Single Trait Research Articles

Timing of reproduction underlies fitness tradeoffs for a salmonid fish

Life history diversity is generated and maintained in part by density‐dependent fitness tradeoffs that inhibit a single trait value from reaching fixation. While central to our understanding of evolution, demonstrating density dependence in the strength of fitness tradeoffs is difficult in natural systems. The timing of reproduction is a key life history trait that determines access to breeding habitat and exposure of offspring to competitive interactions and environmental conditions. Understanding the processes underlying diversity in reproductive timing will aid efforts to increase adaptive capacity under global environmental change. Here, we used detailed field studies, genetic parentage assignment, and simulation modeling to evaluate the fitness tradeoffs associated with the timing of reproduction for Yellowstone cutthroat trout Oncorhynchus clarkii bouvieri in groundwater‐dominated tributaries to the upper Snake River, Wyoming, USA. We conducted our study across two years to understand how the strength of tradeoffs changes with population density. We found that early breeders experienced reduced reproductive success relative to later breeders due to the negative impact of nest superimposition (where later breeders construct nests overlapping those constructed previously) on embryo survival. However, as the risk of superimposition declined in the low‐density year and early breeders experienced fewer losses, reproductive success became more similar among individuals breeding at different times. Further, in the spring following the critical period for growth and survival, offspring of early breeders had experienced longer growing seasons, attained larger body sizes, and were equally abundant relative to those of later breeders, suggesting that fitness losses due to superimposition may be offset by size‐dependent competitive ability and overwinter survival. Our results illustrate a mechanism underlying diversity in the timing of reproduction for salmonids. This type of life history diversity will help to ensure the resilience and stability of salmonid populations attempting to adapt to changing local stressors associated with global climate change.

The genomic architecture of high temperature tolerance in a year class of Atlantic Salmon

The yield from fisheries and aquaculture production has risen dramatically over the last few decades, with rising demands to meet the needs of a growing world population that is also consuming more seafood per capita. Aquaculture products have, in recent years, also overtaken tonnage obtained from wild fisheries. This is particularly true for finfish species such as Atlantic salmon. However, the marine lifecycle stage for Atlantic salmon aquaculture requires ocean-based net pens, which introduces environmental vulnerabilities that can negatively affect production. Of particular concern is the ability of the fish to tolerate rising seawater temperatures in the face of climate change. One way of tackling this challenge is to incorporate selective breeding for Atlantic salmon families that display greater tolerance to elevated seawater temperatures. The goal of this study was to determine the genomic heritability of seawater temperature tolerance in Atlantic salmon and to investigate the genetic architecture of this trait for more effective broodstock management. We report a moderate genomic-based heritability for seawater temperature at mortality in Atlantic salmon (0.208 ± 0.041) with a favourable correlation between growth traits (body mass and fork length) and seawater temperature at mortality. Our results also show that seawater temperature at mortality is a polygenic trait, with no significant quantitative trait loci identified. The moderate genomic heritability for seawater temperature at mortality suggests that this trait could be included in Atlantic salmon breeding programs. Importantly, the favourable correlation between growth traits and seawater temperature at mortality suggests that selection for seawater temperature tolerance would not have a negative impact on growth in this population. No single quantitative trait locus for seawater temperature at mortality was identified, suggesting that genomic selection will be required to achieve an increased tolerance to seawater temperature in this population.

Natural variation in the SVP contributes to the pleiotropic adaption of Arabidopsis thaliana across contrasted habitats

Coordinated plant adaptation involves the interplay of multiple traits driven by habitat-specific selection pressures. Pleiotropic effects, wherein genetic variants of a single gene control multiple traits, can expedite such adaptations. Until present, only a limited number of genes have been reported to exhibit pleiotropy. Here, we create a recombinant inbred line (RIL) population derived from two Arabidopsis thaliana (A. thaliana) ecotypes originating from divergent habitats. Using this RIL population, we identify an allelic variation in a MADS-box transcription factor, SHORT VEGETATIVE PHASE (SVP), which exerts a pleiotropic effect on leaf size and drought-versus-humidity tolerance. Further investigation reveals that a natural null variant of the SVP protein disrupts its normal regulatory interactions with target genes, including GRF3, CYP707A1/3, and AtBG1, leading to increased leaf size, enhanced tolerance to humid conditions, and changes in flowering time of humid conditions in A. thaliana. Remarkably, polymorphic variations in this gene have been traced back to early A. thaliana populations, providing a genetic foundation and plasticity for subsequent colonization of diverse habitats by influencing multiple traits. These findings advance our understanding of how plants rapidly adapt to changing environments by virtue of the pleiotropic effects of individual genes on multiple trait alterations.

Relationships between Environmental Factors and Functional Traits of Macrophyte Assemblages in Running Waters of Greece

The analysis of plant trait composition has raised significant interest among freshwater ecologists as a complementary approach for assessing the effects of environmental change on ecosystem functions. In this study, we investigated patterns of functional traits of the aquatic macrophyte assemblages of 74 lotic ecosystems of Greece, and we identified associations between species traits and environmental variables (hydromorphological and physicochemical parameters) through testing the hypothesis that the environmental features determine the spatial structure of traits. We allocated 12 traits to a total of 39 hydrophyte species, and we conducted RLQ and fourth corner analysis to explore relationships between species, trait composition, and environmental gradients. Based on the results of the RLQ, a hierarchical cluster analysis was conducted to identify groups of plants that share common trait characteristics. Plants were discriminated into five discrete groups based mostly on their life form (e.g., free-floating, rooted submerged etc.) and their ecological preference for nitrogen levels. Hydromorphological parameters had a higher contribution than physicochemical variables in explaining the total variance of the trait data, with water abstraction, channel substrate, and hydrologic alteration being the most important. Our analysis did not reveal significant bivariate relationships between single traits and environmental parameters, although the five groups of macrophyte assemblages appeared to associate with certain environmental gradients. Free-floating and emergent plants were related to higher concentrations of nutrients, whereas rooted submerged plants were related to higher oxygen concentration and increased pH. In addition, free-floating plants were highly associated with metrics of hydromorphological change. Our findings showed clear discrimination of macrophytes based on their functional composition and association of traits with environmental gradients. Thus, further research could explore whether macrophyte functional groups can serve as indicators of environmental change and the overall ecosystem health.

Estimation of heritabilities and additive genetic correlations for reproduction traits in swine: insights for tropical commercial production systems using multiple trait animal models.

This study was to estimate heritabilities, additive genetic correlations, and phenotypic correlations between number of piglets born alive (NBA), litter birth weight (LTBW), number of piglets weaned (NPW) and litter weaning weight (LTWW) in different parities of Landrace (L), Yorkshire (Y), Landrace×Yorkshire (LY), and Yorkshire×Landrace (YL) sows in a commercial swine operation in Northern Thailand. Two models were utilized, a single trait repeatability model (RM) and a multiple trait animal model (MTM). The RM assumed reproductive records from different parities to be repeated values of the same trait, whereas the MTM assumed these records to be different traits. The two models accounted for the fixed effects of farrowing year-season, genetic group of the sow, heterosis, and age at first farrowing, and the random effects of sow, boar, and residual. Heritability estimates from RM were 0.02±0.01 for NBA, 0.10±0.01 for LTBW, 0.04±0.01 for NPW, and 0.11±0.01 for LTWW. Heritability estimates from MTM fluctuated across parities, ranging from 0.04±0.01 in parity 2 to 0.09±0.02 in parity 4 for NBA, 0.07±0.02 in parity 2 to 0.16±0.02 in parity 3 for LTBW, 0.04±0.02 in parity 4 to 0.08±0.01 in parity 1 for NPW, and 0.16±0.02 in parity 1 to 0.20±0.02 in parity 2 for LTWW. Additive genetic correlation estimates from MTM were also variable, ranging from 0.29±0.24 between NBA in parity 1 and NBA in parity 2 to 0.99±0.05 between LTWW in parity 3 and LTWW in parity 4. The findings of this study highlight the advantage of using MTM for the genetic improvement of reproductive traits in swine and contribute to the development of sustainable swine breeding programs in Thailand.

Phenotypic and genetic characterisation of methane emission predicted from milk fatty acid profile of Sarda dairy ewes

