Citizen science across two centuries reveals phenological change among plant species and functional groups in the Northeastern US

Abstract

1. Understanding the breadth and complexity of changes in phenology is limited by the availability of long-term historical data sets with broad geographic range.2. We compare a recently discovered historical data set of plant phenology observations collected across the state of New York (1826-1872) to contemporary volunteer-contributed observations (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) to evaluate changes in plant phenology between time periods. These multi-site, multi-taxa phenology data matched with temperature data uniquely extend historical observations back in time prior to the major atmospheric effects of the Industrial Revolution. 3. The majority of the 36 trees, shrubs and forbs that comprised our analysable data set flowered and leafed out earlier in contemporary years than in the early to mid-19th century. This shift is associated with a warming trend in mean January-to-April temperatures, with flowering and leafing advancing on average 3 days/°C earlier. On average, plants flowered 10.5 days earlier and leafed out 19 days earlier in the contemporary period. Urban areas exhibit more advanced phenology than their rural counterparts overall, and insect-pollinated trees show more advanced phenology than wind-pollinated trees and seasonality and growth form explain significant variation in flowering phenology. The greatest rates of temperature sensitivity and change between time periods for flowering are seen in early-season species, particularly trees. Changes in the timing of leaf out are the most advanced for trees and shrubs in urban areas. 4. Synthesis. Citizen science observations across two centuries reveal a dramatic, climate-driven shift to earlier leaf out and flowering. The magnitude of advancement varies across settings, species and functional groups, and illustrates how long-term monitoring and citizen science efforts are invaluable for ecological forecasting and discovery.

Generating ecological insights from historical data

“…While generally lacking the in‐depth site‐specific detail found in Grinnell's, Bertelsen's, and Thoreau's datasets, these data can still be used to explore broad patterns and test specific hypotheses (Fuccillo Battle et al . 2022).…”

“…In other cases, researchers may find unexpectedly detailed regional-scale datasets. For example, Fuccillo Battle et al (2022) recently began analyzing data from one of the oldest citizenscience networks in North America, a network started by the New York State Regents in 1826 to monitor weather and phenology. After comparing the historical data to modern citizenscience observations to assess changes in plant phenology across the state of New York, the research team found significant variation in phenological changes across functional groups (e.g., insect-versus wind-pollinated taxa) and locations (e.g., urban versus rural), with implications for altered ecological interactions.…”

“…Researchers not only can remotely examine digitized museum specimens for hundreds of thousands of species collected over the past 200 years or more from around the world but also can download more recent taxonomic records from citizen-science platforms like iNaturalist, which has amassed over 100 million observations. While generally lacking the in-depth site-specific detail found in Grinnell's, Bertelsen's, and Thoreau's datasets, these data can still be used to explore broad patterns and test specific hypotheses (Fuccillo Battle et al 2022).…”

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

“…Such community-sourced data have significantly contributed to the accumulation of ecosystem information. These datasets have been instrumental in assessing the impacts of climate change and urbanization on phenology (Fuccillo Battle et al, 2022;Klinger et al, 2023), detecting distribution changes including invasive alien species (Larson et al, 2020;Roy et al, 2023;Wallace & Bargeron, 2014), exploring large-scale geographic variations in traits (Atsumi & Koizumi, 2017;Leighton et al, 2016), and estimating species distributions (Chandler et al, 2017;Feldman et al, 2021;Johnston et al, 2018;Steen et al, 2019). Moreover, the utilization of machine learning to describe population trends based on community-sourced data (Fink et al, 2023) offers opportunities for conducting time-series analyses.…”

Sex-dependent phenological responses to climate vary across species’ ranges

“…Anthropogenic climate change has significantly altered the timing of life history events (i.e., phenology) of plants worldwide ( 1 , 2 ). For instance, plants in New York state USA have been shown to flower 10.5 d earlier on average in contemporary years than in the early to mid-19th century in response to warming spring temperatures ( 3 ). Such shifts in flowering time affect the reproduction and survival of plant species as well as those of other species that interact with them ( 4 – 7 ).…”