Ecophenotypic differentiation among replicate ecotype pairs within a species complex is often attributed to independent outcomes of parallel divergence driven by adaptation to similar environmental ...contrasts. However, the extent to which parallel phenotypic and genetic divergence patterns have emerged independently is increasingly questioned by population genomic studies. Here, we document the extent of genetic differentiation within and among two geographic replicates of the coastal and marine ecotypes of the European anchovy (Engraulis encrasicolus) gathered from Atlantic and Mediterranean locations. Using a genome‐wide data set of RAD‐derived SNPs, we show that habitat type (marine vs. coastal) is the most important component of genetic differentiation among populations of anchovy. By analysing the joint allele frequency spectrum of each coastal–marine ecotype pair, we show that genomic divergence patterns between ecotypes can be explained by a postglacial secondary contact following a long period of allopatric isolation (c. 300 kyrs). We found strong support for a model including heterogeneous migration among loci, suggesting that secondary gene flow has eroded past differentiation at different rates across the genome. Markers experiencing reduced introgression exhibited strongly correlated differentiation levels among Atlantic and Mediterranean regions. These results support that partial reproductive isolation and parallel genetic differentiation among replicate pairs of anchovy ecotypes are largely due to a common divergence history prior to secondary contact. They moreover provide comprehensive insights into the origin of a surprisingly strong fine‐scale genetic structuring in a high gene flow marine fish, which should improve stock management and conservation actions.
Ecophenotypic differentiation among replicate ecotype pairs within a species complex is often attributed to independent outcomes of parallel divergence driven by adaptation to similar environmental ...contrasts. However, the extent to which parallel phenotypic and genetic divergence patterns have emerged independently is increasingly questioned by population genomic studies. Here, we document the extent of genetic differentiation within and among two geographic replicates of the coastal and marine ecotypes of the European anchovy (Engraulis encrasicolus) gathered from Atlantic and Mediterranean locations. Using a genomeâwide data set of RADâderived SNPs, we show that habitat type (marine vs. coastal) is the most important component of genetic differentiation among populations of anchovy. By analysing the joint allele frequency spectrum of each coastalâmarine ecotype pair, we show that genomic divergence patterns between ecotypes can be explained by a postglacial secondary contact following a long period of allopatric isolation (c. 300Â kyrs). We found strong support for a model including heterogeneous migration among loci, suggesting that secondary gene flow has eroded past differentiation at different rates across the genome. Markers experiencing reduced introgression exhibited strongly correlated differentiation levels among Atlantic and Mediterranean regions. These results support that partial reproductive isolation and parallel genetic differentiation among replicate pairs of anchovy ecotypes are largely due to a common divergence history prior to secondary contact. They moreover provide comprehensive insights into the origin of a surprisingly strong fineâscale genetic structuring in a high gene flow marine fish, which should improve stock management and conservation actions.
Barriers to gene flow between divergent populations result in contact (hybrid) zones. Locations where multiple contact zones overlap can be used in comparative studies asking: what mechanisms ...maintain barriers; what is the origin of the genetic variation involved; and do differences in life history affect the nature of barriers? In a review of 23 marine species’ genetic divergence over a postglacial salinity gradient, many showed steep genetic clines supported by divergent selection and/or temporal or spatial segregation. Contacts were primary or secondary and shaped by ancestral variation sometimes involving inversions. The dispersal potential of species seemed less important in shaping clines. Studies of multispecies contact zones will increase our understanding of speciation, but we need to address the taxonomic bias and focus more on postzygotic isolation.
Barriers to gene flow are best studied where divergent populations are in contact, and studies of single-taxon hybrid zones have generated important knowledge about the nature of reproductive barriers.Marine environments, earlier considered to host unstructured species due to high connectivity, offer multispecies contact zones structured by simple physical gradients (e.g., salinity) ideal for comparative studies of divergence and speciation.Overlapping contact zones offer possibilities for comparison of barriers among species of various taxa, life histories, and demographic backgrounds and to test the role of species-specific traits in the formation and function of barriers.Combining genome scans and demographic modelling, barrier regions in the genome can be located and barrier origin traced. With genetic maps, inversions that affect recombination rate (and hence gene flow) can be identified.
We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local ...density and to propagate labels through algorithm-specific labeling decisions. We first improved two of these techniques, a KNN variant of the density peaks clustering method dpc, and a weighted-mode variant of knnclust, so the four methods use the same input KNN graph and only differ by their labeling rules. We propose two regularization schemes for hyperspectral image analysis: (i) a graph regularization based on mutual nearest neighbors (MNN) prior to clustering to improve cluster discovery in high dimensions; (ii) a spatial regularization to account for correlation between neighboring pixels. We demonstrate the relevance of the proposed methods on synthetic data and hyperspectral images, and show they achieve superior overall performances in most cases, outperforming the state-of-the-art methods by up to 20% in kappa index on real hyperspectral images.
