I have a 16 year dataset (2005-2020) modelled with a 2-state MSORD model.
The models I have run so far (see descriptions below);
- Code: Select all
2 S(~-1 + ageclass)Psi(~-1 + to1 + to2:time)pent(~-1 + time:session:stratum)Phi(~four_a:session:stratum)p(~-1 + time:session:stratum)
3 S(~res:time)Psi(~-1 + to1 + to2:time.c)pent(~-1 + time:session:stratum)Phi(~four_a:session:stratum)p(~-1 + time:session:stratum)
5 S(~-1 + time)Psi(~-1 + to1 + to2:time.c)pent(~-1 + time:session:stratum)Phi(~four_a:session:stratum)p(~-1 + time:session:stratum)
4 S(~-1 + trans:time + res:time)Psi(~-1 + to1 + to2:time.c)pent(~-1 + time:session:stratum)Phi(~four_a:session:stratum)p(~-1 + time:session:stratum)
npar AICc DeltaAICc weight Deviance
2 226 180046.1 0.00000 1 179591.4
3 238 180116.3 70.24379 0 179637.3
5 238 180185.4 139.38379 0 179706.5
4 252 180310.0 263.91590 0 179802.6
S(~-1 + ageclass) has 2-ageclass (transients and residents) constant survival
S(~res:time) has time-varying resident survival, constant transient survival
S(~-1 + time) has time-varying survival, no ageclasses.
S(~-1 + trans:time + res:time) has time-varying resident and time-varying transient survival
‘time.c’ indicates the final two psi are constrained so that all S can be estimated
Question 1: it really necessary (or correct) to use a model that is fully time-dependent in all parameters to estimate variance components, when not all parameters can be estimated correctly?
It seems with such highly parameterized models this could often be the case.
For example, model S(~-1 + trans:time + res:time) is the most time-dependent model above.
Several of its transient survival parameters are on the boundary (and this is even with some of the psi being constant). Should I use S(~-1 + trans:time + res:time) for the tasks below?
Task One
The most support is for the constant Survival (2-ageclass) model, but as we would like to monitor annual resident survival, I would like to fit a random effects model that could be an intermediate between S(~-1 + ageclass) and S(~res:time), using ‘S-tilde’ and RMSE as the annual estimates.
Task Two
Find the mean and variance (without sampling variation) of the first 3 resident survival estimates i.e. 2006-2007, 2007-2008, 2008-2009 (transient survival is 2005-2006).(I realise finding the mean and variance of only 3 estimates could be misleading, but it’s all the data we have).
Beta-hat being the mean, and CIs using ‘Estimate of sigma’ process variance.
Question 2: I would also like to test for a trend in abundance in the last 11 annual estimates. It looks as though I can’t compare a model with and without a trend because it is not possible to run a Random Effects ‘Linear Trend’ model on derived parameter.
Is there anything else I can do?
Thank you