I am participating in this SNF-funded project, where the aim is to develop a metapopulation model that incorporates four demographic aspects (density, environmental autocorrelation, and vital-rate reaction norms and covariation). This model will serve to investigate the implications of omitting one or more of these aspects for predicting the dynamics of spatially structured populations. We will test the model with long-term demographic and dispersal data from actively dispersing mammals, passively dispersing plants, and actively and passively dispersing laboratory invertebrates.
This project is led by the Population Ecology Group at the University of Zurich.
Environmental extremes are becoming more frequent. Some natural communities suffer (e.g. they lose biodiversity) under such increased episodes of extreme events while other do not (i.e., they are resilient). I am working as a research fellow at the CREAF, trying to understand the mechanisms that drive this resilience. In particular, I am interested in exploring how life-history characteristics of the species in a given community can mediate changes in community composition after extreme events. To understand these mechanisms, I integrate demographic analyses with community-level metrics. To be able to accomplish this integration better, I have initiated a long-term demographic monitoring project with two Mediterranean shrubland species in mattoral communities affected by drought in the Spanish National Park Doñana.
I am collaborating with Prof Francisco Lloret on this project. He has collected the long-term data from a mattoral community in Doñana that experienced a drought in 2007 and has been recovering ever since.
Virtually all ecosystems are seasonal, and seasonality patterns are projected to change - but we lack an understanding of how such changes will affect populations. One example are arid ecosystems where rain is becoming increasingly scarce while temperatures are on the rise, with both environmental drivers showing highly seasonal patterns. Another example can be drawn from temperate, high-altitude systems, where changes in seasonal snowfall regimes may fundamentally affect hibernating species. Using the Kalahari meerkats (Suricata suricatta) and yellow-bellied marmots (Marmota flaviventris), I am investigating the effects of seasonal changes in interactive environmental drivers on the structure and viability of natural populations. I am particularly interested in compensatory effects (how bad conditions in one season may be compensated for in another season) and how density dependence and trait dynamics (body mass) mediate these effects; and so, I am using integral projection models (IPMs) as a flexible methodological tool.
Results from this research have been featured in some news outlets.
Together with the FEBIMED group of the University of Cadiz, I am managing the long-term demographic study of the dewy pine (Drosophyllum lusitanicum), a carnivorous plant endemic to heathlands of the Strait of Gibraltar and Portugal. Demographic censuses began with my PhD. I used data from annual population cencuses and experiments to investigate population dynamics of dewy pines incorporating key biological/ecological aspects of the species. Currently, we are examining the effects of life-history tradeoffs and individual heterogeneity on population dynamics in different habitat types characterized by various degrees of human-caused disturbances.
Thus far, we have collected demographic data on > 5,000 individuals from 12 populations in 8 years. The dewy pine is a wonderful study system, as resource acquisition does not require detailed knowledge of below-ground dynamics - this carnivorous species obtains most nutrients from insect prey.
Dewy pines are truly unique plants, but also work great as model organisms.
Environmental factors affecting natural populations are changing dramatically across the globe. These changes are often described as being directional (think of increases in temperature trends) or stochastic (think of the fluctuations around those trends). I am interested in another aspect of environmental change: the patterning of environmental states, or their temporal autocorrelation. Specifically, I want to know how individuals and populations will respond if a good year is followed by another good year - or, alternatively, a bad year. To answer this important question, I use cross-taxa comparisons of the sensitivity of demographic processes to changes in patterning. Such comparisons benefit global-change research significantly because a wide range of life histories can respond strongly to patterning
I am also leading a review on mammal responses to climate change