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Many plants are responding to increases in spring temperatures by advancing their leaf‐out and flowering times in temperate regions around the world. The magnitudes of species' sensitivities to temperature vary widely, and patterns within that variation can illuminate underlying phenological drivers related to species' life histories and local‐scale adaptations.The USA National Phenology Network (USA‐NPN) and the National Ecological Observatory Network (NEON) are two rapidly growing, taxonomically and geographically extensive phenology data resources in the USA that offer opportunities to explore emergent properties of spring phenology. Using observations of leaf‐out and flowering in temperate deciduous plant species from USA‐NPN (2009–2024) and NEON (2014–2022), we estimated species‐level flowering (n = 164) and leaf‐out (n = 136) sensitivities to temperatures of the preceding months, obtained through PRISM. We used the results to assess differences in sensitivities between the two datasets and among life history traits (e.g. introduced or native status, seasonal timing and growth habit) and to explore latitudinal patterns in sensitivity among and within species. We found significant relationships between temperature and leaf‐out phenology (2009–2024 for 109 (80%) species, ranging from −7.4 to −1.3 days/°C, and between temperature and flowering phenology for 140 (85%) species, ranging from −8.0 to −1.1 days/°C. Plant sensitivities were highly consistent among the USA‐NPN and NEON datasets, suggesting these datasets can be reasonably combined to expand the coverage of publicly available phenological data across the USA. Introduced species showed stronger sensitivity to temperature than native species for both leaf‐out (−0.8 days/°C difference) and flowering (−0.7 days/°C difference). The strongest (i.e. most negative) leaf‐out sensitivities to temperature were associated with earlier leaf‐out dates and strong flowering sensitivities. Latitudinal analyses within and across species indicate that flowering and leaf‐out sensitivities are both stronger at lower latitudes. Synthesis. Phenological ‘big data’ encompassing over 100 species across the eastern USA shows that leaf‐out and flowering occur earlier with warmer temperatures and that native species and individuals at high latitudes tend to have weaker temperature sensitivities than introduced species and more southern plants; these findings suggest adaptations within and across species to avoid leafing out and flowering under harsh environmental conditions. 

Phenological indices are an effective approach for assessing spatial and temporal patterns and variability in plant development. The Spring Indices (SI-x), two widely adopted phenological indices, have been used in recent decades to predict development of woody plants, and document changes in spring growth timing, especially in North America. However, these two indices (Leaf and Bloom) capture only two “moments” in the continuum of spring when quantities of thermal or photo/thermal energy, associated with seasonal events in plants, are accumulated, limiting their utility to characterize the remainder of the spring season. Further, the Spring Indices do not account for intraspecific variation, limiting their ability to reflect non-cloned plant development. To address these shortcomings, we developed a novel suite of phenological indices that encompass a broader span of the spring season. These indices were constructed using observations contributed to the USA National Phenology Network’s Nature’s Notebook platform across many non-cloned tree and shrub species’ ranges, thereby incorporating differing regional responses within species due to genetic variations. Individual species model predictions of leaf or bloom timing exhibited an average mean absolute error of 8.55 days; most were improved by the inclusion of site-specific latitude, elevation, or 30-year average temperature. Leaf and bloom model outputs for individual species across the spring season were temporally aggregated into four leaf and bloom groups to produce a suite of Spring Development Indices (SDI). Accuracy of the SDI predictionswas 0.89 days lower, on average, than the species models, but 2.65 days better than SI-x. Generally, all SDIs were highly correlated. The SDIs exhibiting the most difference from the others were Early leaf, Very Early bloom, and Late bloom. As such, these SDIs provide novel insights, beyond SI-x, into the relative timing of spring-season “moments” across species in space and time. 

Abstract Climatic change is dramatically altering phenology but generalities regarding tempo and mode of response remain limited. Here we present a general model framework incorporating spring temperature, velocity of spring warming, and species’ thermal requirements for predicting phenological response to warming. A key prediction of this framework is that species active earlier in the season and located in warmer regions where spring temperature velocity is lowest show strongest sensitivity to climatic change and greatest advancement in response to warming. We test this prediction using plant phenology datasets collected in the 1850s and 2010s. Our results strikingly confirm model predictions, showing that while temperature sensitivity is higher in regions with low temperature velocity, the greatest realized change in phenological onset is northern areas where warming rates have been fastest. Our framework offers enhanced utility for predicting phenological sensitivity and responsiveness in temperate regions and across multiple plant species and potentially other groups. 

This paper presents a brief overview and history of “phenoclimatology”, a subdiscipline of climatology, emphasizing atmosphere-biosphere interactions. Here, we describe the establishment and recent growth in models and forecasts created using in situ phenology observations and the factors enabling these advancements, with focus on North America. Most notably, large-scale phenological models paved the way for development of synthetic indices. Such indices can supply an assessment of a location’s general phenological response over a standard period, context for comparing regional or local-scale studies, the ability to analyze changes in damage risks for plants, and reconstruction of the timing of events in years past across many regions. As such, synthetic phenological indices have seen wide adoption in estimating spring-season evolution in real time, anticipating short-term impacts of an early or late start to spring, and in assessing changes in the timing of seasonal transitions associated with climate change. 

Abstract The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project’s needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology.