scholarly journals Short-term effects of controlled mating and selection on the genetic variance of honeybee populations

Heredity ◽  
2021 ◽  
Author(s):  
Manuel Du ◽  
Richard Bernstein ◽  
Andreas Hoppe ◽  
Kaspar Bienefeld
Keyword(s):  
Genetic Variance ◽  
Selection Criterion ◽  
Performance Criterion ◽  
Performance Criteria ◽  
Adaptation To Climate Change ◽  
Short Term ◽  
Breeding Programs ◽  
Breeding Schemes ◽  
And Performance ◽  
Modern Breeding

AbstractDirectional selection in a population yields reduced genetic variance due to the Bulmer effect. While this effect has been thoroughly investigated in mammals, it is poorly studied in social insects with biological peculiarities such as haplo-diploidy or the collective expression of traits. In addition to the natural adaptation to climate change, parasites, and pesticides, honeybees increasingly experience artificial selection pressure through modern breeding programs. Besides selection, many honeybee breeding schemes introduce controlled mating. We investigated which individual effects selection and controlled mating have on genetic variance. We derived formulas to describe short-term changes of genetic variance in honeybee populations and conducted computer simulations to confirm them. Thereby, we found that the changes in genetic variance depend on whether the variance is measured between queens (inheritance criterion), worker groups (selection criterion), or both (performance criterion). All three criteria showed reduced genetic variance under selection. In the selection and performance criteria, our formulas and simulations showed an increased genetic variance through controlled mating. This newly described effect counterbalanced and occasionally outweighed the Bulmer effect. It could not be observed in the inheritance criterion. A good understanding of the different notions of genetic variance in honeybees, therefore, appears crucial to interpreting population parameters correctly.

Effects of reduced variance due to selection in open nucleus breeding systems

1983 ◽  
Vol 34 (1) ◽  
pp. 53 ◽  
Author(s):  
JP Mueller ◽  
JW James
Keyword(s):  
Genetic Variance ◽  
Breeding Systems ◽  
Breeding Programs ◽  
Transfer Rates ◽  
Constant Variance ◽  
High Heritability ◽  
Breeding Schemes ◽  
Nucleus Breeding ◽  
Open Nucleus ◽  
Parameter Theory

In the design of breeding programs the consideration of open nucleus breeding systems may result in a useful alternative. The available theory dealing with these systems assumes constant parameters, but a more exact approach would take into account the possible changes in genetic variance. Numerical evaluation of formulae allowing for loss of genetic variance due to linkage disequilibrium and increase of variance due to mixing groups with different breeding values, suggests that optimum transfer rates, as predicted from the constant parameter theory, are in close agreement with the actual values; however, genetic gain is overestimated by about 20 % in common cattle and sheep nucleus breeding schemes for traits with high heritability, but less for traits with low heritability. The advantage of opening the nucleus is approximated well by constant variance theory.

scholarly journals Identifying the connective strength between model parameters and performance criteria

2017 ◽  
Vol 21 (11) ◽  
pp. 5663-5679 ◽  
Author(s):  
Björn Guse ◽  
Matthias Pfannerstill ◽  
Abror Gafurov ◽  
Jens Kiesel ◽  
Christian Lehr ◽  
...  
Keyword(s):  
Performance Criterion ◽  
Performance Criteria ◽  
Joint Analysis ◽  
Model Parameters ◽  
Flow Conditions ◽  
Target Variable ◽  
Model Parameter ◽  
And Performance ◽  
Parameter Values ◽  
Three Components

Abstract. In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.

scholarly journals Assessing and Comparing Short Term Load Forecasting Performance

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2054 ◽  
Author(s):  
Pekka Koponen ◽  
Jussi Ikäheimo ◽  
Juha Koskela ◽  
Christina Brester ◽  
Harri Niska
Keyword(s):  
Electricity Market ◽  
Load Forecasting ◽  
Performance Criteria ◽  
Short Term ◽  
Comparison Results ◽  
Forecasting Performance ◽  
Network Costs ◽  
And Performance ◽  
Short Term Forecasting ◽  
Forecasting Errors

When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It compares typically applied criteria for tuning and performance assessment of load forecasting methods with estimated costs caused by the forecasting errors. The focus is on short-term forecasting of the loads of energy systems. The estimated costs comprise electricity market costs and network costs. We estimate the electricity market costs by assuming that the forecasting errors cause balancing errors and consequently balancing costs to the market actors. The forecasting errors cause network costs by overloading network components thus increasing losses and reducing the component lifetime or alternatively increase operational margins to avoid those overloads. The lifetime loss of insulators, and thus also the components, is caused by heating according to the law of Arrhenius. We also study consumer costs. The results support the assumption that there is a need to develop and use additional and case-specific performance criteria for electricity load forecasting.

scholarly journals A hybrid optimal contribution approach to drive short-term gains while maintaining long-term sustainability in a modern plant breeding program

2020 ◽  
Author(s):  
Nicholas Santantonio ◽  
Kelly Robbins
Keyword(s):  
Plant Breeding ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Variance ◽  
Hybrid Approach ◽  
Breeding Program ◽  
Short Term ◽  
Breeding Programs ◽  
Plant Breeding Program

1AbstractPlant breeding programs must adapt genomic selection to an already complex system. Inbred or hybrid plant breeding programs must make crosses, produce inbred individuals, and phenotype inbred lines or their hybrid test-crosses to select and validate superior material for product release. These products are few, and while it is clear that population improvement is necessary for continued genetic gain, it may not be sufficient to generate superior products. Rapid-cycle recurrent truncation genomic selection has been proposed to increase genetic gain by reducing generation time. This strategy has been shown to increase short-term gains, but can quickly lead to loss of genetic variance through inbreeding as relationships drive prediction. The optimal contribution of each individual can be determined to maximize gain in the following generation while limiting inbreeding. While optimal contribution strategies can maintain genetic variance in later generations, they suffer from a lack of short-term gains in doing so. We present a hybrid approach that branches out yearly to push the genetic value of potential varietal materials while maintaining genetic variance in the recurrent population, such that a breeding program can achieve short-term success without exhausting long-term potential. Because branching increases the genetic distance between the phenotyping pipeline and the recurrent population, this method requires sacrificing some trial plots to phenotype materials directly out of the recurrent population. We envision the phenotypic pipeline not only for selection and validation, but as an information generator to build predictive models and develop new products.

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