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Thrond O Haugen

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

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Thrond O Haugen

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

  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. yt = b1xt-1 + b2yt-1+ b3zt-1 xt = a1xt-1 + a2yt-1+ a3zt-1 b2 a1 a2 b1 c1 c2 a3 b3 c3 zt = c1xt-1 + c2yt-1+ c3zt-1 Pike and Perch Interactions Linear system!!!!

  4. Population model on log scale (making things linear): xt = a1xt-1 + a2yt-1+ a3zt-1 yt = b1xt-1 + b2yt-1+ b3zt-1 zt = c1xt-1 + c2yt-1+ c3zt-1 x = young pike y = older pike z = perch May be written (after some matrix algebra): xt = a1xt-1 + a2xt-2+ a3xt-3 yt = b1yt-1 + b2yt-2+ b3yt-3 zt = g1zt-1 + g2zt-2+ g3zt-3 B = reproduction rate s1 = survival first year s2 = survival older ones = Nt-1(Bs1 + s2) Density-dependence Nt = exp(yt) = Nt-1exp(b1yt-1+b2yt-2+b3yt-3) = Nt-1R Hence, we expect a three-lagged structure in survival

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

  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. 1 2 3 4 5 1 2 3 4 5 Age Kipling (1983), J. Anim. Ecol. Sex • Differential growth • Differential reproductive effort • Males: fighting costs • Females: gonad costs

  8. Harvest and sex 1 2 3 4 5 1 2 3 4 5 Age Kipling (1983), J. Anim. Ecol.

  9. Hypotheses • Three-lagged density structure of survival • Sfemales>Smales • Sa=1 is size-dependent and more correlated with prey density than Sa>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 46/64 mm gillnet (GN) – for tagging 64 mm gillnet (PGN) – retrieved J F M A M J J A S O N D M J F M A f(t) f(t+1) 5 months 7 months pGN(t) pPT(t) pPGN(t+1) pGN(t+2) pPT(t+2) p(t) p(t+2) Data structure Right-censoring

  11. Pr(A): stays Pr(B): moves Parameterisation (CAS) Si= survival (from) pj= capture probability (to) yij= transition probability (from-to) A: NSN… B: S0N… Conditional Arnason-Schwartz Model

  12. General Model Constraints • Right censoring at winter occasions • Neither S or y are separatetly estimable for winter-to-spring intervals • yw—>s = 0 • Sw—>s = Ss—>w • p could be estimated for each occasion • Different methods and efforts during spring • Ps(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 + 0.37et

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

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

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

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

  19. Survival (a>1) logit[S(a>1)t] = 2.95 – 1.23B – 0.47Nt + 0.29B*Nt – 0.22Nt-1- 0.03Nt-2+ 0.25sex + 0.06Zt logNt-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

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