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Fig. 4 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 4. Seasonal variation in dry mass (A), state-4respiration (B), and cytochrome c oxidase (C) in the pectoral muscle, heart, liver and kidneys of hwameis (Garrulaxcanorus) captured in either summer or winter in Wenzhou, China. Data are shown as mean ± SEM, *p <0.05, **p <0.01, ***p <0.001.

Fig. 3 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 3. Correlations between body mass and resting metabolic rate (RMR) (A), between body mass and EWL (B), between RMR and EWL (C), and between RMR and thermal conductance (D) in Chinese hwameis (Garrulax canorus) captured in either summer or winter in Wenzhou, China.

Fig. 1 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 1. Minimum, maximum and mean ambient daily summer (July to August 2013) and winter (January to February 2014) temperatures in Wenzhou, China. Mean ambient temperature ranged from 31.3 ± 0.2°C in summer to 8.6 ± 0.4°C in winter.

Fig. 2 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 2. Seasonal variation in body mass (A), resting metabolic rate (B), evaporative water loss (C) and thermal conductance (D) in Chinese hwamei (Garrulax canorus) captured in either summer or winter in Wenzhou, China. Data are shown as mean ± SEM, **p <0.01.

Fig. 5 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 5. Correlations between resting metabolic rate (RMR) and state-4 respiration in the pectoral muscle (A), heart (C), liver (E) and kidneys (G), and between RMR and cytochrome c oxidase activity in the pectoral muscle (B), heart (D), liver (F) and kidneys (H), in Chinese hwameis (Garrulax canorus) captured in either summer or winter in Wenzhou, China.

opencc-zeroOct 2020View details →

Data from: Phenotypic flexibility in background-mediated color change in sticklebacks

Phenotypic flexibility may incur a selective advantage in changing and heterogeneous environments, and is increasingly recognized as an integral aspect of organismal adaptation. Despite the widespread occurrence and potential importance of rapid and reversible background-mediated color change for predator avoidance, knowledge gaps remain regarding its adaptive value, repeatability within individuals, phenotypic correlates, and whether its expression is context dependent. We used manipulative experiments to investigate these issues in two fish species, the three-spined (Gasterosteus aculeatus) and nine-spined stickleback (Pungitius pungitius). We sequentially exposed individuals to dark and light visual background treatments, quantified color change from video recordings, and examined associations of color change with phenotypic dimensions that can influence the outcome of predator-prey interactions. G. aculeatus expressed a greater degree of color change compared to P. pungitius. In G. aculeatus, the color change response was repeatable within individuals. Moreover, the color change response was independent of body size but affected by sex and boldness, with males and bolder individuals changing less. Infection by the parasite Schistocephalus solidus did not affect the degree of color change, but it did modulate its association with sex and boldness. G. aculeatus adjusted the expression of color change in response to predation risk, with enhanced color change expression in individuals exposed to either simulated attacks, or olfactory cues from a natural predator. These results provide novel evidence on repeatability, correlated traits, and context dependence in the color change response and highlight how a suite of factors can contribute to individual variation in phenotypic flexibility.

opencc-zeroMay 2022View details →

Data from: Hierarchical variation in phenotypic flexibility across timescales and associated survival selection shape the dynamics of partial seasonal migration

Population responses to environmental variation ultimately depend on within-individual and among-individual variation in labile phenotypic traits that affect fitness, and resulting episodes of selection. Yet, complex patterns of individual phenotypic variation arising within and between time periods, and associated variation in selection, have not been fully conceptualised or quantified. We highlight how structured patterns of phenotypic variation in dichotomous threshold traits can theoretically arise and experience varying forms of selection, shaping overall phenotypic dynamics. We then fit novel multistate models to ten years of band-resighting data from European shags to quantify phenotypic variation and selection in a key threshold trait underlying spatio-seasonal population dynamics: seasonal migration versus residence. First, we demonstrate substantial among-individual variation alongside substantial between-year individual repeatability in within-year phenotypic variation ('flexibility'), with weak sexual dimorphism. Second, we demonstrate that between-year individual variation in within-year phenotypes ('supraflexibility') is structured and directional, consistent with the threshold trait model. Third, we demonstrate strong survival selection on within-year phenotypes, and hence on flexibility, that varies across years and sexes, including episodes of disruptive selection representing costs of flexibility. By quantitatively combining these results, we show how supraflexibility and survival selection on migratory flexibility jointly shape population-wide phenotypic dynamics of seasonal movement.

