DEVELOPMENT AND GROWTH DURING EARLY LIFE HISTORY

5.3 DEVELOPMENT AND GROWTH DURING EARLY LIFE HISTORY

Rates of development and the timing of transi- tional events from one life-history period to the next are, to a certain extent, genetically deter- mined and can be considered as species-specific characteristics. For example, large eggs, such as those of salmonids, take longer to hatch than small eggs, and, in general, eggs from fish living at high latitudes take longer to hatch than those from the tropics (Fig. 5.2)(Kamler 1992; Jobling 1995; Rombough 1997). Environmental factors may, however, have a major modifying influence on rates of development. Ambient temperature, for example, is known to be one of the most potent factors influencing both rates of development and

Rates of Development and Growth

99

A modification has been proposed in which tem-

Marine

perature may be measured from a point other than

Freshwater

0 °C. When T 0 is defined as the new base tempera- ture the expression becomes:

20 ( - TTD 0 ) = constant.

10 In this expression T 0 is the so-called biological

(c)

zero, i.e. the incubation temperature at which, in

5 theory, the time to reach the given stage of devel- Incubation period (days)

3 opment would be infinite. T 0 may be close to 0 °C

2 for cold-water and temperate species, but will be higher in warm-water species. Thus, the introduc-

(b)

1 tion of T 0 would appear to make the hyperbolic

(a)

relationship applicable to the description of 0.5

0 5 10 15 20 25 30 35 developmental rates of a larger number of fish species, especially those which require warm

Temperature ( C) water for egg incubation and embryo development Fig. 5.2 Influence of incubation temperature on the

(e.g. Weltzien et al. 1999). Nevertheless, when the time to hatch for eggs of a range of marine and freshwater

degree-day formula is applied to experimental data fish species. Note that, in addition to temperature, egg

involving a wide range of temperatures it may not size also has a major influence on incubation period. The

be found to be satisfactory. lines indicate estimated incubation times for eggs of (a)

Numerous other mathematical expressions 0.5 mm, (b) 2 mm and (c) 5 mm diameter, respectively.

(Source: from Rombough 1997.) have been used to describe the curvilinear relation-

ship between incubation time and temperature (Cossins and Bowler 1987; Kamler 1992), with

the overall survival of the eggs and larvae (Blaxter both power law (i.e. D = aT b ) and exponential (i.e. 1988, 1992; Kamler 1992; Rombough 1997). Rates D=a e - bT ) equations having been widely used to de- of development are generally found to increase scribe the relationship (Kamler 1992; Rombough with increasing temperatures. However, incuba- 1997). However, both the power law and exponen- tion of eggs at high temperature may result in tial functions have the weakness that they do not increased incidence of malformations and take into account the fact that there are tempera- abnormalities in the embryo, or the death of the ture extremes outside of which egg incubation is eggs. Temperature can also influence size at hatch- impossible, and mortality will be complete. When ing, efficiency of yolk utilization, time to meta- data have been fitted to the power law function, morphosis, behaviour, rates of feeding and the constant b, which describes the inverse rela- metabolic demands.

tionship between incubation time and tempera-

The simplest relationship used to describe the ture, has usually been found to be within the range influence of incubation temperature on rates of de- 1–1.5. With the exponential function the value of b velopment is a hyperbolic function, which encap- will usually be 0.1–0.15. Since Q 10 and the con- sulates the concept of ‘degree-days’. This states stant b in the exponential function relate to each that the product of the incubation temperature (T other as in Q 10 = 10 b , these constants translate to in °C) and the time (D in days) required to reach any Q 10 values of 3–4, meaning that the developmental particular stage of development, e.g. from fertiliza- rate roughly triples or quadruples with each 10 °C tion to hatch, is constant:

increase in temperature. Cold-water species ap- pear to be particularly responsive. Developmental

