ORCID Profile
0000-0002-8556-845X
Current Organisations
Eötvös Loránd University
,
CSIRO
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Microbial ecology | Computational ecology and phylogenetics | Microbiology | Atmospheric composition chemistry and processes |
Publisher: Proceedings of the National Academy of Sciences
Date: 16-07-2015
Publisher: CSIRO Publishing
Date: 2020
DOI: 10.1071/WF20006
Abstract: Sutherland et al. (2020) used simulations from a physics-based numerical fire behaviour model to investigate the effect of the ignition protocol (namely length, direction and rate of ignition) on the spread rates measured in experimental fires. They concluded that the methods used by Cruz et al. (2015) were inadequate as the fires were not spreading at the pseudo-steady state when rate of spread measurements were made, thereby raising questions about the validity of several published experimental and modelling results. Fire spread measurement data from three different outdoor experimental burning studies conducted in grass fuels are used to show that, contrary to the claims of Sutherland et al. (2020), the fire behaviour data collected in Cruz et al. (2015) were from fires spreading in the pseudo-steady-state regime and thus are compatible with data from larger experimental plots. A discussion is presented addressing why Sutherland et al. (2020) simulations were unable to replicate real-world data.
Publisher: CSIRO Publishing
Date: 2020
DOI: 10.1071/WF19139
Abstract: A field-based experimental study was conducted in 50×50m square plots to investigate the behaviour of free-spreading fires in wheat to quantify the effect of crop condition (i.e. harvested, unharvested and harvested and baled) on the propagation rate of fires and their associated flame characteristics, and to evaluate the adequacy of existing operational prediction models used in these fuel types. The dataset of 45 fires ranged from 2.4 to 10.2kmh−1 in their forward rate of fire spread and 3860 and 28000 kWm−1 in fireline intensity. Rate of fire spread and flame heights differed significantly between crop conditions, with the unharvested condition yielding the fastest spreading fires and tallest flames and the baled condition having the slowest moving fires and lowest flames. Rate of fire spread in the three crop conditions corresponded directly with the outputs from the models of Cheney et al. (1998) for grass fires: unharvested wheat → natural grass harvested wheat (~0.3m tall stubble) → grazed or cut grass and baled wheat (& .1m tall stubble) → eaten-out grass. These models produced mean absolute percent errors between 21% and 25% with reduced bias, a result on par with the most accurate published fire spread model evaluations.
Publisher: CSIRO Ecosystem Sciences
Date: 2013
Publisher: CSIRO
Date: 2017
Publisher: CSIRO Publishing
Date: 2017
DOI: 10.1071/WF17097
Abstract: Generalised statements about the state of fire science are often used to provide a simplified context for new work. This paper explores the validity of five frequently repeated statements regarding empirical and physical models for predicting wildland fire behaviour. For empirical models, these include statements that they: (1) work well over the range of their original data and (2) are not appropriate for and should not be applied to conditions outside the range of the original data. For physical models, common statements include that they: (3) provide insight into the mechanisms that drive wildland fire spread and other aspects of fire behaviour (4) give a better understanding of how fuel treatments modify fire behaviour and (5) can be used to derive simplified models to predict fire behaviour operationally. The first statement was judged to be true only under certain conditions, whereas the second was shown not to be necessarily correct if valid data and appropriate modelling forms are used. Statements three through five, although theoretically valid, were considered not to be true given the current state of knowledge regarding fundamental wildland fire processes.
Publisher: Cold Spring Harbor Laboratory
Date: 17-03-2023
DOI: 10.1101/2023.03.17.533068
Abstract: Species tree-aware phylogenetic methods model how gene trees are generated along the species tree by a series of evolutionary events, including the duplication, transfer and loss of genes. Over the past ten years these methods have emerged as a powerful tool for inferring and rooting gene and species trees, inferring ancestral gene repertoires, and studying the processes of gene and genome evolution. However, these methods are complex and can be more difficult to use than traditional phylogenetic approaches. Method development is rapid, and it can be difficult to decide between approaches and interpret results. Here, we review ALE and GeneRax, two popular packages for reconciling gene and species trees, explaining how they work, how results can be interpreted, and providing a tutorial for practical analysis. It was recently suggested that reconciliation-based estimates of duplication and transfer frequencies are unreliable. We evaluate this criticism and find that, provided parameters are estimated from the data rather than being fixed based on prior assumptions, reconciliation-based inferences are in good agreement with the literature, recovering variation in gene duplication and transfer frequencies across lineages consistent with the known biology of studied clades. For ex le, published datasets support the view that transfers greatly outnumber duplications in most prokaryotic lineages. We conclude by discussing some limitations of current models and prospects for future progress. Evolutionary trees provide a framework for understanding the history of life and organising bio ersity. In this review, we discuss some recent progress on statistical methods that allow us to combine information from many different genes within the framework of an overarching phylogenetic species tree. We review the advantages and uses of these methods and discuss case studies where they have been used to resolve deep branches within the tree of life. We conclude with the limitations of current methods and suggest how they might be overcome in the future.
