美国杜克大学David Dunson小组的论文发现了常见到罕见迁移学习（CORAL）能够对25万种稀有的马达加斯加节肢动物进行推理和预测。2025年9月23日，国际知名学术期刊《自然—方法学》发表了这一成果。

在这里，课题组提出了一种“从常见物种到稀有物种的迁移学习”（CORAL）方法，该方法基于从常见物种借鉴的信息，使常见物种和稀有物种的统计和计算效率建模成为可能。该课题组说明，在马达加斯加DNA元条形码数据的背景下，CORAL导致了大大改进的预测和推断，其中包括2874个样本中检测到的255188种节肢动物。

据了解，基于DNA的生物多样性调查产生了大规模的数据，包括多达数百万个物种，其中大多数是罕见的。为了充分利用这些数据进行推断和预测，需要采用建模方法，将物种的发生与环境和空间预测因素联系起来，同时纳入有关其分类或系统发育位置的信息。即使联合物种分布模型在大型群落中的可扩展性已经大大提高，但到目前为止，将数百种物种纳入其中还不可行，这导致了分析的折衷。

附：英文原文

Title: Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

Author: Ovaskainen, Otso, Winter, Steven, Tikhonov, Gleb, Abrego, Nerea, Anslan, Sten, deWaard, Jeremy R., deWaard, Stephanie L., Fisher, Brian L., Furneaux, Brendan, Hardwick, Bess, Kerdraon, Deirdre, Pentinsaari, Mikko, Raharinjanahary, Dimby, Rajoelison, Eric Tsiriniaina, Ratnasingham, Sujeevan, Somervuo, Panu, Sones, Jayme E., Zakharov, Evgeny V., Hebert, Paul D. N., Roslin, Tomas, Dunson, David

Issue&Volume: 2025-09-23

Abstract: DNA-based biodiversity surveys result in massive-scale data, including up to millions of species—of which, most are rare. Making the most of such data for inference and prediction requires modeling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a ‘common to rare transfer learning’ (CORAL) approach, based on borrowing information from the common species to enable statistically and computationally efficient modeling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2,874 samples.

DOI: 10.1038/s41592-025-02823-y

Source: https://www.nature.com/articles/s41592-025-02823-y