T¥D= constant. (5.1) rate is also highly dependent upon egg size, with T¥D= constant. (5.1) rate is also highly dependent upon egg size, with

log D = 1.20 - 0.0494T + 0.203d

(5.3)

where D is incubation time (days), T is tempera- ture (°C) and d is egg diameter (mm). This equation

gave an overall Q 10 of 3.1. Rates of posthatch larval development appear to be less temperature sensi- tive than rates of prehatch embryonic develop-

ment, Q 10 values of about 2 seeming to apply to rates of development of larvae following hatch (Rombough 1997). One should caution, however,

that although it is possible to use Q 10 to relate

temperature to developmental and physiological processes, Q 10 values do not remain constant over wide ranges of temperature. Thus, although Q 10 values can be useful for predictive purposes over narrow temperature ranges, extrapolations to tem- perature extremes should not be attempted.

In addition to influencing rates of development of eggs, embryos and larvae, temperature may af- fect the interactions between growth and differen- tiation. The best-known effect is that on meristic characters. The number of serial structures such as vertebrae, scales and gill rakers is labile and sus- ceptible to environmental influence (Blaxter 1988; Lindsey 1988). There is also some evidence of a prefertilization influence on meristic characters,

i.e. the temperature experienced by the broodstock during gametogenesis may influence the meristic characters of the offspring.

The number of vertebrae tends to be higher in fish from polar and temperate waters than in their relatives from tropical waters. This phenomenon, termed Jordan’s rule, is usually described in rela- tion to latitude, but may be attributable to the fact that fish from different latitudes are usually sub- jected to different thermal environments during early development (Lindsey 1988). In addition, meristic counts of wild fish hatched in the same place often vary between years, and the differences

appear to be associated with year-to-year differ- ences in temperature in the period during spawn- ing and early development (e.g. Brander 1979; Løken et al. 1994). Further, within year-classes a protracted spawning season coupled to changes in water temperature over time may be associated with meristic differences between those individu- als which hatch and develop at different times (Lindsey 1988). Thus, environmental influences on meristic counts may be superimposed upon ge- netically controlled responses (Lindsey 1988). For example, Løken and Pedersen (1996) reported an inverse relationship between incubation tempera- ture and vertebral number in cod, Gadus morhua, although vertebral numbers differed between the offspring of coastal cod and northeastern Atlantic cod when they were reared at the same tempera- ture. Meristic characters can be greatly modified by the environment experienced during early de- velopment. Later, but still quite early in ontogeny, these characters become fixed and remain un- changed thereafter.

Environmental factors, particularly tempera- ture, probably influence meristic characters by differentially affecting the processes of growth through body elongation and differentiation ex- pressed as segment formation; temperature may also have profound influences upon other develop- mental events such as muscle differentiation and the relative timing of organogenesis (Blaxter 1988, 1992; Johnston 1993; Brooks and Johnston 1994; Nathanailides et al. 1995; Johnston et al. 1996, 1997). Environmental factors may also influence sex determination in several animal groups, in- cluding fish (Baroiller et al. 1999; Baroiller and D’Cotta 2001). This phenomenon, known as environmental sex determination (ESD) (Janzen and Paukstis 1991; Crews 1996; Shine 1999; see also Forsgren et al., Chapter 10, this volume), means that the environment experienced by fish during early development can influence pheno- typic sex (Conover et al. 1992; Baroiller et al. 1995, 1996, 1999; Craig et al. 1996; Patino et al. 1996; Römer and Beisenherz 1996; Strüssmann et al. 1996, 1997; Nomura et al. 1998). It is the effects of temperature on sex determination that have been most studied in fish, although both salinity and

Rates of Development and Growth

101

Chapter 5

xenobiotics are known to influence phenotypic ing. Numerical expressions of growth may be sex in some fish species (Baroiller et al. 1999; based on absolute changes in length or weight Baroiller and D’Cotta 2001).

