Selection in the presence of a genotype by environment interaction : response in environmental sensitivity

Transcription

1 Animal Science 23, 76: /3/ $2 23 British Society of Animal Science Selection in the presence of a genotype by environment interaction : response in environmental sensitivity R. Kolmodin 1, E. Strandberg 1, H. Jorjani 1 and B. Danell 1 1 Department of Animal Breeding and Genetics, PO Box 723, S-75 7 Uppsala, Sweden Rebecka.Kolmodin@hgen.slu.se Also a member of staff of the Interbull Centre. Abstract The effect of selection for high phenotypic value in the presence of a genotype by environment interaction (G E, i.e. genetic variation for environmental sensitivity) and an improving environment was studied in a simulation. Environmental sensitivity was evaluated by using reaction norms, which describe the phenotype expressed by a genotype as a function of the environment. Three types of reaction norms (linear, quadratic and sigmoid), and two selection schemes (mass selection and progeny test selection) were studied. Environmental sensitivity was measured as the weighted average of the absolute value of the first derivative of the reaction norm function. Results showed that environmental sensitivity increased in response to selection for high phenotypic value in the presence of G E and an improving environment when reaction norms were linear or quadratic. For sigmoid reaction norms, approximating threshold characters, environmental sensitivity increased within the environmental range encompassing the threshold. With mass selection and/or non-linear reaction norms, environmental sensitivity increased even without environmental change. Keywords: genotype environment interaction, phenotypic plasticity, selection, simulation. Introduction The term phenotypic plasticity is often used in evolutionary biology. Phenotypic plasticity is the ability of a genotype to alter its phenotypic expression in response to environmental influences (Bradshaw, 1965). In an animal breeding context the term environmental sensitivity is more common. In this paper, environmental sensitivity is defined to be synonymous to phenotypic plasticity. The genetic variation in environmental sensitivity is used as a definition of genotype by environment interaction (G E). The reaction norm of a genotype describes the phenotype expressed by that genotype over a range of environments (e.g. Woltereck, 199 in : Suzuki et al., 1981; Stearns, 1989). A general measure of environmental sensitivity in a specific environment is the first derivative of the reaction norm function in that environment (de Jong, 1995). For a linear reaction norm function the first derivative equals the slope, a measure of the environmental sensitivity 375 that is often used in empirical studies (e.g. Finlay and Wilkinson, 1963; Ceccarelli and Grando, 1991). The reaction norm function can be used to predict breeding values for a trait, given a certain environment. Breeding values for reaction norm parameters can be estimated, if phenotypic values of a large number of offspring in a reasonably wide range of environments are available (Kolmodin et al., 22). These breeding values could then be used to monitor the environmental sensitivity of a trait in a population, or to select for high or low environmental sensitivity of a trait, in parallel to genetic improvement of the mean level of the trait. Empirical studies have shown that phenotypic plasticity can change as a result of selection (Falconer, 199; Scheiner and Lyman, 1991; Hillesheim and Stearns, 1991). However, in other experiments, the response in plasticity was not significant (Holloway and Brakefield, 1995; Wijngaarden and Brakefield, 21). Reported estimates of genetic variation and heritability of

2 376 Kolmodin, Strandberg, Jorjani and Danell plasticity in several species vary from non-significant to highly significant (e.g. Scheiner and Lyman, 1989; Weis and Gorman, 199; Holloway and Brakefield, 1995; Scheiner and Yampolski, 1998; Kolmodin et al., 22). In general, genetic variation of the plasticity of a trait is considerably lower than the genetic variation of the mean value of the trait (Scheiner, 1993). The study by de Jong and Bijma (22) gives an algebraic description of the response to selection for a phenotypically plastic trait assuming a constant environment Theoretically, a reaction norm may have any shape, unless restricted by genetic constraints (Gavrilets and Scheiner, 1993) or other costs and limits to plasticity (reviewed by DeWitt et al., 1998). However, within the range of environments normally encountered, it is often reasonable to assume that reaction norms are linear, as it has been described for gall size in the gall fly, Eurosta solidaginis (Weis and Gorman, 199) and for milk protein production (Calus et al., 22; Kolmodin et al., 22) and female fertility in dairy cattle (Kolmodin et al., 22). Assume a population of animals having linear reaction norms with individual variation in slope. The two sires in Figure 1 have equal phenotypic values when the environmental value is 2. If, in a later generation, the environmental value has increased one unit, the progeny of sire A will be favoured over the progeny of sire B. These progeny of sire A, having the steeper reaction norm, respond more strongly to the changes in the environment. Consequently, if the population is selected for high phenotypic value, and the environment is continuously improving, there is reason to believe that the average phenotypic plasticity, or environmental sensitivity, of the population will increase. The described situation may be typical for domestic animals in an intensive production system, where feeding and management are continuously improved, in addition