E-SURGE sex effect on transience vs U-CARE findings

questions concerning analysis/theory using programs M-SURGE, E-SURGE and U-CARE

E-SURGE sex effect on transience vs U-CARE findings

Postby simone77 » Sun Jul 08, 2012 12:37 pm

Hi,

I know it is already summer time and these are not good times to post new topics but unfortunately I have this trouble right now...hope to be lucky...

I am working with resightings of a species of bird and I am interested to estimate transience and survival. In my case study one individual might be randomly: (i) molecularly sexed (no uncertainty), (ii) visually sexed (uncertainty), (iii) molecularly and visually sexed, (iv) neither molecularly nor visually sexed.
Once it was visually sexed, it might be sexed from one team (Monitoring Team nº1) or another (Monitoring Team nº2).

I have modeled my data in a very similar fashion to that Genovart, Pradel and Oro (2012) used in Exploiting uncertain ecological fieldwork data with multi-event capture–recapture modelling: an example with bird sex assignment. They partitioned the transition in two steps (transience and survival) and encounter in several steps to take into account the sex uncertainty.

Here it is a one-page PDF with the states, events, groups, shortcuts, and GEPAT structure I have used for these analyses.
All the models in my candidate set were run with Multiple Random option =5 (to minimize local minima risk), and tolerance to parameter change and tolerance on gradient= 1e-009 (to ensure convergence).
Results indicate no trouble with parameter identifiability.

There are two things that look strange (to me at least):

1. E-SURGE results don't agree at all with those I get in U-CARE.
In U-CARE I found a strong transient effect in males and no transient effect in females and in E-SURGE almost all the models find just the contrary (but see below). I have checked (and tested) data arrangements for both analyses (E-SURGE and U-CARE) one million times and I am sure there are no problems there.

2. In E-SURGE I have found that a different parameterization of Survival had a huge effect on Transience estimates and I did not expect that, given that Survival is conditional on Transience and not viceversa (so I am a bit confused about that).
These are the models structures of the lowest AIC models (but DeltaAIC>5), they are identical except for Survival parameterization:

i. {IS(i) T(sex+dry years) S(sex*dry years) C(sex*time) SEX(time) VIS(time) CORR(sex+Trend)}

ii. {IS(i) T(sex+dry years) S(juvenile*sex+adult*sex*dry years) C(sex*time) SEX(time) VIS(time) CORR(sex+Trend)}

The GEMACO sentence for Transience step is: a(1).[f+t(1 7, 2 3 4 5 6 8 9 10 11 12 13)]+a(2) where t(1 7) represent the dry years, a(2) is set to zero in the IVFV (just two age classes specified in the MODIFY button).

The first model (model i above - lowest AIC) estimates no difference between the transience probability of females and male whereas the second (model ii above) and all the rest in the set of candidate models find a quite strong difference in transience probability between females and males (females much more dispersant).

Any idea on what might be going on?
I thought about a local minima issue but it doesn't make much sense because I found the “wrong” pattern in sex propensity to transience in all the models and I have also re-run the models by increasing a lot the number of multiple random initial values and I always get the same results.

Thanks for any help,

Simone
simone77
 
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Re: E-SURGE sex effect on transience vs U-CARE findings

Postby CHOQUET » Mon Jul 30, 2012 9:19 am

I don't understand

>The GEMACO sentence for Transience step is: a(1).[f+t(1 7, 2 3 4 5 6 8 9 10 11 12 13)]+a(2) where t(1 >7) represent the dry years, a(2) is set to zero in the IVFV (just two age classes specified in the MODIFY >button).

because a(2) must be set to 1 and not to 0 !!.

Minor comments:
a(1).[f+t(1 7, 2 3 4 5 6 8 9 10 11 12 13)] can be replaced by a(1).t(1 7, 2 3 4 5 6 8 9 10 11 12 13)
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Re: E-SURGE sex effect on transience vs U-CARE findings

Postby simone77 » Mon Jul 30, 2012 12:30 pm

Hi Rémi,

Thanks for you answer.
These last weeks I have made some progress with these analyses but there are a few points that are still pending, including some question about your answers.

1. About the discrepancy between E-SURGE and U-CARE on the transient effect.
I looked with more attention to the U-CARE results and I believe that U-CARE findings were affected strongly by the fact the transient effect was more or less strong only in two occasions (post dry years) and by some sparseness of data.

2. Point 2 in the previous post.
I still don't understand this: In E-SURGE I have found that a different parameterization of Survival had a huge effect on Transience estimates and I did not expect that, given that Survival is conditional on Transience and not viceversa (so I am a bit confused about that).

3. About your answers:
3.1
I don't understand

>The GEMACO sentence for Transience step is: a(1).[f+t(1 7, 2 3 4 5 6 8 9 10 11 12 13)]+a(2) where t(1 >7) represent the dry years, a(2) is set to zero in the IVFV (just two age classes specified in the MODIFY >button).

because a(2) must be set to 1 and not to 0 !!.


I believe that, as explained in the supporting information of the Genovart et al. (2012) that you can find here, the transience parameterization refers to the probability an individual moves away (p to be a transient) and for this reason, the p an individual seen more than once is a transient is zero (see line 67).

3.2
Minor comments:
a(1).[f+t(1 7, 2 3 4 5 6 8 9 10 11 12 13)] can be replaced by a(1).t(1 7, 2 3 4 5 6 8 9 10 11 12 13)


I don't understand why they are equivalent, I believed that in that way I was modeling an additive effect of the gender (two levels) and time (two levels) for the individuals seen first time (three parameters to be computed), whereas the other way there is no gender effect (and I should get just two parameters - one intercept and one slope), may you explain me more about that?
simone77
 
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Re: E-SURGE sex effect on transience vs U-CARE findings

Postby CHOQUET » Tue Jul 31, 2012 5:09 am

About 3.1 and 3.2, the correct answer is

a(1).f(1).t(1 7, 2 3 4 5 6 8 9 10 11 12 13)+others

the last parameter being fixed as 0 (I didn't see that you modeled the probability to leave)

By this way, only the first class of age for female is modeled. The other parameters
(males and females of age 2 and more) are fixed to 0 (no transient).
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