(absolute growth), or changes in length or weight

Sex ratios that deviate significantly from the relative to the size of the fish (relative growth). theoretical 1 : 1 amongst groups of fish reared at dif- Length almost always increases with time, ferent temperatures could arise from differential whereas weight can either increase or decrease mortality of the sexes. Thus, deviations of the sex over a given time interval depending upon the ratio from the expected 1 : 1 amongst fish reared influences of the various factors that affect the under different temperature conditions cannot be deposition and mobilization of body materials. taken as providing conclusive evidence of ESD Measurements of growth in relation to time pro- (Strüssmann et al. 1997; Nomura et al. 1998). Con- vide an expression of growth rate. Growth in clusive evidence of ESD has, however, been ob- length can usually be modelled using an asymptot- tained by progeny testing of fish that have been ic curve which tapers off with increasing age (Fig. subjected to different temperature treatments, and 5.3). Growth in weight is usually sigmoidal, i.e. the by demonstration that temperature can induce a weight increment increases gradually up to an in- phenotypic sex change within gynogenetic mono- flection point from where it gradually decreases sex populations (Baroiller et al. 1995, 1996, 1999; again. Thus, growth rates are constantly changing, Nomura et al. 1998; Kitano et al. 1999; D’Cotta and the absolute growth increments will be differ- et al. 2001a,b).

ent for different sizes of fish. Whether or not temperature has any influence on the phenotypic sex of individuals within a given species will depend upon the strength of genetic

sex determination, and when in development the temperature treatment is applied (Baroiller et al.

1995, 1996, 1999; Römer and Beisenherz 1996; Strüssmann et al. 1996, 1997). This is to be ex-

pected because determination of phenotypic sex in fish is also sensitive to sex steroid hormone treat-

ments only at particular stages of development (re- viewed by Purdom 1993 and Patino 1997). It is

Length (cm)

unlikely that the periods of sensitivity to tempera-

ture and sex steroids will differ, because the physi- ological effects of temperature treatment appear to

be mediated via actions on genes coding for P450 steroidogenic enzymes, such as aromatase, and sex

0 5 10 15 20 25 steroid hormone receptors (Crews 1996; Baroiller

et al. 1999; Kitano et al. 1999; Baroiller and Age (years) D’Cotta 2001; D’Cotta et al. 2001a,b).

Fig. 5.3 Length at age plot for female spiny dogfish, Squalus acanthias. The parameters L • and k of the von Bertalanffy growth function are about 104 cm and 0.106,

5.4 GROWTH MODELS

respectively. Spiny dogfish are ovoviviparous, with

AND EQUATIONS

prolonged intrauterine incubation of large eggs. Extrapolation of the regression line gives an intersection

Growth equations are used to describe changes in on the y-axis above the origin, providing an indication of intrauterine growth and an estimate of the length at

the length or weight of a fish with respect to time, ‘birth’ (c.25 cm), whereas the negative intersection on although the constants derived from such empiri- the x-axis gives an estimate of gestation time (c.2 years). cal equations may have no exact biological mean- (Source: data from Holden and Meadows 1962.)

Rates of Development and Growth

A number of mathematical functions have been where L t is length at time t, and t 0 is the theoretical used to describe growth curves, including the ‘age’ of the fish at zero size. Gompertz, the logistic, and a range of straight-line

The two constants L • and k can be estimated and exponential approximations (Beverton and from measurements of fish length at known fish Holt 1957; Ricker 1979; Weatherley and Gill 1987; ages (Gulland 1969; Bagenal and Tesch 1978; Prein Prein et al. 1993; Elliott 1994). Life-history pat- et al. 1993). Before personal computers became terns of fish vary in a consistent fashion, and widely available it was difficult to fit the VBGF to growth and maturation parameters are closely length-at-age data, and several methods were de- interrelated (Adams 1980; Charnov and Berrigan veloped for the estimation of L • and k (Fig. 5.4). 1991; Beverton 1992; Gunderson 1997; He and One method involves making a plot of the annual

Stewart 2001; Hutchings, Chapter 7, this volume). increment of length (L t+ 1 - L t ) against length (L t ), As such, it may be possible to estimate both the age where L t+ 1 is length at age t + 1 and L t is length at and size at first reproduction from the information age t. This gives a straight line with a slope of encapsulated in a growth trajectory plot (Charnov - (1 - e - k ), and an intercept on the abscissa (i.e.

