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45 results for “Quality traits”
Effects of genetic vs. environmental quality on condition-dependent morphological and life history traits in a neriid fly
<p>Condition is assumed to reflect both genes and environment, enabling condition-dependent signals to reveal genetic quality. However, because the phenotypic effects of variation in genetic quality could be masked by environmental heterogeneity, the contribution of genetic quality to phenotypic variation in fitness-related traits and condition-dependent signals remains unclear. We compared effects of ecologically relevant manipulations of environmental quality (nutrient dilution in the larval diet) and genetic quality (one generation of inbreeding) on male and female morphology, life history and reproductive performance in the neriid fly <em>Telostylinus angusticollis</em>. We found that larval diet quality had strong, positive effects on male and female body size, male secondary sexual traits, and aspects of male and female reproductive performance. By contrast, inbreeding had weak effects on most traits, and no trait showed clear and consistent effects of both environmental and genetic quality. Indeed, inbreeding effects on body size and male competitive performance were of opposite sign in rich vs. poor larval diet treatment groups. Our results suggest that environmental quality strongly affects condition, but the effects of genetic quality are subtle and environment-dependent in this species. These findings raise questions about the genetic architecture of condition and the potential for condition-dependent traits to function as signals of genetic quality.</p>
Dataset: Environmental conditions and male quality traits simultaneously explain variation of multiple colour signals in male lizards
<p>Dataset and R code associated with the following publication:</p> <p>Badiane et al. (2022), Environmental conditions and male quality traits simultaneously explain variation of multiple colour signals in male lizards. Journal of Animal Ecology, in press</p> <p>This dataset includes the following files:</p> <p>- An excel file containing the reflectance spectra of all individuals from all the study populations</p> <p>- An excel file containing the variables collected at the individual and population levels</p> <p>- Two R scripts corresponding to the analyses performed in the publication</p>
Fig. 5 in The Impact Of Hydrothermal Conditions During Vegetation Period On Grain Quality Traits Of Oat
Fig. 5. β-glucan content and hydrothermal coefficient of phase 1 and 2 for cultivars A: D – 'Laima', B – 'St.Darta', A – 'Arta', E – 'Cwal', L – 'Scorpion', F – 'Pergamon', 1 – HTC1, 2 – HTC2.
Fig. 3 in The Impact Of Hydrothermal Conditions During Vegetation Period On Grain Quality Traits Of Oat
Fig. 3. Crude fat content and hydrothermal coefficient of phase 1 and 2 for cultivars A: B – 'St.Darta', D – 'Laima', E – 'Cwal', A – 'Arta', F – 'Pergamon', C – 'St.Liva', 1 – HTC1, 2 – HTC2.
Fig. 2 in The Impact Of Hydrothermal Conditions During Vegetation Period On Grain Quality Traits Of Oat
Fig. 2. Crude protein content and hydrothermal coefficient of phase 1 and 2 for cultivars B: K – 'Ingeborg', F – 'Pergamon', H – 'Duffy', L – 'Scorpion', J – 'Kerstin', G – 'Corona', 1 – HTC1, 2 – HTC2.
Figure 1 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria
Figure 1. An indicative map of the sites in southern Bulgaria where P. ridibundus individuals were captured in 2019.
Figure 2 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria
Figure 2. Photos of some asymmetric P. ridibundus individuals from site 1: the Chaya River in southern Bulgaria. Legend: a–d: asymmetric morphological traits on the back of the body and hind limbs of frogs, e–f: asymmetric morphological traits on the fingers of frogs. Trait 1 – number of stripes on the dorsal side of the thigh (femur); trait 2 – number of spots on the dorsal side of the thigh; trait 3 – number of stripes on the dorsal side of the shank (crus); trait 4 – number of spots on the dorsal side of the shank; trait 5 – number of stripes on the foot (pes); trait 6 – number of spots on the foot; trait 7 – number of stripes and spots on the back (dorsum); trait 8 – number of white spots on the ventral side of the second finger of the hind leg; trait 9 – number of white spots on the ventral side of the third finger of the hind leg; trait 10 – number of white spots on the ventral side of the fourth finger of the hind leg.
Dataset for agronomic, quality and resistance traits in tomato BRESOV materials
<p>Different dataset for agronomic data, qualitative data and resistances to pathogens related to tomato BRESOV material</p>
Effects of genetic vs. environmental quality on condition-dependent morphological and life history traits in a neriid fly
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Macroinvertebrate trait, abundance and site quality data from the Green Lakes Valley, 2018
This research was conducted to get a sense of the current spatial distribution of benthic macroinvertebrates among various aquatic habitats throughout the Green Lakes Valley. Benthic macroinvertebrates are small organisms that lack a backbone (invertebrate), live on the bottom (benthic), and are visible with the naked eye (macro). To estimate the community structure of benthic macroinvertebrates communities, 53 benthic macroinvertebrate samples were collected over the summer of 2018 from three alpine lakes and two streams in the Green Lakes Valley, CO at multiple points throughout the season. Benthic macroinvertebrates were sampled from the shoreline, inlet (inflow of water), and outlet (outflow of water) of each sampled lake, alongside estimates for habitat quality like surrounding vegetation, temperature (C°), rock volume (L), nitrate (mg/L,) pH, and dissolved oxygen saturation. The methods from this survey are broadly consistent with historic surveys ranging from 1960 to 1987 that have also sought to characterize aquatic invertebrate community composition within the Green Lakes Valley (Bushnell et al. 1987 The Great Basin Naturalist).
