Genotype-environment interaction and the correlation structure of behavioral elements in paradise fish (Macropodus opercularis)

Physiology& Behavior,Vol. 47, pp. 343-356. ©Pergamon Press plc, 1990. Printed in the U.S.A.

0031-9384/90 $3.00 + .00

Genotype-Environment Interaction and the Correlation Structure of Behavioral Elements in Paradise Fish (Macropodus opercularis) ROBERT GERLAI AND VILMOS CSANYI l

Department of Behavior Genetics, L. Ei~tvi~s University of Budapest Jdvorka S. u. 14., GOD, 2131 Hungary R e c e i v e d 30 S e p t e m b e r 1988

GERLAI, R. AND V. CSANYI, Genotype-environmentinteraction and the correlation structure of behavioral elements in paradise fish (Macropodus opercularis). PHYSIOL BEHAV 47(2) 343-356, 1990.--Three inbred strains and all their possible FI crosses were monitored in one familiar and three unfamiliar situations. Their behavior was described by species-specific elements of the ethogram. Genetic variability was demonstrated both for behavioral elements and for factors extracted by Principal Components Analyses (PCA). We studied how the behavior of genetically different fish changed across situations and examined the interrelations both among variables measured in one situation and between those measured in different ones. Behavioral changes across situations proved to be different for certain strains and crosses, that is, genotype-environment interaction was found. The PCA's carried out for the 4 situations separately yielded unlike factor structures. Another PCA, in which all the variables were included, proved that there was correlation among certain variables measured in different situations. In general, it seems that the corresponding behavioral elements do not always represent the same phene in different situations. We discuss how the genotype-environment interactions can be interpreted, try to define behavioral strategies using the extracted factor structures, and construct a model for the organization of Macropodus behavior. Behavioral elements

Paradise fish

Genotype-environment interaction

Principal components analysis

Diallel cross

In our present exi~dments we measured the fish's behavioral elements in four different environments, among which three were completely unfamiliar for the fish. Novel environments are often preferred in animal behavior sciences since animals display a rich and varied behavioral repertoire in them, and one can assume that the behavioral responses given by animals to the stimuli of unfamiliar environments are highly significant in terms of survival, that is, from an evolutionary point of view. The diallel cross design used here has been widely applied in the genetic analysis of behavior, mostly in rats and mice, e.g., open field behavior in rats (3), avoidance learning in rats (19, 47, 48), emotional defecation in mice (5), locomotor activity and jumping behavior in mice (33,34), exploratory and learning behavior in mice (9). The genetic background of mating speed in Drosophila (18) has also been investigated with this method. A detailed diallel genetic analysis of Macropodus using Hayman's ANOVA and Variance-Covariance Analysis (29,30) will be presented elsewhere. However, one of the questions on which we would like to concentrate here is genotype-environment interaction (GE). GE has been a crucial question in behavior genetics since Tryon's

THE aim of this paper is to describe the behavior of the nine experimental paradise fish populations of a 3 x 3 diallel cross in four different situations, and to obtain information about how the behavior of genetically different fish changes across situations. We would also like to make inferences about the correlation structures of behavioral elements and formulate hypotheses that may explain the interrelations found both among variables measured in one situation and between those measured in different ones. The paradise fish is a new vertebrate study species in behavior genetics. However, its ethology is fairly familiar (16), and it has been used in a selection experiment (17) and in a classical cross analysis (11). When choosing between methods of measuring behavior, ethologists have stressed [e.g., (1,2)] that elements of the ethogram, that is, the species-specific behavioral repertoire, should be used as the relevant behavioral phenotype instead of possible irrelevant test scores. An ethological coding system for recording the behavioral elements of the paradise fish has been defined (13) and various functional relationships of these elements have been studied (11, 14, 42).

IRexluests for reprints should be addressed to V. CsAnyi.

343

344

GERLAI AND CSANYI

experiments (46). According to quantitative genetics, GE is defined as nonhomogeneous variances of nonsegregating generations, such as highly inbred strains and their F1 crosses [e.g., (37,41)]. Ethologists and animal psychologists, on the other hand, tend to define GE as a different change of behavioral responses of genotypically different animals across various environments [e.g., (15)]. In the first case, GE is seen as a result of measurements being made on an unsatisfactory scale and therefore should be made to disappear by appropriate scale transformation so that a genetic analysis can be carried out. In the second case, however, GE is itself the most important question, and its possible evolutionary importance is studied. Broadhurst and Jinks (4) made a distinction between interaction of genotypes with unsystematic and systematic environmental effects. Generalizing this idea, Fulker et al. (19) defined a microand macroenvironmental interaction, where "microenvironment" refers to the unsystematic environmental influences over which the experimenter has only limited and indirect control. This definition corresponds to the environmental error variation of quantitative genetics. "Macroenvironment" refers to systematic effects over which the experimenter has complete control and includes, for example, treatments, different test situations, etc. Obviously, the distinction between micro- and macroenvironmental interaction, that is, between the first and second approach, is not a substantive one, since the same kind of influences are capable of affecting either the micro- or macroenvironment depending on the design of the study. The design of the present experiments allowed us to detect both micro- and macroenvironmental effects. The other relevant question for which we wanted to obtain information is the correlation structure of the behavioral elements in the four test situations. We have already assumed (14) that behavioral elements represent some kind of units since they had identity and they are discrete, that is, there are no continuous transitions among them. Another finding that made this assumption reasonable was that many of them proved to have fairly simple genetic backgrounds (11). In the latter study it was also suggested that using ethological units as inputs of a PCA may be useful, since one may be able to find biologically relevant factors and study the structure of behavioral organization. Factors extracted by a factor analysis are often regarded as real phenes (20). The hierarchical organization of behavior is widely accepted, and the factors and recorded variables are thought to represent different levels in this organization [e.g., (32,43)]. Following this line of reasoning, we have concluded (11) that the factors based on the correlation of our behavioral units represent higher order characters, which we have termed Behavioral Strategies (BS). In different environments one would expect animals to exhibit different BS's. These can be studied if the PCA is applied to the particular test situation. However, one may also be interested in the question of how the behavioral elements which appear to be the same but are recorded in different situations are related to each other, since it is not obvious that they represent the same phene. This question may be answered by applying a PCA in which all the variables measured in the four situations are included. METHOD

Animals and Housing The paradise fish seems an ideal subject for behavioral experiments since, first, it is a diurnal animal; second, being a simpler vertebrate, its actions are less sophisticated and can be described precisely in terms of simple behavior elements; third, an aquarium can be maintained in complete isolation from the observer and easily controlled, thus offering a very constant and undisturbed environment; fourth, paradise fish are prolific and regularly

-

F

!

FIG. I. Observation in the familiar tank (1 st situation). Each fish from the populations of the 3 × 3 diallel cross system of U, P and C strains of Macropodus was monitored individually for 5 min in its "home tank."

produce 1,000 young at each spawning; fifth, there were inbred strains available bred in our laboratory. We chose three inbred strains for the diallel cross which showed marked behavioral differences: the " U , " the " P " and the " C , " proved to be highly different (11,26), and bred their reciprocal F1 generations (UP = U-female × P-male; UC = U-female × C-male; PU = P-female x U-male; PC = P-female x C-male; CU = C-female x U-male and CP = C-female × P-male). At the time of recording, the parental strains had reached the 22nd generation of sib-mating. One pair of parents was used to produce each cross, that is, the crosses were single families. The fish originating from different crosses and strains were raised simultaneously in groups of thirty in 80 liter glass aquaria (60 x 40 × 35 cm). The water was filtered, the temperature was held constant at 28°C, and a 14/10 light/dark cycle was maintained. Each aquarium contained a plant, Hygrophila polysperma, which lives in the natural habitat of the paradise fish. The animals were fed daily on laboratory-made fish food consisting of beef liver, hake, eggs, wheat bran and vitamins. Paradise fish reach maturity at 70°90 days and can live as long as 6 years under laboratory conditions. In these experiments, 120-220-day-old, fully mature fish were tested. The animals were moved to the recording room, where they were housed individually in 6 liter glass aquaria (30 x 15 x 15 cm) for three days. All other conditions were similar. The animals originating from different crosses and strains were then individually tested in a randomized order. To avoid possible subjectivity, each individual fish was given a

