1. Building reliable species distribution models (SDMs) from presence‐only information requires a good understanding of the spatial variation in the sampling effort. However, in most cases, the sampling effort is unknown, leading to biases in SDMs. This study proposes a method to jointly estimate the parameters of sampling effort and species densities to avoid such biases. The method is particularly suited to the analysis of massive but highly heterogeneous presence‐only data.
2. The proposed method is based on estimating the variation in sampling effort over units of a spatial mesh in parallel with the environmental density of multiple species using a marked Poisson process model. Based on simulations with realistic settings, we examined the performance and robustness of parameter estimations. We also analysed a large‐scale citizen science dataset with highly heterogeneous sampling (Pl@ntNet), including around 300,000 occurrences of 150 plant species.
3. We found that sampling effort was correctly estimated when the true sampling effort was constant within the cells of a spatial mesh. Estimation bias arose when sampling effort and environmental drivers strongly covaried within cells. Otherwise, the inference was correct and robust to sampling variation within cells. Running the model on real occurrences of 150 plant species provided an estimated map of relative sampling effort for 15% of French territory. We also found that the density estimated for an exotic invasive plant was consistent with prior data.
4. This is the first method jointly estimating species densities depending on environment, and sampling effort as an explicit spatial function, from occurrence data of multiple species. An asset of the method is that a few frequently observed species greatly contribute to correctly estimate sampling effort, thereby improving density estimation of all other species. This approach can thus provide reliable SDM for large opportunistic presence‐only datasets, with broad spatial variation in sampling effort but also many species, such as datasets from citizen science programmes.

中文翻译：

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从仅机会存在数据联合估计多个物种的空间采样工作量和栖息地适应性

1. 根据仅存在信息构建可靠的物种分布模型（SDM），需要对采样工作中的空间变化有一个很好的了解。但是，在大多数情况下，采样工作是未知的，从而导致SDM出现偏差。这项研究提出了一种方法来共同估计采样工作量和物种密度的参数，以避免这种偏差。该方法特别适合分析大量但高度异构的仅存在数据。
2. 所提出的方法是基于使用标记的泊松过程模型估算与多个物种的环境密度平行的空间网格单位上采样工作量的变化。基于具有实际设置的仿真，我们检查了参数估计的性能和鲁棒性。我们还分析了具有高度异类采样的大规模公民科学数据集（Pl @ ntNet），其中包括150种植物中约30万种。
3. 我们发现，当真实采样工作在空间网格的单元中恒定时，可以正确估计采样工作。当细胞内的采样工作量和环境驱动力强烈协变量时，就会产生估计偏差。否则，该推论是正确的，并且对单元内的采样变化具有鲁棒性。对150种植物的实际发生情况运行该模型，可以为15％的法国领土提供相对采样工作量的估计图。我们还发现，估计外来入侵植物的密度与先前的数据一致。
4. 这是第一种根据环境联合估计物种密度并从多个物种的发生数据中采样采样作为显式空间函数的方法。该方法的优势在于，一些经常观察到的物种对正确估计采样工作做出了巨大贡献，从而改善了所有其他物种的密度估计。因此，这种方法可以为大型机会存在数据集提供可靠的SDM，不仅在采样工作上具有广泛的空间差异，而且还可以对许多物种（例如来自公民科学计划的数据集）进行广泛的变化。