0) equal to L • . This equation is also and Berrigan (1991) and He and Stewart (2001) known as the Brody equation, as mentioned by summarized the interrelationships and provided Schnute and Richards (Chapter 6, Volume 2). This models, Beverton (1992) considered the relation- expression not only establishes the constants in ships within the framework of the growth– the VBGF but also provides an indication of the de- maturity–longevity (GML) plot, and Gunderson cline in the rate of growth with age. The constants

and Berrigan 1991; He and Stewart 2001). Charnov where L t+ 1 - L t =

(1997) reviewed the relationships for viviparous can also be estimated from a plot of L t+ 1 on L t , the and oviparous fish species: viviparity seemed to be

Ford–Walford plot (Fig. 5.4). The rate of growth of associated with reduced reproductive effort, in- the fish slows with age so the plotted line gradually creased age at maturity and low mortality relative approaches a 45° line passing through the origin. to oviparous species of similar size.

The two lines will intersect at L • , the point of in-

The von Bertalanffy growth function (VBGF) tersection indicating when the lengths of the fish

fits many observations on the growth of fish and is at the start (L t ) and end (L t+ 1 ) of the growth period widely used in fisheries research because the con- are identical, i.e. the fish has ceased to increase in stants were readily incorporated into early stock length, and the annual growth increment is zero. assessment models (Beverton and Holt 1957). The The growth constant, k, can also be estimated from simplest derivation of the VBGF is:

the plot of L t+ 1 on L t because the slope of the line is equal to e - k . Much work has been done on develop-

(5.4) ing methods for fitting and testing VBGF data, and with the advent of the personal computer the where L is length, t is time, L • is the asymptotic handling of the data has become much easier length, which is the length the fish would reach if (Gallucci and Quinn 1979; Misra 1986; Ratkowsky it were to grow to an infinite age, and k is the 1986; Cerrato 1990, 1991; Xiao 1994). For a gener- growth constant expressing the rate at which alization of the von Bertalanffy model see Schnute length approaches the asymptote (Gulland 1969; (1981). Ricker 1979). Whereas L • has a straightforward

d Lt d = kL ( • - L )

Bayley (1977) pointed out that a weakness in interpretation, that of k is less easy because it several of the methods is a lack of independence describes the instantaneous growth rate (dL/dt) between the variables plotted. In an attempt to relative to the difference between L • and the overcome the problem, Bayley (1977) devised a length of the fish at a given time. Integration of the method for the estimation of the VBGF constants VBGF gives:

(L • and k) using measurements of instantaneous growth rates (d(lnW)/dt) and a description of

L t = L • 1 - e - ktt ( - 0 ) ( )

(5.5) the length–weight relationship (W = cL m ). From

10 y = 10.422 – 0.1001x or

R 2 = 0.3232

8 ( ln W 2 - ln W 1 ) ( t 2 - t 1 ) =- mk + mkL • ( 1 L ) (5.7)

6 The latter has the form of a linear regression with a slope of mkL • , the intercept is -mk, and a plotted

4 line will intersect with the abscissa at 1/L • , where, by definition, the instantaneous growth rate is

2 zero. Thus, the constants of the VBGF can be estimated from successive measurements of

120 length and weight, and calculation of m in the Length (cm) at age t (years)

Annual length increment (cm)

length–weight relationship; instantaneous growth –2 rate is plotted against the reciprocal of fish length,

–4 the slope and intercept of the regression calcu- lated, and the VBGF constants are then estimated from the values obtained. Bayley (1977) suggested

(b) 120 that this method of analysis could be appropriate y = 0.8999x + 10.422

100 R 2 = 0.9748 for the estimation of the VBGF constants for tropi- cal fish species in which age determination may be

80 extremely difficult. For these species growth is t + 1 (years)

often estimated from data collected following the

60 recapture of released marked fish, where it is not usually possible to control the time over which in-

40 dividual fish are at liberty. Analysis of growth data using this method does, however, require that

20 Length (cm) at age

there has been a marked change in fish weight and

0 length over the growth period.

Length (cm) at age t (years)