GE INTERACTION AND FACTORS IN MACROPODUS BEHAVIOR

345

L

OU

Oi

OS

Tronsparent

i

I

MICRO [

I

~,

COMPUTER j

FIG. 2. Observation in the open field tank (2nd situation), where each fish from the populations of the 3 x 3 diallel cross system of U, P and C strains of Macropodus was monitored individually for 5 min; and the three fields for measures of locomotion: OI = locomotion in the inner part of the open field; OU = locomotion in the outer part (U-segment) of the open field; OS = locomotion in the part of the open field that is located next to the transparent side of the tank.

number before testing, and their exact genotypes were checked only when all recordings had already been made. Each fish was tested individually for 5 minutes in four different situations in the following order: First situation (lst day): a familiar 6-liter tank in the recording room, where the animals had been kept for 3 days before testing (Fig. 1). Second situation (2nd day): an open field, which was a 70 × 70 × 20 cm tank painted white on all but one side. A network of squares (10 x 10 cm) was painted on the bottom (Fig. 2). The fish were individually netted in a plastic pot and were placed into the center of the open field. On the 3rd day the fish were left undisturbed. Third situation (4th day): a small (20 × 20 × 20 cm) unfamiliar tank with transparent glass walls (Fig. 3). Fourth situation (4th day): after the 5-min testing in the 3rd situation the recording was stopped for 5 min and restarted after a rotating black and white disc had been placed above the aquarium (Fig. 3). In all test situations the aquaria were illuminated from above by white phototubes and all the conditions were similar to those during rearing. For the open field test, a television camera was aimed at the transparent side of the tank and a second one was placed above; in all other test situations the fish were monitored from the side. During the recording session the experimenter left

FIG. 3. Observation in the small novel tank (3rd and 4th situations). Each fish from the populations of the 3 × 3 diallel cross system of U, P and C strains of Macropoduswas monitored individually for 5 min in each of the two situations, respectively.

the recording room and observed the fish's behavior on monitors. In the case of the open field, latency time to emerge (LAT) from the pot was recorded, and the animals' movement was monitored for 5 minutes thereafter. The total number of squares entered (locomotion scores) in various parts of the open field were recorded. Designations are as follows (Fig. 2): OIT is the activity score in the inner part of the open field. OOT is the activity score in the outer part of the open field, which was further divided and designated as OUT and OST, where OUT is the activity score in the "U-segment" of the outer part of the open-field, and OST is the activity score in the segment of the outer part located next to the transparent side of the open field. This classification of the activity scores is based on previous studies (23). The behavioral elements measured in the four environments were defined (13) as follows, with relative duration (a), or frequency (b) indicated: Escape (a) ESC: rapid to-and-fro movement, with forceful swimming perpendicular to the glass wall. Swim (a) SWI: fast locomotion mainly using the caudal fin. Move (a) MOV: slow, even locomotion without using caudal fin. Staccato (a) STA: a series of quick starts and sudden stops during locomotion. Creeping (a) CRE: The fish is propelled forward only by pectoral fin fanning, all other fins are closed, pectoral fins beat very quickly. Erratic movement (a) ERA: an intense, extremely rapid, zigzaglike locomotion. Air gulping (b) A-G: being an anabantoid fish,

346

GERLAI AND CSANYI

TABLE 1 RAW SCORES FOR THE BEHAVIOR OF THE 9 POPULATIONS OF THE DIALLEL CROSS MEASURED IN THE FAMILIAR SITUATION

UU

UP

UC

PU

PP

PC

CU

CP

CC

ES1

9.7 12.4

7.0 15.5

19.9 27.1

11.5 22.1

2.6 8.0

3.1 14.2

7.8 17.7

4.6 12,3

11.0 19.4

SW1

5.7 5.2

4.6 5.6

3.9 7.0

3.6 4.8

4.1 5.3

1.8 3.0

2.8 3.3

4.0 6.1

1.7 2.8

MOI

45.1 16.6

41.9 19.6

21.9 18.8

41.5 18.5

43.5 21.3

7.6 9.9

28.3 20.2

21.0 22.2

25.2 24.2

ST1

0.3 1.0

1.5 6.5

1.6 3.8

0.2 0.8

1.6 3.1

1.0 2.3

2.7 4.9

0.9 2.4

0.2 0.8

CR1

0.1 0.6

0.0 0.2

1.8 5.7

0.6 2.7

1.6 5.1

1.7 4.6

1.7 4.6

0.8 2.7

1.2 2.6

PI1

1.3 2.0

1.8 2.6

0.7 ! .4

0.9 1.2

1.2 1.9

0.1 0.2

1.4 2.4

0.7 1.8

0.3 1.0

AGI

7.8 8.4

7.2 13.1

2.9 3.0

5.5 6.2

3.9 2.9

0.8 1.4

3.7 4.6

1.5 1.5

2.6 3.0

FL1

4.6 6.7

2.1 3.4

3.3 7.0

2.6 4.4

1,6 4~0

1.1 3.4

1.6 4.0

2.5 9.5

0.9 2.5

HI1

7.7 5.9

12.5 11.5

12.1 19.0

13.1 14.5

11,7 9,2

8.2 16.1

14.5 16.2

10.5 15.1

9.4 17.0

RE1

7.2 6.1

9.1 8.1

8.0 8.8

8.9 10.1

14.6 17.2

16.7 18.8

11.5 13.2

10.4 16.9

12.2 21.9

OBI

0.1 0.5

0.3 1.8

0.8 2.4

0.0 0.0

0.0 0.0

0.3 1.4

3.7 9.7

0.4 1.6

0.7 2.3

Fill

17.5 21.9

18.5 25.8

26.0 30.4

16.2 24.6

17.7 23.5

58.0 33.7

24.4 29.6

44.1 42.3

37.0 38.8

Sample sizes

41

37

34

39

39

34

38

41

39

Mean above, SD below.

Macropodus from time to time swims to the surface and gulps air. Picking (b) PIC: oriented movement, the jaw is stretched out to get small pieces of food or visible spots in the environment. Floating (a) FLO: the immobile animal floats just beneath the surface, holding its position by beating its pectoral fins. Hanging in midwater (a) HIM: as FLO but at medium depth. Resting (a) RES: as FLO and HIM, but staying on the bottom of the tank. The anal or caudal fm touches the bottom. Oblique plan position (a) OBQ: the body axis of the immobile animal is inclined at 20-40 degrees from the horizontal plane, The dorsal, caudal and anal fins are closed, the pectoral fins are quickly fanning as in CRE. Freezing (a) FRZ: the fish is motionless, only the opercula, and occasionally the eyes, move. The ERA element was measured only in the 4th situation instead of the PIC element, since the former was completely absent in the other three situations while the latter was absent in the 4th one. The 3rd characters of designations of the behavior elements were changed according to which environment the elements were measured in.

