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11 results for “guide tree”
Data from: Variation in population structure and dynamics of montane forest tree species in Ethiopia guide priorities for conservation and research
The greatest extent of Afromontane environments in the world is found in Ethiopia. These areas support exceptional biodiversity, but forest cover and ecological integrity have declined sharply in recent decades. Conservation and management efforts are hampered in part by an inadequate understanding of the basic ecology of major tree species. We investigated population structure and inferred population dynamics from size frequency distributions of 22 forest tree species encountered in montane forests of Ethiopia. We collected new empirical data from four sites in the Bale Mountains, where some of the country's most extensive and least disturbed forests remain, and conducted a systematic review and analysis of all such studies that reported population structure for one or more of these species in Ethiopia. Thirteen widespread montane tree species showed a reverse-J size distribution, indicating a relatively stable population structure. Six other species had size-frequency distributions that indicate episodic recruitment and/or removal of certain size classes. Specific causes of these patterns are uncertain: they may involve timber harvesting, herbivory, fire, or natural disturbances, but patterns were inconsistent and locality dependent. For three other tree species, existing data are inadequate for any interpretation of population structure and dynamics. A species of particular conservation concern that emerged from this analysis was Hagenia abyssinica, which was found in all areas to consist only of larger individuals with no recent recruitment. For management and conservation purposes, the species in most urgent need of new research are those with inadequate or inconsistent data, and H. abyssinica..
Data from: Variation in population structure and dynamics of montane forest tree species in Ethiopia guide priorities for conservation and research
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Data from: Using parsimony-guided tree proposals to accelerate convergence in Bayesian phylogenetic inference
<p>Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as `convergence') and in estimating the correct proportions of the different types of them (known as `mixing'). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical datasets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these datasets, ranging in size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.</p>
Computation Tree Logic guided Reactive Program Repair
<p>This is the supporting data for OOPSLA24 submission: Computation Tree Logic guided Reactive Program Repair. </p> <p>It includes the source code and benchmarks presented in the paper. </p>
Data from: Using parsimony-guided tree proposals to accelerate convergence in Bayesian phylogenetic inference
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Data from: Bayesian species delimitation can be robust to guide tree inference errors
The Bayesian method of species delimitation (Yang and Rannala, 2010) uses a so-called guide tree to reduce the number of models to be evaluated in the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm (Green, 1995). It has been pointed out that the method tends to over-split if a random population tree is used as the guide tree (Fujita and Leaché, 2011). Here we conduct a simulation study to examine the performance of the method under more realistic scenarios, that is, when the guide tree is inferred from the sequence data. We found that Bayesian species delimitation is in general robust to errors in the inferred guide tree.
Data from: Simple chained guide trees give poorer multiple sequence alignments than inferred trees in simulation and phylogenetic benchmarks
Multiple sequence aligners typically work by progressively aligning the most closely related sequences or group of sequences according to guide trees. In PNAS, Boyce et al. report that alignments reconstructed using simple chained trees (i.e., comb-like topologies) with random leaf assignment performed better in protein structure-based benchmarks than those reconstructed using phylogenies estimated from the data as guide trees. The authors state that this result could turn decades of research in the field on its head. In light of this statement, it is important to check immediately whether their result holds under evolutionary criteria: recovery of homologous sequence residues and inference of phylogenetic trees from the alignments. We have done this and the results are entirely opposed to Boyce et al.'s findings.
Data from: Simple chained guide trees give poorer multiple sequence alignments than inferred trees in simulation and phylogenetic benchmarks
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Data from: Bayesian species delimitation can be robust to guide tree inference errors
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DENTRO Tree Matrix: dataset behind DENTRO - a Tree Species Selection Guide for Agroforestry
<p><strong>As a choice has to be made on whether a tree species matches the conditions of an agroforestry project in terms of e.g. soil characteristics and objectives, this dataset can support the tree species selection process.</strong></p> <p>This dataset, called <strong>‘’DENTRO Tree Matrix’’ (EN)</strong> or <strong>‘’DENTRO Bomenmatrix’’ (NL)</strong>, contains information about how tree species match certain relevant parameters varying from abiotic conditions (e.g. soil, weather, light, etc.), animals, objectives (e.g. wood/fruit/nut production and ecological function), and feasibility (e.g. disease risks and planting material availability).</p> <p>In recent years, a growing interest in agroforestry systems has been observed in Belgium and the Netherlands. Therefore, it is important to pay sufficient attention to the tree species selection process as this plays a major role in whether the agroforestry project is successful or not. Also in terms of climate change, it is important to select a tree species that both fits the farmer’s field conditions and can handle well with the extreme weather conditions occurring in the region in which the plot is located. This is why the DENTRO Tree Matrix also includes parameters such as strong wind, drought, frost, heat waves, and floods.</p> <p>The objective of the DENTRO Tree Matrix is to offer knowledge about commonly planted tree species in temperate regions (with a focus on Belgium and The Netherlands), so the right choices considering e.g. field and weather conditions can be made by the end user (farmers, advisors, ...).</p> <p>The DENTRO Tree Matrix also serves as a back-end dataset behind a set of decision support and/or tree species selection tools (currently (<strong>BETULA, AgroforesTreeAdvice, and DENTRO</strong>). </p> <p>The use of BETULA (a), DENTRO (a) and AgroforesTreeAdvice (b) is free, and these tools can be accessed through the following links:</p> <p>(a) <a href="https://www.agroforestryvlaanderen.be/en/agroforestry-planner-2">https://www.agroforestryvlaanderen.be/en/agroforestry-planner-2</a> </p> <p>(b) <a href="https://agroforestreeadvice.sk8.inrae.fr/">AgroforesTreeAdvice (inrae.fr)</a></p> <p>The background info sheet can be read here: <a href="https://www.agroforestryvlaanderen.be/nl/nieuws/boomsoortenselectie-waarmee-rekening-houden">Boomsoortenselectie: waarmee rekening houden? - Agroforestry (agroforestryvlaanderen.be)</a> (currently in Dutch).</p> <p>More info? Contact us through info@agroforestryvlaanderen.be </p>
Computation Tree Logic guided Program Repair With Precise Loop Summaries
<div> <p>This is the supporting data for ICSE25 submission: Computation Tree Logic guided Program Repair With Precise Loop Summaries. </p> <p>It includes the source code and benchmarks presented in the paper. </p> <p> </p> </div>
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