Biology subjects

Dawson, W.

Publications and source records attributed to Dawson, W..

11 recordsLinked to original sources

bioRxiv2025

Global disruption of plant biogeography by non-native species

Biogeographical regions reflect differences in biotic composition resulting from long-term isolation and biogeographical processes, but how human-mediated introductions of non-native species have altered these biogeographical patterns remains unclear. Using global distribution data of 279,441 native and 10,067 non-native seed plant species, we analyzed the impact of species introductions on global organization of biogeographical regions. We show that species introductions have disrupted plant biogeography, causing the loss of distinct floristic realms and subrealms. Due to the impact of non-native species, geographic proximity and dispersal barriers are less important-- particularly as trade-facilitated species exchange drives floristic homogenization--while environmental factors remain critical in structuring floristic regions. Our findings reveal that plant introductions erode biogeographical distinctness and highlight the urgent need for coordinated action to protect native biotas.

ecology↗

bioRxiv2024

Contrasting impacts of invasive Opuntia cacti on mammal habitat use

Biological invasions impact ecosystems worldwide, including through changing the behaviour of native species. Here, we used camera traps to investigate the effects of invasive Opuntia cacti on the habitat use of twelve mammal species in Laikipia County, Kenya, an internationally important region of mammalian biodiversity. We found that Opuntia impacted mammal occupancy and activity, but the strength and direction of the effects varied among species and between seasons, and depended on the spatial scale at which Opuntia was considered. Notably, we observed consistent positive effects for olive baboons and elephants, two major consumers of Opuntia fruit. We also observed seasonally varying effects on the occupancy of two key grazers: Grevys zebra and plains zebra. As well as having important implications for mammal conservation, ecosystem functioning, and the future spread of Opuntia, our findings highlight behavioural changes in large mammals as a potentially important pathway through which invasive species impact ecosystems.

ecology↗

bioRxiv2022

Phylogenetic endemism of the world's seed plants

Assessing phylogenetic endemism, i.e., the distribution of geographically restricted and evolutionarily unique species, is key to understanding biogeographic patterns and processes and critical for global conservation planning. Here, we quantified the geographic distribution and drivers of phylogenetic endemism for ~320,000 seed plants worldwide and identified centers and drivers of evolutionarily young (neoendemism) and evolutionarily old endemism (paleoendemism). Tropical and subtropical islands of the Southern Hemisphere as well as tropical mountainous regions displayed the worlds highest phylogenetic endemism. Tropical moist forests (e.g. Amazonia) and continental islands in south-east Asia emerged as centers of paleoendemism, while both high neo- and paleoendemism were found on ancient continental fragment islands (e.g. Madagascar) and in Mediterranean-climate regions. Global variation in phylogenetic endemism was best explained by a combination of past and present environmental factors (80.3% - 88.1% of variance explained). Geographic isolation and environmental heterogeneity emerged as primary drivers promoting high phylogenetic endemism. Also, warm and wet climates with long-term climatic stability showed a significant positive effect. However, environmental effects on phylogenetic endemism varied with geographic isolation, reflecting the unique evolutionary and biogeographic dynamics on oceanic islands. Long-term climatic stability promoted the persistence of paleoendemics, while isolation promoted higher neoendemism leading to oceanic islands and tropical mountainous regions being centers of both neo- and paleoendemism. Our study provides new insights into the evolutionary underpinnings of biogeographic patterns in seed plants, and by identifying areas of high evolutionary and biogeographic uniqueness, it may serve as a key resource for setting global conservation priorities.

ecology↗

bioRxiv2022

Model selection in occupancy models: inference versus prediction

Occupancy models are a vital tool for applied ecologists studying the patterns and drivers of species occurrence, but their use requires a method for selecting between models with different sets of occupancy and detection covariates. The information-theoretic approach, which employs information criteria such as Akaikes Information Criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information-theoretic approach risks selecting models which produce inaccurate parameter estimates, due to a phenomenon called collider bias. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M-bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz Criterion (or Bayesian Information Criterion, BIC). We found that when M-bias was present in the occupancy process, AIC and BIC selected models which inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site-level occupancy probability. In contrast, M-bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site-level predictions of the AIC/BIC-best model were slightly more accurate. Our results demonstrate that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the models purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modelling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies. Open Research StatementCode to fully reproduce our simulations and analyses is available at: https://zenodo.org/badge/latestdoi/462801230

