rockfish otolith

Age Determination Unit

General Techniques

At the ADU, we utilize a variety of techniques to process age structures. We measure each specimen using calipers and scales before processing. For our groundfish species, we predominately “break and burn” the sagittal otolith, but we also develop thin sections for select specimens. All of our invertebrate samples are prepared by “thin-sectioning” age structures and mounting these onto petrographic slides.
Age Readers then use microscopes to interpret the growth pattern and determine an age estimate.

Otolith Prep

Valve Prep
Scientific NameCommon NameMax AgeMinimum NCapture YearCapture LocationSource
Anoplopoma fimbriaSablefish94b>100001989Aleutian Is.personal communication, Delsa Anderl, NMFS, Seattle
Gadus macrocephalusPacific cod38f>3002001Kachemak BayADFG-Age Determination Unit, Juneau, Alaska
Theragra chalcogrammaWalleye pollock33>10000before 1990Bering SeaMunk (submitted 2004)
Hexagrammos decagrammusKelp greenling18c102000NSEAADFG-Age Determination Unit, Juneau, Alaska
Ophiodon elongatusLingcod36>100002005NSEAADFG-Age Determination Unit, Juneau, Alaska
Pleurogrammus monopterygiusAtka mackerel15 >50002000Aleutian Is.personal communication, Delsa Anderl, NMFS, Seattle
Atheresthes evermaniiKamchatka flounder33c>1201991Bering SeaZimmerman and Goddard 1996
Atheresthes stomiasArrowtooth flounder23 >10001993GOATurnock et al. 1999
Eopsetta jordaniPetrale sole35>60001999BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Errex zachirusRex sole27>2001996GOApersonal communication, Delsa Anderl, NMFS, Seattle
Hippoglossoides elassodonFlathead sole34 >10002009Bering SeaNMFS, AFSC, Seattle
Hippoglossus stenolepisPacific halibut55>100001992Bering SeaInternational Pacific Halibut Commission 1998
Microstomus pacificusDover sole60c>10001990WCPersonal communication, Bob Mikus, ODFW, Newport
Pleuronectes asperYellowfin sole34>2001999Bering Seapersonal communication, Delsa Anderl, NMFS, Seattle
Pleuronectes bilineatusRock sole26 >2001998Bering Seapersonal communication, Delsa Anderl, NMFS, Seattle
Pleuronectes quadrituberculatusAlaska plaice37>5002002Bering SeaNMFS, AFSC, Seattle
Pleuronectes vetulusEnglish Sole22>10001959BCChilton and Beamish 1982
Sebastes aleutianusRougheye rockfish205 >100002000SSEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes alutusPacific ocean perch105>100002008Bering SeaNMFS, AFSC, Seattle
Sebastes babcockiRedbanded rockfish106>10001985SSEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes borealisShortraker rockfish160 >100002002PWSADFG-Age Determination Unit, Juneau, Alaska
Sebastes brevispinisSilvergrey rockfish81>100001981BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Sebastes caurinusCopper rockfish50c>2001992PWSMeyer 2000
Sebastes ciliatus aDark rockfish81a>100002006Chiniak BayADFG-Kodiak
Sebastes crameriDarkblotched rockfish105c>10001990WCPersonal communication, Bob Mikus, ODFW, Newport
Sebastes diploproaSplitnose rockfish86 >2001980BC/WCBennett et al. 1982
Sebastes elongatusGreenstriped rockfish54c11984SSEApersonal communication, Mike Vaughn, ADFG, Sitka
Sebastes entomelasWidow rockfish60>30001996BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Sebastes flavidusYellowtail rockfish64>10000before 1982BCChilton and Beamish 1982
Sebastes helvomaculatusRosethorn rockfish87>1001985SEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes maligerQuillback rockfish90>100001997SSEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes melanopsBlack rockfish56>100002008KodiakADFG-Sport Fish Homer, Alaska
Sebastes melanostomusBlackgill rockfish87c,e>10001985WCpersonal communication, John Butler, NMFS, La Jolla
Sebastes miniatusVermillion rockfish68 2012?WCpersonal communication, Lisa Kautzi, ODFW, Newport
Sebastes mystinusBlue rockfish30c,e>2001993WCpersonal communication, Bob Mikus, ODFW, Newport
Sebastes nebulosusChina rockfish78>10001992SSEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes nigrocinctusTiger rockfish116>10001992SSEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes paucispinusBocaccio rockfish46c>1001994GOAMeyer 2000
Sebastes pinnigerCanary rockfish84>50001980BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Sebastes polyspinisNorthern rockfish88 >10002004Aleutian Is.NMFS, AFSC, Seattle
Sebastes prorigerRedstripe rockfish55 >20001999BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Sebastes reediYellowmouth rockfish99 >40001992BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Sebastes ruberrimusYelloweye rockfish122>100002001SSEAADFG-Age Determination Unit, Juneau, Alaska
Sebastes variegatusHarlequin rockfish43c>3001980BCChilton and Beamish 1982
Sebastes wilsoniPygmy rockfish26c>1001991BCpersonal communication, Shayne MacLellan, CDFO, Nanaimo
Sebastes zacentrusSharpchin rockfish58c>5001999GOAHeifetz et al. 2000
Sebastolubus alascanusShortspine thornyhead rockfish133 >10001998PWSADFG-Age Determination Unit, Juneau, Alaska
Squalus acanthiusSpiny dogfish66 >1000before 1982BCChilton and Beamish 1982
Sphyraena argenteaBarracuda5a,c12003SSEAADFG-Age Determination Unit, Juneau, Alaska
Anarrhichthys ocellatusWolf Eel28a,c12003NSEAsport
Panopea abruptaGeoduck clam146a,c>1000 BCsloan and robinson 1984
Saxidomus giganteusButter clam~70ya,c52009NSEAin process
Strongylocentrotus droebachiensisGreen urchin16a,c>3002000SSEAADFG-Age Determination Unit, Juneau, Alaska
Strongylocentrotus franciscanusRed urchin35a,c>3002001SSEApersonal communication, Ole Shelton, ADFG-Juneau
Sebastes variabilisDusky rockfish32a>10002014Afognak, KodiakADFG-Kodiak
Patinopecten caurinusWeathervane Scallop34a,d>200002021YakutatADFG-Age Determination Unit, Juneau, Alaska
a: non-extensive search of literature, maximum age may be higher than what's shown
b: the "age of record" is identified, though some reports list it as older.
c: non-extensive sampling, or anecdotal reference to larger, possibly older specimens
d: interpretation criteria are being re-evaluated, with expectation of older age assignments for past and present specimens
e: Maximum age of fish south of 48degrees N. latitude listed, for lack of data from northern populations
f: based on new interpretation criteria developed by Munk, 2001.
ADFG: Alaska Department of Fish and Game
CDFO: Canada Department of Fisheries and Oceans
IPHC: International Pacific Halibut Commission
NMFS: National Marine Fisheries Service
ODFW: Oregon Department of Fish and Wildlife
BC: British ColumbiaSCA: Southcentral Alaska
GOA: Gulf of AlaskaSEA: Southeast Alaska
NSEA: northern Southeast AlaskaSSEA: southern Southeast Alaska
PWS: Prince William SoundWC: West Coast (here, for all south of 48 degress N.latitude)