Individual methane emissions are a potential breeding goal for selection plans aimed at improving the sustainability of ruminant farming systems. However, a large-scale recording of individual methane emissions is hampered by the high costs of the equipment and the logistics of the experiments. Equations have been developed to predict methane emissions in dairy cattle based on variables such as dry matter intake (DMI), gross energy intake, body weight and milk fatty acids (FAs) profile. No equations are currently available for dairy sheep. The aims of this study were: (i) to estimate the enteric methane yield (eMY, expressed as g/kg of DMI) and enteric methane intensity (eMI, expressed as g/kg of FPCM) of dairy sheep using equations developed for dairy cattle that consider as predictors milk FA; (ii) to evaluate the effect of stage of lactation, parity and month of lambing on eMY and eMI; (iii) to estimate the heritability of eMY and eMI. For this purpose, 964 individual milk samples from Sarda dairy ewes were analysed for milk composition and FA profile. Nine different equations were used to predict eMY and eMI. Values of eMY ranged from 19.4 g/kg to 20.4 g/kg of DMI, whereas values of eMI ranged from 15.1 g/kg to 21.0 g/kg of DMI, respectively. Stage of lactation was an important factor in the variability of eMY (p < .01). Indeed, this trait increased towards the end of lactation. Both eMY and eMI were negatively correlated with milk yield, lactose concentration and urea and positively correlated with fat concentration and NaCl, respectively. Heritability of eMI and eMY estimated using a single trait model was 0.13 ± 0.05 and 0.05 ± 0.04, respectively. The values of heritability estimated for eMY using a bivariate models ranged from 0.10 to 0.16 and they were higher than the value estimated for eMI. Results of the present study, even if based on prediction equations developed for cows, increased the knowledge about the phenotypic and genetic background of methane emissions in Sarda dairy sheep.

Multimodal Approach for Enhancing Biometric Authentication.

Unimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To address this issue, we propose an enhanced biometric system based on a multimodal approach using two types of biological traits. We propose to combine fingerprint and Electrocardiogram (ECG) signals to mitigate spoofing attacks. Specifically, we design a multimodal deep learning architecture that accepts fingerprints and ECG as inputs and fuses the feature vectors using stacking and channel-wise approaches. The feature extraction backbone of the architecture is based on data-efficient transformers. The experimental results demonstrate the promising capabilities of the proposed approach in enhancing the robustness of the system to presentation attacks.

Genetic Parameter Estimates for the Growth and Morphological Traits of Castanopsis hystrix Families and the Genotype × Environment Interaction Effects

Castanopsis hystrix, a timber resource from Southeast Asia, is characterized by rapid growth and high yield, but plantation quality and efficiency improvements are required. Twenty-year-old C. hystrix experimental forests in Yulin, Liuzhou, and Pingxiang in Guangxi Province, China, comprising 21 open-pollinated half-sib families, were used in this study. Genetic variations in the growth (tree height, diameter at breast height (DBH), and volume (V)) and morphological (height to live crown base (HCB), crown width (CW), and branch angle) traits were assessed, and the genetic parameters were estimated to clarify the genotype × environment interaction effects. The average values for the tree height, DBH, V, HCB, CW, and branch angle were 16.33 m, 17.25 cm, 0.21 m3, 6.68 m, 2.15 m, and 45.45°, respectively. The most important sources of variance for the tree height, DBH, V, and HCB were the block and family, whereas the location, family, and family × location had significant impacts on the tree height, DBH, V, and HCB (p &lt; 0.01). The family heritability for each trait was 0.35, 0.38, 0.62, and 0.19, respectively. Excellent families with strong adaptability and genetic stability were identified using BLUP–GGE biplots for single and multiple traits. The results provide a theoretical basis for the efficient cultivation of C. hystrix in South China.

Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants.

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single-nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi-step method (GBLUP) and single-step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

Study of Combining Ability in Association with Heterosis in Rice (Oryza sativa L.)

In the current investigation, eight lines KNM 12444, KNM 12469, KNM 12472, KNM 13449, KNM 13555, KNM 13568, KNM 14913 and KNM 14985 were crossed with four testers namely Aganni, IBT MRR 24, INRC 3021 and WGL 1119 in Line by Tester mating design during rabi season of 2021-22. The resultant 32 F1’stogether with parents and two commercial checks, KNM 118 and KNM 1638 were evaluated for their combining ability and heterosis at Agricultural Research Station, Kunaram during kharif season, 2022.The analysis of variance for combining ability specified that significant differences were observed due to lines, testers, F1’ hybrids and various interactions indicating the existence of wider adaptability in the material examined. The ratio of gca to sca variances showed that non-additive gene action was predominant in the inheritance of panicle length, plant height, number of productive tillers/plants, grain yield per plant, kernel length, kernel L/B ratio and incidence of gall midge, whereas, preponderance of additive gene action was noticed for 1000 grain weight, days to 50% flowering, number of filled grains per panicle, kernel breadth, hulling percentage, milling percentage and head rice recovery. The gca effects of the parents revealed that, the line KNM 13555 and the testers Aganni, IBT MRR 24 were found to be the best general combiners for single plant yield and its components. Based on the significant sca effects, per se performance, standard heterosis and gall midge incidence, KNM 12469/Aganni followed by KNM 13555/INRC 3021, KNM 13568/IBT MRR 24, KNM 12472/Aganni and KNM 13449/Aganni were identified as promising for single plant yield, its components, quality traits and gall midge incidence. Hence, these parents and F1 hybrids may be further tested for future crop improvement programme.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

