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Phenological response to global climate change can impact ecosystem functions. There are various data sources from which spatiotemporal and taxonomic phenological data may be obtained: mobilized herbaria, community science initiatives, observatory networks, and remote sensing. However, analyses conducted to date have generally relied on single sources of these data. Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources that offer partially nonoverlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at varying macroecological scales. Here, we describe a detailed vision for the harmonization of phenological data, including the direct integration of disparate sources of phenological data using a common schema. Specifically, we highlight existing methods for data harmonization that can be applied to phenological data: data design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. Data harmonization is not a new concept in ecology, but the harmonization of phenological data is overdue.We aim to highlight the need for better data harmonization, providing a roadmap for how harmonized phenological data may fill gaps while simultaneously being integrated into analyses. 

Microbiomes play essential roles in the health and function of animal and plant hosts and drive nutrient cycling across ecosystems. Integrating novel trait-based approaches with ecological theory can facilitate the prediction of microbial functional traits important for ecosystem functioning and health. In particular, the yield-acquisition-stress (Y-A-S) framework considers dominant microbial life history strategies across gradients of resource availability and stress. However, microbiomes are dynamic, and spatial and temporal shifts in taxonomic and trait composition can affect ecosystem functions. We posit that extending the Y-A-S framework to microbiomes during succession and across biogeographic gradients can lead to generalizable rules for how microbiomes and their functions respond to resources and stress across space, time, and diverse ecosystems. We demonstrate the potential of this framework by applying it to the microbiomes hosted by the carnivorous pitcher plant Sarracenia purpurea, which have clear successional trajectories and are distributed across a broad climatic gradient. 

Abstract Many research and monitoring networks in recent decades have provided publicly available data documenting environmental and ecological change, but little is known about the status of efforts to synthesize this information across networks. We convened a working group to assess ongoing and potential cross‐network synthesis research and outline opportunities and challenges for the future, focusing on the US‐based research network (the US Long‐Term Ecological Research network, LTER) and monitoring network (the National Ecological Observatory Network, NEON). LTER‐NEON cross‐network research synergies arise from the potentials for LTER measurements, experiments, models, and observational studies to provide context and mechanisms for interpreting NEON data, and for NEON measurements to provide standardization and broad scale coverage that complement LTER studies. Initial cross‐network syntheses at co‐located sites in the LTER and NEON networks are addressing six broad topics: how long‐term vegetation change influences C fluxes; how detailed remotely sensed data reveal vegetation structure and function; aquatic‐terrestrial connections of nutrient cycling; ecosystem response to soil biogeochemistry and microbial processes; population and species responses to environmental change; and disturbance, stability and resilience. This initial study offers exciting potentials for expanded cross‐network syntheses involving multiple long‐term ecosystem processes at regional or continental scales. These potential syntheses could provide a pathway for the broader scientific community, beyond LTER and NEON, to engage in cross‐network science. These examples also apply to many other research and monitoring networks in the US and globally, and can guide scientists and research administrators in promoting broad‐scale research that supports resource management and environmental policy.