model testing

Community structure is determined by the interplay among different processes, including biotic interactions, abiotic filtering and dispersal. Their effects can be detected by comparing observed patterns of co-occurrence between different species (e.g. C-score and the natural metric) to patterns generated by null models based on permutations of species-by-site matrices under constraints on row or column sums. These comparisons enable us to detect significant signals of species association or dissociation, from which the type of biotic interactions between species (e.g. facilitative or antagonistic) can be inferred. Commonly used patterns are based on the levels of co-occurrence between randomly paired species. The level of co-occurrence for three or more species is rarely considered, ignoring the potential existence of functional guilds or motifs composed of multiple species within the community. Null model tests that do not consider multi-species co-occurrence could therefore generate false negatives (Type II error) in detecting non-random forces at play that would only be apparent for such guilds. Here, we propose a multi-species co-occurrence index (hereafter, joint occupancy) that measures the number of sites jointly occupied by multiple species simultaneously, of which the pairwise metric of co-occurrence is a special case. Using this joint occupancy index along with standard permutation algorithms for null model testing, we illustrate nine archetypes of multi-species co-occurrence and explore how frequent they are in the seminal database of 289 species-by-site community matrices published by Atmar and Patterson in 1995. We show that null model testing using pairwise co-occurrence metrics could indeed lead to severe Type II errors in one specific archetype, accounting for 2.4% of the tested community matrices.

ecology↗

bioRxiv2021

Single-cell Visualization and Quantification of Trace Metals in Chlamydomonas Lysosome-Related Organelles

The acidocalcisome is an acidic organelle in the cytosol of eukaryotes, defined by its low pH and high calcium and polyphosphate content. It is visualized as an electron-dense object by transmission electron microscopy (TEM) or described with mass-spectrometry (MS)-based imaging techniques or multimodal X-ray fluorescence microscopy (XFM) based on its unique elemental composition. Compared to MS-based imaging techniques, XFM offers the advantage of absolute quantification of trace metal content, since sectioning of the cell is not required and metabolic states can be preserved rapidly by either vitrification or chemical fixation. We employed XFM in Chlamydomonas reinhardtii, to determine single-cell and organelle trace metal quotas within algal cells in situations of trace metal over-accumulation (Fe, Cu). We found up to 70% of the cellular Cu and 80% of Fe sequestered in acidocalcisomes in these conditions, and identified two distinct populations of acidocalcisomes, defined by their unique trace elemental makeup. We utilized the vtc1 mutant, defective in polyphosphate synthesis and failing to accumulate Ca to show that Fe sequestration is not dependent on either. Finally, quantitation of the Fe and Cu contents of individual cells and compartments via XFM, over a range of cellular metal quotas created by nutritional and genetic perturbations, indicated excellent correlation with bulk data from corresponding cell cultures, establishing a framework to distinguish the nutritional status of single cells. Significance statementTransition metals are of crucial importance for primary productivity; their scarcity limits crop yield in agriculture and carbon sequestration at global scale. Copper (Cu), iron (Fe) and manganese (Mn) are among the most important trace elements that enable the redox chemistry in oxygenic photosynthesis. The single-celled, eukaryotic green alga Chlamydomonas reinhardtii is a choice experimental system for studying trace metal homeostasis in the context of phototrophy, offering all the advantages of a classical microbial system with a well-characterized photosystem and trace metal metabolism machinery of relevance to plants. This project identifies and differentiates different trace metal storage sites in Chlamydomonas and uncovers the dynamics of trace metal storage and mobilization in situations of fluctuating resources.

plant biology↗

bioRxiv2018

Context-dependent spatial sorting of dispersal-related traits in the invasive starlings (Sturnus vulgaris) of South Africa and Australia.

Species undergoing range expansion frequently experience increased dispersal rates, especially among invasive alien species. Such increased dispersal rates have been attributed to spatial sorting, where traits enhancing dispersal assort towards the expanding range edge while traits enhacing competitiveness are favoured within the core range. To date no single study has compared patterns of spatial sorting across multiple continents for the same species. Here we compared patterns of spatial sorting in Sturnus vulgaris, the European starling (hereafter referred to as starlings), in its invasive ranges in South Africa and Australia. Starlings have experienced similar residence times in these two countries. Using multi-scale pattern analyses and generalized additive models, we determine whether dispersal and foraging traits (i.e. the morphological attributes of wings and bills) were sorted along the distance from introduction site. We found apparent patterns of spatial sorting in Australia, but not in South Africa. This difference may be attributed to differences in dispersal rates, clinal variation, environmental heterogeneity, and thus population demography on the two continents. Genetic data suggests that starlings in South Africa have experienced frequent long distance dispersal events, which could have diluted or overridden patterns of spatial sorting.