Implications of Qualitative Traits Diversity for Future Chickpea Improvement and Conservation

Morphological characterization and diversity estimation are essential in plant breeding. In this endeavor, the researcher performed a visual assessment of 18 phenotypic DUS listed traits of 32 Kabuli chickpea genotypes in rabi 2021-2022. Nine traits were monomorphic, four traits were dimorphic, while five traits were polymorphic. Shannon’s diversity indices were estimated using Microsoft excel. The index ranged from 0.00 to 1.06, with a mean value of 0.36. Seed size (g) obtained the highest value of the 32 genotpyes, few were unique as they could be distinguished based on a single trait, while the majority were very closely related. Cluster analysis revealed that the 32 genotypes were grouped into two major clusters—similar genotypes concerning different traits clustered in the same clusters and vice versa.

Differences in trait–environment relationships: Implications for community weighted means tests

Abstract One of J.P. Grime's greatest achievements was demonstrating the importance of the relationship between the environment and plant functional traits for understanding community assembly processes and the effects of biodiversity on ecosystem functioning. A popular approach assessing trait–environment relationships is the community weighted means (CWMs) method, which evaluates changes in communities' average trait values along gradients, with Grime being among its first practitioners. Today the CWM method is well‐established but some scholars have criticized it for inflated Type I errors. That is, in some scenarios of compositional turnover along a gradient, CWM tests can provide significant results even for randomly generated traits. Null models have been proposed to correct for such effects by randomizing trait values across species (CWM‐sp). We review different approaches relating traits to the environment within the framework of the accepted dichotomy between species‐level (observations are species) versus community‐level (observations are community parameters) analyses. Between these families of analyses and their combinations, a great variety of methods exist that test different trait–environment relationships, each with different null hypotheses and ecological questions. In classic CWM tests, the null hypothesis focuses on characteristics of trait distributions at the community level along gradients. The Type I error rate should not be a priori considered inflated when this test is used to identify changes in community trait structure affecting the functioning of communities. Trait changes observed with CWM tests may be accurate, but the interpretation that a specific trait drives turnover may be fallacious. Approaches like CWM‐sp may be more appropriate for testing other ecological hypotheses, such as whether trait–environment relationships are widespread across species. In effect, this moves the ecological focus towards species‐level analyses, that is on the adaptive value of traits and their relation to species niches. Synthesis. There is no single trait–environment relationship. Species‐level and community‐level analyses, including variants within them, test different relationships with different null hypotheses, such that the potential for inflated error rates can be misleading. Using a spectrum of methods provides a comprehensive picture of the diversity of trait–environment relationships.

Production environment is determinant in the genetic relationship between growth traits of Nile tilapia through a reaction norm model

Genotype by environment interaction (G × E) has been a matter of interest for breeders and fish producers around the world, mainly because the selection performed in one environment may not have the desired response in other environments. Most studies conducted on G × E in aquaculture species have used a single trait approach; however, breeding programs have selected for multiple traits simultaneously. Thus, it was of interest in this study to use a multiple trait reaction norm model to analyze the genetic relationship between growth traits of Nile tilapia in the presence of G × E in a (sub)tropical environment. A multiple trait reaction norm model was applied to data from 12,192 animals evaluated for body weight (BW), trunk length (TL) and head percentage (HP). The adopted environmental descriptor was the solution of contemporary groups for BW. The ratio between slope and additive genetic intercept of the reaction norms ranged from 0.59 to 0.70, which demonstrated the presence of G × E for BW, TL, and HP. The mean of the heritability estimates showed variation of up to 300% along the environmental descriptor. Thus, the selection responses for BW, TL, and HP should be different depending on the production environment. The genetic correlations within each trait across different levels of the environmental descriptor were high and positive between closer environments and reached values close to or below zero between distant environments (−0.34, −0.31, and 0.07 for BW, TL, and HP, respectively). Therefore, the best individuals in one environment will not necessarily be the same in a different environment. Given genetic correlation estimates greater than 0.85 between BW and TL across the environmental descriptor, selection for increased BW or TL will lead to a favorable reciprocal correlated response regardless of the environment. The genetic correlations between BW and HP and between TL and HP across environmental descriptor reached negative values of −0.63 and − 0.77 in better environments, respectively. Thus, selection to increase BW or TL contributes to a more expressive reduction in HP in favorable production environments compared to more restrictive ones.

