[Basta-users] post-fledging survival - recapture probability etc.

[Dries Van de Loock]

Dear BaSTA community,

I'm involved in the same project as Diederik Strubbe on post-fledging
survival of a tropical passerine (see archive
<http://lists.r-forge.r-project.org/pipermail/basta-users/2015-August/thread.html>
).

Nests were monitored until fledging and juveniles were relocated every 5 to
6 days after fledging.

Our input matrix is formatted accordingly : the birthyear is considered the
point of fledging and is set to "1" for all individuals. The resighting
matrix starts at day 1 and is "0" for all individuals on that day. Day 2 in
the matrix is thus the first day after fledging. See screenshot
<https://www.dropbox.com/sh/kk1wztsftfljbw5/AAC4OHKT-pNXepioaR4qf78oa?dl=0>
from RStudio.


When running the models, we bumped into following questions :

(i) *Recapture probability* is around 10 %. We however did not check for
presence/absence of the bird every day after fledging. Usually on the
5th/6th day (range 3-9). This thus underestimates recapture probability
dramatically. We noted on the BaSTA website that future versions would
include covariates on recapture and recovery probabilities. We could for
example add "search effort" as covariate, stating the specific days when we
actually looked for that individual. Is there a possibility of trying this
out already ?

(ii) Our *confidence intervals* on the mortality rates just after fledging
are big. To that extent that it obscures the real shape of the mortality
function when added to the graphs. See PlotFancyBaSTA with CI and regular
plot without CI
<https://www.dropbox.com/sh/kk1wztsftfljbw5/AAC4OHKT-pNXepioaR4qf78oa?dl=0>.
Because we only searched for the juveniles some days after fledging, the
first resightings are only done on day 3 and later. Is it possible that the
particular sampling strategy is on the origin of this big CI's ?

(iii) Based on the lowest DIC from running all basic models (no
covariates), we selected the WEIBULL model with a *Makeham* shape. Adding
the Makeham constant avoids creating immortal individuals when having a
declining mortality shape (i.e. when 0 < b0 < 1)  by setting a "base
mortality". How do we interpret this value when having other shapes (b0 <=
1) ? Why is this in these cases not (partially) embedded within the scale
parameter (b1) ?

(iv) The *serial autocorrelation* of most coefficient estimates appears to
be rather high (> 0.4). Would you recommend readjusting the thinning
argument or other model settings ? Or does this not really effect our
estimates and outcome ?

NB : we noticed a discrepancy between the default values in the package
versus the ones listed in the BaSTA vignette (niter = 11000, burnin = 1001
and thinning = 20 vs. niter = 50000, burnin = 5001 and thinning = 50).
Which settings to you recommend as default values ?


We performed all analysis using BaSTA 1.9.4


Your feedback and suggestions are greatly appreciated,

Best wishes,

Dries

-----
Dries Van de Loock
PhD student
Terrestrial Ecology Unit
Department of Biology
Ghent University
KL Ledeganckstraat 35
B-9000 Ghent, Belgium
Phone: +32 (0)9 265 50 39
http://www.ecology.ugent.be/terec/
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