NOAA Technical Memorandum NMFS 

AUGUST 1984 

INTERPRETING SPOTTED DOLPHIN AGE DISTRIBUTIONS 

Jay Barlow 

Aleta Hohn 

NOAA-TM-NMFS-SWFC-48 

U.S. DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

Southwest Fisheries Center 
NOAA Technical Memorandum NMFS 

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may be referenced in the fornial scientific and technical literature 
NOAA Technical Memorandum NMFS 

This TM series is used lor documentation and timely communication of preliminary results. imterim reports. or 

Special Purpose information. and have not received complete formal revsw. editorial control. or detailed edit in^ 

AUGUST 1984 

INTERPRETING SPOTTED DOLPHIN AGE DISTRIBUTIONS 

Jay Barlow 

Aleta Hohn 

South west Fisheries Center 

National Marine Fisheries Service, NOAA 

La Jolla, California 92038 

NOAA-TM-NMFS-SWFC-48 

U.S. DEPARTMENT OF COMMERCE 

Malcolm Baldrige. Secretary 

National Oceanic and Atmospheric Administration 

John V. Byre. Administrator 

National, Marine Fisheries Service 

William G. Gordon, Assistant Administrator for Fisheries 
CONTENTS 

Page 

ABSTRACT .......................................................... 1 

INTRODUCTION ...................................................... 1 

SECTION 1 . UNDERSTANDING THE AGE DISTRIBUTIONS .................... 2 

SECTION 2 . ESTIMATING NATURAL MORTALITY RATES ..................... 13 

CONCLUSION ........................................................ 17 

ACKNOWLEDGEMENTS .................................................. 19 

LITERATURE CITED .................................................. 20 

i 
Figure 

LIST OF FIGURES 

Page 

1 

2 

3 

4 

Observed age frequency distributions for female 

and male spotted dolphins based on tooth aging 

samples (1973-81) (figures from Hohn and Myrick. 

Age distribution of the kill of spotted dolphins, 

Stenel 1 a attenuata, in the eastern tropical 

Paci f i c , in prep. 1. Sol i d curves represent 

typical stable age distributions for the given 

sample sizes based on a generalized mammalian 

pattern of age specific mortality4 ..................... 4 

Observed age frequency distributions for female 

spotted dolphins based on tooth aging samples 

from 1973-81 (figures from Hohn and Myrick. distribution of the kill of spotted dolphins, 

Stenel 1 a attenuata , in the eastern tropi cal 

Age 

Pacific, in prep.j ..................................... 5 

Mean growth curves for female and male spotted dolphins in the eastern Pacific, based on tooth 

aging. Points represent mean values for each age 

class.................................................. 7 

Age-length relationships for a variety of dolphins. 

