scholarly journals Speciation with gene flow via cycles of isolation and migration: Insights from multiple mangrove taxa

2018 ◽  
Author(s):  
Ziwen He ◽  
Xinnian Li ◽  
Ming Yang ◽  
Xinfeng Wang ◽  
Cairong Zhong ◽  
...  
Keyword(s):  
Gene Flow ◽  
Indian Ocean ◽  
Large Scale ◽  
Marine Species ◽  
Allopatric Speciation ◽  
Unified Framework ◽  
Geographical Isolation ◽  
Mangrove Species ◽  
Geographical Barrier ◽  
Time Required

AbstractAllopatric speciation requiring an unbroken period of geographical isolation has been the standard model of neo-Darwinism. While doubts have been repeatedly raised, strict allopatry without any gene flow remains a plausible mechanism in most cases. To rigorously reject strict allopatry, genomic sequences superimposed on the geological records of a well-delineated geographical barrier will be necessary. The Strait of Malacca, narrowly connecting the Pacific and Indian Ocean coasts, serves at different times either as a geographical barrier or a conduit of gene flow for coastal/marine species. We surveyed 1,700 plants from 29 populations of five common mangrove species by large scale DNA sequencing and added several whole-genome assemblies. Speciation between the two oceans is driven by cycles of isolation and gene flow due to the fluctuations in sea level leading to the opening/closing of the Strait to ocean currents. Because the time required for speciation in mangroves is longer than the isolation phases, speciation in these mangroves has proceeded through many cycles of mixing-isolation-mixing, or MIM cycles. The MIM mechanism, by relaxing the condition of no gene flow, can promote speciation in many more geographical features than strict allopatry can. Finally, the MIM mechanism of speciation is also efficient, potentially yielding mn (m>1) species after n cycles.Significance statementMechanisms of species formation have always been a conundrum. Speciation between populations that are fully geographically isolated, or allopatric speciation, has been the standard solution in the last 50 years. Complete geographical isolation with no possibility of gene flow, however, is often untenable and is inefficient in generating the enormous biodiversity. By studying mangroves on the Indo-Malayan coasts, a global hotspot of coastal biodiversity, we were able to combine genomic data with geographical records on the Indo-Pacific barrier that separates Pacific and Indian Ocean coasts. We discovered a novel mechanism of speciation, that we call mixing-isolation-mixing (MIM) cycles. By permitting intermittent gene flow during speciation, MIM can potentially generate species at an exponential rate, thus combining speciation and biodiversity in a unified framework.

scholarly journals Speciation with gene flow via cycles of isolation and migration: insights from multiple mangrove taxa

2018 ◽  
Vol 6 (2) ◽  
pp. 275-288 ◽  
Author(s):  
Ziwen He ◽  
Xinnian Li ◽  
Ming Yang ◽  
Xinfeng Wang ◽  
Cairong Zhong ◽  
...  
Keyword(s):  
Gene Flow ◽  
Large Scale ◽  
Marine Species ◽  
Mangrove Species ◽  
Coastal Marine ◽  
The Pacific ◽  
Geographical Barrier ◽  
And Migration ◽  
Time Required ◽  
Genome Assemblies

AbstractAllopatric speciation requiring an unbroken period of geographical isolation has been the standard model of neo-Darwinism. While doubts have been repeatedly raised, strict allopatry without any gene flow remains a plausible mechanism in most cases. To rigorously reject strict allopatry, genomic sequences superimposed on the geological records of a well-delineated geographical barrier are necessary. The Strait of Malacca, narrowly connecting the Pacific and Indian Ocean coasts, serves at different times either as a geographical barrier or a conduit of gene flow for coastal/marine species. We surveyed 1700 plants from 29 populations of 5 common mangrove species by large-scale DNA sequencing and added several whole-genome assemblies. Speciation between the two oceans is driven by cycles of isolation and gene flow due to the fluctuations in sea level leading to the opening/closing of the Strait to ocean currents. Because the time required for speciation in mangroves is longer than the isolation phases, speciation in these mangroves has proceeded through many cycles of mixing-isolation-mixing, or MIM, cycles. The MIM mechanism, by relaxing the condition of no gene flow, can promote speciation in many more geographical features than strict allopatry can. Finally, the MIM mechanism of speciation is also efficient, potentially yielding mn (m > 1) species after n cycles.

