I am a plant ecologist whose research falls at the intersection of the climate crisis and the biodiversity crisis. My toolbox strongly features demographic data and models, and sometimes novel statistical methods (e.g., the use of Bayesian statistics for ecological forecasting).
These days, my research is mostly focused on the climate crisis - in terms of the relationship between forests and climate. I’ve been combining tree-ring and forest inventory data to quantify how trees are affected by climate variation and change and how forest ecosystems feedback on climate via their removal of CO₂ from the atmosphere. The climate-growth relationships encoded in tree rings are used to make projections of future tree growth—“ecological forecasts”. This includes collaboration with the US Forest Service and Navajo Forestry Department to develop networks of tree-ring data sourced in forest inventories and tools useful for forest management and carbon accounting, aimed at forest resilience and forest-based “natural climate solutions”. I’ve also been working on developing demography-based models to predict how species' geographic distributions will respond to changing climate (“demographic range modeling”).
In the past, my work focused on the biodiversity crisis - in terms of the origins, dynamics, and conservation of plant diversity. My training (and experience) was in plant population ecology, including rare plant conservation, population viability analysis, evolutionary ecology, and comparative biology.
I work across the divide between basic and applied science, between ecology and evolution, and between the MacArthur and Odum schools of ecology. I am an integrative thinker who is interested in seeing the discipline of ecology grow, become stronger, and be applied to solve the pressing problems of the 21st century in an inter- and trans-disciplinary manner. In addition to my research activities, I sincerely enjoy teaching and mentoring.
Ph.D. in Ecology and Evolutionary Biology, 2003
The University of Arizona
B.A. in Biology, 1993
Forest responses to future climate are highly uncertain, but critical for forecasting and managing for …
A central challenge in global change research is the projection of the future behavior of a system based …
Warming alters the variability and trajectories of tree growth around the world by intensifying or …
Making accurate predictions about species’ future distributions requires a holistic understanding of the …
Abstract Estimates of the percentage of species “committed to extinction” by climate change range from 15% to 37%. The question is whether factors other than climate need to be included in models predicting species’ range change. We created demographic range models that include climate vs. climate-plus-competition, evaluating their influence on the geographic distribution of Pinus edulis, a pine endemic to the semiarid southwestern U.S. Analyses of data on 23,426 trees in 1941 forest inventory plots support the inclusion of competition in range models. However, climate and competition together only partially explain this species’ distribution. Instead, the evidence suggests that climate affects other range-limiting processes, including landscape-scale, spatial processes such as disturbances and antagonistic biotic interactions. Complex effects of climate on species distributions—through indirect effects, interactions, and feedbacks—are likely to cause sudden changes in abundance and distribution that are not predictable from a climate-only perspective.
Abstract Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree-ring and forest inventory data within a Bayesian state-space model at a multi-site, regional scale, focusing on Pinus ponderosa var. brachyptera in the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall–spring maximum temperature, and a positive effect of water-year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%–117%, while the combined effect of climate and size-related trends results in a 56%–91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree-ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill.
Tree-ring time series provide long-term, annually resolved information on the growth of trees. When sampled in a systematic context, tree-ring data can be scaled to estimate the forest carbon capture and storage of landscapes, biomes, and—ultimately—the globe. A systematic effort to sample tree rings in national forest inventories would yield unprecedented temporal and spatial resolution of forest carbon dynamics and help resolve key scientific uncertainties, which we highlight in terms of evidence for forest greening (enhanced growth) versus browning (reduced growth, increased mortality). We describe jump-starting a tree-ring collection across the continent of North America, given the commitments of Canada, the United States, and Mexico to visit forest inventory plots, along with existing legacy collections. Failing to do so would be a missed opportunity to help chart an evidence-based path toward meeting national commitments to reduce net greenhouse gas emissions, urgently needed for climate stabilization and repair.
Understanding and forecasting species’ geographic distributions in the face of global change is a central priority in biodiversity science. The existing view is that one must choose between correlative models for many species versus process-based models for few species. We suggest that opportunities exist to produce process-based range models for many species, by using hierarchical and inverse modeling to borrow strength across species, fill data gaps, fuse diverse data sets, and model across biological and spatial scales. We review the statistical ecology and population and range modeling literature, illustrating these modeling strategies in action. A variety of large, coordinated ecological datasets that can feed into these modeling solutions already exist, and we highlight organisms that seem ripe for the challenge.