Length is scaled as percentage of asymptotic length 

of females (250 cm for bottlenose dolphins, 190 cm 

for spotted dol phi ns) . Sol id 1 ines represent growth 

curves based on mean lengths at ages determined by 

tooth aging. Each symbol represent age-length 

relationship from a different known-age individuals 

(note: more than one measurement for some individuals). 8 

5 Observed age frequency distribution for females in 

a Japanese population of spotted dolphins based on 

tooth aging (Fig. 16 from Kasuya 1976) ................. 10 

6 Observed age frequency distribution for a western 

Atlantic population of bottlenose dolphins based on 

tooth aging (Hohn and Mead, unpubl . data) Males 

and females are pooled.............. ................... 11 

is 
LIST OF FIGURES - Continued 

Figure Page 

7 Age frequency distributions of a hypothetical 

population of spotted dolphins in 1959 and 1974 

(see text). Differences represent effects of 

age-selective time-varying harvests, population 

reduction, and density compensatory increases in 

fecundity .............................................. 16 

iii 
LIST OF TABLES 

Tab1 e Page 

1 Relative age-specific harvest selectivities used 

in the simulation model for population projection.. . . . . 15 

2 Synthesized age frequency distributions for spotted 

dolphins. Included are distributions taken female 

spotted dol phi n age distributions from 1973-77 and 

from 1973-81 (see text for methods) .................... 18 

iv 
INTERPRETING SPOTTED DOLPHIN 

AGE DISTRIBUTIONS 

by Jay Barlow and Aleta A. Hohn 

ABSTRACT 

Previous work has determined the age distribution from a sample of 

spotted dolphins (Stenella lattenuatg) killed in the eastern Pacific 

tuna purse-seine fishery. In this paper we examine the usefulness of this 

age distribution for estimating natural mortality rates. The observed age 

distribution has a deficiency of individuals from 5-15 years and cannot 

represent a stable age distribution. Sampling bias and errors in age 

interpretation are examined as possible causes of the ~~dip't in the 

observed age structure. Natural mortality rates are estimated for the 15+ 

age cl asses based on the assumpti on that these are sampl ed representatively . The resulting annual survival rate (0.82) is too low to allow population 

growth, given what is known about dolphin reproductive rates. 

INTRODUCTION 

A research program began in 1979 at the Southwest Fisheries Center 

to determine the ages of dolphins that are killed in the eastern Pacific 

tuna purse-seine fishery. The initial goal was to develop age estimation 

methods and to determine ages of individual anfmals from a large, random 

sample of male and female spotted dolphins, Sjxxtellq ~QJ&L. Agespecific birth and death rates were to be estimated from this information. 

The ultimate goal was to refine estimates of population growth rates. 

The purpose of this report is to interpret some of the results of 

this age determination study. Methodology for age determination has been 

previously reported by Myrick, Hohn, Sloan, Kimura, and Stanley (1983). 

Studies of age-specific reproductive rates have been resented by Myrick, 

consider the estimation of mortality rates from the age distribution data. 

There are, however, peculiarities in the spotted dolphin age distribution 

that preclude a straightforward estimation of mortal ity rates. Possible 

causes of these peculiarities are discussed in Section 1. Estimates of 

spotted dol pht n mortal ity rates based on a number of simp1 ifying assumptions 

are given in Section 2. 

Hohn, Barlow and Sloanl and Hohn, Chivers and Barlow 9 . This paper will 

'Myrick, A. C., A. A. Hohn, J. Barlow, and P. A. Sloan. 1984. Reproduction 

in female spotted dolphfns, S-a gttenuatg, from the eastern tropical 

Pacific. (manuscript). 

1984. Reproductive maturity 

and seasonality in male spotted dolphins, Stenellq attemWa, in the 

eastern tropical Pacific. (manuscript). 

'Hohn, A. A., S. J. Chivers and J. Barlow. 
2 

Section 1. UNDERSTANDING THE AGE DISTRIBUTIONS 

Aging Methods and Sample Selection 

Ages were determined by counting growth layer groups (GLGs) in the 

dentine and cementum of teeth (Myrick et al. 1983). Teeth were sectioned and 

stained to make the layers more distinct. GLGs are defined as repeating 

series of growth layers which can be defined and counted, but which have no 

implied deposition time (Perrin and Myrick, 1980). However, GLGs in spotted 

dolphins were assumed to be annual, based on analogy to Hawaiian spinner 

dolphins, S. -air ostris, (Myrick, Shal lenberger, Kang and MacKay, in 

press). 

Ages were estimated for a random subset of spotted dolphins in the 

dolphin life-history data base at the Southwest Fisheries Center (SWFC). 

These data are from a sample of the dolphins that were killed in the 

yellowfin tuna purse-seine fishery (see Perrin, Coe and Zweifel (1976) and 

Perrin, Miller and Sloan (1977) for life history sampling criteria). In 

practice, the random subsampling of specimens to be aged was confounded because 

teeth were not collected at sea from all individuals (due to exigencies of the 

commercial fishing operation). The initial procedure for selecting teeth in 

the laboratory was to proceed down a randomized list of all specimens until 

teeth were found for 800 males and 800 females collected during the period 

1973-78. Ages were estimated independently by two tooth readers, with each 

reader making multiple estimates. All these estimates were combined to yield 

a composite or flpooled mean" age for each specimen (Reilly, Hohn, and Myrick 

1983); however, the estimates made by the two readers differed significantly. 

