Xiao Bingqing, Yuan Songxi, Bede-Fazekas Ákos, Zhou Jinxin, Song Xingyu, Lin Qiang, Cui Lei, Zhang Zhixin
State Key Laboratory of Tropical Oceanography, Guangdong Provincial Key Laboratory of Applied Marine Biology South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou China.
University of Chinese Academy of Sciences Beijing People's Republic of China.
Ecol Evol. 2025 Jul 6;15(7):e71747. doi: 10.1002/ece3.71747. eCollection 2025 Jul.
In an era of biodiversity crisis, it is critical to perform biodiversity assessments to better inform conservation strategies. In this regard, species distribution models (SDMs) represent a widely used tool for biodiversity assessment. Despite their popularity, the accuracy of SDM predictions has long been criticized because we have incomplete or biased information on species distribution. To overcome this limitation, researchers have proposed improving predictions of SDMs by integrating different types of distribution data, but this idea has rarely been explored in the marine realm. In this study, we explored the idea of data integration using the Japanese sea cucumber, whose distribution is known to be restricted by freshwater discharge of the Yangtze River. We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert-informed ensemble model that further accounted for distance to the IUCN expert range map. Our results showed that integrating an expert range map into the opportunistic occurrence model improved distribution prediction by avoiding overprediction in the south of the dispersal barrier for this species. Our study highlights the benefits of integrating expert range maps into opportunistic occurrence SDMs, which improve the reliability of species' spatial distributions.
在生物多样性危机的时代，进行生物多样性评估对于更好地为保护策略提供信息至关重要。在这方面，物种分布模型（SDMs）是生物多样性评估中广泛使用的工具。尽管它们很受欢迎，但SDM预测的准确性长期以来一直受到批评，因为我们对物种分布的信息不完整或有偏差。为了克服这一限制，研究人员提出通过整合不同类型的分布数据来改进SDM的预测，但这一想法在海洋领域很少被探索。在本研究中，我们以日本海参为例探讨了数据整合的想法，已知其分布受长江淡水排放的限制。我们首先通过四种建模算法基于机会性出现记录为该物种拟合SDM，然后使用堆叠泛化构建了两种类型的集成模型：一种仅使用四个模型预测的集成模型和一种进一步考虑到与IUCN专家范围地图距离的专家知情集成模型。我们的结果表明，将专家范围地图整合到机会性出现模型中，通过避免该物种扩散屏障以南的过度预测，改进了分布预测。我们的研究强调了将专家范围地图整合到机会性出现SDM中的好处，这提高了物种空间分布的可靠性。
