1. With the advent of technology for data gathering and storage, opportunistic citizen science data are proliferating. Species distribution models (SDMs) aim to use species occurrence or abundance for ecological insights, prediction and management. We analysed a massive opportunistic dataset with over 100,000 records of incidental shipboard observations of marine mammals. Our overall goal was to create maps of species density from massive opportunistic data by using spatial regression for count data with an effort offset. We illustrate the method with two marine mammals in the Gulf of Alaska and Bering Sea.
2. We counted the total number of animals in 11,424 hexagons based on presence-only data. To decrease bias, we first estimated a spatial density surface for ship-days, which was our proxy variable for effort. We used spatial considerations to create pseudo-absences, and left some hexagons as missing values. Next, we created SDMs that used modelled effort to create pseudo-absences, and included the effort surface as an offset in a second stage analysis of two example species, northern fur seals and Steller sea lions.
3. For both effort and species counts, we used spatial count regression with random effects that had a multivariate normal distribution with a conditional autoregressive (CAR) covariance matrix, providing 2.5 million Markov chain Monte Carlo (MCMC) samples (1,000 were retained) from the posterior distribution. We used a novel MCMC scheme that maintained sparse precision matrices for observed and missing data when batch sampling from the multivariate normal distribution. We also used a truncated normal distribution to stabilize estimates, and used a look-up table for sampling the autocorrelation parameter. These innovations allowed us to draw several million samples in just a few hours.
4. From the posterior distributions of the SDMs, we computed two functions of interest. We normalized the SDMs and then applied an overall abundance estimate obtained from the literature to derive spatially explicit abundance estimates, especially within subsetted areas. We also created ‘certain hotspots’ that scaled local abundance by standard deviation and using thresholds. Hexagons with values above a threshold were deemed as hotspots with enough evidence to be certain about them.

中文翻译：

---

来自机会主义公民科学数据的物种密度模型

1. 随着数据收集和存储技术的出现，机会主义的公民科学数据正在激增。物种分布模型 (SDM) 旨在利用物种出现或丰度进行生态洞察、预测和管理。我们分析了一个庞大的机会数据集，其中包含超过 100,000 条海洋哺乳动物偶然船上观察的记录。我们的总体目标是通过对计数数据使用空间回归和努力偏移，从大量机会数据创建物种密度图。我们用阿拉斯加湾和白令海的两种海洋哺乳动物来说明该方法。
2. 我们根据仅存在数据计算了 11,424 个六边形中的动物总数。为了减少偏差，我们首先估计了出货天数的空间密度表面，这是我们努力的代理变量。我们使用空间考虑来创建伪缺失，并留下一些六边形作为缺失值。接下来，我们创建了 SDM，它使用建模的努力来创建伪缺失，并在两个示例物种（北方海狗和 Steller 海狮）的第二阶段分析中将努力面作为偏移量。
3. 对于努力和物种计数，我们使用空间计数回归和随机效应，具有多元正态分布和条件自回归 (CAR) 协方差矩阵，提供 250 万个马尔可夫链蒙特卡罗 (MCMC) 样本（保留 1,000 个）来自后验分配。我们使用了一种新颖的 MCMC 方案，该方案在从多元正态分布中批量采样时为观察到的和缺失的数据保持稀疏精度矩阵。我们还使用截断的正态分布来稳定估计，并使用查找表对自相关参数进行采样。这些创新使我们能够在短短几个小时内抽取数百万个样本。
4. 根据 SDM 的后验分布，我们计算了两个感兴趣的函数。我们将 SDM 归一化，然后应用从文献中获得的整体丰度估计值来推导出空间显式丰度估计值，尤其是在子集区域内。我们还创建了“某些热点”，通过标准偏差和使用阈值来缩放局部丰度。值高于阈值的六边形被视为热点，有足够的证据来确定它们。