scholarly journals Expansion of the mangrove species Rhizophora mucronata in the Western Indian Ocean launched contrasting genetic patterns

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ludwig Triest ◽  
Tom Van der Stocken ◽  
Dennis De Ryck ◽  
Marc Kochzius ◽  
Sophie Lorent ◽  
...  
Keyword(s):  
Gene Flow ◽  
Indian Ocean ◽  
Large Scale ◽  
Geographic Distance ◽  
Western Indian Ocean ◽  
Coastal Current ◽  
Rhizophora Mucronata ◽  
East African ◽  
Mangrove Species ◽  
Mozambique Channel

AbstractEstimates of population structure and gene flow allow exploring the historical and contemporary processes that determine a species’ biogeographic pattern. In mangroves, large-scale genetic studies to estimate gene flow have been conducted predominantly in the Indo-Pacific and Atlantic region. Here we examine the genetic diversity and connectivity of Rhizophora mucronata across a > 3,000 km coastal stretch in the Western Indian Ocean (WIO) including WIO islands. Based on 359 trees from 13 populations and using 17 polymorphic microsatellite loci we detected genetic breaks between populations of the (1) East African coastline, (2) Mozambique Channel Area (3) granitic Seychelles, and (4) Aldabra and northern Madagascar. Genetic structure, diversity levels, and patterns of inferred connectivity, aligned with the directionality of major ocean currents, driven by bifurcation of the South Equatorial Current, northward into the East African Coastal Current and southward into the Mozambique Channel Area. A secondary genetic break between nearby populations in the Delagoa Bight coincided with high inbreeding levels and fixed loci. Results illustrate how oceanographic processes can connect and separate mangrove populations regardless of geographic distance.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Hierarchical Analysis of Genetic Structure in Native Fire Ant Populations: Results From Three Classes of Molecular Markers

Genetics ◽  
1997 ◽  
Vol 147 (2) ◽  
pp. 643-655 ◽  
Author(s):  
Kenneth G Ross ◽  
Michael J B Krieger ◽  
D DeWayne Shoemaker ◽  
Edward L Vargo ◽  
Laurent Keller
Keyword(s):  
Gene Flow ◽  
Genetic Structure ◽  
Solenopsis Invicta ◽  
Large Scale ◽  
Fire Ant ◽  
Nuclear Markers ◽  
Social Forms ◽  
Native Populations ◽  
Nest Level ◽  
Nuclear Differentiation

We describe genetic structure at various scales in native populations of the fire ant Solenopsis invicta using two classes of nuclear markers, allozymes and microsatellites, and markers of the mitochondrial genome. Strong structure was found at the nest level in both the monogyne (single queen) and polygyne (multiple queen) social forms using allozymes. Weak but significant microgeographic structure was detected above the nest level in polygyne populations but not in monogyne populations using both classes of nuclear markers. Pronounced mitochondrial DNA (mtDNA) differentiation was evident also at this level in the polygyne form only. These microgeographic patterns are expected because polygyny in ants is associated with restricted local gene flow due mainly to limited vagility of queens. Weak but significant nuclear differentiation was detected between sympatric social forms, and strong mtDNA differentiation also was found at this level. Thus, queens of each form seem unable to establish themselves in nests of the alternate type, and some degree of assortative mating by form may exist as well. Strong differentiation was found between the two study regions usinga all three sets of markers. Phylogeographic analyses of the mtDNA suggest that recent limitations on gene flow rather than longstanding barriers to dispersal are responsible for this large-scale structure.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

