Allopolyploid Speciation Accompanied by Gene Flow in a Tree Fern

2020 ◽  
Vol 37 (9) ◽  
pp. 2487-2502 ◽  
Author(s):  
Jing Wang ◽  
Shiyong Dong ◽  
Lihua Yang ◽  
Aj Harris ◽  
Harald Schneider ◽  
...  

Abstract Hybridization in plants may result in hybrid speciation or introgression and, thus, is now widely understood to be an important mechanism of species diversity on an evolutionary timescale. Hybridization is particularly common in ferns, as is polyploidy, which often results from hybrid crosses. Nevertheless, hybrid speciation as an evolutionary process in fern lineages remains poorly understood. Here, we employ flow cytometry, phylogeny, genomewide single nucleotide polymorphism data sets, and admixture and coalescent modeling to show that the scaly tree fern, Gymnosphaera metteniana is a naturally occurring allotetraploid species derived from hybridization between the diploids, G. denticulata and G. gigantea. Moreover, we detected ongoing gene flow between the hybrid species and its progenitors, and we found that G. gigantea and G. metteniana inhabit distinct niches, whereas climatic niches of G. denticulata and G. metteniana largely overlap. Taken together, these results suggest that either some degree of intrinsic genetic isolation between the hybrid species and its parental progenitors or ecological isolation over short distances may be playing an important role in the evolution of reproductive barriers. Historical climate change may have facilitated the origin of G. metteniana, with the timing of hybridization coinciding with a period of intensification of the East Asian monsoon during the Pliocene and Pleistocene periods in southern China. Our study of allotetraploid G. metteniana represents the first genomic-level documentation of hybrid speciation in scaly tree ferns and, thus, provides a new perspective on evolution in the lineage.

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
WEERACHON SAWANGPROH ◽  
NILS CRONBERG

Interspecific hybridization had been long recognized as a widespread evolutionary process in vascular plants. In the present review, we summarize knowledge concerning studies of interspecific hybridization in bryophytes before and after the advent of molecular methods. The available data indicate that hybridization is an important evolutionary phenomenon among bryophytes. Evidence for hybridization events before the molecular era is mainly based on studies of intermediacy of parental morphology. The recent molecular marker technology has revolutionized studies of hybridization, generating new insights into the genetic and evolutionary consequences of homoploid and allopolyploid speciation. The current molecular approaches support the prevalence of allopolyploidy in bryophytes. However, we anticipate that homoploid hybridization is under-reported. Finally, we suggest some directions for future studies of hybrid speciation among bryophytes.


The Holocene ◽  
2011 ◽  
Vol 22 (6) ◽  
pp. 705-715 ◽  
Author(s):  
Fengling Yu ◽  
Yongqiang Zong ◽  
Jeremy M Lloyd ◽  
Melanie J Leng ◽  
Adam D Switzer ◽  
...  

2019 ◽  
Vol 8 (12) ◽  
pp. 584 ◽  
Author(s):  
Bernd Resch ◽  
Michael Szell

Due to the wide-spread use of disruptive digital technologies like mobile phones, cities have transitioned from data-scarce to data-rich environments. As a result, the field of geoinformatics is being reshaped and challenged to develop adequate data-driven methods. At the same time, the term "smart city" is increasingly being applied in urban planning, reflecting the aims of different stakeholders to create value out of the new data sets. However, many smart city research initiatives are promoting techno-positivistic approaches which do not account enough for the citizens’ needs. In this paper, we review the state of quantitative urban studies under this new perspective, and critically discuss the development of smart city programs. We conclude with a call for a new anti-disciplinary, human-centric urban data science, and a well-reflected use of technology and data collection in smart city planning. Finally, we introduce the papers of this special issue which focus on providing a more human-centric view on data-driven urban studies, spanning topics from cycling and wellbeing, to mobility and land use.


