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Abstract This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts. 

Abstract Through infection and lysis of their coexisting bacterial hosts, viruses impact the biogeochemical cycles sustaining globally significant pelagic oceanic ecosystems. Currently, little is known of the ecological interactions between lytic viruses and their bacterial hosts underlying these biogeochemical impacts at ecosystem scales. This study focused on populations of lytic viruses carrying the B12-dependent Class II monomeric ribonucleotide reductase (RNR) gene, ribonucleotide-triphosphate reductase (Class II RTPR), documenting seasonal changes in pelagic virioplankton and bacterioplankton using amplicon sequences of Class II RTPR and the 16S rRNA gene, respectively. Amplicon sequence libraries were analyzed using compositional data analysis tools that account for the compositional nature of these data. Both virio- and bacterioplankton communities responded to environmental changes typically seen across seasonal cycles as well as shorter term upwelling–downwelling events. Defining Class II RTPR-carrying viral populations according to major phylogenetic clades proved a more robust means of exploring virioplankton ecology than operational taxonomic units defined by percent sequence homology. Virioplankton Class II RTPR populations showed positive associations with a broad phylogenetic diversity of bacterioplankton including dominant taxa within pelagic oceanic ecosystems such as Prochlorococcus and SAR11. Temporal changes in Class II RTPR virioplankton, occurring as both free viruses and within infected cells, indicated possible viral–host pairs undergoing sustained infection and lysis cycles throughout the seasonal study. Phylogenetic relationships inferred from Class II RTPR sequences mirrored ecological patterns in virio- and bacterioplankton populations demonstrating possible genome to phenome associations for an essential viral replication gene.