to the genetic improvement. When the reaction norms are non-linear, the plasticity is not the same over the entire environmental gradient. Non-linear reaction norms have been estimated for milk production traits in dairy cattle (M. P. L. Calus and R. F. Veerkamp, personal communication), and for several traits in relation to temperature for Drosophila (e.g. David et al., 1997; Morin et al., 1999; Gibert and de Jong, 21). Second degree polynomial functions are used when there is an optimal environmental value that maximizes the phenotypic value (Delpuech et al., 1995; Morin et al., 1999) Figure 1 Reaction norms of two sires (sire A, sire B ). Arbitrary units of phenotypic and environmental values. A sigmoid shaped reaction norm may describe a threshold character with two phenotypic classes (e.g. Roff, 1994; Fairbairn and Yadlovski, 1997) or a situation, where the range of phenotypic values of the trait of interest has upper and lower asymptotes within the studied environmental interval. Sigmoid shaped reaction norms have been found for wing/ thorax ratio (David et al., 1994; Morin et al., 1999) and abdominal pigmentation in Drosophila (David et al., 199; Gibert et al., 1996). Our objective was to study the effect on environmental sensitivity of selection for high phenotypic value in combination with a continuously improving environment, when there was genetic variation for environmental sensitivity. The relevance of the question is demonstrated by a study of G E in Nordic Red dairy cattle (Kolmodin et al., 22). Linear reaction norms were found for milk protein production and female fertility, and there was genetic variation for the environmental sensitivity of these traits. As dairy cattle are selected for high production of milk protein and fertility, and as animal husbandry is improved in parallel, there is reason to believe that the environmental sensitivity of high-producing dairy cattle will increase. The hypothesis of this study was that the average environmental sensitivity of a population selected for high phenotypic value in a continuously improving environment, and in the presence of G E, would increase. The hypothesis was tested in a simulation of selection in combination with different levels of environmental change. The three types of reaction norms reported in the literature, linear, quadratic and sigmoid, were studied.

3 Environmental sensitivity in populations under selection 377 Material and methods Reaction norms were simulated for a population of 2 animals. Selection was practised for 1 generations and the experiment was replicated 1 times. When simulating quadratic or sigmoid reaction norms, the simulation was allowed to run for up to 1 generations to study the development of the shape of the reaction norms. In the base population the average environmental value (x t ) was zero on an arbitrary environmental scale with s.d. 3 units. For each generation, x t was increased ( E) by, 1, 5, 5 or 1 units. The environmental value simulated for each individual was a random deviation, x i, from the average environment of each generation. The generations were not overlapping and the environment was assumed to be constant from birth to selection within each generation. No systematic environmental effects within any one generation were simulated. Two alternative selection schemes were used to select individuals to parent the next generation : (1) mass selection scheme (designated by M) : 1% of both males and females were selected in each generation based on their phenotypic values (y i x t, + x i ), and (2) progeny test scheme (designated by P) : 5% of the males were selected based on their expected phenotypic value in the average environment of each generation (y i x t ), i.e. the expectation of the reaction norm function. This value corresponds to an estimated breeding value based on a large number of offspring normally distributed over all possible environments. In the progeny test scheme there was no selection among females. Mating among selected animals was at random and each mating resulted in an equal number of offspring. To assess the effect of genetic drift, simulations were also run with random selection of individuals to parent the next generation and without environmental change. The same numbers of parents as in the mass selection and progeny test schemes were selected. Genetic variances for the parameters of the linear reaction norms corresponded to those estimated for milk protein production in Nordic Red dairy cattle (Kolmodin et al., 22). The variances of the parameters of the non-linear reaction norms were chosen to result in approximately the same phenotypic variation as for the linear reaction norms. The reaction norm parameters, the individual environmental deviations (x i ) and the residuals (e i ) were independently normally distributed with an expectation of zero. The environmental and residual variances were 9 and 5, respectively. Expectations and variances of the reaction norm parameters were allowed to change in response to selection. Linear reaction norms (experiments L M, L 1 M, L P and L 1 P) Linear reaction norms (designated by L) were simulated as y it = a i + (b f + b i )(x t + x i ) + e i (1) where y it is the phenotype of individual i in generation t, and in the given environment, a i is the intercept (level) of the reaction norm of individual i, b f is the regression coefficient of a fixed regression of phenotypic value on environmental value (fixed slope), b i is the corresponding random regression coefficient (random slope) of individual i, x t is the average environment in generation t, x i is the random deviation from the average environment of individual i, and e i is the random residual. The base population