Pattern in space and time is central to ecology, and adequately designed ecological sampling is needed to resolve those patterns, pursue ecological questions and design conservation strategies. Recently, there has been an explosion of various ecological data due to the proliferation of online data‐sharing platforms, citizen science programs and new technology such as unmanned aerial vehicles (UAVs), but data reliability, consistency and the error properties of the sampling method are usually uncertain. While there are a number of standard survey protocols for different taxa, they often subjectively designed and standardization is meant to facilitate repeatability rather than produce a quantitative evaluation of the data (e.g. error properties). Here, we describe an ecological survey scheme consisting of an ‘algorithm' to be followed in the field that will result in a standard set of data as well as the error properties of the data. While many such sampling schemes could be developed that target different types of organisms, we focus on one case of a moving observer attempting to detect a species in the field (e.g. a birder, UAV, etc.) with the goal of producing a presence–absence map. The multiscale model developed is spatially explicit and accommodates inherent survey tradeoffs such as sampling speed, detectability and map resolution. Given a set of sampling parameters, the model provides estimates of the total sampling time and map accuracy translated into the probability of false negative. Additionally it also provides an actual and sampled occupancy–area curve across mapping resolutions that can be utilized to discuss sampling effects. While the proposed sampling framework is simple, the same general approach could be adapted for other conditions to meet the needs of a particular taxon. If a set of ‘canonical' sampling algorithms could be developed with known mathematical properties, it would enhance reliability and usage of ecological datasets.

中文翻译：

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开发具有已知误差属性的广义采样方案：观察者移动的情况

时空格局对生态至关重要，需要适当设计的生态采样来解决这些格局，寻求生态问题并设计保护策略。近年来，由于在线数据共享平台，公民科学计划和无人驾驶飞机（UAV）等新技术的泛滥，各种生态数据激增，但是数据的可靠性，一致性和抽样方法的误差特性通常是不确定的。尽管有许多针对不同分类单元的标准调查协议，但它们通常是主观设计的，标准化的目的是促进可重复性，而不是对数据进行定量评估（例如错误属性）。在这里，我们描述了一种由“算法”组成的生态调查方案 要遵循的字段将导致一组标准数据以及该数据的错误属性。尽管可以针对不同类型的生物体开发许多此类采样方案，但我们关注的一个案例是，移动的观察员试图在野外发现某种物种（例如，观鸟者，无人飞行器等），目的是为了发现生物–缺席地图。所开发的多尺度模型在空间上是明确的，并且可以适应固有的调查折衷，例如采样速度，可检测性和地图分辨率。给定一组采样参数，该模型将提供总采样时间的估计值，并将映射精度转换为假阴性概率。此外，它还提供了跨映射分辨率的实际和采样占用率-面积曲线，可用于讨论采样效果。虽然建议的采样框架很简单，但是可以将其他通用条件适用于相同的通用方法，以满足特定分类群的需求。如果可以使用已知的数学特性开发一套“规范”的采样算法，它将提高生态数据集的可靠性和使用率。