1. Spatio-temporal variation in pike demography and dispersal: effects of harvest intensity and population density Thrond O Haugen

2. Who’s involved?  Project leader: Nils Chr. Stenseth  Centre for Ecology and Hydrology Ian Winfield  University of Oslo Leif Asbjørn Vøllestad Per Aass (Zoologisk museum)  Management Tore Qvenild (Hedmark) Ola Hegge (Oppland) NIVA Gösta Kjellberg

3. Size-biased harvest of fish  Ecological implications Affects demography directly Effects on population dynamics Affects population density that in turn will affect growth conditions  Evolutionary implications Life-history adaptations to man-made mortality regime and growth conditions

4. General project objectives  In order to gain better knowledge of pike population dynamics: Estimate demographic rates under changing harvesting regimes Quantify natural- and fishing mortality Estimate recruitment to fisheries Estimate dispersal under varying population densities

5. Over to England...

6. Data background  Tagged during spring Three methods Pike gill nets (64 mm mesh size) 46 mm gill nets Perch traps Live recaptures (all re- released)  Winter fisheries by scientists only (64 mm) All individuals retrieved  1949–present

7. Perch trap (PT) – for tagging 64 mm gillnet (PGN) – retrieved 46/64 mm gillnet (GN) – for tagging MAMJJASONDFJ MAFJ M p GN (t) p PT (t) p GN (t+2) p PT (t+2) p PGN (t+1) Right-censoring  (t)  (t+1) 7 months 5 months Discretizing the data p (t)p (t+2)

8. Changed fishing effort

9. Density-dependent growth, but what about survival?

10. 1234512345 Age 64 mm gill net Kipling (1983), J. Anim. Ecol.

11. Specific objectives  We have exact measures on fishing effort Is fishing mortality related to effort? If so: does this apply to all size classes in both basins?  We have population size estimates and information about individual growth Is natural survival density dependent? Is dispersal density dependent? If so: does this apply to all size classes in both basins?

12. Multistate models Probability of survival-migration State 1, 2 or 3  i (1,1)  i (1,2)  i (1,3)  i (2,1)  i (2,2)  i (2,3)  i (3,1)  i (3,2)  i (3,3) Capture probability State 1 State 2 State 3 p i+1 (1,1) p i+1 (1,2) p i+1 (1,3) p i+1 (1) p i+1 (2) p i+1 (3) or From To Jolly MoVe-parameterisation (JMV) Conditional Arnason-Schwartz parameterisation (CAS)

13. The transition parameter  May estimate a separate transition parameter (  ) when conditioning on survival  i,j =  i,j /S i S = fidelity-survival i = from-state j = to-state Note: S is estimated for the “from” state and p for the “to” state in CAS parameterisation

14. Parameterisation A:NSN… B:S0N… Pr(A): Pr(B): moves stays

15. GOF tests for CJS models  A fully efficient GOF test for the CJS model is based on the property that all animals present at any given time behave the same whatever their past capture history (  Test 3) whether they are currently captured or not (  Test 2)

16. NOW: GOF tests also for MS models  A fully efficient GOF test for the JMV model is based on the property that all animals present at any given time on the same site behave the same whatever their past capture history (  Test 3G) whether they are currently captured or not (  Test M)  Methods described in Pradel et al. 2003, Biometrics U-Care 2.0 (ftp://ftp.cefe.cnrs-mop.fr/biom/Soft-CR/)

17. Model constraints (I)  Because of right censoring at winter occasions neither S or  is separatetly estimable for winter-to-spring intervals Could set S=1 and  = 0 for these periods or force estimates to equal over both periods within a year Last approach more often converged

18. Model constraints (II)  p could be estimated for each occasion Three different methods used during spring Different efforts and size selectivity time models the only possibility Same gillnets used during winter fisheries throughout the study Could constrain according to effort Could estimate size-dependent recruitment to fisheries p-estimates performed under maximum temporal variation for S and 

19. Analysis outline 1.Analysis of natural survival Using spring records only Standard CJS modelling Collapsing basin information Exploring effects from gear and density 2.MS modelling Including winter captures (fishing mortality) Recruitment to fisheries Between-basin dispersal

20. GOFs for CJS  For the 1953-1986 period  No evidence for lack of fit for the CJS model No trap happiness or shyness

21. Length- and gear-specific recapture probability p a1(gear*length+length 2 ), a>1(t)

22. Temporal variation in annual natural survival  (gear+t) perch disease

23. Fishing effort and natural survival  (gear+effort PGN )

24. Summary of the CJS results  Natural survival vary over time decreased during 1960-1980 period indication of density dependence?  Capture probability is gear and size specific As known…

25. Do MS-CMR models fit the data?

27. Final CAS model S a1 (basin*length),S a>1 (basin*popsize) P spring (basin+t), P S winter,a1 (length), P N winter,a1 (.), P winter,a>1 (basin+effort)  NS a1 (length),  NS a>1 (density gradient),  SN (t)

28. Size-dependent recruitment to PGN fisheries

29. Effort and fishing mortality

30. Basin- and year-specific survival

31. Length-dependent survival from tagging to first winter S a1(basin*length)

32. Density-dependent survival for tagging age>1 S a>1(basin*popsize)

33. Size- and basin-dependent dispersal during first year following tagging

34. Density- and basin-dependent dispersal for a>1 Increasing relative density in north

35. Summary  Indications of density-dependent dispersal and survival Basin specific responses  Net migration from N to S larger ones migrate with higher probability  3-4 times higher fishing mortality in S  Once lengths of >55 cm is achieved fishing mortality increase with effort  Possible to predict recruitment to fisheries from spring length distributions not for N

36. Further objectives to be addressed  Effect of sex  Population composition Age/size structure  Effects from other environmental variables Eutrophication Prey abundance, i.e. perch abundance Temperature

37. Should I stay or should I go?