variances of the intercept and random slope were 1 and 2, respectively. The corresponding phenotypic variance was approximately 618. In experiments L M and L P the fixed slope was set to zero, i.e. on average there was no environmental sensitivity in the base population and no environment gave a higher average phenotypic value than any other. In experiments L 1 M and L 1 P the fixed slope was set at 1, to define higher environmental values as favourable, i.e. the base population was on average environmentally sensitive. Quadratic reaction norms (experiments QM and QP) In experiments QM and QP, quadratic reaction norms (designated Q) were simulated as a second degree polynomial function: y it = a i + b i (x t + x i ) + c i (x t + x i ) + e i (2) where a i, b i and c i are the reaction norm parameters of individual i, and y it, x t + x i and e i are defined as above. Base population variances of a i, b i, and c i, were 8, 2, and 1, respectively. The corresponding phenotypic variance was approximately 617. The relations between the variance components for the intercept and the slope, and the slope and the quadratic coefficient were chosen to be similar to the relation between the variances of the intercept and the slope of the linear reaction norms estimated from dairy cattle field data (Kolmodin et al., 22). A restriction c i was imposed on the curvature of the reaction norm, to ensure that a maximum phenotypic value existed. Hence, the parameter c i had a

4 378 Kolmodin, Strandberg, Jorjani and Danell truncated normal distribution with an expected value of 25 in the base population. Sigmoid reaction norms (experiments SM and SP) Sigmoid reaction norms (designated by S) were simulated as y it = a i + b i e ci + di (xt + xt) /(l + e ci + di (xt + xt) ) + e i (3) where a i, b i, c i, and d i are the reaction norm parameters of individual i, and y it, x t, x i, and e i are defined as above. The base population variances of a i, b i, c i and d i, were 5, 18, 1 5 and 2, respectively. The corresponding phenotypic variance was approximately 62. The relations between the variance components of the reaction norm parameters were chosen to get more variation in the relative levels of the two asymptotes of the reaction norm function, than in the slope at the inflexion point. This was in accordance with the larger variance in level than in slope of the linear reaction norms estimated from dairy cattle field data (Kolmodin et al., 22). Measure of environmental sensitivity The environmental sensitivity of an individual in a specific environment was measured as the first derivative of the reaction norm function with respect to (x t + x i ) evaluated at x i. The fixed slopes of the linear reaction norms were not included. The overall environmental sensitivity of each individual was measured as the weighted average of the absolute value of the derivative over the environmental range of ± 3 environmental s.d. units (i.e. ± 9 units) from the generation mean. The weights were the values of the probability density function of the environmental distribution in the interval. The population average environmental sensitivity (ES) was the average of the overall environmental sensitivities of all individuals in each generation. Results and discussion The response to selection and environmental change will be reported for ES and individual reaction norm parameters. The response in phenotypic values across the environmental scale will not be dealt with as that was not the main focus of this study. The costs of plasticity or biological restrictions were not accounted for in this simulation. In biological systems it is probable that such restrictions not only exist but have an effect on the shape of the reaction norm. A simulation including such restrictions could give different results from those found in this study. Environmental change The low levels of environmental change ( E to 5) were assumed to be relevant for populations living under natural conditions, in harsh climates and/or in developing countries, where the rate of improvement of animal husbandry may be low. Absence of or slow environmental change may also be relevant for populations kept under standardized conditions, e.g. in test stations, laboratories, or specific pathogen free farms. The high levels of environmental change ( E 5 to 1 ) were assumed to be relevant for farm animals in an intensive production system, where competition and market demands drive the need for continuous improvement of technology, food quality, health care, etc., as well as a genetic progress. Fast 1 1 (a) (b) Figure 2 Average reaction norms (average level and slope of 1 replicates of 2 individuals per generation) in generation ( ), 2 ( ), 4 ( ), 6 ( ), 8 ( ) and 1 ( ) for experiment L P (linear reaction norms, progeny test selection scheme, fixed slope ) plotted over the expected range of environments for each generation (±3 environmental s.d. units from generation average). (a). E = 5, (b) E = 1. 5

