Data from: Making use of multiple surveys: estimating breeding probability using a multievent-robust design capture-recapture model

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.气候变化引发的环境随机性加剧，将使长寿命多次繁殖（iteroparous）物种的生活史特征（尤其是繁殖概率）的时间方差进一步增大。此类变化会降低个体适合度与种群生存力，因此对其开展监测具有重要意义。对于个体检测不完全的野生动物种群而言，采用捕获-重捕法（capture-recapture）估算繁殖概率为最优方案。然而，诸多脊椎动物类群（如两栖类、龟类、海鸟）中的非繁殖个体因不依附于领地或繁殖位点，无法被观测到。尽管可通过不可观测状态对非繁殖个体的暂时迁出进行建模，但捕获-重捕模型中引入不可观测状态存在诸多弊端。因此，处理不可观测生活史状态的最优方案是彻底移除这类状态。本研究通过拟合全新的多事件稳健设计模型（multievent-robust design models）实现该目标，该模型利用全年多次调查获取的信息。我们利用该方法估算了资本繁殖（capital breeding）型雌性南象海豹（Mirounga leonina）的年度繁殖概率。从概念层面而言，本研究方法与巴克-稳健设计（Barker/robust design）的多状态版本思路一致，即结合离散繁殖季采集的稳健设计捕获数据与全年其他时段的观测数据。本方法的一大显著优势在于，当联合分析多源数据时，非繁殖个体状态可变为‘可观测’状态。这使得我们得以检验状态依赖存活（state-dependent survival）是否存在（研究结果一定程度上支持繁殖个体的存活率低于非繁殖个体），并以更高精度估算了往返于非繁殖状态的年度繁殖转换概率（结果显示当前为繁殖个体的个体，未来的繁殖概率往往高于非繁殖个体）。我们使用E-SURGE（2.1.2）软件拟合多事件稳健设计模型，通过隐马尔可夫过程（hidden Markov process）纳入繁殖状态赋值（繁殖个体、非繁殖个体）的不确定性。该灵活的建模方法可轻松适配诸多物种的采样设计，这些物种的观测既可发生在离散繁殖季内，也可发生在繁殖季外。