| 1. |
- Maxwell, Tania L., et al.
(author)
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Global dataset of soil organic carbon in tidal marshes
- 2023
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In: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
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Journal article (peer-reviewed)abstract
- Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.
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| 3. |
- Braga, Mariana P., et al.
(author)
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Bayesian Inference of Ancestral Host-Parasite Interactions under a Phylogenetic Model of Host Repertoire Evolution
- 2020
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In: Systematic Biology. - : Oxford University Press (OUP). - 1063-5157 .- 1076-836X. ; 69:6, s. 1149-1162
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Journal article (peer-reviewed)abstract
- Intimate ecological interactions, such as those between parasites and their hosts, may persist over long time spans, coupling the evolutionary histories of the lineages involved. Most methods that reconstruct the coevolutionary history of such interactions make the simplifying assumption that parasites have a single host. Many methods also focus on congruence between host and parasite phylogenies, using cospeciation as the null model. However, there is an increasing body of evidence suggesting that the host ranges of parasites are more complex: that host ranges often include more than one host and evolve via gains and losses of hosts rather than through cospeciation alone. Here, we develop a Bayesian approach for inferring coevolutionary history based on a model accommodating these complexities. Specifically, a parasite is assumed to have a host repertoire, which includes both potential hosts and one or more actual hosts. Over time, potential hosts can be added or lost, and potential hosts can develop into actual hosts or vice versa. Thus, host colonization is modeled as a two-step process that may potentially be influenced by host relatedness. We first explore the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameter values. We then use our approach, implemented in the program RevBayes, to infer the coevolutionary history between 34 Nymphalini butterfly species and 25 angiosperm families. Our analysis suggests that host relatedness among angiosperm families influences how easily Nymphalini lineages gain new hosts.
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| 5. |
- Braga, Mariana P., et al.
(author)
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Phylogenetic reconstruction of ancestral ecological networks through time for pierid butterflies and their host plants
- 2021
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In: Ecology Letters. - : Wiley. - 1461-023X .- 1461-0248. ; 24:10, s. 2134-2145
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Journal article (peer-reviewed)abstract
- The study of herbivorous insects underpins much of the theory that concerns the evolution of species interactions. In particular, Pieridae butterflies and their host plants have served as a model system for studying evolutionary arms races. To learn more about the coevolution of these two clades, we reconstructed ancestral ecological networks using stochastic mappings that were generated by a phylogenetic model of host-repertoire evolution. We then measured if, when, and how two ecologically important structural features of the ancestral networks (modularity and nestedness) evolved over time. Our study shows that as pierids gained new hosts and formed new modules, a subset of them retained or recolonised the ancestral host(s), preserving connectivity to the original modules. Together, host-range expansions and recolonisations promoted a phase transition in network structure. Our results demonstrate the power of combining network analysis with Bayesian inference of host-repertoire evolution to understand changes in complex species interactions over time.
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| 7. |
- Höhna, Sebastian, 1983-, et al.
(author)
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Probabilistic Graphical Model Representation in Phylogenetics
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Other publication (other academic/artistic)abstract
- Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software design. Moreover, a unified, clear and understandable framework formodel representation lowers the barrier for beginning scientists and non-specialists to grasp the model including the assumptions and parameter/variable dependencies.Graphical models is such a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea isto break complex models into conditionally independent distributions and the strength lies in, amongst others: comprehensibility, flexibility, adaptability and computational algorithms. Graphical models can be used to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference.Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and built these into separate, interchangeable modules. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using either Metropolis-Hastings or Gibbs sampling of the posterior distribution.
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| 8. |
- Höhna, Sebastian, et al.
(author)
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Probabilistic Graphical Model Representation in Phylogenetics
- 2014
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In: Systematic Biology. - : Oxford University Press (OUP). - 1063-5157 .- 1076-836X. ; 63:5, s. 753-771
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Journal article (peer-reviewed)abstract
- Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis-Hastings or Gibbs sampling of the posterior distribution.
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