5 Environmental sensitivity in populations under selection 379 environmental change may also occur when an extensive production system is being upgraded to a more intensive system. Reaction norm parameters and the shape of the reaction norms Short term effects. In the base generation, the average linear reaction norm, as described by the average values of level and slope, was flat (equation 1, Figure 2). The average intercept, a i (equation 1), increased over generations, showing the selection response in the average value of the trait, in all simulated combinations of value of fixed slope, selection scheme and E (not shown). The increase was smaller at higher levels of environmental change (Figure 2a and b). The average random slope of the 1 1 (a) (b) Figure 3 Average reaction norms (average reaction norm parameters of 1 replicates of 2 individuals per generation) in generation ( ), 2 ( ), 4 ( ), 6 ( ), 8 ( ) and 1 ( ) for experiment QP (quadratic reaction norms, progeny test selection scheme) plotted over the expected range of environments for each generation (±3 environmental s.d. units from generation average). (a) E = 5, (b) E = linear reaction norm, b i (equation 1) became steeper and steeper with selection in an improving environment ( E 1, not shown). The increase in slope was larger at higher levels of environmental change (Figure 2a and b). The shape of the average quadratic reaction norm remained quadratic after ten generations of selection, with the peak performance within the expected environmental range of each generation (±3 environmental s.d. units from generation average, Figure 3). There was significant selection response in the mean level of the trait, as seen in the increase in a i (equation 2, Figure 3a and b) from first to tenth generation for all levels of environmental change (including E = ) and for both selection criteria (not shown). The linear coefficient, b i, of the quadratic reaction norm increased significantly from first to tenth generation when the environment was improving (not shown), indicating an increasing environmental sensitivity. The quadratic coefficient, c i, became more negative after selection, increasing the curvature of the reaction norm for mass selection with E 5 and progeny test or random selection for all levels of E (not shown). The quadratic coefficient in generation 1 was more negative with decreasing environmental change, indicating that reaction norms resulting in a maximum performance in an optimum environment could be favourable when the conditions of animal husbandry change slowly. With mass selection and a rapidly changing environment ( E = 1 ) instead, the curvature of the reaction norm diminished, indicating that a reaction norm with an optimum within the observed environmental range was less favourable. With the sigmoid reaction norm function the average reaction norm was flat in the base population (Figure 4a, b and c). The average reaction norm parameters a i, b i, c i, and d i (equation 3) all increased significantly from base to tenth generation for all levels of environmental change (except for d i, which did not change significantly with progeny test selection and E = ). At low levels of environmental change ( E 1) the average sigmoid reaction norm remained more or less flat over generations (Figure 4a). With E 5, the average reaction norm assumed a sigmoid shape after selection (Figure 4b and c). The inflexion point of the average reaction norm did not alter its position on the environmental scale, although the position was not restricted by the reaction norm function. After a large environmental change ( E = 1 ), the threshold was outside the normal environmental range of the population. With E = 1, the average reaction norm in generation 1 was also flat (Figure

6 38 Kolmodin, Strandberg, Jorjani and Danell (a) 14 (b) Figure 4 Average reaction norms (average reaction norm parameters of 1 replicates of 2 individuals per generation) in generation ( ), 2 ( ), 4 ( ), 6 ( ), 8 ( ) and 1 ( ) for experiment SP (sigmoid reaction norms, progeny test selection scheme) plotted over the expected range of environments for each generation (±3 environmental s.d. units from generation average) (solid lines). Dotted lines represent the average reaction norm function plotted over ±3 environmental s.d. units from, i.e. the base generation average. (a) E =, (b) E = 5 (c) E = 1. 4c, solid lines), because in the range of environments that this generation was expected to encounter the expressed phenotypes were at the upper asymptote of the reaction norm function (equation 3). Long term effects. When the simulation of the population with the quadratic reaction norm function was run for further generations of selection in an improving environment, the linear coefficient, b i, increased continuously (mass selection and progeny test selection). The decrease in c i during the first 1 generations of selection turned to an increase towards zero. The consequence was that the average reaction norm approached linearity within the environmental range expected after 5 or 1 generations of environmental change ( E 5 or E = 1, respectively, both selection schemes, not shown). This indicates that a reaction norm with an optimum environment could not be maintained over a long period of environmental change. The constant environmental improvement assumed in this study may not be realistic over a time period of 5 generations, although the constancy of the environmental change may not be crucial. Gavrilets and Scheiner (1993) predict that a population under stabilizing selection in an environment varying temporally within generations 1 (c) will converge on a linear reaction norm, even if nonlinear reaction norms are possible. If directional and stabilizing selection in environments varying between or within generations cause non-linear reaction norms to approach linearity, then one conclusion could be that non-linear reaction norms are transitional states. However, a linear increase in the phenotypic value would probably not be found, for biological reasons, over very large environmental ranges. Extrapolation of estimated reaction norms outside the environmental range of the data should be done with caution. When the simulation of sigmoid reaction norms was run for 5 generations of selection in an improving environment ( E 5), the range increased between the two asymptotes of the sigmoid reaction norm function. With E < 1, the sigmoid reaction norms remained more or less flat.

