1. Estimating fossil species' geographic range is a major goal for paleobiologists. In the deep time, this is most commonly performed by using polygon‐based methods such as the minimum convex polygon (MCP) or the Alpha‐Hull. Unfortunately, such methods provide a poor representation of the fossil species' actual range, because they are unable to take control of the severe stochastic and taphonomic biases.
2. Here, we introduce MInOSSE (massively interpolated occurrences for species spatial estimation), a model‐based method that combines a machine learning algorithm and geostatistical approaches to reconstruct a target fossil species' geographic ranges by relying on the distribution of other coeval species and without using environmental predictors.
3. We tested MInOSSE by using many simulated fossil species' distributions, comparing its performance with MCP and Alpha‐Hull outcomes and applying it to real case studies.
4. In all simulations, MInOSSE outperformed the competing methods. Interestingly, the superior performance of MInOSSE becomes more apparent when the fossil record of the target species is scarce, that is, when appropriate range reconstruction is most problematic with polygon‐based methods.
5. MInOSSE is a powerful tool for researchers interested in studying geographic range evolution, effects of range size on extinction risk, as well as biodiversity dynamics and macroecological patterns in the deep time.

中文翻译：

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MInOSSE：一种重建化石物种地理范围的新方法

1. 估计化石物种的地理范围是古生物学家的主要目标。在较深的时间，这通常是通过使用基于多边形的方法（例如最小凸多边形（MCP）或Alpha-Hull）来执行的。不幸的是，这样的方法不能很好地表示化石物种的实际范围，因为它们无法控制严重的随机和自发偏差。
2. 在这里，我们介绍MInOSSE（用于物种空间估计的大量插值事件），这是一种基于模型的方法，结合了机器学习算法和地统计学方法，无需依靠使用其他同代物种的分布即可重建目标化石物种的地理范围环境预测指标。
3. 我们通过使用许多模拟化石物种的分布来测试MInOSSE，将其性能与MCP和Alpha-Hull结果进行比较，并将其应用于实际案例研究。
4. 在所有模拟中，MInOSSE均优于竞争方法。有趣的是，当目标物种的化石记录不足时，即当基于多边形的方法进行适当的范围重建最成问题时，MInOSSE的优越性能变得更加明显。
5. MInOSSE是有力的研究人员的有力工具，可用于研究地理范围的演变，范围大小对物种灭绝风险的影响，以及深入研究生物多样性动态和宏观生态格局。