Statistical Analysis The data were analysed by two-way ANOVA with repeated measures. Fixed-grouping factor was the strain, the repeat factor

was the test situation. Calculations were carried out by the P2V procedure of the BMDP Statistical Software. Nonparametric Kruskal-Wallis variance analysis was also applied. The method of transformation of the data was chosen on the basis of regression between the standard deviation and means of the groups. The homogeneity of variances across groups was tested by Levene's test. P4M procedure was chosen for Principal Components Analyses. The PCA's were carried out with the oblique rotation method, which can be considered adequate for biological systems. In the case of such systems, the criterion for factors being uncorrelated is arbitrary; furthermore, other examples have shown that orthogonal and oblique rotation lead to essentially identical results (40,44). The criterion for inclusion of the factors (principal components) was set at eigenvalues greater than 1.0. The tolerance limit for matrix inversion was 0.00001. Direct quartimin rotation was performed for simple loadings. The maximum number of iterations for rotations was 50. Kaiser's normalization was applied. PCA's were calculated separately for each of the 4 situations, and there was a 5th one carried out for all the variables. The most rigorous requirements demanding several hundred subjects and at least 6:1 subject-variable ratio to attain component stability were met in each of the five PCA's, since our sample size was 342 and

GE INTERACTION AND FACTORS IN MACROPODUS BEHAVIOR

347

TABLE 2 RAW SCORES FOR THE BEHAVIOR OF THE 9 POPULATIONS OF THE DIA!.I,EL CROSS MEASURED IN THE OPEN FIELD UU

UP

UC

PU

PP

PC

CU

CP

CC

ES2

30.1 19.8

29.7 17.6

32.2 19.0

20.0 14.9

11.3 13.0

12.6 13.7

29.0 16.3

13.1 12.7

5.7 8.3

SW2

27.6 11.9

24.1 14.1

14.2 10.3

15.7 9.2

18.5 12.9

6.6 6.2

13.6 9.0

12.0 11.7

6.2 8.2

MO2

32.8 17.1

26.6 14.6

23.0 10.2

33.4 13.3

37.9 17.0

11.7 14.5

30.3 13.2

15.2 11.6

29.8 20.1

ST2

1.0 2.3

4.5 6.1

8.8 8.8

8.2 10.0

5.3 9.6

26.4 16.5

7.7 9.2

24.3 15.8

3.2 6.0

CR2

2.8 7.2

7.9 9.2

17.7 17.8

16.2 13.2

20.6 19.1

31.3 17.1

14.4 13.2

25.3 17.6

42.3 23.0

PI2

0.8

0.0 0.0

0.0 0.0

0.7 2.2

0.5 1.4

0.1 0.3

0.0 0.2

0.0 0.0

0.3

1.6

AG2

13.6 4.0

9.0 2.9

6.6 2.6

8.4 2.7

7.8 2.8

3.9 2.0

11.9 3.9

5.2 3.0

6.4 2.5

FL2

3.1 2.7

2.2 2.0

0.9 1.4

2.1 2.8

1.9 2.8

1.5 2.5

1.4 1.9

1.8 3.2

5.6 12.4

HI2

0.5 0.9

0.7 1.0

0.0 0.2

0.3 0.7

0.3 0.8

0.2 0.8

0.4 0.7

0.1 0.5

0.6 1.4

RE2

0.2 0.8

0.2 0.9

0.4 0.9

0.3 0.9

0.4 0.8

0.2 0.6

0.3 0.6

1.3 5.1

0.3 0.9

OB2

0.4 0.8

0.6 1.2

1.3 2.2

2.5 7.0

1.6 3.1

6.6 9.0

1.1 2.2

4.3 7.9

3.9 5.4

FR2

0.0 0.0

0.4 2.3

0.4 2.6

0.0 0.0

0.5 1.4

2.1 9.2

0.3 1.3

1.8 4.9

1.2 4.8

OIT

47.2 60.3

36.1 16.4

25.3 11.9

35.3 15.6

42.4 15.4

27.3 9.5

28.1 11.6

30.8 14.4

31.1 14.1

OUT

57.6 30.8

70.4 35.2

36.9 21.3

58.4 24.7

65.1 26.4

25.9 18.0

38.5 24.5

34.4 22.3

25.9 23.1

OST

114.3 52.4

109.0 50.6

83.6 45.3

77.4 44.1

66.6 38.2

48.0 38.0

71.4 25.9

56.3 32.1

33.8 17.4

LAT

4.3 3.5

9.9 7.9

22.4 10.5

15.2 8.7

19.2 10.0

38.3 34.1

18.1 9.9

41.1 24.1

82.0 68.5

1.2

Sample sizes as in Table 1. Mean above, SD below. the numbers of variables included in one PCA were between 11 and 48. [For references of other examples where analyses with even less rigorous requirements proved to be satisfactory see (40).] Basically, there are three approaches by which one may study the correlation structures of the behavioral elements in the present experimental design. First, one may be interested in the within line (and cross) correlations; second the within situation correlations; and third the correlation structure of the whole design. The first ap roach is not considered to be adequate for the purposes of the present study. If data of genetically homogeneous animals were the input of the PCA, then the extracted factors would explain the environmental variances and correlations only. When, e.g., possible redundancy due to the subjective choice of observational variables is the question, then studying environmental correlation may be relevant. One may, however, be interested in the phenotypical eorrelatinn, which contains genetic correlation and thus

seems biologically sound. In this case the data of animals of different genotypes should be included in the analysis. To see whether there are significant strain differences in factors, the PCA's were supplemented with one-way variance analyses carried out on the factor scores for each of the four situations separately. Our 5th PCA reveals the correlation structure of the whole design. From this we may make inferences not only about the correlations among the variables measured in different situations but also about how the factors extracted for the different situations relate to each other. RESULTS The behavioral data of the nine experimental populations are summarized in Tables 1-4 with sample sizes indicated. Adequate scale transformations were sought separately for variables mea-

348

GERLAI AND CSANYI

TABLE 3 RAW SCORES FOR THE BEHAVIOR OF THE 9 POPULATIONS OF THE DIALLEL CROSS MEASURED 1N THE SMALL UNFAMILIAR TANK

UU

UP

UC

PU

PP

PC

CU

CP

CC

ES3

34.0 19.5

58.5 19.0

39.4 23.5

43.3 22.6

24.7 20.9

26.4 21.1

26.9 17.6

20.4 17.5

7.4 11.9

SW3

17.1 14.7

12.1 8.2

5.7 6.2

6.9 6.4

12.8 9.9

4.4 5.1

5.2 7.1

4.8 6.8

0.7 2.5

MO3

41.5 20.5

20.8 15.5

33.9 16.2

29.5 16.3

42.4 18.1

32.7 17.6

48.9 18.2

30.1 14.9

29.6 22.1

ST3

0.2 1.0

1.9 4.2

9.4 12.3

6.7 ll.5

7.0 13.7

16.1 17.7

7.2 11.0

18.1 18.7

0.5 0.9

CR3

1.4 4.8

2.5 4.4

7.6 9.6

8.4 12.0

7.8 6.8

12.9 10.2

6.6 5.4

17.7 10.4

37.4 23.1

PI3

0.6 2.6

0.0 0.0

0.1 0.6

0.1 0.3

0.0 0.0

0.1 0.4

0.0 0.0

0.0 0.0

0.1 0.3

AG3

15.1 6.2

10.4 2.7

6.2 2.1

10.1 3.1

8.9 2.6

4.1 2.1

10.4 3.9

6.0 3.3

5.8 4.8

~3

1.8 2.0

1.1 1.5

0.1 0.4

1.4 2.8

0.9 1.2

0.2 0.5

0.4 0.7

0.2 0.6

0.9 3.3

HI3

0.8 1.2

0.7 1.3

0.2 0.5

0.6 1.1

0.6 1.4

0.2 0.6

0.4 0.7

0.2 0.6

1.0 1.6

RE3

0.8 1.1

0.9 1.1

1.2 1.8

0.8 1.1

1.1 1.7

3.0 2.5

1.7 1.6

1.9 2.4

2.1 3.7

OB3

0.2 0.5

0.2 0.7

1.2 1.7

0.8 1.7

0.6 1.3

1.0 1.4

0.6 1.1

1.5 3.3

1.7 3.3

~3

0.2 0.6

0.2 0.8

0.6 1.7

0.7 2.5

1.1 6.4

4.4 11.9

0.7 1.9

4.5 13.3

18.2 26.3

Sample sizes as in Table 1. Mean above, SD below. sured in different environments, since the assumption of no genotype-environment interaction is not justified when environment is manipulated over a wide range of conditions (22). Appropriate scale transformation (square root) was found for SW1, AG1, ES2, SW2, CR2, AG2, OIT, OUT, and ES3. Inhomogeneity of variances could not be eliminated for the other variables by the BMDP procedure. There was no significant among-population difference found for HIM and RES, F(8,323)= 1.5, p = 0 . 1 5 8 ; F(8,323)= 1.24, p = 0 . 1 9 5 ; however, for the other variables it was revealed as significant, F(8,323)>2.28, p < 0 . 0 2 2 . Significant difference was revealed among situations for all the variables, F(3,969)>8.90, p < 0 . 0 0 0 1 . The situation-population interaction was found to be nonsignificant for RES only, F ( 2 4 , 9 6 9 ) = 0 . 9 0 , p = 0 . 6 0 7 , in all the other cases it was significant, F(24,969)>1.67, p < 0 . 0 2 3 . Since the assumptions of normal distribution of the populations were difficult to check because of the smaller sample sizes, and since the assumption of homogeneous variances was violated in some cases, we also performed a nonparametric variance analysis (Kruskal-Wallis test) to investigate the differences among experimental populations. In general, this analysis gave similar results (Fig. 4) to those of the 2-way ANOVA. The factor structures extracted by PCA for each of the four test situations can be seen in Tables 5-8. The PIC and ERA elements were excluded from the correlation calculations because they occurred very rarely and their values were often zero. The factors

