Core Viewpoint - The BioCLIP 2 model, trained on 2 billion biological images, demonstrates superior species recognition performance and emergent biological understanding beyond species classification, achieving significant advancements in ecological alignment and intra-species differentiation [1][2][5]. Group 1: Model Development and Data Collection - The research team collected 214 million biological images from four major platforms, creating the TreeOfLife-200M dataset, which includes 952,000 different classification labels, making it the largest and most diverse biological image library to date [2][4]. - The model was scaled from ViT-B to ViT-L, increasing the parameter count to facilitate the emergence of new knowledge [4]. Group 2: Performance Metrics - BioCLIP 2 achieved an average accuracy of 55.6% in zero-shot species recognition, outperforming the second-best SigLIP model by 16.1% [5]. - In non-species visual tasks, BioCLIP 2 surpassed common visual models like SigLIP and DINOv2 in habitat recognition, biological attribute identification, new species discovery, and plant disease recognition [8]. Group 3: Emergent Properties - Two emergent properties were identified: 1. Ecological alignment among species with similar lifestyles and ecological significance clustered in feature space, with clearer boundaries as training scale increased [10][11]. 2. Intra-species differentiation, where differences among male, female, and juvenile forms of the same species are distributed orthogonally to inter-species differences, improving with larger training scales [12][14]. Group 4: Training Scale Impact - Experiments showed that increasing training data from 1M to 214M consistently improved performance in non-species visual tasks and enhanced the orthogonality of intra-species differentiation [15].

Group 1 - The core viewpoint of the article highlights the publication of the "Flora of Kenya: Volume 4 Orchidaceae," which is a significant outcome of Sino-Kenyan scientific collaboration [1] - The compilation of the "Flora of Kenya" is a large international cooperation project led by the Sino-African Joint Research Center, the Wuhan Botanical Garden of the Chinese Academy of Sciences, and the National Museum of Kenya [1] - The project aims to document all vascular plants in Kenya, with a total of 31 volumes planned, covering 223 families, 1,773 genera, and over 7,000 species of plants [1] Group 2 - Since its establishment in 2013, the Sino-African Joint Research Center has focused on biodiversity research, ecological environment protection, disaster warning, and modern high-value agriculture, achieving a series of results [2] - The center has recruited and trained 320 master's and doctoral students from African countries and has established regional cooperation offices or joint laboratories in several African nations, forming a cooperative network for scientific research and talent cultivation [2]

据悉，大熊猫国家公园第十次川陕甘三省联合巡护将于2025年10月启幕，由大熊猫国家公园甘肃省 管理局白水江分局接棒主办，续写生态保护新篇章。（蔡丽君 鄢怀林 记者 李全民 青川县委宣传部供 图） "此次行动打破行政壁垒，构建跨区域生态保护网络，为全国自然保护地协同治理提供实践范本，传 递"生态保护无边界"的理念。"大熊猫公园唐家河片区相关负责人介绍。自2019年起，川陕甘三省便积极 探索生态保护合作机制，目前已构建起"规划-法规-司法-执法"四位一体的保护体系。 5月23日，大熊猫国家公园第九次川陕甘三省五市八县联合巡护活动在唐家河片区圆满收官。此次巡 护由大熊猫国家公园唐家河片区承办，以"跨省大巡护·共护大熊猫"为主题，来自三省43家单位的125名 专业人员齐聚一堂，共同开展了为期5天的生态守护行动。 本次巡护系大熊猫国家公园三省联合行动中规模最大、内容最为丰富的一次。巡护工作创新性地采 用"共性+个性"任务清单模式，将启动仪式、技能培训与成果总结有机融合，贯穿巡护全流程。巡护队伍 深入岷山、西秦岭核心区域，沿着10条精心规划的线路展开巡查，总里程达200.6公里。 巡护期间，共发现野生大熊猫、四川羚牛等1 ...