Citizen science databases are increasing in importance as sources of ecological information, but variability in effort across locations is inherent to such data. Spatially biased data—data not sampled uniformly across the study region—is expected. A further introduction of bias is variability in the level of sampling activity across locations. This motivates our work: with a spatial dataset of visited locations and sampling activity at those locations, we propose a model-based approach for assessing effort at these locations. Adjusting for potential spatial bias both in terms of sites visited and in terms of effort is crucial for developing reliable species distribution models (SDMs). Using data from eBird, a global citizen science database dedicated to avifauna, and illustrative regions in Pennsylvania and Germany, we model spatial dependence in both the observation locations and observed activity. We employ point process models to explain the observed locations in space, fit a geostatistical model to explain observation effort at locations, and explore the potential existence of preferential sampling, i.e., dependence between the two processes. Altogether, we offer a richer notion of sampling effort, combining information about location and activity. As SDMs are often used for their predictive capabilities, an important advantage of our approach is the ability to predict effort at unobserved locations and over regions. In this way, we can accommodate misalignment between point-referenced data and say, desired areal scale density. We briefly illustrate how our proposed methods can be applied to SDMs, with demonstrated improvement in prediction from models incorporating effort.

公民科学数据库作为生态信息来源的重要性日益增加，但此类数据固有的跨地区努力的可变性。预计会出现空间偏差数据——数据在整个研究区域中未均匀采样。进一步引入的偏差是不同地点的抽样活动水平的可变性。这激发了我们的工作：通过访问位置的空间数据集和这些位置的采样活动，我们提出了一种基于模型的方法来评估这些位置的工作量。在访问地点和工作量方面调整潜在的空间偏差对于开发可靠的物种分布模型 (SDM) 至关重要。使用来自 eBird 的数据，这是一个致力于鸟类的全球公民科学数据库，以及宾夕法尼亚州和德国的说明性地区，我们对观测位置和观测活动的空间依赖性进行建模。我们采用点过程模型来解释空间中的观测位置，拟合地统计模型来解释位置的观测工作，并探索优先采样的潜在存在，即两个过程之间的依赖关系。总之，我们提供了更丰富的采样工作概念，结合了有关位置和活动的信息。由于 SDM 通常用于其预测能力，因此我们方法的一个重要优势是能够预测未观察到的位置和区域的工作量。通过这种方式，我们可以适应点参考数据之间的错位，比如所需的区域尺度密度。我们简要说明了我们提出的方法如何应用于 SDM，