in the 1st, 2nd, 3rd and 4th situations explained 56%, 57%, 63% and 63% of the total variance in each situation, respectively. Table 9 shows the factor structure for the 4 situations together when all the variables were included in the analysis. In this case the factors explained 68% of the total variance. Owing to the oblique rotation method, significant correlation was found between Factor 1 and Factor 3 in the 1st situation ( r = .266, p < 0 , 0 1 ) , Factor 1 and Factor 2 in the second situation (r = .245, p < 0 , 0 2 ) , and Factor 1 and Factor 2 in the 3rd situation ( r = .306, p < 0 . 0 1 ) , respectively, which may mean that each pair of them represents only one common factor. No one has ever studied the intercorrelations among behavioral elements of paradise fish recorded in different situations. The question of whether a behavioral element that looks structurally the same in different situations represents similar phenes in these situations may be answered by the 5th PCA (Table 9). In short, there are factors that contain twice as many major loadings of behavioral units recorded in one particular situation as the units recorded in others ("situation factors"), e.g., Factor 3 (4th situation), Factor 4 (lst situation), Factor 5 (lst situation), Factor 6 (3rd situation), Factor 8 (4th situation), Factor 9 (2nd situation), Factor 11 (lst situation). While other factors contain one sort of unit from almost all the situations ( " u n i t factors"), e.g., Factor 2 ( S W l , 2, 3, 4). Factor 1 (E,S2, 3, 4), Factor 7 (MO2, 3, 4, and AG2, 4), Factor 10 (ST2, 3, 4) Factor 14 (HI2, 3), these contain different behavioral units recorded in more than one situation as

GE INTERACTION AND FACTORS IN MACROPODUS BEHAVIOR

349

TABLE 4 RAW SCORES FOR THE BEHAVIOROF THE 9 POPULATIONS OF THE DIALLEL CROSS MEASUREDIN THE SMALL UNFAMILIARTANK WITH THE PRESENCE OF A ROTATING DISC PLACED ABOVE IT UU

LIP

UC

PU

PP

PC

CU

CP

CC

ES4

53.0 22.1

47.2 18.8

48.8 28.0

33.0 24.1

15.9 18.2

36.5 27.8

38.0 21.7

24.3 23.5

5.2 11.1

SW4

7.6 8.2

5.5 6.0

2.6 4.0

1.7 3.0

3.4 4.8

3.7 5.2

2.6 3.9

1.7 2.9

0.3 1.2

MO4

25.1 14.7

25.1 12.7

14.3 10.5

21.4 18.1

27.5 21.0

16.4 14.3

18.6 12.8

16.3 15.5

12.3 17.6

ST4

0.8 1.4

3.4 5.4

9.6 12.9

3.8 6.0

9.5 13.2

15.4 17.9

11.3 10.8

17.7 18.4

0.8 2.4

ER4

0.1 0.5

0.3 1.0

0.4 0.7

0.6 1.2

1.6 1.8

0.5 1.1

0.8 1.4

1.0 1.6

0.6 1.0

CR4

2.1 4.3

3.8 5.2

3.2 6.2

8.1 13.1

10.0 7.9

7.3 10.5

5.3 5.8

9.3 12.1

16.6 16.4

AG4

10.9 4.9

7.7 3.7

5.5 3.4

5.9 3.8

5.8 4.4

4.7 3.2

9.6 5.5

4.6 4.0

2.7 3.6

FLA

1.2 1.9

2.1 2.7

0.6 0.9

1.1 1.7

1.1 1.7

0.6 1.0

0.5 1.1

0.2 1.0

0.6 1.7

I-I14

0.2 0.5

0.5 1.1

0.1 0.2

0.3 1.0

0.2 0.6

0.2 0.5

0.1 0.3

0.0 0.2

0.3 0.9

RE4

3.6 3.4

3.4 2.4

2.6 2.7

4.0 3.3

4.6 6.9

3.1 3.0

2.8 2.7

2.9 2.9

3.4 8.9

OB4

1.5

2.4

0.7

1.8

4.5

1.6

2.2 3.5

3.1 3.6

2.5 3.7

3.1 4.7

1.8 2.9

6.1 10.2

3.5 5.8

5.3 7.5

13.4 19.2

22.9 32.8

22.1 31.4

11.0 17.1

13.6 18.2

24.0 29.3

53.0 38.7

FR4

Sample sizes as in Table 1. Mean above, SD below.

well, Finally, there are factors that can be characterized neither by a situation nor by a behavioral unit (mixed factors), e.g., Factor 12, Factor 13 and Factor 15. Owing to the oblique rotation method, significant correlation was found between the following factors: Factors 1-3 ( r = .264, p < 0 . 0 1 ) ; Factors 4-5 (r = .213, p < 0 . 0 5 ) ; Factors 1-9 (r = - .210, p < 0 . 0 5 ) ; Factors 1-6 ( r - - - . 1 8 0 , p < 0 . 0 5 ) ; Factors 2 - 6 ( r = - . 185, p < 0 . 0 5 ) ; Factors 3 - 6 ( r = - . 193, p < 0 . 0 5 ) ; Factors 1-13 (r = - . 188, p < 0 . 0 5 ) , respectively. The analyses of variance carded out on the factor scores demonstrated that there is genetic variability in the extracted factors. The strains and crosses proved to be significantly different for all the factors, F(8,332)>2.85, p < 0 . 0 1 , but for Factor 4 in the 2nd situation, F(8,332) = 1.03, p > 0 . 4 . DISCUSSION A number of studies reporting strain × treatment interactions appeared in the 1960s [for example, (6, 7, 35, 36, 38, 45)], and GE has already been found in paradise fish behavior (12). In the behavior-genetic literature the phenomenon called "heterozygous buffering" or "behavioral homeostasis of heterokaryotypes" is widely known [for references see, e.g., (15,28)]. These expressions mean that hybrids tend to be less affected by environmental " t r a u m a . " This phenomenon can appear either as a decreased phenotypical variance of hybrids relative to the variance of the

inbred strains from where their parents originated, or as a sort of behavioral stability across different situations. Many examples have been reported where this was the case, indeed [for references, see (15, 28, 37, 41)]. However, there have been exceptions [e.g., (39,46)] when the opposite was found, that is, when the F1 variance proved to be greater. Explaining such results, Caspari (8) suggested that for behavioral traits hybrid vigor might manifest itself as a varied adaptive response to the environment in contrast to a restricted and maladaptive response typical of inbred lines. In our study neither a clear-cut indication of heterozygous buffering, nor one of more varied behavior of hybrids was found, despite the detected significant GE. The phenotypical variances of the hybrids in general did not differ in one direction from those of the inbreds, and Tables 1--4 clearly demonstrate that the behavioral changes of the hybrids across environments were neither greater nor smaller than those of the inbreds. Henderson (31) demonstrated by a hypothetical example how two strains with different means can show opposite arousal responses at two levels of stimulation. This idea was generalized by Fuller (21), whose hypothetical example clearly demonstrated how the GE interaction could depend on the experimental parameters chosen, which could alter the direction of behavioral changes of genotypically different animals. We can call this a onedimensional example, in the sense that experimental parameters change along a line, that is, in one dimension. However, when

350

GERLAI AND CS,~NYI

H ~lto

H{=8 >15,5

p < 0.05 ,

H.=s >20.1

p < 0.01

H~, s >26.1

p < QO01 * * *

~,,

11o

BEHAVIORAL ELEMENTS IN THE 4 SITUATIONS

1.

