1. Spatial capture–recapture (SCR) methods use the location of detectors (camera traps, hair snares and live‐capture traps) and the locations at which animals were detected (their spatial capture histories) to estimate animal density. Despite the often large expense and effort involved in placing detectors in a landscape, there has been relatively little work on how detectors should be located. A natural criterion is to place traps so as to maximize the precision of density estimators, but the lack of a closed‐form expression for precision has made optimizing this criterion computationally demanding.
2. Recent results by Efford and Boulanger (2019) show that precision can be well approximated by a function of the expected number of detected individuals and expected number of recapture events, both of which can be evaluated at low computational cost. We use these results to develop a method for obtaining survey designs that optimize this approximate precision for SCR studies using count or binary proximity detectors, or multi‐catch traps.
3. We show how the basic design protocol can be extended to incorporate spatially varying distributions of activity centres and animal detectability. We illustrate our approach by simulating from a camera trap study of snow leopards in Mongolia and comparing estimates from our designs to those generated by regular or optimized grid designs. Optimizing detector placement increased the number of detected individuals and recaptures, but this did not always lead to more precise density estimators due to less precise estimation of the effective sampling area. In most cases, the precision of density estimators was comparable to that obtained with grid designs, with improvement in some scenarios where approximate  < 20% and density varied spatially.
4. Designs generated using our approach are transparent and statistically grounded. They can be produced for survey regions of any shape, adapt to known information about animal density and detectability, and are potentially easier and less costly to implement. We recommend their use as good, flexible candidate designs for SCR surveys when reasonable knowledge of model parameters exists. We provide software for researchers to construct their own designs, in the form of updates to design functions in the r package oSCR.

中文翻译：

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快速，灵活的替代常规网格设计以进行空间捕获-重新捕获

1. 空间捕获-捕获（SCR）方法使用检测器的位置（相机陷阱，头发圈套器和实时捕获陷阱）和检测到动物的位置（其空间捕获历史）来估计动物密度。尽管将探测器放置在景观中通常会花费大量的金钱和精力，但是关于如何放置探测器的工作却相对较少。一个自然的准则是放置陷阱，以使密度估计器的精度最大化，但是缺乏用于精度的闭式表达式使得优化该准则在计算上很困难。
2. Efford和Boulanger（2019）的最新结果表明，可以通过检测到的个体的预期数量和捕获事件的预期数量的函数很好地近似精度，这两者都可以以较低的计算成本进行评估。我们使用这些结果来开发一种用于获取调查设计的方法，该方法可以使用计数或二进制接近检测器或多捕获阱来优化SCR研究的近似精度。
3. 我们展示了如何扩展基本设计协议以合并活动中心和动物可检测性的空间变化分布。我们通过对蒙古雪豹的相机陷阱研究进行仿真，并将我们的设计估算值与常规或优化网格设计得出的估算值进行比较，来说明我们的方法。优化检测器的位置会增加检测到的个体和捕获物的数量，但是由于对有效采样区域的估算不够精确，因此这并不总是导致更精确的密度估算器。在大多数情况下，密度估算器的精度可与网格设计获得的精度相媲美，在某些情况下有所改进，其中大约 <20％且密度在空间上变化。
4. 使用我们的方法生成的设计是透明的，并且具有统计基础。它们可以生产用于任何形状的调查区域，以适应有关动物密度和可检测性的已知信息，并且可能更容易实现且成本更低。如果存在合理的模型参数知识，我们建议将它们用作SCR调查的良好，灵活的候选设计。我们提供软件，研究人员构建自己的设计，以更新的形式在设计功能[R包Ø SCR。