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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.

 

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.

 

Abstract The relationships that control seed production in trees are fundamental to understanding the evolution of forest species and their capacity to recover from increasing losses to drought, fire, and harvest. A synthesis of fecundity data from 714 species worldwide allowed us to examine hypotheses that are central to quantifying reproduction, a foundation for assessing fitness in forest trees. Four major findings emerged. First, seed production is not constrained by a strict trade-off between seed size and numbers. Instead, seed numbers vary over ten orders of magnitude, with species that invest in large seeds producing more seeds than expected from the 1:1 trade-off. Second, gymnosperms have lower seed production than angiosperms, potentially due to their extra investments in protective woody cones. Third, nutrient-demanding species, indicated by high foliar phosphorus concentrations, have low seed production. Finally, sensitivity of individual species to soil fertility varies widely, limiting the response of community seed production to fertility gradients. In combination, these findings can inform models of forest response that need to incorporate reproductive potential.