1. Analysis of flathead catfish population parameters using spine versus otolith age data Jeffrey C. Jolley, Peter C. Sakaris, and Elise R. Irwin Alabama Cooperative Fish and Wildlife Research Unit

2. State Management Overview ~3 states have maximum size limits –MN: 1 over 24” –ND: 1 over 24” –TN: 1 over 34” ~21 states have creel limits ~10 states have minimum size limits –10”(Indiana – all species) – 25”(Mississippi – flatheads)

3. Population parameters Fish aging is generally required for proper assessment of fish populations Various aging techniques have been developed for catfishes Incorrect age estimates may lead to unintended management outcomes

4. Objectives Compare age estimates for two flathead catfish populations via two techniques –otoliths and spines Model and compare populations under varying management regimes using age data Model population growth using survival estimates derived from data

6. Flathead catfish (Pylodictus olivarus)

7. Methods Collection –Boat mounted electrofisher, Gillnets, hoopnets, trotlines, jug-lines, and slatboxes used to facilitate catches Aging –Otoliths and spines removed from all sacrificed fish –Recent studies indicate that otoliths are accurate for aging catfishes

8. Otolith Spine 892 mm; ~11-13 annuli seen 892 mm; Age-25 flathead catfish

9. Models Traditional fishery analysis –FAST (Slipke and Maceina 2000) –Jones (1957) modification of the Beverton-Holt equilibrium yield equation to compute yield (Y) Leslie matrix population modeling –PopTools v. 2.5 (Hood 2003) in Microsoft Excel®

10. PopulationAging Structure NumberMaximum Age Mean Age CoosaOtolith401257 CoosaSpine259186 OcmulgeeOtolith136164 OcmulgeeSpine109154 Results

11. ParameterValue Von Bertalanffy growth coefficientsL ∞ = 1137 mm; K = 0.089; t 0 = 0.262 years Maximum age18 years Conditional natural mortality17% Conditional fishing mortality5% to 50% Log 10 (wt):log 10 (TL)coefficientIntercept = -5.275; slope = 3.12 Age at sexual maturation5 years Fecundity-to-length regressionLog 10 (fecundity) = 2.614. Log 10 (TL) – 3.257 Percent of fish that are females43% for all age groups Percent of females spawning annually100% Minimum length limits254 mm, 381 mm, 508 mm, 635 mm Life history parameters Coosa River- Spines

12. ParameterValue Von Bertalanffy growth coefficientsL ∞ = 1137 mm; K = 0.059; t 0 = -1.185 years Maximum age25 years Conditional natural mortality13% Life history parameters Coosa River- Otoliths

13. Yield – spines versus otoliths Coosa River

14. SPR – spines versus otoliths 29% decrease 18% decrease Coosa River

15. 72% decrease #Memorable fish – spines versus otoliths Coosa River

16. ParameterValue Spines: Von Bertalanffy growth coefficients L ∞ = 1080 mm; K = 0.233; t 0 = 0.131 years Maximum age15 years Conditional natural mortality24% Age at sexual maturation4 years Otoliths: Von Bertalanffy growth coefficients L ∞ = 1080 mm; K = 0.217; t 0 = -0.246 years Maximum age16 years Conditional natural mortality23% Age at sexual maturation4 years Life history parameters Ocmulgee River- Spines versus Otoliths

17. Ocmulgee River – spines vs otoliths

20. Coosa River Ocmulgee River

21. Population Matrix Model Spines versus Otoliths – population growth modeled for Coosa and Ocmulgee flathead catfish –λ, population growth rate –Survival estimates of mature fish derived from catch-curves Assumptions –1:1 sex ratio, survival of YOY and juvenile fish constant –Coosa: age at maturity – 5 yrs –Ocmulgee: age at maturity – 4yrs –Fecundity adjusted for survival

22. Coosa survival-spines versus otoliths ANCOVA: t = 2.73, P=0.01, slopes were different

23. Population growth – spines vs otoliths

24. Ocmulgee survival-spines vs otoliths ANCOVA: t = -0.16, P=0.87, slopes were similar

25. Population growth – spines vs otoliths

26. Ocmulgee versus Coosa - Growth

27. Conclusions Spine derived age information may be adequate at times –Depends on the growth-characteristics of the population –Depends on management concern – maximum yield or trophy fish Otolith derived age information should be used for population growth models, –Especially when individuals in the population are slow- growing.

28. Thanks to Alabama Department of Conservation and Natural Resources M. Maceina D. DeVries Y. Brady J. McHugh S. Rider W. Benson B. Daniels C. Hayer G. Katechis K. Kleiner A. Nicholls M. Ross N. Trippel M. Nash