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

Thu Sep 17 14:44:57 CEST 2015

Dear Fernando,

thanks for your rapid and clarifying reply.

About the resighting covariates. How realistic are the prospects of
implementing this feature in the near future ?

About the large CI. According to the model output, all parameters should
have converged. I have added the traces for all the parameters to the
dropbox folder
<https://www.dropbox.com/sh/kk1wztsftfljbw5/AAC4OHKT-pNXepioaR4qf78oa?dl=0>
.

Best wishes,

Dries

2015-09-17 10:46 GMT+02:00 Fernando Colchero <colchero at imada.sdu.dk>:

> Dear Dries,
>
>    Thanks for sending this very detailed explanation. I’ll try to answer
> to each of your points below:
>
> (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 ?
>
>
> Fernando: Unfortunately we have not yet been able to implement this.
>
> (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 ?
>
>
> Fernando: By looking at your plots, particularly the PlotFancy, I suspect
> that not all your parameters had converged… Did you have a chance at
> checking the traces?.
>
> (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) ?
>
>
> Fernando: Actually embedding the third parameter (c) into b1 does not
> solve the problem of starting mortality at 0, inherent to the Weibull
> model, while the intended effect of the Makeham constant is to shift up or
> down the mortality. If it’s embebed in b1, then you are only changing
> mortality proportionally.
>
> (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 ?
>
>
> Fernando: Yes, I would recommend running the model for more iterations
> with a large thinning argument. If you run it in parallel then you can run
> more chains for less iterations but with higher thinning, that’ll be even
> better and it would save you quite a bit of time.
>
> 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 ?
>
>
>
> Fernando: Thanks for spotting this! We’ll check the settings.
>
> Best,
>
> Fernando
>
> *Fernando Colchero*
> Assistant Professor
> Department of Mathematics and Computer Science
> Max-Planck Odense Center on the Biodemography of Aging
>
> Tlf.               +45 65 50 23 24
> Email           colchero at imada.sdu.dk
> Web             www.sdu.dk/staff/colchero
> Pers. web   www.colchero.com <http://www.sdu.dk/staff/colchero>
> Adr.              Campusvej 55, 5230, Odense, Dk
>
> *University of Southern Denmark*
>
> On 16 Sep 2015, at 15:13, Dries Van de Loock <dries.vandeloock at ugent.be>
> wrote:
>
> 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/
> _______________________________________________
> Basta-users mailing list
> Basta-users at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/basta-users
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.r-forge.r-project.org/pipermail/basta-users/attachments/20150917/a4f163ff/attachment.html>