I am a quantitative ecologist interested in disentangling the roles of ecological processes in producing population- and community-level patterns in order to forecast responses to environmental change. Climate change, land use change and nonnative species interact to pose new challenges to species and my goal is to predict the species responses to these challenges. As a modeler, I try to balance mathematically rigorous analyses with biological sensibilities to produce models that accurately reflect our data and assumptions, while highlighting which parts of our inference derive from each.
Some Current Projects
Advancing demography in ecology with integral projection models: a practical guide
Integral Projection Models (IPMs) link demography to the ecological processes that shape populations. Here, we review important resources for building IPMs and describe in detail how to build IPMs for a variety of life histories of increasing complexity and biological realism. Throughout, we emphasize how IPMs can offer mechanistic insights into population-level patterns. IPMs have the flexibility to represent life histories at any desired level of biological detail. These life histories are characterized by using observations of individuals to determine generalities in vital rates among individuals, and used to predict population dynamics and emergent biological patterns.
Demographically driven distribution models: Advantages of using integral projection models to incorporate demography into species distribution models
To understand a species' response to a changing environment, it is critical to relate demographic heterogeneity back to its environmental drivers. Typically climate variables that represent average weather over a 30 or 50-year interval are used to characterize the environment, however these are only proxies for more physiologically relevant predictors (e.g. maximum drought length). We're developing a daily weather data set from which to construct these predictors that includes uncertainty. I'm developing demographic models that relate vital rates back to these environmental drivers. This will allow us to map predicted population viability, including uncertainty, based on environmental conditions across the species' range.
Experimental demography of two invasive plants and their native analogs along environmental gradients using integral projection models
Invasive species' geographic distributions are often not at equilibrium in their invasive ranges. Therefore, inferring population dynamics based on current locations of populations may under- or over-estimate population growth rates and potential spread. We took an experimental approach to investigating the underlying demographic processes that drive population dynamics across a range of environmental conditions that are hypothesized to be invasible. The link between environment and demography is particularly important for understanding invasive species distributions to improve early detection abilities and to design appropriate management actions.
A practical guide to Maxent: What it does, and why inputs and settings matter
We've developed a suite of methods to better explore distribution models built with Maxent. In my opinion, many models built with default settings in Maxent are overly complex and therefore compromise their transferability and interpretation. We've outlined modeling strategies to produce simpler models that better reflect biological assumptions and hypotheses. These methods will help users explore the consequences of Maxent's assumptions and better understand their models to produce more robust predictions.
Back to the basics of species distribution modeling: what do we learn from complex versus simple response curves?
with Matthew Smith, Jane Elith, Wilfried Thuiller, Niklaus Zimmerman, Tom Edwards, Antoine Guisan, Signe Normand, and Rafael Wuest
Species Distribution Models have become one of the most widely used tools in ecology, evolutionary biology, and conservation to understand the spatial distributions of species and their potential drivers. Part of this popularity is surely due to the recent development and refinement of modeling algorithms that allow greater flexibility in model features and assumptions. Here, we take a look at what distribution modelers have gained from this proliferation of algorithms and outline situations to help modelers choose which algorithms or algorithm types might be more suitable under different situations. A useful distinction between SDM algorithms is in the complexity of occurrence-environment relationships that they predict. We evaluate the consequences of different choices of complexity in occurrence-environment relationships.
Community abundance patterns in South African fynbos
This work is part of a larger collaborative effort focused on understanding the different aspects of biodiversity in plant communities in the Greater Cape Floristic Region (GCFR) of South Africa. The larger project seeks to understand the relationship between functional traits and patterns of genetic, functional and taxonomic diversity. Our goal is to integrate knowledge collected at these different scales to predict species and community responses, enhanced by understanding their evolutionary past. I'm focusing on determining how community-level distributions of functional traits vary along ecological gradients and how this variation can predict species' abundance patterns. Here are some brief articles about the project: link, link.