Subsequently, ages were estimated for all females which were collected in 

1981 and for which teeth were available (n=321). Only one age estimate was 

made by each reader for the 1981 sample, and these 2 estimates were averaged to 

obtain an age estimate for each specimen. For the 1981 sample, between reader 

differences were small (Reilly et al. 1983). Ages given in this report refer 

to the composite or average estimate for each specimen (unless otherwise 

noted). This report only considers aged specimens (1088 females and 793 males) 

from the northern offshore stock (Perrin, Sloan and Henderson 1979) of spotted 

dolphins. 

Very young individuals (those with neonatal color patterns) are 

underrepresented in the aged subsample relative to the 1 ife-history sample3. 

Despite instructions to sample randomly at sea, teeth were apparently less 

likely to be collected from very small specimens. 

The Problem 

Age frequency distributions for the random samples of male and female 

3Hohn, A. A. and A. C. Myrick. In prep. Age distribution of the kill of 

spotted dolphins, Stenel la muata, in the eastern troplcal Pacific. 
3 

spotted dolphins are shown in Fig. 1. Superimposed on these figures is an 

age distribution characteristic of a population in stable age distribution and 

which has a stereotypic mammalian pattern of age-specific mortal ity4 . There is a notable dearth of specimens in age classes 5 to 15. This vvdipvv 

in apparent frequencies cannot occur in populations that have any conceivable 

stab1 e age structure. 

Three hypotheses could explain this aberrant age structure: 

Hypothesis (1) : (Non-stable age structure) The observed age distributions 

reflect the actual population age structure, which has been subject to 

a large perturbation and is not in stable age distribution; 

Hypothesis (2): (Sampling bias) The observed age distributions are a 

biased sample of the true population age structure; 

Hypothesis (3): (Variable rate of tooth layer deposition) The rate of 

tooth-layer deposition is not constant at 1 GLG per year, so that 

frequency distributions of GLGs are not the same as age frequency 

distributions. 

All are examined in some detail below. 

Non-Stable Age Distribution 

If the paucity of specimens in age classes 5-15 were due to a large 

perturbation in the underlying age structure, we would expect to see the 

perturbation propagate through the age structure with time. For instance, if 

a minima in age class abundance in 1973 occurred at 8 years of age, we 

would expect that minima to occur at 16 years in 1981. Comparing the 1973 

and 1981 age structures (Fig. 21, a minima occurs near 8 years for both, and 

in 1981 a maxima in age class abundances occurs near 16 years. The cumulative 

age frequency distributions were tested pairwise for individual years (1973 

to 1981 for females, 1973 to 1978 for males). In only 2 of these 36 pairwise 

comparisons were the between year differences significant (Kolmogorov/Smirnov 

test, p < 0.05). This number of significant results would be expected given 

random variation (alpha error). On the basis on these observations, the first 

hypothesis is regarded as untenable. 

Sampling Bias 

Juveniles (age classes 5-15) may be underrepresented in tuna purse-seine 

samples relative to the underlying population. This could occur if 

juvenile dolphins segregate from other dolphins, and if schools of juvenile 

dolphins were less likely to be captured by tuna fishermen. 

presented evidence for such segregation. Furthermore, they found vvjuveni le 

schoolsvv to have a smaller characteristic school size, which might make them 

Hohn and Scott5 

‘Barlow, J. 1984. Variability, trends and biases in reproductive rates 

of spotted dolphins (Stenella p-a). (manuscript). 
4 

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Figure 1. Observed age frequency distributions for female and male spotted 

dolphins based on tooth aging samples (1973-81)(figures from Hohn and 

Myrick. Age distribution of the kill of spotted dolphins, Stenellg 

attenuata, in the eastern tropical Pacific, in prep.). Solid curves 

represent typical stable age distributions for the given sample siges 

based on a generalized mammalian pattern of age specific mortality . 
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less attractive to tuna fishermen. These observations support Hypothesis (2) 

above. It is not known if the magnitude of this effect is sufficient to 

account for the apparent bias in age distributions. 

