LIMPET tagging of Hawaiian odontocetes: assessing reproduction and estimating survival of tagged and non tagged individuals
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1 LIMPET tagging of Hawaiian odontocetes: assessing reproduction and estimating survival of tagged and non tagged individuals Robin W. Baird 1, Alex N. Zerbini 1, Sabre D. Mahaffy 1, Gregory S. Schorr 1, Daniel L. Webster 1, Dan J. McSweeney 2, Russel D. Andrews 3 1 Cascadia Research Collective, Olympia, WA USA 2 Wild Whale Research Foundation, Holualoa, HI USA 2 University of Alaska Fairbanks and Alaska SeaLife Center, Seward, AK USA Presentation at the Workshop on Impacts of Cetacean Tagging: a review of follow up studies and approaches, Dunedin, New Zealand, December 8, 2013
2 Using LIMPET tags to assess odontocete movements and habitat use in Hawaiʻi
3 Do LIMPET tags influence odontocete survival and reproduction? NOPP grant Improving attachments of remotelydeployed dorsal fin mounted tags: tissue structure, hydrodynamics, in situ performance, and tagged animal follow up PI: Russ Andrews Pilot whale 80 days Resident populations Long term photo identification catalogs Social clusters identified Cluster 1 HIGm0893 Cluster 3 Baird et al. 2012
4 Limitations for assessing reproduction post tagging Bias towards tagging males (avoiding females with small calves, targeting larger individuals) Long inter birth interval for most species Long intervals between re sightings Females with calves born since tagging: Pygmy killer whales (1 of 3 known or suspected females) Cuvier s beaked whales (2 of 3 females that have been re sighted)
5 Re sightings post tag loss Pygmy killer whales 5 of 7 (71%) tagged prior to 2011 re sighted at years* Melon headed whales, Kohala resident stock 2 of 3 (67%) tagged prior to 2012 re sighted at 0.8 to 2.7 years* Cuvier s beaked whales 6 of 10 (60%) re sighted at years* (3 of 4 females re sighted) Blainville s beaked whales, insular population 7 of 10 re sighted at years* (6 of 7 females resighted at years*) *post tag loss
6 False killer whales 142 distinctive & very distinctive individuals photo IDs between 2003 and 2013, with 1,280 records Included capture histories of 24 individuals tagged between (total of 25 deployments, one whale tagged twice) Two analyses undertaken: 1) all three social clusters considered* (tags deployed in clusters 1 and 3 only); 2) cluster 1 only (~50% of records, 16 of 25 tag deployments) *Cluster as a co variate Dan J. McSweeney
7 Short finned pilot whales 620 distinctive & very distinctive individuals photo IDd between 2003 and 2013, with 6,094 records, in 34 social clusters Included capture histories of 46 individuals tagged between , in 15 social clusters Five tagged twice (51 deployments) Two analyses undertaken: 1) all individuals considered; 2) only social clusters (15) with tagged individuals considered* *Cluster as a co variate Dan J. McSweeney
8 Survival estimation Modeling in R Mark Cormack Jolly Seber model to estimate apparent survival (Phi) and capture probability (p) Number of models run including a time varying tag effect as a covariate Overdispersion computed using TEST1 and TEST2 in program RELEASE Model selection with Akaike Information Criteria for small samples after accounting for overdispersion (QAICc)
9 Jeff Hogan False killer whales Approach 1 (all three clusters) 16 models run with a combination of effects Phi (Apparent survival) null model cluster tag cluster + tag p (Capture probability) null model time cluster time + cluster Top 4 models shown (100% of model weight) Model # par QAICc ΔQAICc weight Phi(~1)p(~Cluster + time) Phi(~Tag)p(~Cluster + time) Phi(~Cluster)p(~Cluster + time) Phi(~Cluster + Tag)p(~Cluster + time)
10 Jeff Hogan False killer whales Approach 1 (all three clusters) Model average estimates of apparent survival Parameter estimate se Lower CL Upper CL Phi (cluster 1 + tag) Phi (cluster 1) Phi (cluster 2 + tag) Phi (cluster 2) Phi (cluster 3 + tag) Phi (cluster 3) Capture probabilities by cluster Cluster Mean capture probability (p)
11 Jeff Hogan False killer whales Approach 2 (cluster 1 only) 6 models run with a combination of effects Phi (Apparent survival) null model tag p (Capture probability) null model time All 6 models shown Model # par QAICc ΔQAICc weight Phi(~1)p(~time) Phi(~Tag)p(~time) Phi(~1)p(~Tag) Phi(~Tag)p(~Tag) Phi(~1)p(~1) Phi(~Tag)p(~1)
12 Jeff Hogan False killer whales Approach 2 (cluster 1 only) Model average estimates of apparent survival Parameter estimate se Lower CL Upper CL Phi (Tag) Phi (.)
13 Short finned pilot whales Approach 1 (all 34 clusters) 4 models run with a combination of effects Robin W. Baird Phi (Apparent survival) null model tag p (Capture probability) null model time All models shown Model # par QAICc ΔQAICc weight Phi(~1)p(~time) Phi(~Tag)p(~time) Phi(~1)p(~1) Phi(~Tag)p(~1)
14 Short finned pilot whales Approach 1 (all 34 clusters) Model average estimates of apparent survival Robin W. Baird Parameter estimate se Lower CL Upper CL Phi (tag) Phi (~1) But survival estimates low for a relatively long lived species
15 Short finned pilot whales Approach 2 (15 clusters w/ tagged individuals) 16 models run with a combination of effects Robin W. Baird Phi (Apparent survival) null model Cluster Tag Cluster + tag p (Capture probability) null model time Cluster Tag Top 3 models shown (100% of model weight) Model # par QAICc ΔQAICc weight Phi(~1)p(~Cluster) Phi(~Tag)p(~Cluster) Phi(~Cluster)p(~Cluster)
16 Short finned pilot whales Approach 2 (15 clusters with tag deployments) Model average estimates of apparent survival Robin W. Baird Parameter estimate se Lower CL Upper CL Phi (tag) Phi (~1)
17 Take home: survival of tagged and untagged individuals not significantly different* *Power to detect an effect is very low, given average capture probability, proportion of population tagged Dan J. McSweeney Robin W. Baird
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