AGE DETERMINATION,

5.5 AGE DETERMINATION,

Fig. 5.4 Graphical methods for estimating the

BACK-CALCULATION AND

parameters L • and k of the von Bertalanffy growth function using data for female spiny dogfish, Squalus

VALIDATION TECHNIQUES

acanthias , as an example (see Fig. 5.3 for length at age plot). (a) A plot of the annual length increment against

The ability to determine the age of fish is an impor- length gives a straight line with a slope of -(1 - e - k ) and

tant tool in fisheries research, and age data are vital an intercept on the abscissa at L • . (b) A Ford–Walford

for both growth modelling and the study of popula- plot gives a regression line (solid line) with a slope of e k .

tion dynamics. There are several approaches to The point of intersection of the lines indicates L • .

ageing fish, including ‘direct’ observation of indi- viduals, length–frequency analysis and the analy- sis of various hard structures (Bagenal and Tesch 1978; Weatherley and Gill 1987; Busacker et al.

these data Bayley (1977) derived an equation that 1990; Devries and Frie 1996; Campana 2001). led to a linear transformation of the non-linear

The most accurate method for collecting age VBGF:

data is direct observation of individuals, but this is time-consuming and costly. However, under some

d ( ln W ) d t = ( mLkL ) [ ( • - L ) ] = mk L [ ( • L ) - 1 ]

circumstances it may be the only way in which re- (5.6) liable age and growth information can be obtained.

The method involves the release of marked fish into natural systems. Marked fish may be either hatchery-reared fish of known age or fish captured and marked in situ. The marked fish are later re- captured. The period of time between release and recapture is quantified and combined with data re- lating to changes in body size for use in growth models. Data collected using the mark-and- recapture method can also be used to gain insights into the size of the fish population, provided that certain assumptions are met (Youngs and Robson 1978; Guy et al. 1996). One prerequisite of the method is that the released fish can be easily recog- nized at the time of recapture. In other words, the mark applied must be distinct and, if not perma- nent, at least long-lasting. Further, if data from marked fish are to be of value in the estimation of growth rates and population sizes, the marks ap- plied should not influence either growth or the vul- nerablity of the fish to predators, i.e. mortality rates should not be affected.

Several marking and tagging techniques are available (Laird and Stott 1978; Guy et al. 1996; Campana 2001). Fish may be marked by fin mutila- tion, hot and cold branding or tattooing; marks may also be applied to the fish via subcutaneous in- jections of dyes, liquid latex or fluorescent materi- als. There are also many types of tags, some of which, such as the anchor tag and the plastic flag tag, are applied externally, whereas others, such as visible implant tags, coded wire tags and passive integrated transponders (PIT tags), are subcuta- neous or internal. The advantage of tags over other marking techniques is that tags can be numbered serially, allowing for individual recognition. Chemical marks may be induced in the body tis- sues by feeding, injecting or immersing the fish in solutions of a chemical that is taken up and incor- porated into the tissue in question. The hard calci- fied tissues, such as scales, otoliths and skeletal elements, are the most common tissues used be- cause they incorporate certain chemicals perma- nently and in a form that can provide a ‘time mark’. Examples of chemical markers include fluorescent compounds such as tetracycline and calcein, and metallic elements such as strontium and rare earth elements. Chemical marking techniques are par-

ticularly valuable in validation studies designed to cross-check fish ages as determined by other methods (Weatherley and Gill 1987; Brown and Gruber 1988; Casselman 1990; Rijnsdorp et al. 1990; Devries and Frie 1996; reviewed by Campana 2001).

Length–frequency analysis may be used to dis- tinguish between different age groups of fish pro- vided that the distribution of lengths is unimodal around a value that is distinct for the age group. Several graphical and statistical methods have been developed to enable age groups to be sepa- rated using an analysis of length–frequency data (reviewed by MacDonald 1987; Pitcher, Chapter 9, Volume 2). Although length–frequency analysis appears simple in principle, it may not be so in practice. Slower and disparate growth amongst older fish makes for less distinct peaks at older ages, and long spawning seasons lead to an increase in the spread of the distributions of length (Bagenal and Tesch 1978; Devries and Frie 1996).

Although direct observation and length– frequency analysis are used in age determination studies, the most frequently used method is the ex- amination of hards parts such as scales, otoliths, spines, vertebrae and the opercular and dentary bones (Bagenal and Tesch 1978; Weatherley and Gill 1987; Brown and Gruber 1988; Casselman 1990; Rijnsdorp et al. 1990; Devries and Frie 1996; Campana 2001). This method is based on the fact that as a fish grows by increasing in length there will also be increases in the size of the hard body parts (Table 5.1). However, the use of hard parts for age determination also relies on the appearance of growth zones, rings or checks in these parts. These growth marks are referred to as annual marks, annual rings or annuli when they are taken to represent age in years, and daily rings or daily increments when they indicate daily growth.