Genetic dissection and origin of pleiotropic loci underlying multi-level fiber quality traits in upland cotton (Gossypium hirsutum L.)

Cotton fiber quality is a persistent concern that determines planting benefits and the quality of finished textile products. However, the limitations of measurement instruments have hindered the accurate evaluation of some important fiber characteristics such as fiber maturity, fineness, and neps, which in turn has impeded the genetic improvement and industrial utilization of cotton fiber. Here, 12 single fiber quality traits were measured using Advanced Fiber Information System (AFIS) equipment among 383 accessions of upland cotton (Gossypium hirsutum L.). In addition, eight conventional fiber quality traits were assessed by the High Volume Instrument (HVI) System. Genome-wide association study (GWAS), linkage disequilibrium (LD) block genotyping and functional identification were conducted sequentially to uncover the associated elite loci and candidate genes of fiber quality traits. As a result, the previously reported pleiotropic locus FL_D11 regulating fiber length-related traits was identified in this study. More importantly, three novel pleiotropic loci (FM_A03, FF_A05, and FN_A07) regulating fiber maturity, fineness and neps, respectively, were detected based on AFIS traits. Numerous highly promising candidate genes were screened out by integrating RNA-seq and qRT-PCR analyses, including the reported GhKRP6 for fiber length, the newly identified GhMAP8 for maturity and GhDFR for fineness. The origin and evolutionary analysis of pleiotropic loci indicated that the selection pressure on FL_D11, FM_A03 and FF_A05 increased as the breeding period approached the present and the origins of FM_A03 and FF_A05 were traced back to cotton landraces. These findings reveal the genetic basis underlying fiber quality and provide insight into the genetic improvement and textile utilization of fiber in G. hirsutum.

The complex II resistance mutation H258Y in succinate dehydrogenase subunit B causes fitness penalties associated with mitochondrial respiratory deficiency.

The acaricides cyflumetofen, cyenopyrafen and pyflubumide inhibit the mitochondrial electron transport chain at complex II [succinate dehydrogenase (SDH) complex]. A target site mutation H258Y was recently discovered in a resistant strain of the spider mite pest Tetranychus urticae. H258Y causes strong cross-resistance between cyenopyrafen and pyflubumide, but not cyflumetofen. In fungal pests, fitness costs associated with substitutions at the corresponding H258 position that confer resistance to fungicidal SDH inhibitors have not been uncovered. Here, we used H258 and Y258 near-isogenic lines of T. urticae to quantify potential pleiotropic fitness effects on mite physiology. The H258Y mutation was not associated with consistent significant changes of single generation life history traits and fertility life table parameters. In contrast, proportional Sanger sequencing and droplet digital polymerase chain reaction showed that the frequency of the resistant Y258 allele decreased when replicated 50:50 Y258:H258 experimentally evolving populations were maintained in an acaricide-free environment for approximately 12 generations. Using in vitro assays with mitochondrial extracts from resistant (Y258) and susceptible (H258) lines, we identified a significantly reduced SDH activity (48% lower activity) and a slightly enhanced combined complex I and III activity (18% higher activity) in the Y258 lines. Our findings suggest that the H258Y mutation is associated with a high fitness cost in the spider mite T. urticae. Importantly, while it is the most common approach, it is clear that only comparing life history traits and life table fecundity does not allow to reliably estimate fitness costs of target site mutations in natural pest populations. © 2023 Society of Chemical Industry.

Applying a geometric morphometric surface semilandmark-based approach for assessing sexual dimorphism in cranial bones

Traditionally, sex estimation in human skulls has been carried out applying non-metric and inter-landmark distance approaches. Such estimation has substantially improved with the introduction of geometric morphometric semilandmark-based registration, representing a useful tool for capturing the complex morphology of 3D surfaces, particularly in human skulls. Taking in account these improvements, the main purpose of this study is to show the benefits of these techniques for assessing by means of a quantitative, hypothesis testing approach the differential phenotypic expression of sexual dimorphism in human cranial traits, using the frontal bone, the mastoid process of the temporal bone, and the zygomatic bone as proxies. According to our main results: i) accuracy and precision of sex estimation is higher when the shape variable is included in the analysis than when it is not, ii) numerical classification tends to be more accurate in males than in females for both shape and size components of sexual dimorphism, and iii) a single trait or two traits in combination would be more successful in sex classification than the three traits taken as a whole. The introduction in the present study of a surface semilandmark-based approach for quantitatively assessing sexual dimorphism in human skull considerably improves the capture of sexual dimorphic signatures, corroborating the importance of the information carried by the surface of the frontal bone, mastoid process, and zygomatic bone. Our results suggest that shape and size components should be considered as relevant factors in making a differential, bone- dependent sex assessment in human crania.

