Monitoring drought-induced tree mortality
In 2011, Texas experienced the most severe one-year drought since record keeping began, killing millions of trees across the state. We developed two remote sensing approaches to quantify canopy loss from the 2011 drought. Firstly, in central Texas, we conducted a time-series analysis to identify areas with persistent canopy loss, using Landtrendr. Within these areas, we estimated percent tree canopy loss using a zero-or-one inflated beta regression model (Schwantes, et al., RSE, 2016). Secondly, to create canopy loss maps across the entire state of Texas, we used Random Forest, a machine learning algorithm, to identify drought-impacted areas with greater than 25% canopy loss. We scaled from field estimates to 1-m orthophotos to 30-m Landsat imagery (Schwantes et al., GCB, 2017). Time-series, geospatial, machine-learning, and statistical analysis were all conducted using ArcGIS, Python, R, ENVI, and IDL.
Identifying climatic thresholds associated with canopy loss and forecasting how likely these thresholds would be crossed in the future
Focusing on a 100-km by 1,000-km transect spanning the State's five-fold east–west precipitation gradient (1,500 to 300 mm), we compared spatially explicit 2011 climatic anomalies to our canopy loss maps. Much of the canopy loss occurred in areas that passed specific climatic thresholds in temperature and vapor pressure deficit anomalies, percent deviation in precipitation, and 2011 difference between precipitation and potential evapotranspiration. Future climate data under the representative concentration pathway 8.5 trajectory project that average values will surpass the 2011 VPD anomaly during the 2070–2099 period and the temperature anomaly during the 2040–2099 period (Schwantes et al., GCB, 2017).
Accounting for landscape heterogeneity to improve spatial predictions of tree vulnerability following drought
We employed a non-linear stochastic model of soil moisture dynamics across a watershed, accounting for spatial differences in aspect, topography, and soils. Using MATLAB, we modeled water stress for Juniperus ashei, a dominant tree/shrub that experienced significant mortality during the record 2011 drought. We then projected future dynamic water stress through the 21st century using future climate projections from 10 global climate models under two scenarios, and comparing models with and without landscape diversity. Favorable microsites or refugia may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of a heterogenous landscape could be overwhelmed. When projecting future water stress, we can account for these potential refugia by incorporating landscape diversity into models (Schwantes, et al. New Phytologist, 2018).
As an undergraduate student at the University of Virginia, I researched how reproductive effort and nutrient availability could affect nutrient resorption for Pentaclethra macroloba, a dominant nitrogen fixing tree in La Selva, Costa Rica. The study found that both N and P resorption proficiency, measured as the senesced-leaf nutrient concentration, decreased when phenological demands, fruit production, were high. As a dominant tree species, nutrient resorption dynamics of P. macroloba could have important effects on nutrient cycling in this ecosystem (read more here)