7 Environmental sensitivity in populations under selection 381 Roff (1994) studied the population level reaction norm for the proportion of individuals in one of the categories of a categorical trait. With the model assumptions that the threshold point is normally distributed in the population and the underlying trait distribution is fixed, this reaction norm has a sigmoid shape and selection can change the location of the threshold, i.e. the inflexion point, but not the shape of the reaction norm. This environmental threshold model has some experimental support from studies on wing dimorphic insects. Owing to different model assumptions the results of the present study are not directly comparable to the results of Roff (1994). Environmental sensitivity Linear reaction norms. The results corroborated the hypothesis of increased environmental sensitivity in response to selection for high phenotypic value in a continuously improving environment. In general, the ES of a population having linear reaction norms increased over generations and increased more with higher levels of environmental change (Figure 5a and b). ES increased more with the mass selection scheme than with the progeny test scheme at low levels of environmental change ( E 5). At high levels ( E 5 ) ES increased more with the progeny test scheme. The standard errors were 1 to 5% of the value of the ES in the different generations, selection schemes and levels of E. With the mass selection scheme, ES increased with or without environmental change. The increased ES even without environmental change can be explained as follows. Directional mass selection is an example of what Falconer (199) refers to as synergistic selection, i.e. selection and environmental effects act to change the phenotype in the same direction. Synergistic selection is expected to increase environmental sensitivity because The individuals selected are those that have experienced a favourable environment and have the genetic ability to perform well in that environment (Falconer, 199). (a) (c) ES L M 1 L 1 M 1 L 1 M 5 L M 5 L 1 M 5 L 1 M 1 L 1 M L M 5 L M 1 L M Generation ES Generation QP 1 QM 1 QP 5 QP QP 1 QP 5 QM 5 QM 5 QM 1 QM ES (b) LP 1 LP 5 LP 5 LP 1 LP Generation ES (d) SP 5 SP 1 SP SM SM 1 SM 5 SP 5 SM 5 SP 1 SM Generation Figure 5 Average environmental sensitivity (ES) in generations to 1 for different levels (, 1, 5, 5 and 1 ) of environmental change per generation. (a) Linear reaction norms, mass selection scheme and a fixed slope of (L M) and 1 (L 1 M), (b) linear reaction norms and progeny test selection scheme (LP), (c) quadratic reaction norms, mass selection (QM) and progeny test (QP) selection scheme, (d) sigmoid reaction norms, mass selection (SM) and progeny test (SP) selection scheme. ES for environmental change of, 1 or 5 units per generation are indistinguishable, or nearly so, from each other.

8 382 Kolmodin, Strandberg, Jorjani and Danell With the simulated mass selection scheme, parents were selected based on the phenotype they would have in an environment with a random deviation from the average value. The ES of a population having linear reaction norms was, by definition, the average of the absolute value of the random slope, b i. With a fixed slope, b f, of, three combinations of environmental deviations and random slopes resulted in high phenotypic values : a positive environmental deviation and a positive random slope, a negative environmental deviation and a negative random slope, and a high intercept combined with a small environmental deviation from the average value. Because both individuals with positive and negative random slopes could be selected, the variance of the slopes increased. Thus, the average absolute value of the slope (ES) increased, although b i did not increase significantly. This means that there were more animals with steep reaction norms in the later generations. Without environmental change, with mass selection and b f = 1 the individuals selected were those with a positive environmental deviation and a positive random slope. Hence, b i and b i both increased with selection for high phenotypic value, and the difference between them was small. The resulting increase in ES seen in this study when linear reaction norms were simulated has some support in the literature. Simmonds (1981) reported that newer varieties of cereals, developed in environments of high yield potential, were more responsive than older varieties and reached the conclusion that historically rising E (environment) has apparently evoked responsive genotypes. For potatoes, though, no such trend was seen. Simmonds (1991) simulated selection for high yield of cultivars in environments of constant high, medium or low yield potential, respectively. After two cycles of selection in an environment of high yield potential, the slope of a regression of cultivar yield on the environmental value, measured as the mean yield of a set of cultivars (i.e. a linear reaction norm for cultivar yield) had increased significantly. Falconer (199) reviewed experiments on mice, pigs and a number of plant species, selected for increased or decreased phenotypic value in favourable or unfavourable environments. He found that, in general, selection for high phenotypic value