Gouvêa Lidiane, Fragkopoulou Eliza, Legrand Térence, Serrão Ester A, Assis Jorge
Centre of Marine Sciences, University of Algarve, Faro, Portugal.
Faculty of Bioscience and Aquaculture, Nord Universitet, Bodø, Norway.
Data Brief. 2024 Jan 2;52:110023. doi: 10.1016/j.dib.2023.110023. eCollection 2024 Feb.
Data on contemporary and future geographical distributions of marine species are crucial for guiding conservation and management policies in face of climate change. However, available distributional patterns have overlooked key ecosystem structuring species, despite their numerous ecological and socioeconomic services. Future range estimates are mostly available for few species at regional scales, and often rely on the outdated Representative Concentration Pathway scenarios of climate change, hindering global biodiversity estimates within the framework of current international climate policies. Here, we provide range maps for 980 marine structuring species of seagrasses, kelps, fucoids, and cold-water corals under present-day conditions (from 2010 to 2020) and future scenarios (from 2090 to 2100) spanning from low carbon emission scenarios aligned with the goals of the Paris Agreement (Shared Socioeconomic Pathway 1-1.9), to higher emissions under reduced mitigation strategies (SSP3-7.0 and SSP5-8.5). These models were developed using state-of-the-art and advanced machine learning algorithms linking the most comprehensive and quality-controlled datasets of occurrence records with high-resolution, biologically relevant predictor variables. By integrating the best aspects of species distribution modelling over key ecosystem structuring species, our datasets hold the potential to enhance the ability to inform strategic and effective conservation policy, ultimately supporting the resilience of ocean ecosystems.
面对气候变化,有关海洋物种当代和未来地理分布的数据对于指导保护和管理政策至关重要。然而,现有的分布模式忽略了关键的生态系统构建物种,尽管它们提供了众多的生态和社会经济服务。未来的分布范围估计大多仅适用于区域尺度上的少数物种,并且通常依赖于过时的气候变化代表性浓度路径情景,这阻碍了在当前国际气候政策框架内对全球生物多样性的估计。在此,我们提供了980种海洋构建物种(包括海草、海带、岩藻和冷水珊瑚)在当前条件下(2010年至2020年)以及未来情景(2090年至2100年)的分布范围图,涵盖了从符合《巴黎协定》目标的低碳排放情景(共享社会经济路径1-1.9)到减排策略降低情况下的高排放情景(SSP3-7.0和SSP5-8.5)。这些模型是使用最先进的高级机器学习算法开发的,将最全面且经过质量控制的出现记录数据集与高分辨率、与生物学相关的预测变量相联系。通过整合物种分布建模在关键生态系统构建物种方面的最佳内容,我们的数据集有潜力提高为战略和有效的保护政策提供信息的能力,最终支持海洋生态系统的恢复力。