Statistical methods for unidirectional switch designs: Past, present, and future

2017 ◽  
Vol 27 (9) ◽  
pp. 2872-2882 ◽  
Author(s):  
Zhuozhao Zhan ◽  
Geertruida H de Bock ◽  
Edwin R van den Heuvel
Keyword(s):  
Statistical Methods ◽  
Large Scale ◽  
Sample Size Calculation ◽  
Complex Interventions ◽  
New Drugs ◽  
Control Treatment ◽  
Future Research ◽  
Control Group ◽  
Unified Framework ◽  
New Treatment

Clinical trials may apply or use a sequential introduction of a new treatment to determine its efficacy or effectiveness with respect to a control treatment. The reasons for choosing a particular switch design have different origins. For instance, they may be implemented for ethical or logistic reasons or for studying disease-modifying effects. Large-scale pragmatic trials with complex interventions often use stepped wedge designs (SWDs), where all participants start at the control group, and during the trial, the control treatment is switched to the new intervention at different moments. They typically use cross-sectional data and cluster randomization. On the other hand, new drugs for inhibition of cognitive decline in Alzheimer’s or Parkinson’s disease typically use delayed start designs (DSDs). Here, participants start in a parallel group design and at a certain moment in the trial, (part of) the control group switches to the new treatment. The studies are longitudinal in nature, and individuals are being randomized. Statistical methods for these unidirectional switch designs (USD) are quite complex and incomparable, and they have been developed by various authors under different terminologies, model specifications, and assumptions. This imposes unnecessary barriers for researchers to compare results or choose the most appropriate method for their own needs. This paper provides an overview of past and current statistical developments for the USDs (SWD and DSD). All designs are formulated in a unified framework of treatment patterns to make comparisons between switch designs easier. The focus is primarily on statistical models, methods of estimation, sample size calculation, and optimal designs for estimation of the treatment effect. Other relevant open issues are being discussed as well to provide suggestions for future research in USDs.

scholarly journals Algebraic multigrid methods

Acta Numerica ◽  
2017 ◽  
Vol 26 ◽  
pp. 591-721 ◽  
Author(s):  
Jinchao Xu ◽  
Ludmil Zikatanov
Keyword(s):  
Minimization Problem ◽  
Large Scale ◽  
Multigrid Method ◽  
Approximate Solutions ◽  
Multigrid Methods ◽  
Algebraic Multigrid ◽  
Unified Framework ◽  
Smoothed Aggregation ◽  
Algebraic Multigrid Methods ◽  
Coarse Space

This paper provides an overview of AMG methods for solving large-scale systems of equations, such as those from discretizations of partial differential equations. AMG is often understood as the acronym of ‘algebraic multigrid’, but it can also be understood as ‘abstract multigrid’. Indeed, we demonstrate in this paper how and why an algebraic multigrid method can be better understood at a more abstract level. In the literature, there are many different algebraic multigrid methods that have been developed from different perspectives. In this paper we try to develop a unified framework and theory that can be used to derive and analyse different algebraic multigrid methods in a coherent manner. Given a smoother$R$for a matrix$A$, such as Gauss–Seidel or Jacobi, we prove that the optimal coarse space of dimension$n_{c}$is the span of the eigenvectors corresponding to the first$n_{c}$eigenvectors$\bar{R}A$(with$\bar{R}=R+R^{T}-R^{T}AR$). We also prove that this optimal coarse space can be obtained via a constrained trace-minimization problem for a matrix associated with$\bar{R}A$, and demonstrate that coarse spaces of most existing AMG methods can be viewed as approximate solutions of this trace-minimization problem. Furthermore, we provide a general approach to the construction of quasi-optimal coarse spaces, and we prove that under appropriate assumptions the resulting two-level AMG method for the underlying linear system converges uniformly with respect to the size of the problem, the coefficient variation and the anisotropy. Our theory applies to most existing multigrid methods, including the standard geometric multigrid method, classical AMG, energy-minimization AMG, unsmoothed and smoothed aggregation AMG and spectral AMGe.

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