Résumé
Increased environmental stochasticity due to climate change will intensify
temporal variance in the life-history traits, and especially breeding
probabilities, of long-lived iteroparous species. These changes may
decrease individual fitness and population viability and is therefore
important to monitor. In wild animal populations with imperfect individual
detection, breeding probabilities are best estimated using
capture-recapture methods. However, in many vertebrate species (e.g.,
amphibians, turtles, seabirds), non-breeders are unobservable because they
are not tied to a territory or breeding location. Although unobservable
states can be used to model temporary emigration of non-breeders, there
are disadvantages to having unobservable states in capture-recapture
models. The best solution to deal with unobservable life-history states is
therefore to eliminate them altogether. Here, we achieve this objective by
fitting novel multievent-robust design models which utilize information
obtained from multiple surveys conducted throughout the year. We use this
approach to estimate annual breeding probabilities of capital breeding
female elephant seals (Mirounga leonina). Conceptually, our approach
parallels a multistate version of the Barker/robust design in that it
combines robust design capture data collected during discrete breeding
seasons with observations made at other times of the year. A substantial
advantage of our approach is that the non-breeder state became
“observable” when multiple data sources were analyzed together. This
allowed us to test for the existence of state-dependent survival (with
some support found for lower survival in breeders compared to
non-breeders), and to estimate annual breeding transitions to and from the
non-breeder state with greater precision (where current breeders tended to
have higher future breeding probabilities than non-breeders). We used
program E-SURGE (2.1.2) to fit the multievent-robust design models, with
uncertainty in breeding state assignment (breeder, non-breeder) being
incorporated via a hidden Markov process. This flexible modelling approach
can easily be adapted to suit sampling designs from numerous species which
may be encountered during and outside of discrete breeding seasons.