How well do the spring indices predict phenological activity across plant species?

Area of Science:

• Phenology
• Ecological modeling
• Plant science

Background:

• Spring indices, developed from historical lilac and honeysuckle data, model the onset of spring biological activity.
• The USA National Phenology Network (USA-NPN) provides widely used maps of spring index onset dates for natural resource management.
• The predictive accuracy of these indices for diverse plant species remains underexplored.

Purpose of the Study:

• To evaluate the accuracy of spring index models in predicting leaf and flowering onset dates for 19 deciduous tree and shrub species.
• To assess model performance for the original lilac and honeysuckle species used in their development.
• To determine how prediction accuracy varies by species, latitude, and phenological index (leaf vs. bloom).

Main Methods:

• Utilized a dataset of 37,819 recent plant phenology observations (1981-2017).
• Compared observed leaf and flowering onset dates against predictions from USA-NPN gridded spring index maps.
• Analyzed prediction concordance across different species, plant types (trees vs. shrubs), and latitudinal gradients.

Main Results:

• Spring index models showed variable predictive performance across the 19 tested species.
• The Bloom Index generally predicted flowering onset better than the Leaf Index predicted leaf onset.
• Prediction accuracy was higher for shrubs than for trees and decreased at higher latitudes.

Conclusions:

• Spring indices serve as valuable indicators for predicting phenological events in a broad range of North American trees and shrubs.
• Understanding species-specific and latitudinal variations in model performance is crucial for accurate phenological forecasting.
• These findings provide a baseline for assessing climate change impacts on plant phenology across the USA.