Performing Department
O`Neill SPEA
Non Technical Summary
The timing of phenological events (spring leaf emergence) exerts strong control over exchanges of carbon, water, and energy between the land surface and the atmosphere at the stand scale. Spring leaf emergence initiates the deciduous forest's growing season and represents a significant driver of ecosystem productivity, soil resource acquisition, and carbon dynamics. Observations of stand-scale phenology, from remote sensing and repeat digital imagery networks have substantially advanced our understanding of the variability in phenological timing. However, while the importance of stand-level phenology on ecosystem processes is clear, we have a very limited understanding of intra- and inter-specific variability in species-level phenology.Understanding how tree species respond to these changes is critical for understanding future forest ecosystem services including protection of soil and water resources and mitigation of climate change. Most studies to date have been focused on responses at the forest scale, with much less attention given to the species-specific responses, which is a significant gap because species respond differently to environmental change. Species that leaf out earlier may have a competitive advantage in terms of carbon uptake leading to increases in growth in some species and decreased growth for others. This has implications for the value of the timber being harvested in a forest stand, depending on whether the higher or lower value timber species are experiencing increased growth. However, any advantage in carbon gain may come with disadvantages to the water budget. If high water users are leafing out earlier with warmer springs, this could reduce summer water supply in years with less precipitation which will potentially lead to water shortages for human populations living downstream, depending on surface water supply.By leveraging existing networks including digital imagery of forest canopies, tree-ring chronologies, and carbon and water cycling, I will quantify species-specific leaf phenological variability and determine the role this varibility has in tree growth, carbon, and water cyling dyanmics. Individual tree species will be identified within the digital image of the canopy by use of a UAV affixed with a digital camera that will be flown above the forest canopy. Visual inspection and integration of forest tree species identification into the existing digital image of the canopy will allow for the development of a species-specific time series of leaf phenological transitions. Once these transitions are extracted, I will assess the effect of the interannual variability in spring leaf emergence date on variability in basal area increment, gross primary productivity, and evapotranspiration.By evaluating completing these methods, I will devlop a framework to systematically assess species-level phenological transitions for use by anyone with access to the Phenocam network. I will provide information to forest managers regarding how shifting phenological patterns are affecing tree growth across species to better inform forest policy and land management practices under a future characterized by a warming climate.
Animal Health Component
0%
Research Effort Categories
Basic
90%
Applied
10%
Developmental
(N/A)
Goals / Objectives
The major goals of this include understanding how species-specific leaf phenological variability affects interannual variability on tree growth and elemental (carbon, water, and energy) cycling, and to provide a framework for a systematic and scalable approach for species-level leaf phenological observations, allowing for the application of this approach anywhere where these data are available. Additionally, I aim to use insight gleaned from this project to communicate the impacts of environmental change of forest structure and composition to assist in making informed decisions about future forest policy and management decisions.Objectives: 1) quantify variability within and across species in spring leaf phenological events across multiple forests stands in the eastern US; and, 2) determine how phenological variation affects tree growth (increase, decrease, no change) and covaries with carbon water, and energy cycling.
Project Methods
Collect and synthesize community science data efforts from National Phenology Network and National Ecological Observatory Network. Quantify species leaf emergence variability from ground observations. Collate Phenocam images and select individuals/ species within 'master' image to track phenophases. Extract RGB digital numbers to assess change in % greenness, with particular focus to spring leaf emergence. Collect and synthesize data from selected eddy-covariance flux towers. Quantify interannual and intra-annual variability of evapotranspiration and gross primary production. Travel to field sites to fly a UAV equipped with a digital camera to identify canopy tree species within the viewshed of the Phenocam. Incorporate aerial species identification into Phenocam images. Develop species-specific timeseries of phenological transitions. Quantify species leaf emergence variability across the canopy. Develop code for extracting the "start of spring" for each species present in the forest canopy and train undergraduate student. Explore flux tower data to investigate how variable spring start dates influence variability in carbon and water cycling. Determine variable spring start dates on magnitude of tree growth for each canopy species across sites. Annual basal area increment (BAI) will be estimated using tree-ring chronologies of each species. Pearson correlations will be computed to assess relationships between climate spring transition date and BAI. Evaluate relationships between spring leaf emergence metrics and ecological fluxes. Evaluate implications of oak decline and forest compositional shifts.