Find research datasets worth reusing

Figure 8. Lectotype Icteranthidium floripetum A in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 8. Lectotype Icteranthidium floripetum A, face; B, dorsal view; C, labels; D, vertex; E, T1–T3.

Figure 18. Gonostyli, Pseudoanthidium stigmaticorne. A in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 18. Gonostyli, Pseudoanthidium stigmaticorne. A, Cape Lukull, Crimea; B, Bet She'an, Israel; C, Agadir, Morocco; D, Restinclières, France; E, Cacela Velha, Portugal; F, Seui, Sardinia.

Figure 25. A in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 25. A, Pseudoanthidium canariense, female (Santa Cruz de Tenerife, Canary Islands); B, P. tropicum, female (Bandar Abbas, Iran); C, P. canariense, male (Santa Cruz de Tenerife, Canary Islands); D, P. tropicum, male (Bandar Abbas, Iran); E, T7, P. canariense (Santa Cruz de Tenerife, Canary Islands); F, T7, P. tropicum (Bandar Abbas, Iran).

Figure 7. Lectotype, Anthidium sinuatum. A in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 7. Lectotype, Anthidium sinuatum. A, lateral view; B, labels; C, metasoma; D, T5–T6; E, mesonotum.

Figure 21. Dorsal habitus, females.A in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 21. Dorsal habitus, females.A, vertex Pseudoanthidium tenellum (Pestszentimre, Hungary); B, vertex P. palestinicum (Rehovot, Israel); C, mesonotum P. tenellum (Pestszentimre, Hungary); D, mesonotum P. palestinicum (Rehovot, Israel); E, metasoma P. tenellum (Pestszentimre, Hungary); F, metasoma P. palestinicum (Rehovot, Israel).

Figure 6 in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 6. Palaearctic distribution of A, Pseudoanthidium nanum; B, P. scapulare; C, P. stigmaticorne; D, P. tenellum.

Figure 24 in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 24. Third coxal tooth (circled by white ring), Pseudoanthidium cribratum, male. A, ventral view (Damascus, Syria); B, lateral view (Yasuj, Iran).

Figure 16. Lectotype Pseudoanthidium karakalense. A in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 16. Lectotype Pseudoanthidium karakalense. A, labels; B, dorsal view; C, latero-ventral view S2; D, gonostyli; E, S8; F, S3; G, S4; H, S5 (showing sternal combs), S6.

Figure 2 in Paraphyly and low levels of genetic divergence in morphologically distinct taxa: revision of the Pseudoanthidium scapulare complex of carder bees (Apoidea: Megachilidae: Anthidiini)

Figure 2. Best-scoring maximum likelihood tree based on analysis of COI data. Numbers shown at nodes are maximum likelihood bootstrap values based on 1000 bootstrap replicates in RAxML. Only bootstrap values greater than 50% are shown. Terminals are labelled with a DNA extraction code, species name, collection locality and either as male (m) or female (f). Barcodes were obtained using Sanger sequencing technology, except for those corresponding to specimens 1802 and 1805, which were obtained from non-UCE assemblies generated during UCE sequencing. A, Pseudoanthidium tenellum, Burgenland, Austria (m), photo Bernhard Jacobi; B, P. cribratum, Bukhara, Uzbekistan (f), photo Jessica Litman; C, P. canariense, Santa Cruz de Tenerife, Canary Islands, Spain (m), photo Jessica Litman; D, P. stigmaticorne, Crimea, Russia, photo Alexander V. Fateryga; E, P. scapulare, Portugal, photo Ian Cross; F, P. nanum, photo Entomologie/Botanik, ETH Zürich / Albert Krebs; G, P. palestinicum, photo Jessica Litman.

Genetic variation in leaf traits and gas exchange response to vapor pressure deficit in two contrasting conifer species

1. Mechanistically predicting the evolutionary response of tree species to climate change requires an understanding of genetic variation in relevant traits. Here we compared the phenotypic and genetic variation in the Leaf Economics Spectrum (LES) traits and the response of gas exchange to vapor pressure deficit (VPD) in lodgepole pine (Pico) and white spruce (Pigl), an early and a late successional species dominating the boreal forests of western Canada. 2. We measured gas exchange, foliar nitrogen, and lamina mass to area ratio in 697 app. 30-year-old trees in two field progeny trials. We analyzed the response of gas exchange rates to VPD using a novel quantitative genetic model, the function-valued trait approach. 3. Pico showed greater phenotypic variation in the LES traits and greater genetic variation in photosynthetic rate than Pigl, but the species showed no significant difference in their phenotypic correlations between the LES traits. Pico showed a less sensitive stomatal response to VPD than Pigl and no significant genetic variation in stomatal sensitivity. In contrast, Pigl showed a positive correlation between the genetic values of stomatal sensitivity to VPD and stomatal conductance under low VPD. 4. Our study region is projected to see an increase in VPD with climate change; the less sensitive and genetically diverse stomatal response to VPD in Pico could make this species more vulnerable to climate change-induced droughts.

opencc-by-4.0Oct 2022View details →

Genetic basis of cytonuclear conflicts in citrus hybridization, domestication, and diversification