For example, the number of scales of a bony fish remains nearly constant throughout life, so as the fish grows the scales must inevitably increase in size in more or less the same proportion. New ma- terial is added to the scale as it increases in size: in the elasmoid scales (cycloid or ctenoid) of teleost fishes this material appears as a series of bony ridges, or circuli. The arrangement of the circuli is

Rates of Development and Growth

105

106

Chapter 5

not regular because the fish does not grow at the same rate throughout the year. When food is plen- tiful, the fish grow rapidly and the scales increase in size by the addition of large numbers of circuli well separated from each other; when growth slows down or ceases the circuli are much closer together. Thus, periods of slow growth are denoted by a close clumping of circuli to form a distinct ‘ring’. In temperate regions, the growth of fish is usually rapid during the spring and summer but is slow during the winter. These seasonal changes are reflected in the deposition of circuli in the scales. Thus, the winter growth check enables the age of the fish to be determined by counting the number of ‘rings’ formed by areas of close circuli on the scales. The greater the seasonal growth dif- ferences, the clearer the growth ‘rings’ on the scale: for this reason they are most easily seen on the scales of fish from high latitudes, where growth is distinctly seasonal. Thus, scales may be the hard part of choice for ageing fish from high latitudes, and they have the added advantage that their col- lection need not involve killing the fish. However, scales can be demineralized during prolonged peri- ods of food deprivation and scales can be regener- ated after damage; this can lead to errors in age determination unless sufficient care is taken with

sampling (Casselman 1990; Devries and Frie 1996; Campana 2001).

It may be difficult to determine the age of tropi- cal and subtropical fish species by examination of the scales or other hard parts, because the growth ‘rings’ may not necessarily be annual. The for- mation of ‘rings’ may be associated with factors unrelated to specific seasonal changes, such as unpredictable changes in food supply, spawning events or density-related changes in growth (Bagenal and Tesch 1978; Weatherley and Gill 1987; Devries and Frie 1996). Thus, if age determi- nation is attempted using readings of these growth ‘rings’ there must be some sort of validation by other methods (reviewed by Campana 2001). Unfortunately it may not be possible to use length–frequency analysis because the fish may spawn throughout much of the year and life cycles are short, so a ‘direct’ method using mark-and- recapture may be the method of choice.

Similarly, it is not possible to determine the age of elasmobranchs using scales because, unlike the scales of bony fishes, the placoid scales of sharks and rays do not increase in size as the fish grows. Instead, new scales are added in between the exist- ing ones. This is a continuous process so that at any one time there will be a number of newly erupted

Table 5.1 Relationships between otolith length (OL) and fish standard length (FSL) (both in mm) for some common boreal marine fish species. (Source: from Jobling and Breiby 1986.)

Fish species

FSL size range (mm) FSL = a + b OL Intercept (a)

Slope (b) Herring, Clupea harengus

76 51–279

-8.50

58.46 Pearl-side, Maurolicus muelleri

74 22–67

-12.80

35.31 Lanternfish, Benthosema glaciale

37 37–85

-3.31

31.79 Capelin, Mallotus villosus

96 49–159 24.02 44.31 Sandeel (sand-lance), Ammodytes tobianus

42 65–137 14.93 50.81 Atlantic cod, Gadus morhua

67 26–317 0.41 22.44 Haddock, Melanogrammus aeglefinus

65 20–283 11.06 17.24 Saithe (coal-fish), Pollachius virens

37 53–259

-4.24

23.50 Blue whiting, Micromesistius poutassou

27 56–318

-25.54

23.17 Norway pout, Trisopterus esmarki

75 72–252

-23.26

25.14 Silvery pout, Gadiculus argenteus thori

37 79–149 4.53 18.14 Red-fish (Norway haddock), Sebastes spp.

7818 –238

3.75 19.45

scales, as well as some in the process of disintegra- tion. However, age determination of some elasmo- branchs may be possible by examination of growth zones within vertebrae or spines (Holden and Meadows 1962; Holden 1974; Nammack et al. 1985; Brown and Gruber 1988).

When the scales and other hard parts increase in size in proportion to the size of the fish (e.g. Table 5.1), they may not only be used in age determina- tion but can also be considered to represent a diary recording the growth history of the fish. Thus, using knowledge about the relationship between the size of the hard part and fish length, it may be possible to back-calculate the length of the fish at a given age by examination of the positioning of the various growth ‘rings’ (Bagenal and Tesch 1978; Weatherley and Gill 1987; Devries and Frie 1996). The data required for back-calculation are the length of the fish at capture, the radius of the hard part at capture (measured from the nucleus to the margin), and the radius of the hard part to the outer edge of each of the growth ‘rings’ (either annuli or daily increment rings). Back-calculation of length at any given age is then usually carried out by one of four methods: the direct proportion method, the Fraser–Lee method, various curve-fitting proce- dures or the Weisberg method (Bagenal and Tesch 1978; Devries and Frie 1996). The method to use depends upon the type of relationship between the length of the fish and the dimensions of the hard part used in the back-calculation procedure.

The direct proportion method can be used when the relationship between body length and hard- part radius is linear and has an intercept that does not differ from the origin: in this situation the growth of the hard part is directly proportional to the growth in length of the fish. The Fraser–Lee method is applicable when the intercept of the relationship between fish length and hard- part radius is not at the origin. Under these circumstances, length at time t (L t ) can be back- calculated using the formula:

(5.8)

where L c is the length of the fish at the time of cap- ture, R c is the radius of the hard part at capture, R t

L aRR t a c c = t - ( ) [ ] +

is the radius of the hard part at time t, and a is the intercept of the regression line relating hard-part radius to fish length (Bagenal and Tesch 1978; Devries and Frie 1996). In some cases, the use of simple linear regression may be precluded due to a lack of linearity between the dimensions of the hard parts and the body, or because there are differ- ent body length to hard-part relations among age groups. Under such circumstances various curve- fitting procedures and covariance analysis may be used to address the problems (Bagenal and Tesch 1978; Bartlett et al. 1984). The Weisberg method is more complex than the others. It involves a model- ling approach that enables age group and annual environmental effects to be distinguished. Thus there is a separation and identification of changes in growth from one time period to another, such as years of particularly good or poor growth, that may

be superimposed upon age effects (Weisberg and Frie 1987; Weisberg 1993).