in a favourable, though not improving, environment increased the environmental sensitivity. With the progeny test selection scheme ES increased significantly over generations with E 5 (Figure 5b). As expected, ES did not change with zero or very low environmental change ( E 1), as selection was based on the expected phenotypic value in the average environment. Without environmental improvement, this average environment remained the same (zero) in all generations. The phenotypic value in an environment of value zero depended only on the intercept, a i (equation 1). As ES did not depend on a i, selection should not affect ES. This means that without a systematic change in the environment, a progeny test selection scheme for high phenotypic value or selection on predicted breeding values mainly based on progeny information, should not result in an increase in ES, unless there is a correlation between the level and the slope of the reaction norm. When parents were selected according to the progeny test scheme, the value of the fixed slope, b f, had no effect on the estimated reaction norm parameters and hence not on ES. With random selection of parents there was no significant change in ES or reaction norm parameters (not shown). Quadratic reaction norms. The ES increased significantly from base to tenth generation for all levels of environmental change (Figure 5c). The ES increased more with the progeny test selection scheme than with mass selection. In general, the increase in ES was larger with higher levels of environmental change. The standard errors were 1 to 2% of the value of the ES in the different generations, selection schemes and levels of E. The increase in ES when there was no environmental change can be explained by genetic drift of the linear and quadratic coefficients, b i and c i (equation 2, ES = weighted average of b i + 2ci (x t + x i ) ). Also with random selection of parents, ES increased from base to tenth generation. Although b i did not change significantly with E =, the variation between replicates increased as expected due to random drift occurring independently in the different replicates (Falconer and Mackay, 1996). As ES was a measure of absolute values, the increased variation between replicates also increased the ES. The random drift of c i was asymmetric, because of the restriction c i. The replicate average of c i could increase only to asymptotically approach zero, but could decrease indefinitely. Thus the average c i became more negative by random drift, and ES increased. An example of a quadratic reaction norm, with relevance to animal production, may be found for growth rate in broilers with respect to ambient temperature. Broilers between 4 and 8 weeks of age have maximal growth rate between 18ºand 2ºC (Yalcin et al., 21), which can be thought of as a

9 Environmental sensitivity in populations under selection 383 quadratic reaction norm. Heat stress, reducing growth performance, has a larger effect in broilers with high growth potential than in slower-growing chickens (e.g. Cahaner and Leenstra, 1992; Yunis and Cahaner, 1999; Yalcin et al., 21). High sensitivity to heat stress may be a correlated response to selection for increased growth rate (Cahaner and Leenstra, 1992; Yunis and Cahaner, 1999). It could also be an example of the results seen in this study, if one thinks of the selection for high phenotypic value as selection for increased growth rate, of the environment as a temperature gradient, and of heat stress as environmental sensitivity. The scenario of E =, would correspond to a situation where the breeding population has been kept in conditions of well regulated temperature for several generations. The simulated environmental improvement would correspond to improvements of control and optimization of the ambient temperature. Sigmoid reaction norms. At low and intermediate levels of environmental change ( E 5) ES increased significantly over generations with mass and progeny test selection. ES was higher after progeny test selection than after mass selection. The ES increased even without environmental change (Figure 5d), because the parameters that increased the phenotypic value in the environment of value zero also increased the ES. The standard errors of ES were 1 to 8% of the value of the ES in the different generations, selection schemes and levels of E. With high levels of environmental change ( E 5 ), the ES increased for some generations and then decreased (Figure 5d). In the later generations, sensitivity to environmental change occurred more in the less frequent environments, because the threshold was not close to the average environment of the population (Figure 4c). However, there was variation in individual ES, even when the average reaction norm was flat. With random selection of parents there was no significant change in ES or reaction norm parameters (not shown). The dotted lines in Figure 4c illustrate the reaction norms over the environmental range encountered in the base generation. As can be seen in Figure 4c, all generations showed environmental sensitivity in the environmental range encompassing the threshold. The interpretation of this result is that a large decrease in the environmental value, beyond the range of environments