Variable Rate of Tooth-layer Deposition 

There is no direct evidence for variable deposition rate in dolphin 

tooth layers. By analogy, however, we compare results from spotted dolphin 

age analyses with those from other dolphin species to help evaluate whether 

GLGs represent annual increments. 

Growth Curve Comparisons 

It is sometimes possible to verify aging methods by comparing growth 

curves based on estimated ages of wild animals to growth curves of known-age 

captive animals (Laws 1966). The mean lengths of male and female spotted 

dolphins are plotted in Fig. 3 as a function of age. We compare these 

spotted dolphin growth curves to growth In captive-born and known-aged wild 

bottlenose dolphins (TursioDs trun catus). 

Individual growth curves for known-age bottlenose dolphins are shown 

in Fig. 4 (data from Myrick and Cornel1 (captive dolphins); Ridgway, 

pers comm, (captive); Harrison, Brownell and Boice 1972 (captive); Irvine, 

Scott, Wells, Kaufmann, and Evans 1979 (wlld); and Wells, Irvine, and Scott 

1980 (wild)). Published growth curves based on tooth aging are also shown 

for two wild populations of bottlenose dolphins (Sergeant, Caldwell and 

Caldwell 1973; Hohn 1980) and one western Pacific population of spotted 

dolphins (Kasuya 1976). The average length at a given age for female spotted 

dolphins in this study (Fig. 3) is repeated in Fig. 4. All the growth curves 

based on tooth-layers tend to underestimate the rate at which captive dolphins 

can grow. Within species, growth is relatively determinate in mammals (Case 

1978), hence this degree of growth enhancement due to captivity does not seem 

likely. Typically, if growth is increased by improved diet, asymptotic lengths 

also change. Captive dolphins are not growing to greater asymptotic lengths 

than wild specimens (Ridgway and Fenner 1982). Also, the length of known-age 

wild specimens are similar to captive dolphins of the same age (Fig. 4). 

Spotted dolphins apparently grow more slowly than known-age bottlenose 

dolphins. True growth rates would be faster if young spotted dolphins laid 

down more than one GLG per year (Hypothesis 3). 

'Hohn, A. A. and M. D. Scott. 1983. Segregation by age in schools of 

spotted dolphins in the eastern tropical Pacific. Abstract, Fifth 

Biennial Conference on the Biology of Marine Mammals, Boston. 

'Myrick, A. C. and L. H. Cornell. In prep. Calibration of dental layers 

in captive bottlenose dolphins, TursiopS &run catus , using tetracycline 

labels and tooth extractions. 
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Age at Sexual Maturation 

The age of sexual maturation in spotted dolphins as estimated from tooth 

layer data is higher than that of bottlenose dolphins for which ages are known 

directly. The mean age of sexual maturity in spotted dolphins was determined 

from tooth layer counts to be 11.3 years for females (range of 10.6 to 12.0 

for the wo readers)' and 15.1 for males (range of 13.6 to 16.6 for the two 

9.0 and 9.8 years. 

readers) 3 . For the two readers, the youngest sexually mature females were 

For known-age captive bottlenose dolphins the youngest to give birth 

was 7 years old (Ridgway and Benirschke 19771, implying sexual maturity at 

age 6. Captive dolphins may mature at an earlier age if they have a greater 

fat reserve than wild dolphins (Frisch and Revelle 1970); however, a known-age 

bottlenose dolphin in a wild population gave birth at age 8 (Wells and Scott, 

pers. comm.), implying sexual maturity at age 7. 

Spotted dolphins are smal ler than bottlenose dolphins and thus might be 

expected to mature at a younger age. Based on tooth-layer ages for spotted 

dolphins and on a small sample of known ages for bottlenose dolphins, spotted 

dolphins appear to mature at an older age than bottlenose dolphins. If 

expectations were true, and spotted dolphins mature earl ier than bottlenose 

dolphins, then more than one GLG (as defined in the spotted dolphin age study) 

must be deposited per year in young spotted dolphins. 

