Hi Simone (et al)

Sorry I've been a bit slow on this. In summary:

1)

According to some hidden group you are not considering, different apparent survival rates could be a source of overdispersion. Females and males, for example, or juveniles and adults. If you have data on groups that may absorb some variance in apparent survival, you should try to run the GOF tests separately.

I did fit TSM models to those data that showed evidence for transience, and then recalculated the c-hat by removing the GOF 3.SR test component as suggested in Pradel et al 2005. All good here, but see bit on sex-specific survival further below.

2)

An idea regarding your second question: when the data are sparse, U-CARE employs the Fisher exact test rather than directly calculating the Chi2. The Chi2 is then back-calculated as the value of a Chi2(1) distribution, which yields the corresponding p-value. I'm curious if, when the data are sparse, the Chi2 that you see in the output of each test is the same as the Chi2 values that U-CARE adds up in the "Sum of TESTs over groups."

If I have understood this correctly, YES, they match:

Sum of TESTs over groups Chi2 = 20.4419

Test 3.SR Chi2 = 18.916 + Test3.SM Chi2 = 1 + Test2.CT Chi2 = 0.51026 = Chi2 of 20.43

(note that the Test 2.CL is not calculated in this case because the number of occasions < 5

3) You were also right in saying that in some cases, part of the heterogeneity might be due to differences between groups, in this case sexes. For a reduced dataset only containing Males and Females with an adhoc approach (i.e. sightings previous to the assignment of the sex are ignored, see Nichols et al 2004) GOF for the full-time dependent CJS is accepted (in U-CARE, with two groups for male/female and doing the sum of tests over groups); df =8, Quadratic Chi2 = 10.61, p-level = 0.225; not significant for transients (p-value = 0.19) or for trap-dep (p-value = 1). However, looking further into the individual test components shows that there is some evidence for transience in the females only (p-level, one-sided test for transience = 0.044)

I tested this same dataset in R2ucare, and followed the advice given in the documentation for the package, which says "For groups, just treat the group separately as in the Dipper example below". So I did split my dataset into Females and Males and ran separate GOF tests for each. When doing this, the general GOF test was rejected for the Female data, with the individual 3.SR test for transience being significant:

- Code: Select all
`> overall_CJS(SkAdL_sex.fem.hist, SkAdL_sex.fem.freq)`

chi2 degree_of_freedom p_value

Gof test for CJS model: 10.328 4 0.035

> test3sr_females

$test3sr

stat df p_val sign_test

9.471 2.000 0.009 1.704

$details

component stat p_val signed_test test_perf

1 2 9.102 0.003 3.017 Chi-square

2 3 0.369 0.544 -0.607 Chi-square

The GOF test and individual components were fine for the male dataset.

Three questions here regarding groups and testing:

A) Why would the sum of tests over groups accept a general model for the male/female data (2 groups) if one of the tests (3SR in this case) is significant for one of the groups?

B) If following the advice given in the R2ucare package to split data into the two groups, how does one calculate the c-hat in R2ucare to apply to sex-specific survival models (in MARK/RMark) which will contain both groups?

C) and related to the previous one: if fitting sex-specific models with transience (TSM) only affecting one group (i.e. females in this case), how do I recalculate the c-hat (in U-CARE or in R2ucare) to exclude the 3SR component when there are two groups in the data?

Any advice on these last questions would be highly appreciated.

Many thanks,

Monica