that the later generations of the population are expected to encounter, could have drastic effects on the average phenotypic value of the population. For example, in a population living in a very good environment, all animals may be healthy. If, for some reason, the population encounters a worse environment, the disease incidence could increase dramatically, reducing the average phenotypic value of health. Environmental sensitivity as a breeding goal trait More knowledge is needed about the shape of reaction norms for important traits in our domestic animal populations. The (co)variation of reaction norm parameters for different environmental descriptors needs to be studied. With this knowledge, a decision could be made as to whether to include the environmental sensitivity of a trait in the breeding goal. This would be possible if reaction norms for the sires of the breeding population were routinely estimated from field data. The first derivative of the reaction norm function for a certain trait would then give the predicted breeding value for environmental sensitivity for this trait. In this study environmental sensitivity was measured as the absolute value of the first derivative of the reaction norm function averaged over the environmental range of interest and weighted by the environmental probability density function. This was relevant because the study was focused on the average environmental sensitivity of a population that encounters a range of environments. If the interest is in the environmental sensitivity of each individual, or if a certain response to environmental change is expected or desired, one could instead use the actual value of the derivative of the reaction norm function (de Jong, 1995) in the environment of each individual, because it distinguishes between individuals with positive and negative derivatives of the reaction norm function. In an intensive farming system, where the environment can be kept adequate, a high sensitivity (responsiveness) towards environmental change may be desired. The benefit from improvements of for example management and feeding would be substantial. The risk of environmental deterioration, causing drastic reduction in the value of the responsive trait, would be relatively low. However, the system would be susceptible to disturbances, e.g. food quality problems or disease. Breeding for high sensitivity could be of ethical concern, if animals as a result became restricted to highly controlled environments for their welfare or even survival. Low sensitivity of production traits could be useful in agricultural systems where the environment is unpredictable and cannot be controlled, as may be the case for subsistence farmers in developing countries. In such situations yield stability is of paramount importance to minimize the risk of crop failure (Ceccarelli, 1994). Similarly, the livestock must

10 384 Kolmodin, Strandberg, Jorjani and Danell be tolerant to harsh conditions. The disadvantage of breeding animals for low environmental sensitivity would be a lesser incentive for actions to improve animal husbandry, because of the less sensitive animals small response to improved conditions. Also, breeding for low sensitivity could be of ethical concern, if animals as a result lost their ability to react and respond to stressful treatment. Conclusion We detected significant selection response in environmental sensitivity, or phenotypic plasticity, when selection was for high phenotypic value in an improving environment. The environmental sensitivity of traits having linear or quadratic reaction norms can be expected to increase, as a result of traditional selection programmes combined with environmental improvements in animal husbandry. Environmental sensitivity may increase even with small environmental changes, as might be experienced by populations living under natural or standardized conditions. Sigmoid reaction norms, approximating threshold characters, showed environmental sensitivity in the environmental range encompassing the threshold. After a large environmental change, the environment of the population was not in the range of the threshold. The reaction norms appeared flat, although the reaction norm function did not approach linearity. This means that if the environment was to deteriorate, environmental sensitivity would be expressed and the average performance of the population would be reduced drastically. Even when the average environment is not in a state of change, there is a random variation of the environment experienced by each individual. With mass selection, environmental sensitivity may increase as a result of selecting individuals that are able to take advantage of a favourable environment. References Bradshaw, A Evolutionary significance of phenotypic plasticity in plants. Advances in Genetics 13: Cahaner, A. and Leenstra, F Effects of high temperature on growth and efficiency of male and female broilers from lines selected for high weight gain, favourable feed conversion, and high or low fat content. Poultry Science 71: Calus, M. P. L., Groen, A. F. and Jong, G. de. 22. Genotype x environment interaction for protein yield in Dutch dairy cattle as quantified by different models. Journal of Dairy Science 85: Ceccarelli, S