Dataset Information

Blind testing of shoreline evolution models.

ABSTRACT: Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

SUBMITTER: Montano J

PROVIDER: S-EPMC7005834 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Blind testing of shoreline evolution models.

Montaño Jennifer J Coco Giovanni G Antolínez Jose A A JAA Beuzen Tomas T Bryan Karin R KR Cagigal Laura L Castelle Bruno B Davidson Mark A MA Goldstein Evan B EB Ibaceta Raimundo R Idier Déborah D Ludka Bonnie C BC Masoud-Ansari Sina S Méndez Fernando J FJ Murray A Brad AB Plant Nathaniel G NG Ratliff Katherine M KM Robinet Arthur A Rueda Ana A Sénéchal Nadia N Simmons Joshua A JA Splinter Kristen D KD Stephens Scott S Townend Ian I Vitousek Sean S Vos Kilian K

Scientific reports 20200207 1

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models an ...[more]

PMID: 32034246

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Project description:Although large scale informatics studies on introns can be useful in making broad inferences concerning patterns of intron gain and loss, more specific questions about intron evolution at a finer scale can be addressed using a gene family where structure and function are well known. Genome wide surveys of tetraspanins from a broad array of organisms with fully sequenced genomes are an excellent means to understand specifics of intron evolution. Our approach incorporated several new fully sequenced genomes that cover the major lineages of the animal kingdom as well as plants, protists and fungi. The analysis of exon/intron gene structure in such an evolutionary broad set of genomes allowed us to identify ancestral intron structure in tetraspanins throughout the eukaryotic tree of life.We performed a phylogenomic analysis of the intron/exon structure of the tetraspanin protein family. In addition, to the already characterized tetraspanin introns numbered 1 through 6 found in animals, three additional ancient, phase 0 introns we call 4a, 4b and 4c were found. These three novel introns in combination with the ancestral introns 1 to 6, define three basic tetraspanin gene structures which have been conserved throughout the animal kingdom. Our phylogenomic approach also allows the estimation of the time at which the introns of the 33 human tetraspanin paralogs appeared, which in many cases coincides with the concomitant acquisition of new introns. On the other hand, we observed that new introns (introns other than 1-6, 4a, b and c) were not randomly inserted into the tetraspanin gene structure. The region of tetraspanin genes corresponding to the small extracellular loop (SEL) accounts for only 10.5% of the total sequence length but had 46% of the new animal intron insertions.Our results indicate that tests of intron evolution are strengthened by the phylogenomic approach with specific gene families like tetraspanins. These tests add to our understanding of genomic innovation coupled to major evolutionary divergence events, functional constraints and the timing of the appearance of evolutionary novelty.

Dataset Information

Blind testing of shoreline evolution models.

Publications

Blind testing of shoreline evolution models.

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