Age Distribution Comparisons 

Age distributions have been determined for several other dolphin 

populations using the tooth layer method. Western Pacific spotted dolphins 

were sampled from the Japanese drive fishery for dolphins (Kasuya 1976). 

The age distribution of these spotted dolphins is similar to those of the 

eastern Pacific, with a dearth of individuals in age classes 4-13 (Fig. 5) 

(Kasuya 1976, his Figs. 16 8, 17). He also found that the "dip" in the age 

distribution was more pronounced for females. 

Age distributions of bottlenose dolphins in the western Atlantic show 

a similar pattern. Samples were obtained from a shore-based net fishery in 

1927-28 on the outer banks of North Carolina (Mead 1975). Although sample 

size is small, age distributions from these samples appear to show a dearth 

of specimens from 8 to 16 years (Fig. 6) (A. A. Hohn and J. G. Mead, unpubl. 

data 1. 

Samples from three different dolphin populations all show similar 

patterns in their age distributions. This may be evidence of a cmon social 

structure whereby juveniles segregate from adults and their dependent calves 

(Hypothesis 2). To be consistent with observations, the juvenile schools 

would always have to be under-sampled. Alternatively, consistent errors 

in interpreting GLGs as annual layers for young dolphins could also explain 

the consistency in these patterns (Hypothesis 3). 
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Calibration of Aging Methods 

The most direct verification of aging methods uses known-age individuals. 

Using 4 captive bottlenose dolphins (2 born in captivity), Sergeant (1959) 

concluded that GLGs (as defined by him) are deposited annually. Sergeant, 

Cal dwell and Caldwell (1973) reported on 13 more captive bottlenose dolphins 

(one born in captivity and 21 yrs old), again concluding that their GLGs are 

annual. Additional studies on captive bottlenose dolphins (Hui 1980) reported 

the same conclusion. Myrick et al. (in press) examined teeth from one 

captive-born Hawaiian spinner dolphin (S. Ionair ostris 1; again the number 

of GLGs matched the age of the specimen. 

Additional evidence for the annual deposition of GLGs is from 

tetracycl ine-marked dolphins. This approach has been used on baaenorhvnchus 

obsc urus (n=3, Best 19761, DelDhinus Mphis (n=3, Gurevich, Stewart, and 

Cornel1 19801, and S. longbostis (n=6, Myrick et al. in press). In each 

case, the GLGs were interpreted to be annual. 

Annual growth layers have also been verified in teeth of tropical, 

terrestrial species based on known-age specimens. Spinage (1976) found one 

layer per year in three species (waterbuck, lion, and giraffe) and 2 annual 

layers in another species (black rhinoceros). In the latter case, the two 

layers could be distinquished, and thus both layers fit into the current 

concept of a growth layer group (GLG). Spinage (1976) suggests that 

differences in deposition patterns are influenced by environmental factors, 

specifically annual vs. biannual rainfall patterns. 

GLGs in teeth have been found to be annual in most species for which 

this has been examined. Some of these studies can be faulted, however, 

for their lack of blind tests. Blind GLG counts by experienced readers can 

differ markedly when approximate age is not known (Kimura, 1980). Between 

reader differences may result from subjectivity in defining GLGs. This 

subjectivity can result in defining GLGs that do not represent annual patterns. 

Regardless, previous calibration studies of dolphin age determination tend 

to discount Hypothesis (3). 

