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opennotspecifiedDec 2017View details →

FIGURE 4. Mean reflectance spectra for ten body regions measured from three Melozone leucotis subspecies, M. l in Phenotypic variation and vocal divergence reveals a species complex in White-eared Ground-sparrows (Cabanis) (Aves: Passerellidae)

FIGURE 4. Mean reflectance spectra for ten body regions measured from three Melozone leucotis subspecies, M. l. leucotis (solid lines, N = 13), M. l. nigrior (dotted lines, N = 13), and M. l. occipitalis (dashed lines, N = 8). The gray area around each line represents standard error of the mean calculated at every 1nm.

FIGURE 8 in Phenotypic variation of Leptodactylus cupreus Caramaschi, São-Pedro and Feio, 2008 (Anura, Leptodactylidae)

FIGURE 8. Advertisement call of Leptodactylus cupreus (Porto Seguro, state of Bahia). A—Detailed oscillogram of one call; B—audiospectrogram of the same call; C—oscillogram of three notes emitted in one second; D—audiospectrogram of the three notes shown in C.

FIGURE 7 in Phenotypic variation of Leptodactylus cupreus Caramaschi, São-Pedro and Feio, 2008 (Anura, Leptodactylidae)

FIGURE 7. Female of Leptodactylus cupreus, dorsal and ventral views (CFBH 23632 SVL = 55.5 mm). Notice the absence of the chisel-like snout and the general similarity with males (Fig. 3).

FIGURE 6 in Phenotypic variation of Leptodactylus cupreus Caramaschi, São-Pedro and Feio, 2008 (Anura, Leptodactylidae)

FIGURE 6. Boxplots of morphometric comparisons within Leptodactylus cupreus populations. Morphometric parameters followed by: –M) measurements of males from this study; –C) measurements of males taken by Caramaschi et al. (2008); and –F) measurements of females. Maximum (Max) and minimum (Min) values found within each population sample are represented by the upper and lower surfaces, respectively, of the quadrangle. The upper and lower lines of each boxplot represent the value of the sum and the subtraction, respectively, of the average of the sample and its correspondent standard deviation.

FIGURE 4 in Phenotypic variation of Leptodactylus cupreus Caramaschi, São-Pedro and Feio, 2008 (Anura, Leptodactylidae)

FIGURE 4. Spines on dorsal surface of tibia. A—Leptodactylus cupreus, CFBH 23632; B—L. mystaceus, CFBH 509; C—L. mystacinus, CFBH 9804.

FIGURE 2 in Phenotypic variation of Leptodactylus cupreus Caramaschi, São-Pedro and Feio, 2008 (Anura, Leptodactylidae)

FIGURE 2. Variation of the white line in the posterior region of the thigh. A—Leptodactylus cupreus, CFBH 23632, well marked; B, C and D—L. cupreus, CFBH 26359, CFBH 32113 and CFBH 32114 respectively, weakly marked; E—L. mystaceus, CFBH 17242, well marked; F—L. mystacinus, CFBH 18386, absent.

FIGURE 3 in Phenotypic variation of Leptodactylus cupreus Caramaschi, São-Pedro and Feio, 2008 (Anura, Leptodactylidae)

FIGURE 3. Variation of flanks and hands color. A and B—Leptodactylus cupreus from Lagoa das Bromélias, Parque Estadual da Serra do Brigadeiro, municipality of Ervália, state of Minas Gerais (photos: Renato Neves Feio); C—L. cupreus from RPPN Estação Veracel, municipality of Porto Seguro, state of Bahia; D—Female of L. cupreus from RPPN Serra Bonita, municipality of Camacan, state of Bahia. A and B—copper or darkish brown flanks and hands light grey with yellow and/or copper dorsal flecks; C and D—black flanks and light gray hands.

opencc-zeroNov 2023View details →

Duck pan-genome reveals two transposon-derived structural variations caused bodyweight enlarging and white plumage phenotype formation during evolution

Structural variations (SVs) are a major source of domestication and improvement traits. We present the first duck pan-genome constructed using five genome assemblies capturing ~40.98 Mb new sequences. This pan-genome together with high-depth sequencing data (>46.5X) identified 101,041 SVs, of which substantial proportions were derived from transposable element (TE) activity. Many TE-derived SVs anchored in a gene body or regulatory region are linked to domestication and improvement. By combining quantitative genetics with molecular experiments, we dissect how TE-derived SVs change gene expression of IGF2BP1 and generate novel transcripts of MITF, shaping body weight and plumage color. In the IGF2BP1 locus, the TE-derived SV explains the largest effect on body weight among avian species (27.61% of phenotypic variation). Our findings highlight the importance of using a pan-genome as a reference in genomics studies and explore the roles of TE-derived SVs in trait formation and in livestock breeding.

Fig. 4. 2B in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 4. 2B-PLS analysis of T. septentrionis. (A) Forewing size; (B) Forewing shape; (C) Hindwing size; and (D) Hindwing shape.

Fig. 7 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 7. Forewing shape analysis. Mlp female (A) % of variance, (B) PC1, (C) PC2, (D) PC3. Mlp male (E) % of variance, (F) PC1, (G) PC2, (H) PC3. Wyn female (I) % of variance, (J) PC1, (K) PC2, (L) PC3. Wyn male (M) % of variance, (N) PC1, (O) PC2, (P) PC3.

