1 / 37

Thrond O Haugen

Spatio-temporal variation in pike demography and dispersal: effects of harvest intensity and population density. Thrond O Haugen. Who’s involved?. Project leader: Nils Chr. Stenseth Centre for Ecology and Hydrology Ian Winfield Universit y of Oslo Leif Asbjørn Vøllestad

ataret
Download Presentation

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: 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 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) Discretizing the data Right-censoring

  8. Changed fishing effort

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

  10. 64 mm gill net 1 2 3 4 5 1 2 3 4 5 Age 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. 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) Multistate models To State 1 State 2 State 3 From Capture probability pi+1 (1,1) pi+1 (1,2) pi+1 (1,3) Jolly MoVe-parameterisation (JMV) pi+1 (1) pi+1 (2) pi+1 (3) or Conditional Arnason-Schwartz parameterisation (CAS)

  13. The transition parameter • May estimate a separate transition parameter (y) when conditioning on survival • yi,j = fi,j/Si 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. Pr(A): stays Pr(B): moves Parameterisation A: NSN… B: S0N…

  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 y is separatetly estimable for winter-to-spring intervals • Could set S=1 and y = 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 y

  19. Analysis outline • Analysis of natural survival • Using spring records only • Standard CJS modelling • Collapsing basin information • Exploring effects from gear and density • 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 pa1(gear*length+length2), a>1(t)

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

  23. Fishing effort and natural survival f(gear+effortPGN)

  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?

  26. Final CAS model Sa1(basin*length),Sa>1(basin*popsize) Pspring(basin+t), PSwinter,a1(length), PNwinter,a1(.), Pwinter,a>1(basin+effort) yNSa1(length), yNSa>1(density gradient), ySN(t)

  27. Size-dependent recruitment to PGN fisheries

  28. Effort and fishing mortality

  29. Basin- and year-specific survival

  30. Length-dependent survival from tagging to first winter Sa1(basin*length)

  31. Density-dependent survival for tagging age>1 Sa>1(basin*popsize)

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

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

  34. 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

  35. 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

  36. Should I stay or should I go?

More Related