2.

3.

4.

FIG. 4. Differences among the populations of the 3 × 3 diallel cross system of U, P and C strains of Macropodus in behavioral elements of the 4 test situations expressed by the H values of Kruskal-Wallis nonparametric variance analysis test.

probably overemphasized the importance of GE and underrated the essentially uniform patterns that characterize a species. If we accept this view and look at the actual data, we can say that in spite of the significant genotype-environment interactions obtained, the behavior of the paradise fish is environment specific. In the familiar situation the most striking findings are the relatively high values of the MOV, PIC, FLO, HIM. RES and FRZ elements, which means that in this situation the fish move slowly, seek food and behave rather passively: they float either under the surface, or at medium depth, or on the bottom, or freeze. Previous studies (11,14) confirm that this sort of behavior characterizes territorial fish or fish that have been habituated to their environment. In the open field the fish become more active: MOV is still high, ESC increases and SWI is the highest compared to the corresponding elements measured in other environments. It has been found (23) that swimming behavior occurs in every segment of the open field, while escaping is frequent only by the transparent glass wall. Obviously, high " s w i m m e r s " can explore the field better. This exploration apparently loses its importance when the fish are in the small restricted aquarium (3rd situation). In the open field the values of all but one of the elements (FLO) mentioned as high in the familiar situation decreased dramatically. The values of STA, CRE and OBQ elements seem to be high in this situation. Previous studies (11, 14, 27) have suggested that these behavior elements indicate a sort of frightened state in the fish. In the small tank (3rd situation) the " f e a r behavior" is still pronounced, but OBQ decreases. The time percentages of passive behavioral elements are still low, but RES and FRZ seem to increase. Interestingly, swimming behavior decreases while escaping increases, which means that fish in a restricted small aquarium tend to avoid rather than to explore their environment. This avoidance can either be active (increased ESC) or passive (increased RES and FRZ). In the small tank with the rotating disc (4th situation) the fear behavior is still pronounced and OBQ is also high again. FRZ and RES increase more, ESC is still high and SWI decreases. These changes may indicate that in this situation the fish are more

more complex environmental situations are used, as is the case in the present study, this change can be multidimensional, which makes the results even more difficult to interpret. However, Fuller (21) has claimed that behavior geneticists

TABLE 6 SORTED FACTOR LOADINGS FOR THE 2ND TEST SITUATION (OPEN FIELD)

TABLE 5 SORTED FACTOR LOADINGS FOR THE IST TEST SITUATION (FAMILIARTANK)

FR1 MO1 HI1 OB1 ST1 RE1 ES1 SW1 AG1 CR1 FL1

Factor

Factor

Factor

Factor

1

2

3

4

.985 - .787 - .585

- .260 - .575 .781 .747 .909 .827

-.407 -.333

.421 .385

.472 -.319

Factor loadings smaller than ABS(0.25) are not presented in the table.

ES2 OST CR2 OUT OIT MO2 SW2 ST2 HI2 FL2 RE2 FR2 AG2 OB2 LAT

Factor

Factor

Factor

Factor

1

2

3

4

-

,962 .903 .715

.458

- .340

.685 .617 .581 .565

- .316

.536 - .677 .631 .509 .806

.778 .443 - .344 - .469

.290 - .315

.342

Factor loadings smaller than ABS(0.25) are not presented in the table.

GE INTERACTION AND FACTORS IN MACROPODUSBEHAVIOR

STRAINS

UU I

UP I

LIE; t

PU i

PP I

PC I

351

CU I

CP I

CC I

FACTORS F/,

TERRITORIALITY

F2

FRIGHTENED STATE

F3

? CORRELATES WITH

??

Z O

SOCIAL ATI'RP£:TION I ACTIVE )

gl

Z

F1

PASSIVE DEFENSE BASIC ACTWITY ( ? ) CORRELATES WITH F1

t-"!

HABITUATED STATE

b--

PASSIVE DEFENSE CORRELATES WITH F1

?

tn

HABITUATED STATE FRIGHTENEO STATE 1. FRIGHTENED STATE n .

FIG. 5. Factor score pattern of the populations of the 3 x 3 diallel cross system of U, P and C strains of Macropodus. Factor scores were calculated according to the factors extracted for the 4 test situations separately.

" f r i g h t e n e d , " probably by the rotating disc, and are trying to avoid danger. The other findings we would like to discuss are the differences among experimental populations in the 4 situations. Although the H values of the Kruskal-Wallis test (Fig. 4) reach the level of significance ( p < 0 . 0 5 ) in several variables, it seems that the differences among populations are somewhat smaller in the 1st and 4th situations. In the familiar tank (lst situation) fish of different genotypes tend to behave more similarly than in the open field or in the small tank. It has been stated [e.g., (9,27)] that animals in novel environments display a rich and varied behavioral repertoire. Our findings support this statement. A possible explanation for animals showing less variable behavior in familiar environments is that

they do not need to respond to new stimuli, while in a novel situation they have to choose some defensive strategies against the potential dangers. Given that these strategies are genetically variable in paradise fish (11), they cause greater among-population variance when genotypically different strains and crosses are measured. Interestingly, PIC and FLO behavioral elements are less variable in the 3 novel situations, which may mean that there has been selection against them. It has been suggested (9,25) that in novel environments it is adaptive for the animals to explore and collect information about their new surroundings, so it is masonable to suppose that being engaged with eating instead of investigating or avoiding the novel area may not be adaptive for

TABLE 7 SORTED FACTOR LOADINOS FOR THE 3RD TEST SITUATION (SMALL NOVEL TANK)

TABLE 8 SORTED FACTOR LOADINGS FOR THE 4TH TEST SITUATION (SMALL NOVEL TANK WITH ROTATING DISC)

OB3 ST3 CR3 FR3 RE3 FL3 HI3 MO3 AG3 ES3 SW3

Macropodus. The between-population variances seem to be smaller again in

Factor

Factor

Factor

Factor

Factor

Factor

Factor

Factor

1

2

3

4

1

2

3

4

-.872 .755 .745 .609 .566

- .346

.785 .753 .528

FR4 MO4 ES4 AG4 SW4 CR4 OB4

- .325 .418 .877 .647 .780 .741

.255 .420 - .517 .787 .779

.956 - .480 - .396 - .291

- .415 - .407 - .395

- .477

Factor loadings smaller than ABS(0.25) are not presented in the table.

ST4 RE4 HI4

.387 .287

- .266 .279 - .274

.686 -.532 .305

-

.270 .785 .354

Factor loadings smaller than ABS(0.25) are not presented in the table.

352

G E R L A I A N D CS,~NYI

TABLE 9 SORTED FACTOR LOADINGS FOR THE 4 SITUATIONS (ALL VARIABLES INCLUDEDi Factors 1

ES2 OST CR2 AG2 SW4 SW3 SW1 FL4 AG4 HI1 MO1 FRI ES1 RE1 FR3 RE3 MO3 MO2 OB4 CR4 RE2 FR2 RE4 OB1 ST1 CR1 FL1 OIT HI4 HI2 FL3 OB3 MO4 ST4 FR4 AGI OB2 CR3 ES3 FL2 ST2 OUT HI3 ST3 SW2 AG3 LAT ES4

2

3

4

5

6

.93 .84 - .74 .54

7

8

10

9

11

12

13

14

15

.29 .80 .69 .53

-

.26

.28

.32

.76 .57

.30

.25 .88 .67 - .57

- .47 .83 .76 .80 .68

.28 .88 .51

-

.41

.86 .64

- .27

-

.32

-

.27

.66 .61 .62

.26 .79 .71 .56 .73 .65 .69 .67 .57

.33

- .38

.47

.39 - .37 - .35 .44

- .38 - .42 .35

-

.32 .26

-

.25

-

.44 .41

.35

-

.64 .30

-

.29

.39

.44 - .30 -.37

-.39

-.29 .40

.38 .29

.42

-

.31

.27

.31

.44

.30

- .27 .39 .32 - .43 .32

-

.40

-

.26

-.40

-

.26

- .34 .32

-

.42

.39

Factor loadings smaller than ABS(0.25) are not presented in the table.

the 4th situation, a l t h o u g h , E S C and F R Z are still highly variable. In this case the rotating disc above the tank m a y frighten the fish (27) and force t h e m to u s e o n e o f their strong d e f e n s i v e strategies, in other words, there m a y h a v e b e e n selection p r e s s u r e s against u s i n g other strategies than e s c a p i n g or freezing w h e n there is a sign o f a potential predator.