evolutionary biology↗

bioRxiv2018

zetadiv: an R package for computing compositional change across multiple sites, assemblages or cases

Spatial variation in compositional diversity, or species turnover, is necessary for capturing the components of heterogeneity that constitute biodiversity. However, no incidence-based metric of pairwise species turnover can calculate all components of diversity partitioning. Zeta ({zeta}) diversity, the mean number of species shared by any given number of sites or assemblages, captures all diversity components produced by assemblage partitioning. zetadiv is an R package for analysing and measuring compositional change for occurrence data using zeta diversity. Four types of analyses are performed on bird composition data in Australia: (i) decline in zeta diversity; (ii) distance decay; (iii) multi-site generalised dissimilarity modelling; and (iv) hierarchical scaling. Some analyses, such as the zeta decline, are specific to zeta diversity, whereas others, such as distance decay, are commonly applied to beta diversity, and have been adapted using zeta diversity to differentiate the contribution of common and rare species to compositional change.\n\nHighlightsO_LIAn R package to analyse compositional change using zeta diversity is presented.\nC_LIO_LIZeta diversity is the mean number of species shared by any number of assemblages\nC_LIO_LIZeta diversity captures all diversity components produced by assemblage partitioning\nC_LIO_LIAnalyses relate zeta diversity to space, environment and spatial scale\nC_LIO_LIAnalyses differentiate the contribution of rare and common species to biodiversity\nC_LI

ecology↗

bioRxiv2017

The application of zeta diversity as a continuous measure of compositional change in ecology

Zeta diversity provides the average number of shared species across n sites (or shared operational taxonomic units (OTUs) across n cases). It quantifies the variation in species composition of multiple assemblages in space and time to capture the contribution of the full suite of narrow, intermediate and wide-ranging species to biotic heterogeneity. Zeta diversity was proposed for measuring compositional turnover in plant and animal assemblages, but is equally relevant for application to any biological system that can be characterised by a row by column incidence matrix. Here we illustrate the application of zeta diversity to explore compositional change in empirical data, and how observed patterns may be interpreted. We use 10 datasets from a broad range of scales and levels of biological organisation - from DNA molecules to microbes, plants and birds - including one of the original data sets used by R.H. Whittaker in the 1960s to express compositional change and distance decay using beta diversity. The applications show (i) how different sampling schemes used during the calculation of zeta diversity may be appropriate for different data types and ecological questions, (ii) how higher orders of zeta may in some cases better detect shifts, transitions or periodicity, and importantly (iii) the relative roles of rare versus common species in driving patterns of compositional change. By exploring the application of zeta diversity across this broad range of contexts, our goal is to demonstrate its value as a tool for understanding continuous biodiversity turnover and as a metric for filling the empirical gap that exists on spatial or temporal change in compositional diversity.

ecology↗

bioRxiv2017

Heterogeneity in local density allows a positive evolutionary relationship between self-fertilisation and dispersal

Theoretical work predicts that dispersal and self-fertilisation (selfing) should always be negatively correlated and the Good Coloniser Syndrome (GCS) of high dispersal and selfing should not occur when both traits are free to evolve. This contradicts positive relationships between selfing and dispersal in empirical data. Critically, previous work assumes density of adults is spatially and temporally homogeneous, so selfing results in homogeneity in propagule production and competition, which eliminates the benefit of dispersal for escaping from local resource competition. We investigate the joint evolution of dispersal and selfing in a demographically structured metapopulation model where local density varies due to stochastic extinction-recolonisation dynamics. Increasing local extinction rate reduces local density across the metapopulation, which favours high selfing to mitigate mate limitation, but increases heterogeneity in density, which favours high dispersal for escape from competition. Together, these effects produce a positive relationship between selfing and dispersal, and evolution of the GCS. Nevertheless, the relationship between selfing and dispersal is context-dependent, as varying dispersal cost yields a negative relationship. Our results imply that if spatiotemporal heterogeneity in environmental suitability increases towards the range edge, the GCS may evolve there, favouring further range expansion (Cf. Bakers Law).

evolutionary biology↗