Sumnary 

In examining causes for the observed tldiptf in the age structure of 

female spotted dolphins, we have found that non-stable age structure cannot 

be the cause of this peculiarity, but that sampling bias or age determination 

errors could be. Evidence of social segregation is strong5, but this alone 

cannot cause a bias unless sampling is also selective. Additional work on 

sampling bias is needed. In spotted dolphins, GLGs in teeth have not been 

proven to correspond to age in years. Based on comparing patterns of agespecific growth and maturation to known-age bottlenose dolphins, age 

estimates for spotted dolphins may be too high (ie. more than 1 GLG may be 

deposited annually, at least for young dolphins). Comparisons to GLG patterns 

in other dolphins suggest that 1 GLG per year would be expected. Resolution 

of this apparent conflict will require additional information. 
13 

Section 2. ESTIMATING NATURAL MORTALITY RATES 

This section deals with the estimation of survival rates from the above 

age frequency distributions. The first section raised doubts about the 

suitability of the data for this exercise, specifically whether sampling or 

age determination are biased. This section simply examines some of the 

possibilities for mortality estimation if Hypothesis 2 is true and 

Hypothesis 3 is false (ie. ages are known, but frequency distributions are 

biased for younger age classes). 

Survival or mortality rates can be estimated from the age frequency 

distribution of a population provided that 1) the population is in "stable 

age distribution" and 2) the population growth rate is known (Caughley 1977). 

The first of these conditions would be met if age-specific birth and death 

rates had remained constant for a sufficient period of time prior to sampling 

the age distributions (Lopez 1961). The second would be met if population 

growth rates were measured by direct observation (Barlow 1982) or if a 

stationary population were assumed (hence zero growth, Caughley 1966). 

For spotted dolphins, problems exist with both assumptions. Given 

varying levels of fishery-related mortal ity since 1959, we know that agespecific mortality rates have not remained constant. We do not know the 

population growth rates of the spotted dolphin populations (although an 

attempt has been made to define 1 i kel y ranges 7, . 

It is, however, possible to address the more limited problem of 

estimating pre-1959 mortality rates. If we assume that dolphin stocks 

were in equilibrium prior to 1959, we can then assume that age-specific 

birth and death rates were constant prior to that time and that the population 

growth rate was zero. Historical rates of natural mortality can be estimated 

from current age distributions (Ohsumi 1978). Although teeth were not 

collected for age determination until the 1970~~ details of the 1959 age 

distribution can be derived from what is known of the 1973-81 age 

distributions. These pre-1959 mortality rates are important because they 

correspond to natural mortality rates, or the mortality rates prior to 

incidental fishing mortality. 

Two additional assumptions are necessary to estimate natural mortality 

by these means. First, there must be no compensatory changes in adult 

mortality rates with population density. Eberhardt (1977) and Fowler (1981) 

suggest that adult mortality is less likely than other vital rates to show 

density dependent changes in marine mamnals . Second, fishery-related dolphin 

mortality must not be selective with respect to age, or the age-specific 

selectivity must be known. If the first assumption is true and if fisheryrelated mortality has not been selective, the age distribution of individuals 

over 14 years of age would be the same In 1973 as it was in 1959. 

'Reilly, S. B. and J. Barlow. 1984. Rates of increase in delphinid 

cetaceans. (manuscript). 
14 

Effects of Selective Harvest 

If dolphin harvests between 1959 and 1973 were not distributed evenly 

over all ages, estimation of mortality rates becomes more difficult. No data 

exist from which 1959-72 selectivities could be estimated; however, as was 

discussed in Section 1, considerable age selectivity may have existed in the 

1973-81 harvests (Fig. 1). A simulation model is used to examine the effect 

of selective harvests on the estimation of annual mortality rates. 

A population is assumed to be in stable age distribution with an 

equilibrium population size of 6 million in the year 1959. Assume a 13-year 

age of first reproduction for females, a fecundity of 0.15, and a constant 

survival rate of 0.95. The population is projected to the year 1974 using 

a Leslie matrix model. Each year, harvests are applied to each age class 

based on the age-specific selectivities given in Table 1. Density dependent 

changes are allowed in age of maturation and fecundity, but not in survival 

rates. This example was patterned after spotted dolphins, and the actual 

estimates of dolphin mortality (Smith 1983, Table 4) were used for harvest 

numbers. Details of the density dependence are not relevant so long as adult 

mortality does not change through time. 