Interspecific hybridization events are on the rise in natural systems due to climate change disrupting species barriers. Across taxa, microsatellites have long been the molecular markers of choice to ...identify admixed individuals. However, with the advent of high‐throughput sequencing easing the generation of genome‐wide datasets, incorrect reports of hybridization resulting from microsatellite technical artefacts have been uncovered in a growing number of taxa. In the marine zooplankton genus Calanus (Copepoda), whose species are used as climate change indicators, microsatellite markers have suggested hybridization between C. finmarchicus and C. glacialis, while other nuclear markers (InDels) never detected any admixed individuals, leaving the scientific community divided. Here, for the first time, we investigated the potential for hybridization among C. finmarchicus, C. glacialis, C. helgolandicus and C. hyperboreus using two large and independent SNP datasets. These were derived firstly from a protocol of target‐capture applied to 179 individuals collected from 17 sites across the North Atlantic and Arctic Oceans, including sympatric areas, and second from published RNA sequences. All SNP‐based analyses were congruent in showing that Calanus species are distinct and do not appear to hybridize. We then thoroughly re‐assessed the microsatellites showing hybrids, with the support of published transcriptomes, and identified technical issues plaguing eight out of 10 microsatellites, including size homoplasy, paralogy, potential for null alleles and even two primer pairs targeting the same locus. Our study illustrates how deceptive microsatellites can be when applied to the investigation of hybridization.
We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a red-green-blue composite. Band selection is achieved by means of ...information measures at the first, second, and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informative content.
Hyperspectral (HS) imaging has been used extensively in remote sensing applications like agriculture, forestry, geology and marine science. HS pixel classification is an important task to help ...identify different classes of materials within a scene, such as different types of crops on a farm. However, this task is significantly hindered by the fact that HS pixels typically form high-dimensional clusters of arbitrary sizes and shapes in the feature space spanned by all spectral channels. This is even more of a challenge when ground truth data is difficult to obtain and when there is no reliable prior information about these clusters (e.g., number, typical shape, intrinsic dimensionality). In this letter, we present a new graph-based clustering approach for hyperspectral data mining that does not require ground truth data nor parameter tuning. It is based on the minimax distance, a measure of similarity between vertices on a graph. Using the silhouette index, we demonstrate that the minimax distance is more suitable to identify clusters in raw hyperspectral data than two other graph-based similarity measures: mutual proximity and shared nearest neighbours. We then introduce the minimax bridgeness-based clustering approach, and we demonstrate that it can discover clusters of interest in hyperspectral data better than comparable approaches.
Changing environmental conditions can lead to population diversification through differential selection on standing genetic variation. Structural variant (SV) polymorphisms provide examples of ...ancient alleles that in time become associated with novel environmental gradients. The European plaice (Pleuronectes platessa) is a marine flatfish showing large allele-frequency differences at two putative SVs associated with environmental variation. In this study, we explored the contribution of these SVs to population structure across the North East Atlantic. We compared genome-wide population structure using sets of RAD-sequencing SNPs with the spatial structure of the SVs. We found that in contrast to the rest of the genome, the SVs were only weakly associated with an isolation-by-distance pattern. Indeed, both SVs showed important variation in haplogroup frequencies, with the same haplogroup increasing both along the salinity gradient of the Baltic Sea, and found in high frequency in the northern-range margin of the Atlantic. Phylogenetic analyses suggested that the SV alleles are much older than the age of the Baltic Sea itself. These results suggest that the SVs are older than the age of the environmental gradients with which they currently co-vary. Altogether, our results suggest that the plaice SVs were shaped by evolutionary processes occurring at two time frames, firstly following their origin, ancient spread and maintenance in the ancestral populations, and secondly related to their current association with more recently formed environmental gradients such as those found in the North Sea-Baltic Sea transition zone.
We propose a novel Mean-Shift method for data clustering, called Robust Mean-Shift (RMS). A new update equation for point iterates is proposed, mixing the ones of the standard Mean-Shift (MS) and the ...Blurring Mean-Shift (BMS). Despite its simplicity, the proposed method has not been studied so far. RMS can be set up in both a kernel-based and a nearest-neighbor (NN)-based fashion. Since the update rule of RMS is closer to BMS, the convergence of point iterates is conjectured based on the Chen's BMS convergence theorem. Experimental results on synthetic and real datasets show that RMS in several cases outperforms MS and BMS in the clustering task. In addition, RMS exhibits larger attraction basins than MS and BMS for identical parametrization; consequently, its kernel variant requires a lower aperture of the kernel function, and its NN variant a lower number of nearest neighbors compared to MS or BMS, to achieve optimal clustering results. In addition, the NN version of RMS does not need to specify a convergence threshold to stop the iterations, contrarily to the NN-BMS algorithm.
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