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

1. Capture-recapture software packages have proven to be very powerful tools for analyzing 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. 2. We have developed a statistical method and propose an R package for analyzing such data based on an extension of classical survival analysis models incorporating an inhomogeneous Poisson process for modeling 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). Second, 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. 3. Results of the simulated data analysis show that the method performs better than its discrete-time counterpart for analyzing 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 sub-adults, varies between study sites, and increases with physical condition, 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. 4. In conclusion, continuous-time approaches – such as that presented in this paper – should be preferred when data acquired from continuous-time protocols is analyzed.1. 标记重捕法（Capture-recapture）软件包已被证实是分析野生种群存活影响因子的高效工具。然而，此类软件包均局限于离散时间采样方案，目前仍缺乏适用于连续时间采集数据的标准化存活分析工具。 2. 本研究开发了一种统计方法，并基于经典存活分析模型的扩展框架，结合非齐次泊松过程（inhomogeneous Poisson process）对捕获历史进行建模，据此提出了一款用于分析此类数据的R包（R package）。首先，本研究通过连续时间采样方案生成模拟数据，以此完成两项任务：其一，对比离散时间与连续时间分析方法的存活估计偏差；其二，针对四类协变量验证本研究开发的R包的性能与准确性，这四类协变量分别为：个体间差异变量（如性别）、随时间变化的变量（如气候因子）、兼具个体与时间双重差异的变量（如身体状况），以及作为分类变量的年龄。其次，本研究将该R包应用于凯尔盖朗群岛（Kerguelen archipelago）的猫科动物存活分析真实数据集（涵盖20年间682只猫的观测数据）作为示例。 3. 模拟数据分析结果表明，相较于离散时间方法，本研究提出的连续时间方法在分析连续时间采集的数据时表现更优。除兼具时间与个体双重差异的协变量外，该方法对所有参数均可提供无偏估计——这一结果符合预期，因本研究设定中此类协变量仅在捕获时刻更新，未实现连续化更新。将该R包应用于凯尔盖朗猫数据集后，结果显示幼崽的存活率低于成体与亚成体，存活率随研究样地不同存在差异，且随身体状况提升而升高，该效应在雌性个体中较雄性更为显著。此外，本研究还测试了性别、季节、存活的时间线性趋势以及归一化差分植被指数（NDVI, Normalized Difference Vegetation Index）等协变量，但未发现其存在显著影响。不过，受限于较低的重捕率，置信区间范围过大，无法排除此类效应的存在，因此仍需开展进一步分析以实现该场景下协变量的严谨检验。 4. 综上，当分析连续时间采样方案获取的数据时，应优先选用本研究提出的连续时间分析方法。