存在/不存在数据以及存在/不存在数据与仅存在数据的融合的优先采样
Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data
摘要：
存在/不存在数据和仅存在数据是用于了解区域上的物种分布的两个常规来源。我们阐明了两种类型的数据之间的基本建模差异。最简单地说，位置在存在/不存在数据下被认为是固定的;位置在仅存在数据下是随机的。“存在的可能性”的定义在两者之间是不相容的。因此，我们采取的问题，在文献中的建模策略，忽略了这种不兼容性，假设存在/不存在建模可以诱导从存在的唯一规范，因此，存在的唯一和存在/不存在的数据源的融合是常规的。我们认为，存在/不存在的数据应在点水平建模。也就是说，我们需要指定一个表面，它提供了在该区域中任何位置存在的概率。从这个表面的实现是一个二进制地图，产生的结果伯努利试验在所有位置。仅存在数据应被建模为由强度函数的规范驱动的点模式。我们进一步认为，只有存在/不存在的数据，优先采样，使用共享的过程的角度来看，可以提高我们的估计存在/不存在的表面和预测的存在。我们还认为，优先采样可以使两种数据类型的概率一致的融合。我们用两个真实的数据集来说明，一个存在/不存在，一个只存在于美国新英格兰的入侵物种存在。我们表明，潜在的偏差在采样位置可以影响推理方面的存在/不存在，并表明，推理可以改善与优惠的采样想法。我们还提供了两个数据集的概率相干融合，以再次改善关于存在/不存在的推断。
英文摘要：
Presence/absence data and presence-only data are the two customary sources for learning about species distributions over a region. We illuminate the fundamental modeling differences between the two types of data. Most simply, locations are considered as fixed under presence/absence data; locations are random under presence-only data. The definition of "probability of presence" is incompatible between the two. So, we take issue with modeling strategies in the literature which ignore this incompatibility, which assume that presence/absence modeling can be induced from presence-only specifications and therefore, that fusion of presence-only and presence/absence data sources is routine. We argue that presence/absence data should be modeled at point level. That is, we need to specify a surface which provides the probability of presence at any location in the region. A realization from this surface is a binary map yielding the results of Bernoulli trials across all locations. Presence-only data should be modeled as a point pattern driven by specification of an intensity function. We further argue that, with just presence/absence data, preferential sampling, using a shared process perspective, can improve our estimated presence/absence surface and prediction of presence. We also argue that preferential sampling can enable a probabilistically coherent fusion of the two data types. We illustrate with two real datasets, one presence/absence, one presence-only for invasive species presence in New England in the United States. We demonstrate that potential bias in sampling locations can affect inference with regard to presence/absence and show that inference can be improved with preferential sampling ideas. We also provide a probabilistically coherent fusion of the two datasets to again improve inference with regard to presence/absence.
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