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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 The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges.