Temporal Signatures, or, What Are Biochronologies Good For?


G. R. Spangler, University of Minnesota


We think the major use for biochronologies based upon incremental growth of hard parts of fishes will be as an aid to determining fish ages in connection with population studies. This is the idea of specific periods of time generating "temporal signatures" in the growth increments of bony parts. The drawback to this is that you have to have a biochronology (see heading of that name on home page) to begin with, but letÕs deal with that in a minute. HereÕs how the thing works. Suppose you do have a biochronology for your favorite fish species from a particular place, say, Lovewell Reservoir, Kansas, on the Republican River system (Is it true that a certain senator from that state has proposed a name change for the reservoir to something reminiscent of a salamander?). Suppose the biochronology runs from 1960 to 1990, and you have just caught (in 1995) a smallish (5 kg?) flathead catfish (Pylodictus olivaris ). You measure the growth increments on the spine cross-sections, run them through a linear model program (Weisberg, 1993) to separate out the year-effects, then plot them as a jagged pattern, much like the GI3 pattern (only much shorter) in fig. 9 of Pereira, et al. (1995--Otolith Symposium). Now, all you have to do to ascertain the age of your fish is to "slide" its own saw-tooth pattern alongside the master chronology that you have developed for Lovewell Reservoir, until the best match is found to the master chronology. Voila! The age of the fish is the difference between 1995 (when you caught the fish) and the first year of the series of years that best match your fish. This is simple to do graphically if you have a graph of every fishÕs growth increments, and, in fact, that is how the dendrochronologists match up various periods in the history of forest growth based on tree rings. For fish it is a bit more of a statistical problem. In Ogle et al. (1994), we show a statistical method for successively comparing an individual fishÕs growth increments with every possible position along a master chronology. We used growth increments on scale samples from Red Lakes walleye (Stizostedion vitreum ). The point in the series where there is a minimum sum of squares of the deviations between the master chronology and the fishÕs individual pattern is judged to be the point of best match. From this we assign a probability that the fish belongs to such-and-such a year-class. The advantages of this method over the standard approach of looking at scales or rings on spines or otoliths is that we can automate the process, assign a specific probability for membership in a particular year-class, and, more importantly, the age analyst doesnÕt have to see a complete record of growth. If the last few increments are not visible on the scale because of resorption or erosion of the outer edge, all is not lost. From the matching of the first 6-8 increments, we can still determine which era best corresponds, and assign an age to the fish. Getting the master chronology is a bit of work, and it requires some maintenance sampling, although annual sampling may not be necessary. The original master chronology may not be very precise, but it will improve in quality with age as more years of data are tacked onto the most recent end of it. It can initially be based on known-aged fish, or very young fish that you can age confidently from scales or spines.


Well, that is probably more about biochronology and temporal signatures than you may ever want to know, but it sums up why we are interested in developing the technology further. There is no reason not to use the method for other species too, including invertebrates, if they can be aged. So far, we have developed biochronologies for freshwater drum (Aplodinotus grunniens , PereiraÕs papers) and walleye, Stizostedion vitreum , (Ogle et al., 1994; Cyterski, 1995), and we are working on shallow-water cisco (Coregonus artedii ) and lake trout (Salvelinus namaycush ) from Lake Superior. I suspect these methods will be useful for a host of fishes that show density-dependent growth, perhaps even for flathead catfish!


References

  • Cyterski, M. J. (1995). A Growth History of Red Lake Walleye (Stizostedion vitreum ) Developed Through Scale Analysis. M. Sc. Thesis, Dept. Fisheries and Wildlife, University of Minnesota. 140 pp.
  • Frie, R. V. (1982). Measurement of fish scales and backcalculation of body lengths using a digitizing pad and microcomputer. Fisheries, 7, 5-8.
  • Ogle, D. H., Spangler, G. R., & Shroyer, S. M. (1994). Determining fish age from temporal signatures in growth increments. Can. J. Fish. Aquat. Sci., 51(8), 1721-1727.
  • Pereira, D. L., Bingham, C., Spangler, G., Cohen, Y., Conner, D., & Cunningham, P. (1995). Growth and recruitment of freshwater drum (Aplodinotus grunniens ) as related to long-term temperature patterns. Can. Spec. Publ. Fish. Aquat. Sci., 121, 617-629.
  • Pereira, D. L., Bingham, C., Spangler, G., Conner, D., & Cunningham, P. (1995). Construction of a 110-year biochronology from sagittae of freshwater drum (Aplodinotus grunniens ). In D. H. Secor, J. M. Dean, & S. Campana (Eds.), Recent Developments in Fish Otolith Research, (Vol. 19, pp. 735). Columbia: The Belle W. Baruch Library in Marine Science, University of South Carolina Press.
  • Weisberg, S. (1993). Using hard-part increment data to estimate age and environmental effects. Can. J. Fish. Aquat. Sci., 50(6), 1229-1237.