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Jan. 14, 2004Paulsen - multi-site multi-year survival 1 Methods to compare multi-stock and multi- site differences in survival rates Overview Methods –Survival.

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Presentation on theme: "Jan. 14, 2004Paulsen - multi-site multi-year survival 1 Methods to compare multi-stock and multi- site differences in survival rates Overview Methods –Survival."— Presentation transcript:

1 Jan. 14, 2004Paulsen - multi-site multi-year survival 1 Methods to compare multi-stock and multi- site differences in survival rates Overview Methods –Survival rates vs. % detected –Regression model –AICc Data Results Discussion

2 Jan. 14, 2004Paulsen - multi-site multi-year survival 2 Overview Q: How to “best” explain differences in parr-to- smolt survival rates across sites and years? 10 sites, 10 years of survival estimates Survival rates, not just # detected/# released How to calculate survival rates from release of tagged parr in subbasin to LGR Regression models using dummies (e.g., release site) and continuous covariates (parent spawner density) Akaike information criterion (AIC) and weights to select most plausible model Results

3 Jan. 14, 2004Paulsen - multi-site multi-year survival 3 Survival Rate (# LGR / # released) * (1/proportion guided into bypass) 1000 released, 100 detected, 0.5 bypass efficiency 100/1000 * 1/0.5 = 20% survival rate Easy to do with MARK, SURPH, SAS, etc.

4 Jan. 14, 2004Paulsen - multi-site multi-year survival 4 Survival rates vs. proportion detected

5 Jan. 14, 2004Paulsen - multi-site multi-year survival 5 Statistical model Equation (s) b – intercept S – site or subbasin dummies Y – year dummies L – length at tagging D – redd density C – Climate (drought) index H - # of habitat actions i, t = site, years indices

6 Jan. 14, 2004Paulsen - multi-site multi-year survival 6 AICc weights Akaike Information Criterion, “corrected” for small sample site (10 sites * 10 years = 100 observations) Can construct weights for a given set of observations and models. Interpreted as the probability of each model, given the data and models estimated Usually, the overwhelming majority of the weight goes to one or two models Provides a nice way to select best (highest-weighted) models Accounts for number of models estimated Models do not need to be “nested”

7 Jan. 14, 2004Paulsen - multi-site multi-year survival 7 Data 100 observations – 10 sites, 10 years Dependent variable: ln (parr-to-smolt survival) Independent Variables: Site and year dummies Parent (brood) year redd density Drought index Length at tagging # of habitat actions

8 Jan. 14, 2004Paulsen - multi-site multi-year survival 8

9 Jan. 14, 2004Paulsen - multi-site multi-year survival 9

10 Jan. 14, 2004Paulsen - multi-site multi-year survival 10

11 Jan. 14, 2004Paulsen - multi-site multi-year survival 11

12 Jan. 14, 2004Paulsen - multi-site multi-year survival 12

13 Jan. 14, 2004Paulsen - multi-site multi-year survival 13

14 Jan. 14, 2004Paulsen - multi-site multi-year survival 14 Results – AICc weights

15 Jan. 14, 2004Paulsen - multi-site multi-year survival 15 Best Model Parameters

16 Jan. 14, 2004Paulsen - multi-site multi-year survival 16 Discussion Model diagnostics (influence, leverage, etc.) also worthwhile Survival calculations straight-forward Regression models and AICc use well- established techniques Results suggest that both environmental factors and habitat actions important Worked examples, not rigorous analysis Lots of surprises likely

17 Jan. 14, 2004Paulsen - multi-site multi-year survival 17 Questions?


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