ecology↗

bioRxiv2021

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Evolved SARS-CoV-2 variants are currently challenging the efficacy of first-generation vaccines, largely through the emergence of spike protein mutants. Among these variants, Delta is presently the most concerning. We employ an ab initio quantum mechanical model based on Density Functional Theory to characterize the spike protein Receptor Binding Domain (RBD) interaction with host cells and gain mechanistic insight into SARS-CoV-2 evolution. The approach is illustrated via a detailed investigation of the role of the E484K RBD mutation, a signature mutation of the Beta and Gamma variants. The simulation is employed to: predict the depleting effect of the E484K mutation on binding the RBD with select antibodies; identify residue E484 as a weak link in the original interaction with the human receptor hACE2; and describe SARS-CoV-2 Wuhan strand binding to the bat Rhinolophus macrotis ACE2 as more optimized than the human counterpart. Finally, we predict the hACE2 binding efficacy of a hypothetical E484K mutation added to the Delta variant RBD, identifying a potential future variant of concern. Results can be generalized to other mutations, and provide useful information to complement existing experimental datasets of the interaction between randomly generated libraries of hACE2 and viral spike mutants. We argue that ab initio modeling is at the point of being aptly employed to inform and predict events pertinent to viral and general evolution.

biophysics↗

bioRxiv2021

Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding

The main protease (Mpro) of SARS-CoV-2 is central to its viral lifecycle and is a promising drug target, but little is known concerning structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of classical molecular mechanics and quantum mechanical techniques, including automated docking, molecular dynamics (MD) simulations, linear-scaling DFT, QM/MM, and interactive MD in virtual reality, to investigate the molecular features underlying recognition of the natural Mpro substrates. Analyses of the subsite interactions of modelled 11-residue cleavage site peptides, ligands from high-throughput crystallography, and designed covalently binding inhibitors were performed. Modelling studies reveal remarkable conservation of hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular at the P2/S2 sites. The binding modes of the natural substrates, together with extensive interaction analyses of inhibitor and fragment binding to Mpro, reveal new opportunities for inhibition. Building on our initial Mpro-substrate models, computational mutagenesis scanning was employed to design peptides with improved affinity and which inhibit Mpro competitively. The combined results provide new insight useful for the development of Mpro inhibitors.

bioinformatics↗

bioRxiv2021

Capacity of countries to reduce biological invasions

The extent and impacts of biological invasions on biodiversity are largely shaped by an array of socio-ecological predictors, which exhibit high variation among countries. Yet a global synthetic perspective of how these factors vary across countries is currently lacking. Here, we investigate how a set of five socio-ecological predictors (Governance, Trade, Environmental Performance, Lifestyle and Education, Innovation) explain i) country-level established alien species (EAS) richness of eight taxonomic groups, and ii) country capacity to prevent and manage biological invasions and their impacts. Trade and Governance together best predicted the average EAS richness, increasing variance explained by up to 54% compared to models based on climatic and spatial variables only. Country-level EAS richness increased strongly with Trade, whereas high level of Governance resulted in lower EAS richness. Historical (1996) levels of Governance and Trade better explained response variables than current (2015) levels. Thus, our results reveal a historical legacy of these two predictors with profound implications for the future of biological invasions. We therefore used Governance and Trade to define a two-dimensional socio-economic space in which the position of a country captures its capacity to address issues of biological invasions. Our results provide novel insights into the complex relationship between socio-ecological predictors and biological invasions. Further, we highlight the need for designing better policies and management measures for alien species, and for integrating biological invasions in global environmental scenarios.

ecology↗

bioRxiv2021

Mechanistic Insight from Full Quantum Mechanical Modeling: Laccase as a Detoxifier of Aflatoxins

