“Synthesizing Multiple Data Sources to Understand the Population and Community Ecology of California Trees”
Introduced by John Battles
Many management and regulatory decisions are urgent and we are forced to act with less information than we would prefer. With increasing computing power and sophistication of statistical modeling software,
an option which is becoming more feasible is to revisit old datasets to create new insights. My dissertation work has synthesized and augmented several different datasets, including 34 years of Sierra Nevada forest inventories from the UC Center for Forestry’s Blodgett
Forest Research Station, experimental data from several different studies on blue oak from the UC Sierra Foothills Research and Extension Center, and historical aerial photography and current USDA high-resolution images of Humboldt County. I have used hierarchical state-space statistical models, integral projection population models, and object-based image analysis to gain new insights from these datasets. These tools and data sources have enabled me to learn about tree growth and mortality in the mixed-conifer forests of the Sierra Nevada, blue oak population viability in the woodlands of the Sierra foothills, and the encroachment of conifers into black and white oak woodland in the north coast.
White fir trees in the Sierra Nevada grow more quickly in less-dense stands and larger individuals grow still more quickly. Certain sampling plots have higher or lower growth, and certain individual trees grow more quickly or slowly over their entire lifetime. In that same forest, mortality in canopy tree species is concentrated in susceptible subsets of the population, and mortality drivers are highly species-specific. Though larger trees typically have higher survival, some species show senescence, and some have higher mortality at their high-elevation range limits, or when exposed to more intense competition in the stand, or with less incoming solar radiation, or with increasing water deficit. Only a handful of species show a time trend in mortality, and this may or may not result in community composition shifts depending on the corresponding recruitment rates of those species. Full population models for blue oak give a sense of the population growth rate and sensitivity of that rate to underlying demographic parameters such as growth of small individuals or survival of large individuals. In the north coast over the last 60 years, Douglas-fir and other shade-tolerant species have encroached into oak woodland, and at the same time cover has increased in all forest types. Complex and differential management histories at different sites, however, complicate the story of encroachment in different locations.
Taken together, these studies paint a picture of forests and woodlands in an era of fire suppression slowly moving along a trajectory of increasing density and shifting community composition. Modern analytical tools have allowed me to appropriately account for complexities and carefully synthesize existing data sources in order to better understand the population and community ecology of important forest and woodland resources. For these kinds of resources, which change slowly and involve species which grow incrementally, making best use of existing long-term data will be critical for their future management.
This research wouldn’t be possible without the hard work of the many technicians and researchers whose data I am privileged to analyze and augment. I would also like to express my deep gratitude to my many wonderful friends, collaborators, and mentors in ESPM for many years of enjoyable, rewarding, and meaningful work, from geeking out about statistical models to constructing metaphors of the practice of science. Likewise, I am deeply grateful to my partner Jon, my parents, my grandparents, and all my friends and family who support me in so many ways in everything I do, and particularly in my PhD work. I am also grateful for the ecosystems and individuals I study and for the chance to study them, and for the more-than-human world to which we belong.