zenodo36/100

E. coli-ΦX174 genotype to phenotype map reveals flexibility and diversity in LPS structures

Here are the raw sequencing data of all E. coli C and ΦX174 strains obtained during this study. A short description of each file's content can be found below.   Bacteria <ul> <li>"E.coliC_res.zip": Whole genome sequencing results of E. coli C WT and ΦX174-resistant strains (generated by fluctuation experiments). All E. coli C samples were prepared for whole-genome sequencing from one millilitre of stationary-phase cultures. Genomic DNA was extracted using the Wizard® Genomic DNA purification Kit (Promega, Germany). Bacterial samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany) using an Illumina Nextera DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</li> </ul>   <ul> <li>"E.coliC_res_excluded.zip": Whole genome sequencing results of the four ΦX174-resistant E. coli C strains (generated by fluctuation experiments) that have been excluded from the analyses: E. coli C R1, R3, R15, and R19. We found that both E. coli C R3 and R15 were not isogenic. While whole-genome sequencing from their respective glycerol stocks showed only a single mutation in galE, whole-genome sequencing carried on colony re-streaks showed that additional mutations were systematically associated with the single mutation in galE (see file "E.coliC_res_excluded_10_clones). E. coli C R1 displayed an unstable resistant phenotype. Finally, E. coli C R19 was partially resistant to ΦX174 WT. It carries a single mutation in the yajC gene, which encodes for a periplasmic protein. No apparent link to the LPS biosynthesis or assembly has been discovered yet, but yajC might play a role in phage DNA injection into the bacterium’s cytoplasmic membrane.</li> </ul>   <ul> <li>"E.coliC_res_excluded_10_clones.zip": Whole genome sequencing results of 10 randomly chosen isolates of E. coli C R1, R3, and R15.</li> </ul>   <ul> <li>"EcoliC.gb": E. coli C WT strain used as reference.</li> </ul>   Bacteriophages   <ul> <li>“PhiX174_PCR_399r_400f.zip”: ΦX174 samples were prepared for whole genome re-sequencing from 1 mL of phage lysate. Genomic DNA was extracted using the QIAprep Spin Miniprep Kit (QIAGEN), then amplified by performing 20 cycles of PCR using Q5® High-Fidelity 2X Master Mix (NEB). The sets of primers used for the amplification of ΦX174 whole genome are:</li> </ul>   PhiX174_399_r: CTTGACTCATGATTTCTTACC PhiX174_400_f: TTACTGAACAATCCGTACGTTTC   DNA samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany). Sequencing was performed using an Illumina MiSeq DNA Flex Library Prep Kit to produce 150 bp paired-end reads.   <ul> <li>“PhiX174_PCR_2361r_2362f.zip”: ΦX174 samples were prepared for whole genome re-sequencing from 1 mL of phage lysate. Genomic DNA was extracted using the QIAprep Spin Miniprep Kit (QIAGEN), then amplified by performing 20 cycles of PCR using Q5® High-Fidelity 2X Master Mix (NEB). The sets of primers used for the amplification of ΦX174 whole genome are:</li> </ul>   PhiX174_2361_r: TCGCTTGGTCAACCCCTCAG PhiX174_2362_f: AGCGCGGTAGGTTTTCTGCT   DNA samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany). Sequencing was performed using an Illumina MiSeq DNA Flex Library Prep Kit to produce 150 bp paired-end reads. <ul> <li>“PhiX174_excluded.zip”: Since we removed R19 from the final analysis, we also removed its corresponding evolved phage obtained during the first evolution experiment (ΦX174 R19 T1). We also removed the phage infecting R5 (ΦX174 R5 T2) because it was not isogenic (confirmed by Sanger Sequencing).</li> </ul> <ul> <li>“PhiX174_Sequencing_Sanger.zip”: Both F and H genes were amplified by performing 35 cycles of PCR using Phusion® High-Fidelity PCR Master Mix with HF Buffer. The sequencing primers are listed in the Information_Sanger_Samples_ID.xlsx file.</li> </ul>   <ul> <li>“PhiX174_ref.gb”: PhiX174 WT strain used as reference.</li> </ul>   We also include the raw data and pictures from our spotting tests and plaque assays used to complete the final matrix of infection. <ul> <li>Spotting_tests_reanalyzed.xlsx”: Raw data from the pictures (see Photos_matrices.zip) used to generate the Hierarchical agglomerative clustering analysis of the host ranges of evolved phage and Infection matrix of evolved ΦX174 phages on the 31 resistant E. coli C strains.</li> </ul> The Photos_matrices.zip folder contains   <ul> <li>“SSA_matrix_Bact_Lawn”: pictures of all evolution experiments obtained from the spotting test method where we spotted phages on bacterial lawns.</li> </ul>   <ul> <li>“SSA_matrix_Phi_Lawn”: pictures of all evolution experiments obtained from the spotting test method where we spotted bacteria on phage lawns.</li> </ul>   <ul> <li>“Mismatches”: We performed plaque assays when combinations of phages and bacteria showed discrepancies between the two spotting test methods</li> </ul>   <ul> <li>“Plaque_assays_R12_R14”: pictures of plaque assays where we tested the sensitivity of E. coli C R12 and R14 toward a subset of evolved phages</li> </ul>   <ul> <li>“Plaque_assays_of_PhiX174R22T1c1/R28T1c1_vs_WT”: pictures of plaque assays where we tested the sensitivity of E. coli C WT toward phages infecting R22 and R28.</li> </ul>   <ul> <li>“displayImage.R”. Script made to look for the desired combination of phage and bacterium.</li> </ul>   Finally, we include the raw OD measurments:   <ul> <li>“All_EcoliC_growth_raw_data.xlsx”: raw OD measurements of each E. coli C resistant strains used in this study.</li> </ul>

opencc-by-4.0Sep 2022View details →

publicJun 2022View details →

Data from: Hierarchical variation in phenotypic flexibility across timescales and associated survival selection shape the dynamics of partial seasonal migration

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