Abstract:
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), nonbreeders are unobservable because they are not tied to a territory or
breeding location. Although unobservable states can be used to model temporary
emigration of nonbreeders, 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 nonbreeder 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 nonbreeders), and to estimate annual breeding transitions to
and from the nonbreeder state with greater precision (where current breeders
tended to have higher future breeding probabilities than nonbreeders). We used program
E‐SURGE (2.1.2) to fit the multievent‐robust design models, with uncertainty in
breeding state assignment (breeder, nonbreeder) being incorporated via a hidden
Markov process. This flexible modeling approach can easily be adapted to suit sampling
designs from numerous species which may be encountered during and outside
of discrete breeding seasons.
