Data from: An R package for analyzing survival using continuous-time open capture-recapture models

Capture–recapture software packages have proven to be very powerful tools
for analysing factors affecting survival in wild populations. However, all
such packages are limited to discrete-time protocols. Appropriate survival
analysis tools are still lacking for data acquired from continuous-time
protocols. We have developed a statistical method and propose an r package
for analysing such data based on an extension of classical survival
analysis models incorporating an inhomogeneous Poisson process for
modelling capture histories. First, data were simulated from a
continuous-time protocol. These data were used to (i) compare survival
estimation biases of discrete- and continuous-time approaches and (ii)
investigate the performance and accuracy of our r package for four types
of covariates: factors varying between individuals (like sex), in time
(like climatic factors), both in time and between individuals (like
physical condition) and age (as a categorical factor). Secondly, the r
package has been applied to a real data set for survival analysis of cats
in the Kerguelen archipelago (regrouping 682 cats over 20 years) as an
illustrative example. Results of the simulated data analysis show that the
method performs better than its discrete-time counterpart for analysing
data acquired from continuous-time protocols. It provides unbiased
parameter estimates for all parameters except those that vary both in time
and between individuals – which is not surprising, since in our case,
these factors were not updated in continuous time (i.e. only upon
capture). When applied to the Kerguelen cat data set, the results suggest
that survival is lower in juveniles than in adults and subadults, varies
between study sites and increases with physical condition, and this latter
effect being more important in females than in males. Sex, season,
temporal linear trend in survival and the NDVI vegetation index were also
tested but were not found to be significant. However, confidence intervals
were too large (due to a low recapture rate) for excluding such effects.
Further analyses are still needed for rigorous covariate testing in this
context. In conclusion, continuous-time approaches – such as that
presented in this paper – should be preferred when data acquired from
continuous-time protocols is analysed.