Generalized Otolith Dissection

There are several ways to dissect otoliths from fish. Project considerations or fish type will direct the most appropriate method:

Are you collecting sagittae, lapillae, and asteriscae? - see .
Do you need to collect a pure sample of brain tissue as well? - see .
Just collecting sagittae? - see
Is it a hard-headed fish? - see Figure , , , and .

Generalized Disection Figure 1
Figure 1. To access all otoliths from a fish, a cut bisecting the cranium dorso-ventrally will splay halves open to see symmetrical location of lapillae, asteriscae, and sagittae otoliths.


Generalized Disection Figure 2
Figure 2. Collecting uncontaminated brain tissue in addition to sagittae otoliths, requires a cut allowing removal of the brain tissue first. Sagittal otoliths are then removed by angling forceps to the ventral posterior of the cranium, pulling otoliths from the sagittal wells.


Generalized Disection Figure 3
Figure 3. Basic cut to remove sagittae otoliths from most fish.

Generalized Disection Figure 4
Figure 4. Otoliths of rockfish are removed from the bottom of the cranium, accessed through the gill.

Generalized Disection Figure 5
Figure 5. Sever the gill at its dorsal insertion point.

Generalized Disection Figure 6
Figure 6. Posterior of the pseudobranch, scrape away tissue overlying a translucenct area of the bottom of the cranium(seen at tip of forceps). Behind this translucent spot will be one sagittae otolith.

Generalized Disection Figure 7
Figure 7. The translucent area chips easily with the point of a knife. Enlarge a hole through which the otolith may be removed.

Quality Control and Assessment in Aging of Biological Structures

Biologists are taught early the difference between accuracy and precision1 in the estimation of parameters. In age-reading of biological structures, "accuracy" represents the ability to estimate age closest to the true age of the organism. "Precision" measures the ability to repeat a previous estimate of age. Without knowing the true age of the organism, scientists are left with only "precision" in assessing the quality of the data.

Precision of data is assessed by rereading part or all of the sample. Several statistical analyses are commonly used in characterizing the ability to repeat the estimates. Examples are: average percent error, coefficient of variation2, percent agreement3, and index of precision4. Some statistics do a better job than others in providing a balanced picture of aging error: "coefficient of variation" may incorporate an aspect of the age range of the sample, while "percent agreement" only describes the portion of the sample which is in agreement. A graphical depiction of error5 can be quite useful in quickly seeing trends of one reader vs another.

The ADFG Age Determination Unit routinely tests at minimum 20% of all samples read. Results from these precision tests are monitored, but are also actively used in improving age data. If species-specific control limits are transgressed, data are detained for additional evaluation by experienced readers. Data which fall within control limits are released without additional reading. Progress of new age readers6 is tracked using precision test results to monitor their consistency in producing data which falls within acceptable tolerances.

Accuracy versus Precision1

Accuracy is not the same thing as precision. "Accuracy" represents the ability to estimate age closest to the true age of the organism. "Precision" measures the ability to repeat a previous estimate of age (regardless of accuracy). Age data may be accurate but imprecise with repeated estimates, accurate and precise, inaccurate but precise, or inaccurate AND imprecise! The following figure (Figure 1) graphically demonstrates these distinctions. In absence of the means to estimate accuracy, age reading labs generally measure precision. While precision is often described as a proxy for accuracy, this is a dangerous assumption if no studies of ground-truthing the age-reading criteria have been performed.

Accuracy versus Precision (Figure 1)

Literature cited
Kelley, W.D., Ratliff, Jr., T.A., Nenadic, C. 1992. Basic statistics for laboratories: a primer for laboratory workers. New York,NY:Van Nostrand Reinhold.


Coefficient of Variation (CV)2

Coefficient of variation (below) is commonly used by age-reading labs in characterizing error in data. Chang (1982), and Kimura and Lyons (1991) both describe its application and promote its use. It is an unbiased estimate of error and incorporates the age range of the sample into the measure of precision. For example, error of a few years when the age range of the sample is 30-80 years old is less of a concern than if the range is 5-20 years old. Some species are quite difficult to read and a CV of .1 may be routine and acceptable while a CV of .1 for an easy to read species may be unacceptable. Easy to read species may have very low CV?s, for example, less than .03. Kimura and Lyons (1991) also characterize use of CV as simply describing the ease or difficulty in reading a species.

(1) coefficient of variation Coefficient of Variation Equation

Literature cited
Kimura, D. K., and J.L. Lyons. 1991. Between-reader bias and variability in the age-determination process. Fishery Bulletin 89:53-60
Chang, W.Y.B. 1982. A statistical method for evaluating the reproducibility of age determination. Canadian Journal of Fisheries and Aquatic Sciences, 39:1208-1210.


Average Percent Error (APE)3

"Average percent error" (below), formulated by Beamish and Fournier (1981), reduces bias in error estimates by comparing individual estimates to the mean of the estimates, as opposed to comparing one estimate directly to the other. APE is commonly used by several age-reading labs in describing error. The Age Determination Unit uses this estimate for establishing species-specific control limits.

(1) average percent error, APE = Average Percent Error Equation (Beamish and Fournier, 1981)

Literature cited
Beamish, R.J., and D.A. Fournier. 1981. A method for comparing the precision of a set of age determinations.
Canadian Journal of Fisheries and Aquatic Sciences. 38:982-983


Index of Precision (D)4

The index of precision (D) describes that portion of error “contributed by each observation to the average age-class” (Chang 1982). This statistic incorporates the frequency of similar estimates between readers and seems to “credit” the error rate as more readers come up with similar estimates. For example, if 2 readers produced estimates of 23 and 25, X = 24, Average Error (AE)=.04166, and D=.04166. Additional readers repeatedly getting similar results will continue to improve the estimate of error, for example, 4 readers with the estimates of 23, 25, 23, 25, X=24, AE = .04166, but D=.024056.
(1) index of precision, Index of Precision Equation

Literature cited
Chang, W.Y.B. 1982. A statistical method for evaluating the reproducibiliity of age determination.
Canadian Journal of Fisheries and Aquatic Sciences, 39:1208-1210.


Graphical Depiction of Error ~ Age Bias Plots5

Age bias plots (Campana et al 1995) are one of the easiest and visual ways to quickly assess error. They plot one set of estimates against another, showing trends throughout the age range of the sample. For example, APE or CV describe general error within the sample. Age bias plots may suggest trends in this error: higher error in the younger fish vs older, underaging or overaging between readers. They are an excellent complement to any age-reading quality control program.

Literature cited
Campana, S.E., M.C. Annand, and J. I. McMillan. 1995. Graphical and statistical methods for determining the consistency of age determinations. Transaction of the American Fisheries Society 124:131-138..
Graphical Depiction of Error (Figure 1)
Figure 1. An age bias plot showing relatively consistent error throughout the age range. The line represents perfect agreement.

Graphical Depiction of Error (Figure 2)
Figure 2. These data show fair agreement between Reader 1 and 2 up to age 9, but a trend of younger ages by Reader 2.

Monitoring Training of New Age Readers6

The Age Determination Unit has a standard process for training readers. Training of age readers begins with sessions at a teaching microscope, where species-specific age reading criteria are described, relevant to the growth pattern. They next spend at least two weeks examining specimens, with free access to age data. The next step is to assess their progress, by conducting "blind" precision tests where they are given a sample without access to age data. Problem areas are often revealed in this process and highlight difficult to interpret growth patterns; these specimens are then examined at the teaching scope. Over time, the error will (should) stabilize. When consistency is achieved the reader begins "production reading" responsibilities which generally involves a lower level of oversight by an experienced reader. Figure 1 is an example of a control chart (Shewart 1931) used in tracking progress of new readers, and documenting consistency of experienced readers (application parameters and control limits developed by Munk, unpublished data).

New Age Reader Progress (Figure 1)