Specific adaptation and breeding for marginal conditions. Euphytica 77: Ceccarelli, S. and Grando, S Selection environment and environmental sensitivity in barley. Euphytica 57: David, J. R., Capy, P. and Gauthier, J Abdominal pigmentation and growth temperature in Drosophila melanogaster: similarities and differences in the norms of reaction of successive segments. Journal of Evolutionary Biology 3: David, J. R., Gibert, P., Gravot, E., Petavy, G., Morin, J., Karan, D. and Moretau, B Phenotypic plasticity and developmental temperature in Drosophila: analysis and significance of reaction norms of morphometrical traits. Journal of Thermal Biology 22: David, J. R., Moretau, B., Gauthier, J. P., Pétavy, G., Stockel, A. and Imasheva, A. G Reaction norms of size characters in relation to growth temperature in Drosophila melanogaster: an isofemale analysis. Genetics, Selection, Evolution 26: Delpuech, J., Moretau, B., Chiche, J., Pla, E., Vouidibio, J. and David, J. R Phenotypic plasticity and reaction norms in temperate and tropical populations of Drosophila melanogaster: ovarian size and developmental temperature. Evolution 49: DeWitt, T. J., Sih, A. and Wilson, D. S Costs and limits to phenotypic plasticity. Trends in Ecology and Evolution 13: Fairbairn, D. J. and Yadlovski, D. E Coevolution of traits determining migratory tendency: correlated response to selection on wing morphology. Journal of Evolutionary Biology 1: Falconer, D. S Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genetical Research 56: Falconer, D. S. and Mackay, T. F. C Introduction to quantitative genetics, fourth edition. Longman Group, Essex. Finlay, K. W. and Wilkinson, G. N The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research 14: Gavrilets, S. and Scheiner, S. M The genetics of phenotypic plasticity. V. Evolution of reaction norm shape. Journal of Evolutionary Biology 6: Gibert, P. and Jong, G. de. 21. Temperature dependence of development rate and adult size in Drosophila species: biophysical parameters. Journal of Evolutionary Biology 14: Gibert, P., Moretau, B., Moretau, J. and David, J. R Growth temperature and adult pigmentation in two Drosophila sibling species: an adaptive convergence of reaction norms in sympatric populations? Evolution 5: Hillesheim, E. and Stearns, S. C The responses of Drosophila melanogaster to artificial selection on body weight and its phenotypic plasticity in two larval food environments. Evolution 45: Holloway, G. J. and Brakefield, P. M Artificial selection of reaction norms of wing pattern elements in Bicyclus anynana. Heredity 74:

11 Environmental sensitivity in populations under selection 385 Jong, G. de Phenotypic plasticity as a product of selection in a variable environment. The American Naturalist 145: Jong, G. de and Bijma, P. 22. Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livestock Production Science 78: Kolmodin, R., Strandberg, E., Madsen, P., Jensen, J. and Jorjani, H. 22. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agriculturæ Scandinavica, Section A, Animal Science 52: Morin, J. P., Moretau, B., Petavy, G. and David, J. R Divergence of reaction norms of size characters between tropical and temperate populations of Drosphila melanogaster and D. simulans. Journal of Evolutionary Biology 12: Roff, D. A The evolution of dimorphic traits: predicting the genetic correlation between environments. Genetics 136: Scheiner, S. M Genetics and evolution of phenotypic plasticity. Annual Review of Ecology and Systematics 24: Scheiner, S. M. and Lyman, R. F The genetics of phenotypic plasticity. I. Heritability. Journal of Evolutionary Biology 2: Scheiner, S. M. and Lyman, R. F The genetics of phenotypic plasticity. II. Response to selection. Journal of Evolutionary Biology 4: Scheiner, S. M. and Yampolski, L. Y The evolution of Daphnia pulex in a temporally varying environment. Genetical Research 72: Simmonds, N. W Genotype (G), environment (E) and GE components of crop yields. Experimental Agriculture 17: Simmonds, N. W Selection for local adaptation in a plant breeding programme. Theoretical and Applied Genetics 82: Stearns, S. C The evolutionary significance of phenotypic plasticity. BioScience 39: Suzuki, D. T., Griffiths, A. J. F. and Lewontin, R. C An introduction to genetic analysis. W. H. Freeman and Company, San Francisco. Weis, A. E. and Gorman, W. L Measuring selection on reaction norms: an exploration of the Eurosta-solidago system. Evolution 44: Wijngaarden, P. J. and Brakefield, P. M. 21. Lack of response to artificial selection on the slope of reaction norms for seasonal polyphenism in the butterfly Bicyclus anynana. Heredity 87: Woltereck, R Weitere experimentelle Untersuchungen über Artveränderung, speziell über das Wesen quantitativer Artunterschiede bei Daphniden. Verhandlungen der Deutschen Zoologischen Gesellschaft 199: Yalcin, S., Özkan, S., Türkmut, T. and Siegel, P. B. 21. Responses to heat stress in commercial and local broiler stocks. 1. Performance traits. British Poultry Science 42: Yunis, R. and Cahaner, A The effects of the Naked Neck (Na) and Frizzle (F) genes on growth and meat yield of broilers and their interactions with ambient temperatures and potential growth rate. Poultry Science 78: (Received 3 May 22 Accepted 3 February 23)