Fig. 10 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 10. FE-SEM image analysis of T. septentrionis. Forewing brown regions (A–F), and bluish-white regions (G–J) of T. septentrionis. Hindwing brown regions (K–Q), and bluish-white regions (R–T). FE-SEM image of the male pouch scale of T. septentrionis (U–X). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1. T in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 1. T. septentrionis forewing (A) with 25 landmarks; hindwing (B) with 18 landmarks. male pouch represented in white circle.

Fig. 9 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 9. Morphospace analysis. (A) forewing PCs morphospace analysis, PC1 vs. PC2; (B) forewing CVs morphospace analysis; (C) hindwing PCs morphospace analysis, PC1 vs. PC2; (D) hindwing CVs morphospace analysis. Alphabet code used in image – first three letter code indicated as the collection site and fourth letter for sex (male or female). For eg: MlpF – Malappuram female.

Fig. 6 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 6. Sexual wing asymmetry (male vs. female) of T. septentrionis wings validated by Discriminant function analysis (DFA). Forewing of T. septentrionis (A–D) and hindwing of T. septentrionis (E–H). Alphabet code used in image – first three letter code indicated as the collection site and fourth letter for sex (male or female). For eg: MlpF – Malappuram female.

Fig. 8 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 8. Hindwing shape analysis. Mlp female (A) % of variance, (B) PC1, (C) PC2, (D) PC3. Mlp male (E) % of variance, (F) PC1, (G) PC2, (H) PC3. Wyn female (I) % of variance, (J) PC1, (K) PC2, (L) PC3. Wyn male (M) % of variance, (N) PC1, (O) PC2, (P) PC3.

Fig. 5 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 5. Wing asymmetry (right or left) of T. septentrionis wings validated by Discriminant function analysis (DFA). Forewing of T. septentrionis (A–D) and hindwing of T. septentrionis (E–H). Alphabet code used in image – first three letter code indicated as the collection site and fourth letter for sex (male or female), fifth letter for wing side (right or left). For eg: MlpFR – Malappuram female right.

Fig. 2. 30 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 2. 30 years mean climate data (A) Mlp region mean temperature, and precipitation; (B) Mean temperature of Mlp; (C) Wyn region mean temperature, and precipitation; (B) Mean temperature of Wyn.

opencc-zeroDec 2021View details →

Context dependent variation in corticosterone and phenotypic divergence of Rana arvalis populations along an acidification gradient

Background Physiological processes, as immediate responses to the environment, are important mechanisms of phenotypic plasticity and can influence evolution at ecological time scales. In stressful environments, physiological stress responses of individuals are initiated and integrated via the release of hormones, such as corticosterone (CORT). In vertebrates, CORT influences energy metabolism and resource allocation to multiple fitness traits (e.g. growth and morphology) and can be an important mediator of rapid adaptation to environmental stress, such as acidification. The moor frog, Rana arvalis, shows adaptive divergence in larval life-histories and predator defense traits along an acidification gradient in Sweden. Here we take a first step to understanding the role of CORT in this adaptive divergence. We conducted a fully factorial laboratory experiment and reared tadpoles from three populations (one acidic, one neutral and one intermediate pH origin) in two pH treatments (Acid versus Neutral pH) from hatching to metamorphosis. We tested how the populations differ in tadpole CORT profiles and how CORT is associated with tadpole life-history and morphological traits. Results We found clear differences among the populations in CORT profiles across the developmental stages, but only weak effects of pH treatment on CORT. Tadpoles from the acid origin population had, on average, lower CORT levels than tadpoles from the neutral origin population, and the intermediate pH origin population had intermediate CORT levels. Overall, tadpoles with higher CORT levels developed faster and had shorter and shallower tails as well as shallower tail muscles. Conclusions Our common garden results indicate among population divergence in CORT levels, likely reflecting acidification mediated divergent selection on tadpole physiology, concomitant to selection on larval life-histories and morphology. However, CORT levels were highly environmental context dependent. Jointly these results indicate a potential role for CORT as a mediator of multi-trait divergence along environmental stress gradients in natural populations. At the same time, the population level differences and high context dependency in CORT levels suggest that snapshot assessment of CORT in nature may not be reliable bioindicators of stress.

opencc-zeroDec 2021View details →

Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice

To understand the genetic basis of main culm panicle node number, maximum node production rate, and degree-days to heading in rice (Oryza sativa), we conducted genome-wide association studies using a diversity panel of 220 rice accessions and 854,832 SNP markers generated using genotyping-by-sequencing (GBS), with 1X coverage. The raw genotype data was filtered, selecting single nucleotide polymorphisms (SNPs) having less than 50% missing data and minimum allele frequency (MAF) >5%. After initial filtering, imputation was conducted using BEAGLE V4.0 in 1,075,302 SNP markers. After imputation, the dataset was filtered a second time by removing SNPs with less than 5% MAF and more than 5% missing data. A total of 854,832 SNPs were used in the genome-wide association analyses. The dataset representing the genotype data of 854,832 SNP markers by 220 rice accessions is presented here.