1 / 20

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.

kendall-sutton
Télécharger la présentation

Thrond O Haugen

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related