Finally, the last question we d i s c u s s is the correlation structure o f the behavioral e l e m e n t s . T h e interpretation o f the results o f the P C A ' s m a y be controversial. H o w e v e r , as h a s been s u g g e s t e d (21), factors o f a multivariate correlation analysis c a n be considered as real p h e n e s , and there h a s b e e n an e x a m p l e (11) s u g g e s t i n g that if the correlation matrices are b a s e d on units o f b e h a v i o r then

GE INTERACTION AND FACTORS IN MACROPODUS BEHAVIOR

353

j ENVI FAM, UARl RONMENT

[NOVEL] ENVIRONMENT

KEY STIMULI t PERCEPTION (~ SELECTIVE ATTENTIONJ} (NEURALANALYSIS~

z~ ~

W

l

T

H

J

LARGE II SMALC IIF~GHTENINGI II II STIMULUSI

t

REFERENCESTRUCTURE ) (:INTERNALSTATE? (:NEURALANALYSIS?

[IFRz IIH,M IIESC IIFRZ IIH,MIIoBo IlSWl ]IoBoIIEsc IIHfM I |IRESlIFLO IlSWl IIRES IIFLO IISTA I ISTAIIA-o II"OV I ~/ I"°V IIA-G IICRE JlA-r" IICRE I lCeEI IA-nl /

~/ |

L

oP

of.

or

of

of"

ISTA IIERE IIESC IISTA Iltsc I ~lOSOllSWl I ISTAII A-n I .

.

.

or

or

I-~'~lTEI .

.

experimenfolfindings

.

.

I

t

BEHAVIORAL ACTION C ~.AVIORAL ~. STRATEGIES .,/

11/¢I~ \",\

f BEHAVIORAL ~. ELEMENTS //

.

flleoreficalmode[

FIG. 6. The proposed organization of Macropodusbehavior: Experimental findings and a theoretical model. (Note: according to this model, there can be genetic variability in several points in this organization, which explains the variability found both in factors and in behavioral elements, e.g., in perception, in neural analysis, or in the choice of behavioral strategies, etc.) the extracted factors are biologically relevant and represent a higher level in the organization of behavior. On the other hand, a factor is still an artificially constructed variable. Moreover, factors from two calculations are difficult to identify or differentiate, since there are no statistical methods available which would ensure an objective assessment of their possible differences. The interpretation, in which we identify and label the factors, may, therefore, be only an approximative judgement. However, the correlation structure revealed by the PCA may still be relevant. When one looks through the results of the correlation studies carded out with paradise fish of different genotypes and in different environments (11, 13, 14, 23, 26), it seems reasonable to accept that the correlations found between certain behavioral units are not invariable, that is, our observational variables are not redundant. On the other hand, the correlation structures are environment specific, that is, some invariances characterizing the behavior of paradise fish in a certain situation do exist. The fLrst question to be addressed is whether there are differences among the factor structures of the four situations. Apart from the lack of statistical accuracy in assessing the differences, it seems reasonable to accept that these structures are not the same at all, despite the fact that almost the same behavioral elements were recorded and used for the calculations and that the same number of factors (four) were extracted for each of the four situations. The factor structure of the 1st situation seems to be the most unique, while those of the three novel situations are not markedly different from one another. The second problem, the unbiased interpretation and naming of the factors, looks quite difficult. However, for the sake of simplicity it may be practical to attempt it. Moreover, one may be able to find clues for identification of the factors as well. Factor 1 of the 1st situation (home tank) may be called "Quiet state" or "Territoriality"; the fish showing this behavior may either freeze (FRZ) or hang in midwater (HIM), move slowly (MOV) and swim fast (SWI). It does not seem probable that this passive behavior reflects a sort of fear (26,27) or passive defense (11), since none of the three fear units [OBQ, STA, CRE; see (26,27)] have major

loadings on its factor. Factor 2, however, may be labelled Frightened State. Even though the fish in their home tank were not exposed to frightening stimuli, they might have received some from the very few accidental disturbances. Like Factor 1, Factor 3 may also represent a quiet, restful state and may not be independent of the former, as is indicated by the significant correlation found between them. Factor 4 might be called "escape," but this only means a tendency to leave the place. In the present case "escaping" might be due to some effect of social attraction, and could be interpreted as an attempt to approach the conspecifics. Factor 4 might reasonably be called "Social Attraction." The animals in their own home aquaria could see each others' movements, since the side walls of their tanks were semiopaque. In the open field situation the factor structure looks quite similar to that obtained in a previous study (11). Incorporating the interpretation of the factors obtained in that study we may call Factor 1 Active Defense (the fish either escapes or shows some signs of frightened state), Factor 3 Habituated State (floating and slow locomotion) and Factor 4 Passive Defense (motionless state). Factor 2 could be named Exploration (swimming inside the field). However, this may not be an independent factor, since its correlation with Factor 1 is significant, that is, there may be a common activity factor behind them. Factor 1 of the 3rd situation (small novel tank) contains the major loadings of the three units that are typical of a frightening situation, and we might therefore call it "frightened state." Although it looks very much the same as Factor 1 of the 2nd situation, the signs of the loadings on the former factor are just the opposite of those on the latter. Thus, Active Defense in this (and also in the open field) situation can manifest either as "fear" or as active fleeing. However, as the loadings on Factor 2 of the 3rd situation indicate, an escape reaction can also be the alternative of freezing or motionless state, which has been interpreted as Passive Defense. Factor 3 is very similar to the factor that was interpreted as Habituated State in the open field (Factor 3 of the 2nd situation). Factor 4 is unique in the sense that it explains one element (MOV) very well and contains the major loading of one