Results of the simulation model show that selective havests can affect 

the estimation of natural mortality rates. The initial age distribution in 

1959 (Fig. 7, upper line) show a constant slope which corresponds to the 

stationary age distribution of a population with a constant mortality rate 

of 0.95. The projected age distribution in 1974 (Fig. 7, lower line) has a 

variable slope due to the effects of a density dependence and a time varying 

age-sel ective harvest. If the harvest had not been age-sel ective, the 

portion of the 1973 age distribution over 15 years would have had the same 

slope as in 1959. Using the 15+ age classes, the Chapman and Robson (1960) 

mortality estimator give a value of 0.932 rather than the true value of 0.95. 

In this case, non-selective harvests resulted in a slight under-estimate of 

pre-1959 natural mortal ity rates. 

Pooling Age Distributions 

Age distributions for individual years (Fig. 2) do not contain sufficient 

numbers of older individuals to estimate mortality re1 iably for each year. 

The problem is how to pool the years so as to obtain a single natural mortality 

estimate. Ages less than 17 years were not used, in order to avoid the 

potential bias in age frequency distributions (above). The method used here 

was to sum the frequency of individuals in age class 17 in the 1973 sample, 

plus the frequency in age class 18 in 1974, plus the frequency in age class 19 

in 1975, etc., to get a first age class. The second and subsequent age 

classes were summed in the same wayI using successively higher starting ages 

in 1973. Since this method can introduce errors if mortality rates change 

appreciably with ager sums were pooled over a span of only 5 years (1973-771, 

as well as over the entire data set (1973-81). The effect of age-varying 

mortality rates has not been investigated. 
15 

Table 1. Relative age-specific harvest selectivities used in the simulation 

model for population projection. 

Age 

C1 ass 

Re1 ative 

Sel ect ivi ty 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

. 

1st 

1.0 

1.0 

1 .o 

0.9 

0.8 

0.7 

0.6 

0.5 

0.5 

0.6 

0.7 

0.8 

0.9 

1 .o 

. 

. 

1 .o 
16 

00 0 

00 0 

000 0 ooo(0 * 0 

0 

r 
17 

Results 

The synthesized age distributions from 1973-77 and 1973-81 are given in 

Table 2. If all of the above assumptions were met, these should approximate 

the age distribution in 1959 for individuals of 17 years or older. The 

Chapman-Robson model was used to estimate a constant survival rate from 

these age frequency distributions (Chapman and Robson 1960) . Annual survival 

rates were 0.829 (s.e.=0.012) for 1973-77 data and 0.818 (s.e.=O.O12) for 

1973-81 data. Attempts were made to estimate age-specific survival rates 

from these data by fitting non-linear functions to this age frequency data 

(Barlow 1982). The non-linear functions gave better fits to the data, but 

these improvements were not statistically significant. Expected age 

frequencies predicted by the geometric (Chapman-Robson) model were not 

significantly different from the observed age frequencies (chi-square test). 

Since we are not able to discern age-specific changes in survival rates, 

pooling the entire 1973-81 data set is justified. 

Discussion 

The estimation of natural survival rates for spotted dolphins is 

dependent on many assumptions. Some of these assumptions (e.g. non-selective 

harvesting) are undoubtedly not met. Additional simulations may be useful 

to determine the sensitivity of this method to violations in the assumptions. 

First, however, attempts should be made to resolve the questions of age 

estimation raised in Section 1. Without accurate age estimates, mortality 

rates simply cannot be estimated by these means. 

The survival rate estimated above (0.82) is less than expected for an 

adult survival rate for spotted dolphins. The simulation model indicated 

that selective harvests could cause this value to be 2 percentage points low. 

However, even assuming survival rates of 0.85 and high estimates for 

fecundity (age-at-first-birth = 7 yrs, calving interval = 2 years), this 

adult survival would result in a population declining at more than 5% per 

year7. This is obviously not an equilibrium survival rate. This low 

estimate may be an artifact of the difficulty in aging older individuals. 

Alternatively, the estimate may be accurate as a composite estimate for older 

age classes, though younger age classes may have much higher survival rates. 

