1. It is important to understand metapopulation dynamics and underlying demographic processes in heterogeneous landscapes. Traditionally, demographic parameters are estimated using capture–recapture data that can be difficult to collect. Spatially explicit dynamic N‐mixture models allow inference for demographic parameters, including dispersal, using count data of unmarked animals, but these models have only been shown effective under constant demographic parameters and dispersal between adjacent local populations.
2. In this study, I aimed to compensate the weakness of spatially explicit dynamic N‐mixtures and multistate capture–recapture models by jointly analysing count and capture–recapture data. This spatially explicit integrated population model allows for spatiotemporal variation of demographic parameters in relation to environmental and density covariates and dispersal between any local populations. I conducted simulations to evaluate this model (a) for species with distinct life histories under different detection and capture probabilities, (b) when spatial sampling intensity varied, (c) when the length of survey period varied, (d) when the robust sampling design was adopted or not and (e) when auxiliary information is partially available. I also provided an empirical example of Gadwall Mareca strepera metapopulation dynamics in North American prairies.
3. The results showed that the model provided unbiased parameter estimates under a variety of ecological and sampling conditions, even when the spatial sampling intensity of capture–recapture survey was low (20% of the patches) with the complement of count data (≥60% of the patches). Also, the model only required a relatively short survey period (6~8 years) to provide unbiased inferences. The robust sampling design was not necessary for the model to provide unbiased inferences when spatial counts were intense, but became critical when spatial counts were sparse. Parameter estimates remain unbiased when auxiliary information is partially available. The model showed that Gadwall had low emigration probability (11.8%) but could disperse more than 200 km.
4. Based on the results, I provide recommendations about the trade‐off between spatial sampling intensity, length of survey period and the use of the robust sampling design when applying this model in real‐world studies. The model could have wide applications in the interface of metapopulation ecology and landscape ecology.

中文翻译：

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关于空间显式综合人口模型的抽样设计

1. 重要的是要了解异质景观中的种群动态和潜在的人口统计学过程。传统上，人口统计参数是使用难以收集的捕获-捕获数据估算的。空间显式动态N混合模型允许使用未标记动物的计数数据推断人口统计参数，包括分布，但是这些模型仅在恒定的人口统计参数和相邻局部种群之间的分散下显示有效。
2. 在本研究中，我旨在通过联合分析计数和捕获-捕获数据来弥补空间显式动态N混合和多状态捕获-捕获模型的不足。这种空间明确的综合人口模型允许人口参数相对于环境和密度协变量的时空变化以及任何本地人口之间的分散。我进行了仿真以评估该模型（a）针对具有不同检测和捕获概率的不同生命历史的物种，（b）当空间采样强度变化时，（c）当调查周期的长度变化时，（d）当进行稳健采样时设计是否被采用；以及（e）当辅助信息部分可用时。我还提供了Gadwall Mareca strepera的经验示例 北美大草原的种群动态。
3. 结果表明，即使在捕获-捕获调查的空间采样强度较低（占斑块的20％）且计数数据为补充（≥60％）的情况下，该模型也可以在各种生态和采样条件下提供无偏参数估计。补丁）。此外，该模型仅需要相对较短的调查周期（6〜8年）即可提供无偏见的推断。健壮的采样设计对于模型在空间计数密集时提供无偏见的推断不是必需的，但是在空间计数稀疏时变得至关重要。当辅助信息部分可用时，参数估计值保持不变。该模型表明，盖德沃尔的移民机率低（11.8％），但可以散布超过200公里。
4. 根据结果​​，我提供了有关在实际研究中应用此模型时在空间采样强度，调查周期长度和使用稳健采样设计之间进行权衡的建议。该模型在后代种群生态学和景观生态学的接口中具有广泛的应用价值。