354

other element only (ESC), that is, slow locomotion is independent of all but the ESC element in this situation. Factor 1 in the 4th situation (small novel tank with a rotating disc placed above it) contains major loadings of those elements that are most significant in the factors of the 2nd and 3rd situations interpreted as Active Defense, but could also be interpreted as an inverse of Passive Defense. Frightened State seems to form a separate factor (Factor 2); however, it might also be interpreted as an inverse of another strategy, Active Defense, since it may manifest either as typical fear behavior (OBQ, CRE, STA) or as fleeing. Factor 3 may be interpreted as Habituated State, and it contains negative loadings of SWI and STA, which indicates that fast swimming or staccato may be the alternative of the quiet habituated state. Factor 4 is difficult to interpret because there are several active and passive elements with loadings of both positive and negative signs on it. The correspondence between the sum of the factors extracted for the four situations (sixteen) and the number of factors obtained in the 5th PCA (fifteen), where all the variables recorded in the four situations were included, might suggest that the factors obtained in different situations are independent of each other. However, this is not the case: even though there are factors concerning one single situation, there are others that characterize more than one, that is, there may be a connection between factors extracted (or between variables measured) in different situations. According to the loadings of the variables on the first factor (Table 9), this may be interpreted as an escape reaction (or Active Defense behavioral strategy) that represents the same phene throughout the 3 novel situations (situations 2, 3 and 4). The most striking feature of Factor 2 is that it contains almost exclusively the major loadings of SWI elements recorded in all four situations, which indicates that activity expressed in fast swimming may manifest irrespective of the environmental situation. Factor 3 refers to situations 3 and 4 (small aquaria), and looks similar to factors interpreted as Habituated State. Factor 4 contains major loadings of the elements recorded in the 1st situation (home tank). Interestingly, the signs of the loadings on it are just the opposite of those obtained for a factor of the 1st situation that was interpreted as Quiet State in the present study. Cs~inyi et al. (14) found significant connections among exactly the same behavioral elements that have positive major loadings on Factor 4 of the 5th PCA. They concluded that these elements characterized territorial behavior. Considering these results, we may infer that "quiet state" is the passive alternative of territoriality. Since Factor 4 contains elements of the 1st situation and does not correlate with factors referring to other situations, it seems reasonable to accept that this sort of behavior refers only to territoriality elicited in a familiar environment and has no connection with the factor interpreted as Habituated State (1 l), even though the latter contains loadings of the same sort of behavioral elements. Factor 5, which correlates with Factor 4, refers to the 1st situation again. This factor seems to be similar to that interpreted as Social Attraction in a familiar environment. The most important measure with major, and positive, loading is ESC (escaping). Escape, however, refers only to a tendency to leave, and implies neither positive nor negative emotions. Since our fish could see each others' shapes through the semiopaque side walls of their tanks, "escape" could be the measure of the approach reaction elicited by the sight of the conspecifics. This idea seems to be supported by the fact that ES1 does not correlate with any fear indicator units (e.g., OBQ, STA, CRE). The positive correlation between Factor 4 (Territoriality) and Factor 5 (Social Attraction) is quite understandable: the territorial behavior always implies some sort of social interaction. Factor 6 exclusively contains the loadings of the elements

GERLAI AND CS,~.NYI

recorded in the 3rd situation and is very similar to that labelled as Passive Defense. In spite of the fact that this Passive Defense is built up by elements similar to those found in corresponding factors, it seems to be unique in the sense that it refers to only one situation (small novel aquarium). Factor 7 supports the assumption that novel situations are somewhat similar in terms of activity measures. It contains positive major loadings of MOV and A-G elements recorded in the 2nd, 3rd and 4th situations. Factor 8 refers uniquely to the 4th situation and may be interpreted as Frightened State, whose one alternative is the expression of fear (OBQ, STA, CRE) and the other is escape reaction (ESC). According to the present results this escape reaction has no connection with the Active Defense strategy. Factor 9 may be similar to the factor interpreted as Passive Defense and refers only to the open field (2nd) situation. It thus looks as though there are two Passive Defense strategies that look very much the same; however, they refer to different environments (2nd and 3rd situations), and actually do not correlate with each other. Factor 10 reveals that the STA element recorded in different novel situations may represent the same phene. It may look as though it indicates the existence of an independent " f e a r " factor in novel environments. However, there may be another probable explanation: the loadings in Factor 10 show that STA correlates positively with either active or passive elements present in different factors, so the ethological interpretation of this factor may be controversial; on the other hand, STA is found to be the alternative of Habituated State in all the novel situations, which may make it reasonable to assume that the factors named Habituated State in novel environments are not independent of each other. Factor 11 clearly shows that Frightened State in a familiar environment expressed by OBQ, STA and CRE elements does not correlate with any other behavior shown either in the same familiar situation or in novel ones. We could not interpret Factor 12, 13 and 15 unambiguously, since they contain elements recorded in different situations belonging to different factors in each situation. Factor 14 may indicate that Habituated States in the 2nd and 3rd situations are similar phenes. In another study (24) with the same experimental set up, where a replicated diallel cross was analysed, we found nonsignificant " B l o c k s " items for all but the SWI variables (swimming recorded in the 1st, 2nd, 3rd and 4th situation), which indicated that behavior did not change over blocks (replications). This finding supported that our recording methods were reliable and characterized the strains and crosses irrespectively of the date of recording sessions. Thus, it can be assumed that the outline presented here is not biased by environmental error variation, and consequently reveals something about the organization of Macropodus behavior. Figure 5 shows the factor score pattern of the nine experimental populations in the four situations. The factors arranged according to our interpretation so that the patterns can be more easily compared across situations. These patterns also suggest that the three novel situations (especially the open field and the small novel tank) are similar to each other. The U strain is generally the most active and least passive one in these situations, P is intermediate and C is the most passive one (it is notable that there is no variability between the strains and crosses in Passive Defense in the open field, and all the factor scores are quite small). The scores for Habituated State of U and C strains are relatively high while that of the P strain is low. In Habituated State there are examples (PC, CP in the open field; UC, CU, PC, CP in the small novel tank; and all the Fls in the small novel tank with the disc,

GE INTERACTION AND FACTORS IN MACROPODUS BEHAVIOR

respectively) where the scores of F1 crosses exceed one of their parents in one direction (heterosis). The comparison of the scores of other factors extracted for different situations is difficult since, according to the 5th PCA, these factors (the four ones in the 1st situation and those interpreted as Frightened State in the 4th situation) may uniquely refer to one single situation only. The factor score pattern (Fig. 5) also suggests that the distribution of factors (Fig. 6) across different situations is caused, at least partly, by genetic effects, that is, the behavioral strategy performed by a fish in a certain situation depends on its genotype. In Fig. 6 we summarized the organization of paradise fish responses to different situations which we think is the most probable conclusion from the results of the principal components analyses. The experimental findings (left side of Fig. 6) demonstrate clearly that it is often difficult to interpret behavior only on the basis of observed elements. The behavioral elements should therefore be considered as structural units in the sense that their appearance and "anatomy" are invariable, however, they may correlate with each other variably, and may belong to different independent factors according to the given environmental situation. Factors seem to form a branch of phenes that can have ethologically relevant meaning and can explain the interrelations among behavior elements; however, they still retain the problem similar to that caused by the structural feature of the units (see, e.g., the contradiction of finding two similar-looking factors extracted in different situations to be independent). This latter

355

phenomenon may be due to genetic variability in other levels of the behavioral organization, e.g., in perception. The fight side of Fig. 6 represents our theoretical model whose definitions and nomenclature have been discussed elsewhere (10). According to this model there can be several points in the organization of behavior where genetic variability may be expected. This outline (Fig. 6) is a fast step towards the understanding of the organization of Macropodus behavior. It suggests that the paradise fish can differentiate between familiar and novel environments and can further analyse novel situations in terms of their features. The present study highlights the facts that, fwst, Macropodus " s e e s " a familiar situation as very different from a novel one; second, that the novel tanks have features in common, but that, third, they are recognized as different, and not all the behaviors of Macropodus are shown in them in the same way. We do not yet know if there are key stimuli on which Macropodus concentrates; however, it is very probable that there are important cues to which the fish adapts in order to recognize. We can thus assume that certain kinds of behavior are connected with particular features of the environment and this behavioral response is adaptive. Further studies are needed to examine in detail what feature of the environment elicits a certain sort of behavior, and to hypothesize its adaptivity. We also need to study whether there is genetic variability in other organizational levels and to investigate perception and reference structure of Macropodus and the epigenesis of the latter.

