Within-species patterns challenge our understanding of the leaf economics spectrum.

The utility of plant functional traits for predictive ecology relies on our ability to interpret trait variation across multiple taxonomic and ecological scales. Using extensive data sets of trait variation within species, across species and across communities, we analysed whether and at what scales leaf economics spectrum (LES) traits show predicted trait-trait covariation. We found that most variation in LES traits is often, but not universally, at high taxonomic levels (between families or genera in a family). However, we found that trait covariation shows distinct taxonomic scale dependence, with some trait correlations showing opposite signs within vs. across species. LES traits responded independently to environmental gradients within species, with few shared environmental responses across traits or across scales. We conclude that, at small taxonomic scales, plasticity may obscure or reverse the broad evolutionary linkages between leaf traits, meaning that variation in LES traits cannot always be interpreted as differences in resource use strategy.

MeadoWatch: a long-term community-science database of wildflower phenology in Mount Rainier National Park.

We present a long-term and high-resolution phenological dataset from 17 wildflower species collected in Mt. Rainier National Park, as part of the MeadoWatch (MW) community science project. Since 2013, 457 unique volunteers and scientists have gathered data on the timing of four key reproductive phenophases (budding, flowering, fruiting, and seeding) in 28 plots over two elevational gradients alongside popular park trails. Trained volunteers (87.2%) and University of  Washington scientists (12.8%) collected data 3-9 times/week during the growing season, using a standardized method. Taxonomic assessments were highly consistent between scientists and volunteers, with high accuracy and specificity across phenophases and species. Sensitivity, on the other hand, was lower than accuracy and specificity, suggesting that a few species might be challenging to reliably identify in community-science projects. Up to date, the MW database includes 42,000+ individual phenological observations from 17 species, between 2013 and 2019. However, MW is a living dataset that will be updated through continued contributions by volunteers, and made available for its use by the wider ecological community.