Quality-quantity tradeoffs drive functional trait evolution in a model microalgal "climate change winner"
<p>Phytoplankton are the unicellular photosynthetic microbes that form the base of aquatic ecosystems, and their responses to global change will impact everything from food web dynamics to global nutrient cycles. Some taxa respond to environmental change by increasing population growth rates in the short-term, and are projected to increase in frequency over decades. To gain insight into how these projected "climate change winners" evolve, we grew populations of microalgae in ameliorated environments for several hundred generations. Most populations evolved to allocate a smaller proportion of carbon to growth while increasing their ability to tolerate and metabolise reactive oxygen species (ROS). This tradeoff drives the evolution of traits that underlie the ecological and biogeochemical roles of phytoplankton. This offers evolutionary and a metabolic frameworks for understanding trait evolution in projected "climate change winners", and suggests that short-term population booms have the potential to be dampened or reversed when environmental amelioration persists.</p>
Supplementary data: Effect of genotype by environment interaction (GEI) analysis for potato tuber yield and their quality traits in organic multi-environment domains of Poland
<p>Climate and raw data supplementary to the related publication in the journal Agriculture (ISSN 2077-0472).</p>
Supplementary Table 1. Raw data of egg quality parameters for 990 egg samples with ATOL (Animal Trait Ontology for Livestock) descriptors, as function of hen age, pen no. and genotype in 15 replicates.
<p>Data table of egg quality parameters</p>
Table 2 in Effects of different combinations of N, P and K at different time interval on vegetative, reproductive, yield and quality traits of mango (Mangifera Indica. L) cv. Dusehri
<p><b>Table 2.</b> Effect of different fertilizer combinations of N, P and K on reproductive physiology of mango cv. Dusehri.</p><table><tbody><tr><th><b>Treatments</b></th><th><b>Growth size (mm)</b></th><th><b>Total No. of Panicle/Tree</b></th><th><b>Total no. of flowers / Panicle</b></th><th><b>Sex Ratio (%)</b></th><th><b>Fruit Drop (%)</b></th><th><b>Fruit Retention (%)</b></th><th><b>Total no. of fruit/tree</b></th><th><b>Yield (Kg/Tree)</b></th><th><b>Fruit Length (cm)</b></th><th><b>Fruit Weight (g)</b></th><th><b>Pulp Weight (g)</b></th><th><b>Stone Weight (g)</b></th><th><b>Peel Weight (g)</b></th><th><b>TSS (%)</b></th><th><b>Total Acidity (%)</b></th><th><b>TSS/Acid Ratio</b></th><th><b>Vit. C (mg/100 mL)</b></th><th><b>Total Sugar (%)</b></th></tr></tbody><tbody><tr><th>T1 (Control)</th><td>149.34d ± 3.89</td><td>397.67h ± 3.51</td><td>543.21h ± 3.61</td><td>51.17c ± 2.10</td><td>94.85a ± 1.40</td><td>1.83e ± 0.15</td><td>186.72g ± 4.51</td><td>40.01e ± 4.51</td><td>15.4b ± 3.17</td><td>155.15e ± 6.34</td><td>76.30g ± 2.22</td><td>24.14f ± 2.02</td><td>28.61f ± 1.86</td><td>20.29d ± 1.05</td><td>0.52a ± 0.005</td><td>22.43</td><td>31.26f ± 0.92</td><td>14.52c ± 0.25</td></tr><tr><th>T2 (N)</th><td>166.67b ± 4.47</td><td>508.57f ± 4.51</td><td>612.47f ± 4.58</td><td>54.42bc ± 1.05</td><td>94.86a ± 2.41</td><td>5.57d ± 0.57</td><td>213.34f ± 3.06</td><td>52.70cd ± 4.50</td><td>16.4b ± 3.11</td><td>175.50bc ± 5.50</td><td>82.41f ±1.90</td><td>30.04de ± 2.21</td><td>33.70e ± 1.38</td><td>21.06cd ± 0.95</td><td>0.49b ± 0.004</td><td>25.16</td><td>42.22c ± 1.18</td><td>15.24bc ± 0.41</td></tr><tr><th>T3 (P)</th><td>156.26cd ± 4.85</td><td>467.33g ± 4.04</td><td>593.34g ± 3.61</td><td>53.57bc ± 1.01</td><td>91.72ab± 2.51</td><td>8.82bc ± 0.72</td><td>241.40e ± 4.04</td><td>50.14d ± 5.03</td><td>18.3ab ± 2.75</td><td>169.24cd ± 5.41</td><td>85.23f ± 1.96</td><td>28.50e ± 2.00</td><td>35.62de ±1.94</td><td>22.07bc ± 1.10</td><td>0.45c ± 0.005</td><td>29.13</td><td>39.37d ± 0.98</td><td>15.82bc ± 0.29</td></tr><tr><th>T4 (K)</th><td>164.80bc ± 4.95</td><td>634.57c ± 4.51</td><td>730.19c ± 4.56</td><td>57.39bc ± 2.38</td><td>94.26ab ± 1.79</td><td>5.83d ± 0.48</td><td>278.33d ± 3.51</td><td>55.23cd ± 4.50</td><td>19.3ab ± 2.99</td><td>182.01b ± 5.47</td><td>99.92e ± 1.89</td><td>31.26e ± 1.76</td><td>40.84c ± 1.35</td><td>21.41cd ± 1.25</td><td>0.37d ± 0.002</td><td>43.27</td><td>36.62e ± 1.21</td><td>16.89b ± 0.55</td></tr><tr><th>T5 (NP)</th><td>166.48b ± 4.98</td><td>584.47d ± 3.51</td><td>639e.46 ± 4.04</td><td>56.61bc ± 1.59</td><td>93.34ab ± 2.15</td><td>6.92cd ± 0.33</td><td>288.62c ± 4.51</td><td>57.31c ± 5.03</td><td>17.2b ± 3.29</td><td>160.46de ±4.86</td><td>107.34c ± 2.11</td><td>35.63bc ± 2.02</td><td>37.81d ± 1.88</td><td>23.43ab ± 0.93</td><td>0.35d ± 0.001</td><td>40.48</td><td>42.09b ± 0.47</td><td>16.65b ± 0.61</td></tr><tr><th>T6 (NK)</th><td>160.75bc ± 5.05</td><td>684.66b ± 4.50</td><td>810.62b ± 4.59</td><td>60.17b ± 2.53</td><td>90.28cd ± 2.71</td><td>9.74ab ± 0.66</td><td>320.32b ± 3.05</td><td>66.61b ± 4.49</td><td>18.5ab ± 2.73</td><td>180.32b ± 5.35</td><td>118.04b ± 1.94</td><td>37.21b ± 1.81</td><td>46.92b ± 1.65</td><td>23.30ab ± 0.08</td><td>0.32e ± 0.002</td><td>51.28</td><td>51.48b ±1.07</td><td>16.07b ± 0.71</td></tr><tr><th>T7 (PK)</th><td>159.42bc ± 4.98</td><td>559.71e ± 3.49</td><td>701.17d ± 3.61</td><td>55.31bc ± 1.02</td><td>92.96ab ± 2.56</td><td>7.49bcd ± 0.52</td><td>274.37d ± 2.52</td><td>54.85cd ± 4.51</td><td>18.1b ± 3.01</td><td>178.24bc ± 6.53</td><td>103.51d ± 1.79</td><td>33.07cd ± 1.95</td><td>42.14c ± 1.43</td><td>2.35bc ± 0.05</td><td>0.31e ± 0.002</td><td>52.41</td><td>51.55b ± 0.95</td><td>15.01b ± 0.37</td></tr><tr><th>T8 (NPK)</th><td>177.51a ± 4.92</td><td>845.64a ± 3.61</td><td>974.52a ± 4.58</td><td>69.18a ± 2.87</td><td>86.10e ± 2.85</td><td>13.85a ± 0.43</td><td>379.05a ± 3.00</td><td>82.35a ± 3.51</td><td>23.3a ± 3.10</td><td>197.05a ± 5.62</td><td>135.32a ± 2.09</td><td>43.53a ± 2.07</td><td>52.09a ± 1.77</td><td>24.53a ± 0.06</td><td>0.26f ± 0.001</td><td>73.53</td><td>57.63a ± 0.07</td><td>20.48a ± 0.53</td></tr></tbody></table><p>Values within each column followed by the same letter are not significantly different at P <0.5 level.</p><p>Values within each column followed by the same letter are not significantly different at <i>P</i> <0.05 level.</p><p>Values within each column followed by the same letters are not significantly different at <i>P</i> <0.05 level.</p>
Quality-quantity tradeoffs drive functional trait evolution in a model microalgal “climate change winner”
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Linking leaf economic traits with forage quality across temperate grasslands under ambient and drought conditions
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Tomato fruit quality traits and metabolite content are affected by reciprocal crosses and heterosis
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Data from: Male mate choice, male quality, and the potential for sexual selection on female traits under polygyny.
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Milkweed trait values associated with aridity gradients and drought-induced changes in hostplant quality
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Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach
Background <p>Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (<i>Prunus persica</i> (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time.</p> Results <p>Phenotypic data of seven F<sub>1</sub> low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40–46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (<i>D</i>-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles.</p> Conclusions <p>A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.</p>
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