Climate change and species interactions
We know that both climate and species interactions can and do set species range limits, but we have very little understanding of what fraction of range limits are set by climate v. species interactions, or when and where each of these factors might dominate. A long-standing but surprisingly little-tested hypothesis, first proposed by Darwin, suggests that abiotic stress more commonly sets range limits in colder, drier, or higher locations, while species interactions set limits in apparently less stressful locations. For my dissertation work, I tested this hypothesis in Kenya, where I worked in UHURU (Ungulate Herbivory Under Rainfall Uncertainty), an herbivore exclosure experiment arrayed across a pronounced aridity gradient. UHURU is led by Robert Pringle, Jacob Goheen, and Todd Palmer, and is located at Mpala Research Centre. In UHURU, I studied the effect of mammalian herbivory (a negative interaction that reduces performance), pollination (a positive interaction that increases performance), and plant-plant interactions (which should shift from positive to negative with increasing rainfall, consistent with the stress-gradient hypothesis) on Hibiscus meyeri, a common understory forb. I compared population growth rate across the aridity gradient in the presence v. absence of each of these four species interaction types. I found strong support for Darwin's hypothesis, with stronger effects of all four species interactions on population growth rate in mesic than in arid areas (Louthan et al. 2018).
This study found stronger effects of species interactions in mesic areas not because of the commonly cited reason (higher densities of interacting species in low-stress areas), but because aridity changed the sensitivity of population growth rate to vital rates that these four species interactions affected (Louthan et al. 2018). This work provides empirical evidence disentangling the disparate ways that different aspects of species interactions could easily generate (or prevent) the pattern proposed by Darwin (outlined in Louthan, Angert, and Doak 2015). Future work in this system will tackle other questions about how abiotic and biotic forces interact with each other to govern climate change responses, as well as understand how herbivore feeding rates change with herbivore body size and plant community.
The SGH predicts a complex pattern of facilitation and competition across stress gradients. To better understand the population-level effects of SGH in the context of Darwin's hypothesis, I conducted extensive experimental work on a Rocky Mountain succulent, Sedum lanceolatum, during my dissertation. This work is similar to that described above for H. meyeri.
Climate change responses across species
My postdoc appointment is part of a larger project designed to look at the effects of climate on 8 different species (birds, amphibians, plants, and butterflies), projecting shifts in distributions with climate change, and identifying what characteristics of these species render them more or less vulnerable to climate change. This work is conducted in collaboration with Brian Hudgens, Nick Haddad, Jeff Walters and Lynne Stenzel (and Bill Morris). One of my major roles in this project is constructing integral projection models and projecting range shifts for a very well-studied bird species, the red-cockaded woodpecker. Jeff Walters has led a team of researchers in collecting data on this species, and now has ~47,300 bird-years of data across three geographically distinct sites. We are using this system to ask how asynchrony in vital rates through time or space generates variation in population growth rate in time or space, and how dispersal might attenuate or amplify synchrony in population growth rate across space.
Dan Doak and I have been working together to assess the impact of measurement error on the predictions of demographic matrix models and integral projection models. Measurement error is a insidious problem in size-, age-or stage-structured population models; researchers rarely know which state variable(s) perfectly predict an individual's fate (e.g., either total biomass of a plant, or total biomass of photosynthetic tissue might perfectly predict survival or growth), and even if they know which state variable best predicts fate, they often cannot measure this variable (e.g., total biomass must be measured destructively). Our work has shown that if individuals are measured with error, population growth rate is usually overestimated, though the magnitude and even direction of misestimation depends on life history strategy (Louthan and Doak in press). This result suggests that predictions of population growth rate (as well as associated metrics like extinction risk) might be optimistic, such that declining populations might be even more at-risk than we thought.