Publisher: CSIRO
Date: 2014
Publisher: CSIRO Ecosystem Sciences
Date: 2014
Publisher: Cold Spring Harbor Laboratory
Date: 11-08-2023
DOI: 10.1101/2023.08.08.552427
Abstract: Most of life’s ersity and history is microbial but it has left a meagre fossil record, greatly hindering understanding of evolution in deep time. However, the co-evolution of life and the Earth system has left signatures of bacterial metabolism in the geochemical record, most conspicuously the Great Oxidation Event (GOE) ∼2.33 billion years ago (Ga, (Poulton et al. 2021)), in which oxygenic photosynthesis and tectonism (Eguchi, Seales, and Dasgupta 2019) transformed Earth’s biosphere from dominantly anaerobic to aerobic. Here, we combine machine learning and phylogenetic reconciliation to infer ancestral transitions to aerobic lifestyles during bacterial evolution. Linking these transitions to the GOE provides new constraints to infer the timetree of Bacteria. We find that extant bacterial phyla are truly ancient, having radiated in the Archaean and the Proterozoic: the oldest include Bacillota (Firmicutes), which radiated 3.1-3.7 Ga, Cyanobacteria (3.3-3.5 Ga) and Patescibacteria (3-3.5 Ga). We show that most bacterial phyla were ancestrally anaerobic and that most transitions to an aerobic lifestyle post-dated the GOE. Our analyses trace oxygen production and consumption back to Cyanobacteria. From that starting point, horizontal transfer seeded aerobic lifestyles across bacterial ersity over hundreds of millions of years. Our analyses demonstrate that the ersification of aerobes proceeded in two waves corresponding to the GOE and to a second sustained rise in atmospheric O 2 at the dawn of the Palezoic (Krause et al. 2022).
Publisher: American Association for the Advancement of Science (AAAS)
Date: 07-05-2021
Abstract: The origin of the eubacteria and phylogenetic relationships between subgroups have been difficult to resolve. Applying a phylogenetic analysis and recent computational methods to the expanded ersity of bacterial sequences from metagenomic analyses, Coleman et al. infer the root of the eubacterial tree (see the Perspective by Katz). The root was determined without using the Archaea as an outgroup, to avoid the possibility of a false result due to long branch attraction. This method places the eubacterial root in the neighborhood of Fusobacteriota. Using this information, the authors reconstructed the eubacterial ancestor, identifying that this organism likely had a double-membrane cell envelope, flagellum-mediated motility, antiphage defense mechanisms, and erse metabolic pathways. Science , this issue p. eabe0511 see also p. 574
Publisher: Cold Spring Harbor Laboratory
Date: 15-07-2020
DOI: 10.1101/2020.07.15.205187
Abstract: Bacteria are the most abundant and metabolically erse cellular lifeforms on Earth. A rooted bacterial phylogeny provides a framework to interpret this ersity and to understand the nature of early life. Inferring the position of the bacterial root is complicated by incomplete taxon s ling and the long branch to the archaeal outgroup. To circumvent these limitations, we model bacterial genome evolution at the level of gene duplication, transfer and loss events, allowing outgroup-free inference of the root 1 . We infer a rooted bacterial tree on which 68% of gene transmission events are vertical. Our analyses reveal a basal split between Terrabacteria and Gracilicutes, which together encompass almost all known bacterial ersity. However, the position of one phylum, Fusobacteriota, could not be resolved in relation to these two major clades. In contrast to recent proposals, our analyses strongly reject a root between the Candidate Phyla Radiation (CPR) and all other Bacteria. Instead, we find that the CPR is a sister lineage to the Chloroflexota within the Terrabacteria. We predict that the last bacterial common ancestor was a free-living flagellated, rod-shaped cell featuring a double membrane with a lipopolysaccharide outer layer, a Type III CRISPR-Cas system, Type IV pili, and the ability to sense and respond via chemotaxis.
Publisher: Informa UK Limited
Date: 03-07-2015
Publisher: Imprensa da Universidade de Coimbra
Date: 2018
Publisher: CSIRO Land and Water
Date: 2019
Publisher: Portland Press Ltd.
Date: 14-11-2018
DOI: 10.1042/ETLS20180021
Publisher: CSIRO Publishing
Date: 2018
DOI: 10.1071/WF18161
Abstract: This paper represents our response to the questioning by Mell et al. (2018) of our interpretation (Cruz et al. 2017) of five generalised statements or mantras commonly repeated in the wildland fire behaviour modelling literature. We provide further clarity on key subjects and objectively point out, using ex les from relevant scientific findings, that our discussion of the identified mantras presented in Cruz et al. (2017) was indeed not ill-conceived as suggested by Mell et al. (2018).
Publisher: CSIRO
Date: 2016
Publisher: Elsevier BV
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: CSIRO Publishing
Date: 13-12-2022
DOI: 10.1071/WF21068
Abstract: Reliable and accurate models of the speed of a wildfire front as it moves across the landscape are essential for the timely prediction of its propagation, to devise suitable suppression strategies and enable effective public warnings. We used data from outdoor experimental fires and wildfires to derive an empirical model for the rate of fire spread in eucalypt forests applicable to a broad range of wildfire behaviour. The modelling analysis used logistic and non-linear regression analysis coupled with assumed functional forms for the effect of different environmental variables. The developed model incorporates the effect of wind speed, fine dead fuel moisture, understorey fuel structure, long-term landscape dryness and slope steepness. Model evaluation against the data used for its development yield mean absolute percentage errors between 35 and 46%. Evaluation against an independent wildfire dataset found mean percentage errors of 81 and 84% for two landscape dryness conditions. For these wildfires, the mean error was found to decrease with increasing rates of spread, with this error dropping below 30% when observed rates of spread were greater than 2 km h−1. The modular structure of the modelling analysis enables subsequent improvement of some of its components, such as the dead fuel moisture content or long-term dryness effects, without compromising its consistency or function.
Location: Australia
Start Date: 2023
End Date: 12-2025
Amount: $374,079.00
Funder: Australian Research Council
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