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This article is a Commentary onParket al. (2023),239: 2153–2165.

Plants track changing climate partly by shifting their phenology, the timing of recurring biological events. It is unknown whether these observed phenological shifts are sufficient to keep pace with rapid climate changes. Phenological mismatch, or the desynchronization between the timing of critical phenological events, has long been hypothesized but rarely quantified on a large scale. It is even less clear how human activities have contributed to this emergent phenological mismatch. In this study, we used remote sensing observations to systematically evaluate how plant phenological shifts have kept pace with warming trends at the continental scale. In particular, we developed a metric of spatial mismatch that connects empirical spatiotemporal data to ecological theory using the “velocity of change” approach. In northern mid‐to high‐latitude regions (between 30–70°N) over the last three decades (1981–2014), we found evidence of a widespread mismatch between land surface phenology and climate where isolines of phenology lag behind or move in the opposite direction to the isolines of climate. These mismatches were more pronounced in human‐dominated landscapes, suggesting a relationship between human activities and the desynchronization of phenology dynamics with climate variations. Results were corroborated with independent ground observations that indicate the mismatch of spring phenology increases with human population density for several plant species. This study reveals the possibility that not even some of the foremost responses in vegetation activity match the pace of recent warming. This systematic analysis of climate‐phenology mismatch has important implications for the sustainable management of vegetation in human‐dominated landscapes under climate change.

A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.