The latter might be expected given the mortality patterns seen in other 

mammals (Caughley 1966). 

CONCLUSION 

Current age distributions provide little direct information which will 

allow us to refine estimates of dolphin population growth rates. Until 

uncertainty in age estimation and sampling bias can be resolved, analysis 

of age distributions will be of little value. However, several of the 

approaches used here may be useful in resolving these uncertainties. 

Potential exists to use growth curves of known-age dolphins to verify 

age estimation procedures. Given our limited samples, we do not know the 

inter-specific range of variability in dolphin growth. Growth curves are 

needed for a variety of dolphin species. Also, we do not know the possible 
18 

Table 2. Synthesized age frequency distributions for spotted dolphins. 

Included are distributions taken female spotted dolphin age 

distributions from 1973-77 and from 1973-81 (see text for 

methods 1 . 

Age 

C1 ass 

Age Class 

Abundance 

( 1973 -77 1 

Age Class 

Abundance 

(1973-81) 

a 

b 

d 

e 

f 

9 

h 

i 

j 

k 

1 

m 

n 

P 

9 

r 

C 

0 

Total 

33 

42 

33 

33 

18 

16 

13 

21 

13 

10 

6 

8 

3 

3 

0 

3 

0 

2 

44 

51 

38 

42 

19 

23 

14 

23 

15 

12 

6 

9 

3 

3 

0 

3 

0 

2 

257 3 07 
19 

range of intra-specific growth in dolphins and whether captivjty can 

appreciable alter growth rates. Unpublished data of this sort may exist at 

a number of marine aquariums throughout the world. Additional data on 

captive animals can certainly be gathered, and the possibility exists for 

gathering more data from certain wild populations (M. D. Scott, pers. comm.). 

Additional data on age at sexual maturation in captive dolphins may 

also help verify age estimation procedures. Estimates for species other 

than bottlenose dolphins would be especially useful. Data on marked, wild 

populations would be of great value also. 

In addition to uncertainty in age estimation, uncertainty exists in 

the degree of sampling bias. Even if social segregation is extreme, a 

large bias would require that the fishing operations be selective, either 

consciously or unconsciously. Work is underway to determine whether known 

levels of selectivity are sufficient to explain the apparent level of 

sampling bias (A. A. Hohn and M. D. Scott, pers. comm.). 

Much work has yet to be done before questions of sampling bias and 

age estimation in spotted dolphins can be completely resolved. In this paper 

we have raised more questions than we have answered. We hope that by 

bringing these questions to light, we can ensure that they will be answered. 

The answers may be broadly applicable and useful in other dolphin population 

studies. 

ACKNOWLEDGEMENTS 

We wish to thank A. Myrick and S. Ridgway for graciously sharing 

unpublished data on captive dolphin growth. Tooth GLGs were counted by 

A. Myrick and the author (A.A.H.); teeth were prepared by P. Sloan, D. 

Stanley, and M. Kimura. Helpful comments and critical reviews were provided 

by J. Breiwick, D. Chapman, A. Dizon, D. Goodman, F. Hesterr A. Myrick, 

W. Perrin, S. Reilly, D. Siniff, T. Smith, and L. Vetter. 
20 

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RECENT TECHNICAL MEMORANDUMS 

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Distribution of four dolphins (Stenella spp. and Delphinus 

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W. F. PERRIN, M. D. SCOTT, G. J. WALKER, F. M. RALSTON 

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(December 1983) 

Annotated references to techniques capable of assessing the 

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Summary of environmental and fishing information on Guam 

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"The Hawaiian Monk Seal of Laysan Island: 1982" 

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Atlas of airborne sea surface temperature observations In 

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Potential impact of deep seabed mining on the larvae of 

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Sampling commercial rockfish landings In California. 

A.R. SEN 

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Histopathological manual for monitoring health of striped 

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J.A. WHIPPLE, M. JUNG, R.B. MacFARLANE and R. FISCHER 

(August 1984) 

Hawaiian monk seal population research, Lisianski island, 

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H.S. STONE 

(August 1984)