2020 ◽  
Vol 68 (3) ◽  
pp. 949-964
Author(s):  
Dimitris Bertsimas ◽  
Bradley Sturt

The bootstrap method is one of the major developments in statistics in the 20th century for computing confidence intervals directly from data. However, the bootstrap method is traditionally approximated with a randomized algorithm, which can sometimes produce inaccurate confidence intervals. In “Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms,” Bertsimas and Sturt present a new perspective of the bootstrap method through the lens of counting integer points in a polyhedron. Through this perspective, the authors develop the first computational complexity results and efficient deterministic approximation algorithm (fully polynomial time approximation scheme) for bootstrap confidence intervals, which unlike traditional methods, has guaranteed bounds on its error. In experiments on real and synthetic data sets from clinical trials, the proposed deterministic algorithms quickly produce reliable confidence intervals, which are significantly more accurate than those from randomization.


2016 ◽  
Vol 283 (1823) ◽  
pp. 20152334 ◽  
Author(s):  
Christopher H. Martin ◽  
Jacob E. Crawford ◽  
Bruce J. Turner ◽  
Lee H. Simons

One of the most endangered vertebrates, the Devils Hole pupfish Cyprinodon diabolis , survives in a nearly impossible environment: a narrow subterranean fissure in the hottest desert on earth, Death Valley. This species became a conservation icon after a landmark 1976 US Supreme Court case affirming federal groundwater rights to its unique habitat. However, one outstanding question about this species remains unresolved: how long has diabolis persisted in this hellish environment? We used next-generation sequencing of over 13 000 loci to infer the demographic history of pupfishes in Death Valley. Instead of relicts isolated 2–3 Myr ago throughout repeated flooding of the entire region by inland seas as currently believed, we present evidence for frequent gene flow among Death Valley pupfish species and divergence after the most recent flooding 13 kyr ago. We estimate that Devils Hole was colonized by pupfish between 105 and 830 years ago, followed by genetic assimilation of pelvic fin loss and recent gene flow into neighbouring spring systems. Our results provide a new perspective on an iconic endangered species using the latest population genomic methods and support an emerging consensus that timescales for speciation are overestimated in many groups of rapidly evolving species.


2011 ◽  
Vol 20 (18) ◽  
pp. 3812-3822 ◽  
Author(s):  
JO S. HERMANSEN ◽  
STEIN A. SAETHER ◽  
TORE O. ELGVIN ◽  
THOMAS BORGE ◽  
ELIN HJELLE ◽  
...  

2019 ◽  
Vol 37 (4) ◽  
pp. 1211-1223 ◽  
Author(s):  
Tomáš Flouri ◽  
Xiyun Jiao ◽  
Bruce Rannala ◽  
Ziheng Yang

Abstract Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here, we implement the multispecies-coalescent-with-introgression model, an extension of the multispecies-coalescent model to incorporate introgression, in our Bayesian Markov chain Monte Carlo program Bpp. The multispecies-coalescent-with-introgression model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Reanalysis of data sets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.


2016 ◽  
Vol 6 (3) ◽  
pp. 173-188 ◽  
Author(s):  
Vladimir Stanovov ◽  
Eugene Semenkin ◽  
Olga Semenkina

Abstract A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.


2016 ◽  
Vol 25 (03) ◽  
pp. 1650010
Author(s):  
Khadijeh Mahdikhanlou ◽  
Hossein Ebrahimnezhad

In this paper, a novel shape descriptor for shape recognition is proposed. An evolutionary process is introduced in which a contour is reconstructed from the bounding circle of the shape. In this evolutionary process, circle points always move toward the shape in normal direction until they arrive at the shape contour. Three different descriptors are extracted from this process: the first descriptor is defined as the number of steps that every circle point should pass from circle to shape contour which is called evolution steps (ES). The second descriptor is considered as the boundary distance (BD) of the sample points at the end of the evolution process. The third descriptor is the mean of curvature of the evolution lines that are created by moving points, (MCEL). In matching stage, dynamic programming is employed to best matching between shapes. Finally, normalizing the features makes them to be invariant to scale. Sparse representation as a new framework for classification is applied in the recognition stage. The proposed descriptors are evaluated for task of shape recognition on several data sets. Experimental results demonstrate the advantaged performance of the proposed method in shape recognition.


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