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A spatial integrated population model applied to black-footed albatross Simon Hoyle Mark Maunder.

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Presentation on theme: "A spatial integrated population model applied to black-footed albatross Simon Hoyle Mark Maunder."— Presentation transcript:

1 A spatial integrated population model applied to black-footed albatross Simon Hoyle Mark Maunder

2 Background Black-footed albatross Phoebastria nigripes Breed on north-western Hawaiian islands –Genetically distinct population on Torishima island About breeding pairs in Hawaiian population Conservation status –Endangered (IUCN) –Petition (US ESA)

3 Project PFRP – integrated modelling for protected species Current status –2 models Hoyle & Maunder Maunder, Alvarez-Flores & Hoyle

4 Model structure Spatial effects at 2 levels –Sub-populations on islands and island groups Kure, Midway, Pearl & Hermes Reef, Lisianski, Laysan, French Frigate Shoals (FFS) FFS also broken down into Disappearing, East, Little Gin, Gin, Round, Trig, Tern, and Whale-Skate islands –Spatial and temporal overlap of birds and fishing effort Bycatch from pooled population Birds interact with fishing vessels Bird distribution depends on age and stage Integrated analysis –Fitting to multiple data types

5 Data types by model

6 Nest and fledgling counts Collated in Cousins and Cooper 2001 Timing of counts –Year = fledge year –Nests = counts of nests or pairs, Dec – Jan –Fledglings = counts of chicks or fledglings, May – July

7 Nest and fledgling counts - uncertainty CV Direct counts of breeding pairs5% Based on sample counts & transects10% Extrapolations of total bird counts20% Estimated from chicks counted later, or doubt expressed 30% Uncertainty reportedAs given

8 Trends vary by island

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11 Breeding success rates

12 Survival rates – correlated with fishing effort

13 Return rates

14 Breeding season range

15 Summer range

16 Albatross distribution 1

17 Albatross distribution 2

18 Fishing effort Effort typeMAHHM Hawaiian longline – shallow and deepYY Californian longline – shallowNY Alaskan groundfish longline – BSAI and GOANY Non-US longline – shallow and deepNY Large mesh driftnet (Japan)YY Squid driftnet (Japan, Korea, Taiwan)YY

19 HA and CA longline effort

20 Driftnet effort

21 Non-US longline effort

22 Bycatch estimates

23 Fishing selectivity Catch at age –Recapture of tagged birds (C&C 2000) –Ageing of Canadian longline bycatch Above strongly suggest higher juvenile selectivity Potential extra sources –Timing of Hawaiian longline fishery captures –Samples from driftnet fisheries at Burke Museum, U Washington (60+) –Samples from Hawaii LL fishery

24 Population model (HM) Multi-state age-based model Recruitment Dynamics Initial conditions Likelihoods

25 Important population processes Spatial and temporal relationships of fishing effort and bird distribution Selectivity (catchability of different life stages) Skipping breeding & breeding success by island Density dependence Movement between islands? –Recruitment –As adults

26 Migration Seems important for dynamics at the local scale –Whale-Skate island disappears –After a number of years, Trig island pop increases(?) –Tern island grows too fast? Impractical to estimate, and may not be significant between island groups –No linkage with Torishima

27 Model structure 5 stages –juvenile –immature –successful breeder –unsuccessful breeder –non-breeder Population structured by colony, time, stage, & age Dynamics in 2 stages: N’(t)=N(t)survival N(t+1)=N’(t)transition

28 Dynamics

29 Distribution of birds and effort Effort in each fishery by grid varies through time Bird distribution varies by season, stage, and year Spatial and temporal effects in Hawaiian catch rates Distribution can be inferred from telemetry, Hawaiian bycatch

30 Mortality

31 Stage transitions (breeding) Attempt breeding (i.e. NB or SB/UB) Successful breeding (i.e. SB or UB)

32 Density dependence Not currently in model Likely to act on probability of successful breeding, age at first breeding, & probability of attempting to breed

33 Initial conditions Initial recruitment per colony Rzero c Projected across age classes at time t start given natural mortality and transition rates

34 Expected values and Likelihoods For fitting to –Bycatch –Counts (nests and fledglings) –Tagging data –Breeding success

35 Bycatch (lognormal LL) Counts (lognormal LL)

36 Observed survival Transition rates use a similar approach – adding up estimated transitions Likelihood –Multivariate normal fit to Hessian Tagging data

37 Breeding success (binomial likelihood) Expected value

38 Estimated parameters Currently only –Rzero c –  c (colony effect on p(breed successfully) –q f (catchability by fishery) All other parameters currently fixed

39 Results Fit to –Count data –Mark-recapture survival Return rate –Bycatch estimates –Breeding success Total bycatch Population trajectories

40 Survival and return rate

41 Return rate

42 Bycatch estimates

43 Breeding success

44 Count data

45 Total bycatch

46 Conclusion Modelling in progress –Algorithm working –Preparing new distribution data Important question for managers is overall population trend Interesting to examine what processes may explain differing population trends by island –Breeding success –Recruitment/migration rates –Differential fishing mortality

47 The end

48 To Do Initial conditions – check and fix Density dependence


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