In December of 2015, I made my way to rural Peru for a few weeks, my first visit to South America. While I was there, I observed a devotion to family and leisure activity, topics that were not heavily prioritized in my experience in Arizona. Upon my return, I became more involved in leisure activities, particularly running, hiking, yoga, and climbing. These involvements noticeably benefitted my health and well-being. The way the Peruvians I met prioritized these subjects fascinated me, and I wanted to study this difference between Arizona and Peru. In July of 2017, I returned to Peru for a semester abroad with my bags packed and the following research questions: 1) Are differences in motivation for rock climbing between Arizona and Peruvian climbers associated with cultural values? 2) Do leisure activities and the amount of time spent on them have an effect on quality of life? 3) Does the degree of climbing specialization impact perceptions of well-being? 4) What characteristics impact perceptions of quality of life among climbers? Are these characteristics affected by country of origin? My prediction was that Peruvians had higher quality of life due to their emphasis on leisure. Through this study, I learned that this conclusion was not as simple as I anticipated.

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters.

Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO₂) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6  ×  10⁵ km² region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO₂ enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments.

Predictions of how growth responded to elevated CO₂ strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO₂ study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO₂, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.