184.cp.variations.map.vcf.zip--the chloroplast variation map of 184 samples 184.mt.variations.map.vcf.zip----the mitochondrial variation map of 184 samples aligment1.fa-- the group of grapefruit, sweet orange and sour orange for mitochondrial heteroplasmy analysis aligment2.fa-- the group of lemon for mitochondrial heteroplasmy analysis coverage depth of 184 short reads samples.zip -- the coverage depth of BAMs in mitochondrial variation map in 184 samples Fortunella hindsii mitochondrial genome.gb -- the conservation proteins annotation of kumquat reference mitochondrial genome GWAS-118.samples.input.phenotype.for GWAS.txt -- the inputfiles for GWAS  GWAS-LD.linked.input.gwas.vcf.gz -- the LD linked variations for GWAS LD.purning.nuclear.variation.map.vcf.gz -- LD purning nuclear variations for demography analysis normalization.RNA-seqs.txt -- the nomarlization data of expression of mitochondrial genome nuclear.variation.map.vcf.gz -- the nuclaer variation map ORFs annotation based on Augustus.gtf -- the annotation gene structure using Augustus  pan-genome and aligned mitochondrial genomes.zip -- the pan-genome(.fa and .gfa) and aligned mitochondrial genomes scaffold-assemblies.zip -- the scaffolds of mitochondrial genomes

Data from: Life histories as mosaics: plastic and genetic components differ among traits that underpin life-history strategies

Life-history phenotypes emerge from clusters of traits that are the product of genes and phenotypic plasticity. If the impact of the environment differs substantially between traits, then life histories might not evolve as a cohesive whole. We quantified the sensitivity of components of the life history to food availability, a key environmental difference in the habitat occupied by contrasting ecotypes, for 36 traits in fast-and slow-reproducing Trinidadian guppies. Our dataset included six putatively independent origins of the slow-reproducing, derived ecotype. Traits varied substantially in plastic and genetic control. Twelve traits were influenced only by food availability (body lengths, body weights), five only by genetic differentiation (inter-birth intervals, offspring sizes), ten by both (litter sizes, reproductive timing), and nine by neither (fat contents, reproductive allotment). Ecotype-by-food interactions were negligible. The response to low food was aligned with the genetic difference between high- and low-food environments, suggesting that plasticity was adaptive. The heterogeneity among traits in environmental sensitivity and genetic differentiation reveals that the components of the life history may not evolve in concert. Ecotypes may instead represent mosaics of trait groups that differ in their rate of evolution.

Genetic diversity, differentiation and historical origin of the isolated population of rooksCorvus frugilegusin Iberia

Current bird populations in southern temperate latitudes often represent relicts of glacial refugia from which northern populations expanded as the climate became suitable following the last glacial maximum, 18 000 years before present. Alternatively, these southern populations could be the result of the fragmentation of large distributions and other processes not related to glaciations, like recent recolonization from northern populations and human impact in historical times. Here, we investigate the origin of a small, isolated population of rooks Corvus frugilegus in north-western Iberia. We use genetic data from mitochondrial sequence markers and seven microsatellite loci to assess levels of genetic diversity, structure and gene flow among extant populations in Iberia and its broad distribution across western Europe. Microsatellite markers revealed the existence of two genetic clusters corresponding to Iberia and the remaining European populations, respectively. Haplotype networks based on mtDNA markers revealed a marked star-like phylogenetic pattern and evidence of a recent population expansion in northern Europe, but not in the Iberian population. Our results suggest that contemporary gene flow between Iberia and western Europe is restricted, and that breeding recruitment over recent generations in the Iberian population is local. The results are consistent with a relatively recent post-glacial colonization of Europe and western Siberia by rooks surviving the last glacial maximum in an Iberian refugium, and likely from refugia in other southern peninsulas. The unique ecological features and genetic differentiation of the Iberian rooks underscore the importance of ensuring the long-term conservation of this declining population.

FIGURE 6 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 6. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids is based on partial sequences of the 28S rDNA gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.

FIGURE 3 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 3. Distribution of 28S rDNA-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids.

FIGURE 5 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 5. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids is based on partial sequences of the 16S rDNA gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. The abbreviation "Hap" denotes haplotype. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.

FIGURE 2 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 2. Distribution of 16S rDNA-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids. For the 28S rDNA, the genetic distance for H. polii was the lowest with H. forskali (0.01) but the highest with H. sanctori (1.08) (Fig. 2A). For H. sanctori, the lowest pairwise distance was with H. polii (1.08), while the highest was with P. longicauda (1.163) (Fig. 2B).

FIGURE 4 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 4. Distribution of H3-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids.

FIGURE 7 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori

FIGURE 7. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids, based on partial sequences of H3 gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.

Host genetics, phenotype and geography structure the microbiome of a foundational seaweed

Interactions between hosts and their microbiota are critical to the functioning and resilience of eukaryotic macro-organisms. Critically, for hosts that play foundational roles in communities, understanding what drives these interactions is essential for informing restoration and conservation of entire ecosystems. Here, we investigated the relative influence of host traits and the surrounding environment on microbial communities associated with the foundational seaweed Phyllospora comosa. We collected data on 16 morphological and functional phenotypic traits, host genetics (using 354 Single Nucleotide Polymorphisms) and surface-associated microbial communities (using 16S rRNA gene amplicon sequencing) from 160 individuals sampled from eight sites spanning Phyllospora's entire latitudinal distribution (1300 km). Combined, these factors explained 54% of the overall variation in Phyllospora's associated microbial community structure, much of which was related to the local environment (~32%). We found that putative "core" microbial taxa (i.e. present on all Phyllospora individuals sampled) exhibited slightly higher association with host traits when compared to "variable" taxa (not present on all individuals). We identified several key genetic loci and phenotypic traits in Phyllospora that were strongly related to multiple microbial amplicon sequence variants, including taxa with known associations to seaweed defense, disease and tissue degradation. This information on how host-associated microbial communities vary with host traits and the environment enhances our current understanding of holobionts and how they are structured. Such understanding can be used to inform management strategies of these important and vulnerable habitats.