Phenology is the study of biological activities influenced by seasonal environmental changes. These changes drive ecosystem functions and services and have been found to be influenced by climate change. The methods for documenting phenology have expanded from ground observation to more advanced methods including digital repeat camera and satellite imagery. Additionally, citizen science programs have contributed observations to nationwide databases. I utilized Harvard Forest Long Term Ecological Research (LTER) and data provided by the USA National Phenology Network’s (USA-NPN) citizen science project Nature’s Notebook to examine the phenological trends of five deciduous tree species throughout the northeastern United States. I assessed the accuracy of predicting phenological events and the degree to which various abiotic factors influenced phenology. To predict leaf out dates, an accumulated growing degree day (AGDD) model was developed. The model gave an overall root mean squared error (RMSE) of 25.5% and mean absolute error (MAE) of 12.6%. Canopy position was found to significantly affect the timing of leaf out and leaf fall for trees located at Harvard Forest. Additionally, leaf fall showed less variability than leaf out throughout all USA-NPN sites. Finally, ANOVA and Tukey’s HSD revealed varying degrees of significance for the timing of phenological events for species based on urbanization status. Overall, biotic factors played differing roles in the timing of phenology for all species across all sites. As interest in phenology continues to increase, there is great potential for further integration of scientific and citizen science data to assess trends and predict future changes.
Research Experience for Undergraduates (REU) Scholarship
Harvard Forest 25th Annual Student Symposium, 2017