Adaptive plasticity to drought of Grime's CSR strategies

Grime's strategies (competitor, stress tolerator, ruderal; CSR) represent viable trait combinations with which species deal with environmental conditions. CSR strategies are broadly used to understand plant adaptation to the environment, yet their plastic responses have received little attention. A globally‐calibrated tool (StrateFy) estimates CSR strategies using specific leaf area (SLA), leaf dry matter content (LDMC) and leaf area (LA) data, but these three traits can hardly characterise whole‐plant responses to the environment individually. CSR strategies reflect tradeoffs among growth, survival and reproduction, at both leaf and whole‐plant levels, thus integrating several functions. We hypothesised that CSR strategies and the three constituent traits would show independent plasticity patterns, and that CSR strategies would be more likely to show adaptive responses, i.e. to fit expected functional responses to environmental gradients. We compared phenotypic plasticity to drought in single traits (SLA, LDMC and LA) with the integrated plasticity of the resulting CSR strategy. The study species was the invasive plant Mesembryanthemum crystallinum, which is distributed in arid and semiarid Chile. We found that trait plasticity was rather idiosyncratic and contrary to what would be expected from a functional adjustment to drought: LDMC did not change (expected response: increase) and SLA increased (expected response: decrease). Conversely, plastic responses of CSR strategy and LA were consistent with functionally adaptive responses to drought in all populations: S‐strategy increased, while C‐strategy and LA decreased. We advocate the use of Grime's CSR theory as an integrative approach to further our understanding of adaptive plasticity in plants.

Bold–shy continuum does not account for egg rejection behaviour in the Japanese tit

Abstract In response to brood parasitism, which significantly reduces the reproductive success of avian hosts, hosts have evolved the ability to recognize and reject parasitic eggs. Many studies have revealed the factors influencing egg recognition and egg rejection. Recent work has demonstrated that host personality along the bold–shy continuum can influence egg rejection behaviour. In addition, the egg rejection behaviour of hosts differs among populations, suggesting that individuals with different personalities might evolve various adaptive behaviours under different parasitic pressures, but there have been few relevant experiments to test this. In the present study, two populations of the Japanese tit, Parus minor, distributed in China were used to examine whether there were differences in egg rejection behaviour of individuals with different personalities. The results showed that egg rejection rates of bold individuals (which remained in the nest box when encountering human invaders) and shy individuals (which immediately flew away from the nest box when encountering human invaders) were similar in both populations of the Japanese tit, and there were also no significant differences in egg rejection rates between individuals of the same personality in the two populations. This study suggests that host personality along the bold–shy continuum does not account for egg rejection behaviour in Japanese tits. This might be attributable either to other factors that affect egg rejection behaviour counteracting the effect of personality or to the presence of multiple personality traits that act together in the Japanese tits, counteracting the effect of a single personality trait.

Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize.

The study of yield and resistance/tolerance to pest are related traits fundamental for maize breeding programs. Genomic selection (GS), which uses all marker information to calculate genomic breeding values, is presented as an emerging alternative to phenotypic and marker-assisted selections for improving complex traits controlled by many genes with small effects. Therefore, although phenotypic selection (PS) has been effective for increasing resistance and yield under high infestation with maize stem borers, higher genetic gains are expected to be obtained through GS based on the complex architecture of both traits. Our objective was to test whether GS is more effective than PS for improving resistance and/or tolerance to maize stem borers and grain yield. For this, we compared different selection programs based on phenotype and genotypic value for a single trait, resistance or yield, and for both traits together. We obtained that GS achieved the highest genetic gain for yield, meanwhile phenotypic selection for yield was the program that achieved the highest reduction of tunnel length, but was ineffective for increasing yield. However, phenotypic or genomic selection for increased resistance may be more effective in improving both traits together; although the gains per cycle would be small for both traits.