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

2. Previously on Windermere Pike Trilogy  Presented the data series 1949–2001 (N = 5560) Individually tagged pike All the covariates you can dream of  Presented a parameterisation that works Multistate Cormack-Jolly-Seber model MS-GOF tells us to proceed (Pradel et al. 2003) Low or no over-dispersion  Presented preliminary results

3. z t = c 1 x t-1 + c 2 y t-1 + c 3 z t-1 x t = a 1 x t-1 + a 2 y t-1 + a 3 z t-1 y t = b 1 x t-1 + b 2 y t-1 + b 3 z t-1 b1b1 c1c1 c2c2 a3a3 a1a1 a2a2 b3b3 b2b2 c3c3 Pike and Perch Interactions Linear system!!!!

4. Density-dependence x t = a 1 x t-1 + a 2 y t-1 + a 3 z t-1 y t = b 1 x t-1 + b 2 y t-1 + b 3 z t-1 z t = c 1 x t-1 + c 2 y t-1 + c 3 z t-1 x = young pike y = older pike z = perch Population model on log scale (making things linear): N t = exp(y t ) = N t-1 exp(  1 y t-1 +  2 y t-2 +  3 y t-3 ) x t =  1 x t-1 +  2 x t-2 +  3 x t-3 y t =  1 y t-1 +  2 y t-2 +  3 y t-3 z t =  1 z t-1 +  2 z t-2 +  3 z t-3 May be written (after some matrix algebra): = N t-1 R Hence, we expect a three-lagged structure in survival = N t-1 (Bs 1 + s 2 ) B = reproduction rate s 1 = survival first year s 2 = survival older ones

5. Degree-days over 14 °C Juvenile instantaneus mortality All aspects? Age 1 Age 2–3 Age >3

6. Spatial aspects  Two basins  Differ in productivity and morphology Northern basin Deep, Steep and stony littoral Mesotrophic Southern basin Shallow Sheltered and weedy littoral Eutrophic

7. 1234512345 Age Kipling (1983), J. Anim. Ecol. Sex  Differential growth  Differential reproductive effort Males: fighting costs Females: gonad costs

8. 1234512345 Age Kipling (1983), J. Anim. Ecol. Harvest and sex

9. Hypotheses  Three-lagged density structure of survival S females >S males S a=1 is size-dependent and more correlated with prey density than S a>1  Fishing mortality (a>1) is correlated with effort and highest for females  Recruitment to fisheries is size-dependent and highest for females  Dispersal is density-dependent

10. 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 Data structure p (t)p (t+2)

11. Parameterisation (CAS) A:NSN… B:S0N… Pr(A): Pr(B): moves stays S i = survival (from) p j = capture probability (to)  ij = transition probability (from-to) Conditional Arnason-Schwartz Model

12. General Model Constraints  Right censoring at winter occasions Neither S or  are separatetly estimable for winter-to-spring intervals  w—>s = 0 S w—>s = S s—>w  p could be estimated for each occasion Different methods and efforts during spring P s (t) Consistent winter fisheries throughout the study Covariate-specific estimates

13. Results

14. Fishing mortality (a>1) Logit[p(a>1) t ] = logit[F(a>1) t ] = -1.01 + 0.58B + 0.12sex + 0.37e t

15. Recruitment to winter fisheries Logit[p(a=1)] = -1.61 + 0.79B + 0.01sex + 0.57l + 0.32e t North South e = 0

16. Dispersal (a=1) logit[  (a=1)] = -2.17 + 1.64B – 0.11sex + 0.09l – 0.14B*l

17. Dispersal (a>1) logit[  (a>1) t ] = -3.94 + 1.16sex + 1.11B + 0.33g t – 1.03B*g t Increasing relative density in south

18. Survival (a=1) logit[S(a=1) t ] = 9.74 – 0.53B – 1.45sex + 0.14Z t - 2.01N t + 7.36l – 1.98N t *l logN = 3 Z = 0

19. Survival (a>1) logit[S(a>1) t ] = 2.95 – 1.23B – 0.47N t + 0.29B*N t – 0.22N t-1 - 0.03N t-2 + 0.25sex + 0.06Z t logN t-1 and t-2 = 3 Z = 0

20. Summary  Models with delayed density structure on survival are generally supported Though—no more than two delays Highly size-dependent survival at a=1 Inverse sex effect for a=1 and a>1  Dispersal is density dependent Males migrate more than females for a>1 Inverse size-dependence between basins for a=1  Fishing mortality is basin and effort dependent Recruitment to fisheries is not sex dependent and can be predicted from size distribution Fishing mortality is highest in northern basin