We demonstrate a path towards full Quantum Mechanics (QM) characterization of enzymatic activity. As a case-study, we investigate the detoxification of aflatoxin, a carcinogenic food contaminant, by laccase, a versatile oxidase capable of--but not efficient for--degrading aflatoxin. We use a combination of quantitative experimentation and QM modeling to show that low enzymatic steric affinity for aflatoxin is the main bottleneck, rather that the oxidative activity of laccase. To identify the structural elements responsible for low reaction rates, we perform a density functional theory (DFT) based modeling of both the substrate and the enzyme in a full QM simulation of more than 7,000 atoms. Thanks to our approach we point to amino acid residues that determine the affinity of laccase for aflatoxin. We show that these residues are substrate-dependent, making a full QM approach necessary for enzyme optimization. Altogether, we establish a roadmap for rational enzyme engineering applicable beyond our case study.

biophysics↗

bioRxiv2020

Full quantum mechanical modeling of the enzyme-substrate system: how laccase detoxifies aflatoxin

This work focuses on: 1) the development of a methodology to perform a full Quantum Mechanics (QM) characterization of enzymatic activity; 2) the development of a rational approach to laccase engineering as a food bioremediator. Aflatoxins are among the most dangerous natural carcinogens, and regularly contaminate reserves of staple crops worldwide. Decontamination of aflatoxin-polluted food is of great interest for ensuring food safety, and bioremediation is regarded as the most promising solution. The fungal isoforms of laccase display the rare potential to detoxify aflatoxin by tackling its aromatic moieties.. Yet, because of a generally low efficiency, large-scale application of naturally occurring isoforms has so far been unfeasible. We perform a combination of quantitative experimentation and quantum mechanical modeling on aflatoxin and reveal that: (1) detoxification efficiency is limited by the low enzymatic affinity for the substrate; and (2) aflatoxin is not detoxified by oxidative activity of laccase alone, but requires additional stimulation from the environment. QM modeling also allowed identification of the residues in the laccase tertiary structure that determine affinity of the enzymatic pocket for aflatoxin. We conclude that, for our case-study, a full QM approach is mandatory as a first step towards rational optimization. We detail a feasible approach towards this endeavor and argue that our full QM characterization can serve as a roadmap for enzyme development in other applications pertaining laccase as well as other enzymes.

biophysics↗

bioRxiv2019

Disharmony of the world’s island floras

Disharmony is a key concept in island biology that describes the biased representation of higher taxa on islands compared to their mainland source regions. Although differential colonization success of taxa is predicted by selective dispersal, environmental and biotic filtering, the empirical evidence for disharmony remains largely anecdotal. Here, we develop a novel method for delineating island source regions and present the first global quantitative assessment of island disharmony. We analyzed the overall compositional bias of 320 island floras and examined the over- or under-representation on islands for 450 plant families. We found that the compositional bias of island floras is strongly predicted by geographical and climatic island characteristics (isolation, area, geologic origin, temperature, precipitation), whereas the representation of individual families is only weakly related to family-specific characteristics (family size, family age, taxonomic group, functional traits), indicating that the taxonomic scope of the disharmony concept has historically limited its wider applicability. Our results provide a strong foundation for integrating disharmony with quantitative functional and phylogenetic approaches in order to gain a deeper understanding of assembly processes on islands.

ecology↗

bioRxiv2017

Free energy based high-resolution modeling of CTCF-mediated chromatin loops for human genome

A thermodynamic method for computing the stability and dynamics of chromatin loops is proposed. The CTCF-mediated interactions as observed in ChIA-PET experiments for human B-lymphoblastoid cells are evaluated in terms of a polymer model for chain folding physical properties and the experimentally observed frequency of contacts within the chromatin regions. To estimate the optimal free energy and a Boltzmann distribution of suboptimal structures, the approach uses dynamic programming with methods to handle degeneracy and heuristics to compute parallel and antiparallel chain stems and pseudoknots. Moreover, multiple loops mediated by CTCF proteins connected together and forming multimeric islands are simulated using the same model. Based on the thermodynamic properties of those topological three-dimensional structures, we predict the correlation between the relative activity of chromatin loop and the Boltzmann probability, or the minimum free energy, depending also on its genomic length. Segments of chromatin where the structures show a more stable minimum free energy (for a given genomic distance) tend to be inactive, whereas structures that have lower stability in the minimum free energy (with the same genomic distance) tend to be active.

genomics↗