Climate Change

Climate change motivates much of my research. Specifically, I seek to quantify how climate change will alter the temporal and spatial structure of key environmental factors across the globe. In marine systems, I am particularly interested in the effects of climate change on coastal upwelling, a key oceanographic current that promotes the productivity of nearshore marine ecosystems by bringing cold and nutrient rich water to the surface where it can subsidize the base of the food web.

In a retrospective analysis of long-term observational data from the West Coast of the United States, my colleagues and I showed that upwelling events have become longer, stronger and more frequent over the 20th century (Iles et al. 2012). A similar prospective analysis conducted at the global scale using climate models suggests that these results are likely to hold in the future, as upwelling is predicted to become stronger and more persistent over the course of the 21st century, particularly away from the equator (Wang et al. 2015). These spatiotemporal trends in upwelling have the potential to fundamentally reorganize life in the coastal ocean.

I am also interested in quantifying changes in the spatial and temporal structure of temperature fluctuations under climate change. For instance, using output from climate models, an Honors undergraduate student and I showed that air temperature is expected to become more temporally and spatially autocorrelated over time (Di Cecco and Gouhier 2018). These dual trends have the potential to interact synergistically and promote extinction risk by eroding spatiotemporal refugia and spatial rescue effects in interconnected ecosystems (Duffy et al. 2022).

Population Dynamics

The spatial and temporal dynamics of ecosystems depend on the complex interplay between local species interactions, regional dispersal and environmental forcing. One of my main interests is to construct spatially-implicit and spatially-explicit metacommunity models in order to understand how processes operating at different spatial scales govern the structure and stability of natural systems (Gouhier et al. 2010a, Gouhier et al. 2010b, Gouhier et al. 2011, Townsend and Gouhier 2019).

To bridge the gap between theory and reality, I use spatiotemporal statistical methods to test model predictions and determine the processes that give rise to patterns of biodiversity across scales. This includes testing the effectiveness of existing multivariate frameworks (Salois et al. 2018), identifying novel statistical signatures of local processes scaling-up in the temporal (non-stationarity) and spatial (non-linearity) structure of synchrony (Gouhier et al. 2010a), and creating software packages to facilitate the use of these spatiotemporal analyses by the broader scientific community (Gouhier and Guichard 2014).

Not Even Wrong

The field of ecology has become increasingly dominated by attempts to link the loss of biodiversity to reduced ecosystem functioning. In recent years, my colleague Pradeep Pillai and I have shown that the main statistical methods used to quantify the relationship between biodiversity and ecosystem functioning are fundamentally flawed (Pillai and Gouhier 2019, Pillai and Gouhier 2019a, Pillai and Gouhier 2019b, Pillai and Gouhier 2020). Specifically, when attempting to partition the effect of biodiversity on ecosystem functioning, these methods inflate the net biodiversity effect and yield incorrect estimates of selection and complementarity. The only reliable finding that can be gleaned from these methods is that biodiversity tends to promote ecosystem functioning, a result so trivial that it becomes self-evident to undergraduate students when they are introduced to elementary concepts such as Lotka-Volterra competition. Unfortunately, this is just the tip of the iceberg when it comes to problems in the degenerate biodiversity-ecosystem functioning research program.