REFERENCES 1. Abeelen, van, J. H. F. Mouse mutants studied by means of ethological methods. I. Ethogram. Genetica 34:79-94; 1963. 2. Aboelen, van, J. H. F. Ethology and the genetic foundations of animal behavior. In: Royce, J. R.; Mos, L. P., eds. Theoretical advances in behavior genetics. Alphen aan den Rijn: Sijthoff and Noordhoff; 1979:101-119. 3. Broadhurst, P. L. Application of biometrical genetics to the inheritance of behaviour. In: Eysenck, H. J., ed. Experiments in personality, vol. I. Psychogenetics and psychopharmacology. London: Routledge and Kegan Paul; 1960:3-102. 4. Broadhurst, P. L.; Jinks, J. L. Stability and change in the inheritance of behaviour in rats: A further analysis of statistics from a diallel cross. Proc. R. Soc. Lond. [Biol.] 165:451)--472; 1966. 5. Bruell, J. Genetics and adaptive significance of emotional defecation in mice. Ann. NY Acad. Sci. 159:825-830; 1969. 6. Carran, A. B. Passive avoidance and strain differences associated with differences in emotionality: A test of Mowrer's theory. Psychonom. Sci. 7:263-264; 1967. 7. Carran, A. B.; Yeudall, L. T.; Royce, J. R. Voltage level and skin resistance in avoidance conditioning of inbred strains of mice. J. Comp. Physiol. Psychol. 58:427-436; 1964. 8. Caspari, E. Genetic basis of behavior. In: Roe, A.; Simpson, G. G., eds. Behavioral evolution. New York: Yale University Press; 1958: 103-127. 9. Crusio, W. E.; Abeelen, van, J. H. F. The genetic architecture of behavioural responses to novelty in mice. Heredity 56:55-63; 1986. 10. Cs~nyi, V. Contribution of the genetical and neural memory to animal intelligence. In: Jerison, H. J.; Jerison, I., eds. Intelligence and evolutionary biology. NATO ASI Series, vol. G17; 1988:299-318. 11. CsL,ayi, V.; Gedai, R. Behavior-genetic analysis of the open-field behavior of the paradise fish (Macropodus opercularis). J. Comp. Psychol. 102:326-336; 1988. 12. Csfmyi, V.; Gervai, J. Genotype-environment interaction in passive avoidance learning of the paradise fish (Macropodus opercularis). Acta Biol. Hung. 36:259-267; 1985 13. Cs~myi,V.; T6th, P.; Altbacker, V.; D6ka, A.; Gervai, J. Behavioural elements of the paradise fish (Macropodus opereulafis). I. Regularities of defensive behaviour. Acta Biol. Hung. 36:93-114; 1985. 14. Cs/myi, V.; T6th, P.; Altb/icker, V.; D6ka, A.; Gervai, J. Behavioural

elements of the paradise fish (Macropodus opereularis). II. A functional analysis. Acta Biol. Hung. 36:115-130; 1985. 15. Ehrman, L.; Parsons, P. A. The genetics of behavior. Sannderland, MA: Sinauer; 1976. 16. Forselius, S. Studies of anabantoid fishes. I. A quantitative description of the reproductive behaviour. Zool. Bidr. 32:93-302; 1957. 17. Francis, R. C. The effects of bidirectional selection for social dominance on agonistic behavior and sex ratios in the paradise fish (Macropodus opercularis). Behaviour 90:25-45; 1984. 18. Fulker, D. W. Mating speed in male Drosophila melanogaster: A psychogenetic analysis. Science 153:203-205; 1966. 19. Fulker, D. W.; Wilcock, J.; Broadhurst, P. L. Studies in genotypeenvironment interaction. I. Methodology and preliminary multivariate analysis of a diallel cross of eight strains of rat. Behav. Genet. 2:261-287; 1972. 20. Fuller, J. L. The taxonomy of psychopbenes. In: Royce, J. R.; Mos, L. P., eds. Theoretical advances in behavior genetics. Alphen aan den Rijn: Sijthoff and Noordhoff; 1979:483-513. 21. Fuller, J. L. Ethology and behavior genetics. In: Fuller, J. L.; Simmel, E. C., eds. Behavior genetics: Principles and applications. Hillsdale, NJ: Lawrence Erlbaum Associates; 1983:337-362. 22. Fuller, J. L.; Thompson, W. R. Foundations of behavior genetics. Saint Louis, MO: The C. V. Mosby Company; 1978. 23. Gerlai, R.; Cs~tnyi, V. The behaviour of the paradise fish (Macropodus opercularis) in two different open-fields. A correlation study. Acta Biol. Hung. 38:225-234; 1987. 24. Gerlai, R.; Cs~tnyi, V. Diallel genetic analysis of the elements of paradise fish's (Macropodus opercularis) ethogram. Acta Biol. Hung.; in press. 25. G-erlai, R.; Crusio, W. E.; Cs~yi, V. Inheritance of species-specific behaviors in the paradise fish (Macropodus opercularis): A diallel study. Behav. Genet.; in press. 26. Gervai, J.; Cs/myi, V. Behavior-genetic analysis of the paradise fish (Macropodus operculafis). I. Characterization of the behavioral responses of inbred strains in novel environments: A factor analysis. Behav. Genet. 15:503-519; 1985. 27. Gervai, J.; Csfiayi, V. Exploratory and fear behaviour in the macropodus opercularis subspecies, M. o. opercularis and M. o. concolor. Acta Biol. Hung. 37:277-285; 1986.

356

28. Hay, D. A. Essentials of behaviour genetics. Oxford: Blackwell Scientific Publications; 1985. 29. Hayman, B. I. The analysis of variance of diallel tables. Biometrics 10:235-244; 1954. 30. Hayman, B. I. The theory and analysis of diallel crosses. Genetics 39:789-809; 1954. 31. Henderson, N. D. The confounding effects of genetic variables in early experience research: Can we ignore them? Dev. Psychobiol. 1:146-152; 1968. 32. Henderson, N. D. Adaptive significance of animal behavior: The rote of gene-environment interaction. In: Royce, J. L.; Mos, L. P., eds. Theoretical advances in behavior genetics. Alphen aan den Rijn: Sijthoff and Noordhoff; 1979:243-299. 33. Henderson, N. D. Genetic influences on locomotor activity in 11-day-old house mice. Behav. Genet. 11:209-225; 1981. 34. Henderson, N. D. A fit mouse is a happy mouse: Jumping behavior in a 15-day-old Mus musculus. Dev. Psychobiol. 14:459-472; 1981. 35. Levine, S.; Wetzel, A. Infantile experiences, strain differences and avoidance conditioning. J. Comp. Physiol. Psychol. 56:879-881; 1963. 36. Lindzey, G.; Winston, H. D.; Manosevitz, M. Early experience, genotype and temperament in Mus musculus. J. Comp. Physiol. Psychol. 56:622-629; 1963. 37. McClearn, G. E. Strain differences in activity of mice: Influence of illumination. J. Comp. Physiol. Psychol. 53:142-143; 1960. 38. McClearn, G. E. Genotype and mouse activity. J. Comp. Physiol. Psychol. 54:674-676; 1961.

GERLAI AND CSANYI

39. Maier, S.E.; Vandenhoff, P.; Crowne, D. P. Multivariate analysis ol putative measures of activity, exploration, emotionality, and spatial behavior in the hooded rat (Rattus norvegicus). J. Comp. Psychol. 102:378-387; 1988. 40. Mather, K.; Jinks, J. L. Biometrical genetics: The study of continuous variation, 2nd ed. London: Chapman and Hall; 1971. 41. Mather, K.; Jinks, J. L. Biometrical genetics, 3rd ed. London: Chapman and Hall; 1982. 42. Nagy, A.; D6ka, A.; Cs,'inyi, V. A microcomputer method for recording and analysing behavioural elements. Acta Biol. Hung. 36:239-245; 1986. 43. Royce, J. R. The factor-gene basis of individuality. In: Royce, J. R.; Mos, L. P., eds. Theoretical advances in behavior genetics. Alphen aan den Rijn: Sijthoff and Noordhoff; 1979:579-649. 44. Royce, J. R.; Carran, A.; Howarth, E. Factor analysis of emotionality in ten inbred strains of mice. Multivar. Behav. Res. 5:19-48; 1970. 45. Thompson, W. R.; Olian, S. Some effects on offspring behavior of maternal adrenalin injections during pregnancy in three inbred strains of mice. Psychol. Rep. 8:87-90; 1961. 46. Tryon, R. C. Genetic differences in maze-learning ability in rats. In: 39th Year Book Nat. Soc. Stud. Educ.; 1940:111-119. 47. Wilcock, J.; Fulker, D. W. Avoidance learning in rats: Genetic evidence for two distinct behavioral processes in the shuttle box. J. Comp. Physiol. Psychol. 82:247-253; 1973. 48. Wilcock, J.; Fulker, D. W.; Broadhurst, P. L. Analysis of two-way escape avoidance conditioning measures from a diallel cross of eight strains of rats. Behav. Genet. 11:339-358; 1981.