Ecology and Evolution: Spatial and temporal metapopulation dynamics

Faculty mentors: Michael Antolin, Donald Estep, Colleen Webb

Project Objectives and Aims:

Analytical areas within ecology address how organisms respond to environmental variation, with effects on 1.) behavior and physiology of individuals, 2.) short term population growth, 3.) species composition of ecological communities, and 4.) eventual evolutionary change. In most cases, elucidating fundamental mechanisms requires more time than the life of single investigator and entails interactions changing on different spatial and temporal scales. This complexity makes mathematical modeling an important tool for investigating many ecological/evolutionary problems. The initial aim of this project is to introduce undergraduates to the quantitative approaches necessary for investigating ecological problems; later approaches may include more explicit evolutionary models.

Project Background:

The dynamics of ecological communities change at multiple spatial and temporal scales. Recently, larger-scale climatic patterns have been recognized as a particularly important driver of population dynamics. This project aims to examine the interaction between temporal, climate drivers and spatial, metapopulation dynamics. We will use a combination of field investigations and modeling to study the community of ectoparasitic fleas that live on Black-tailed prairie dogs on the Pawnee National Grasslands (PNG) in north-central Colorado (e.g. Webb et al. 2006). For this project, we will focus on resource competition and how multiple species can be maintained within a metapopulation context.

Prairie dogs live communally in towns and experience catastrophic die-offs because of outbreaks of plague (Antolin et al. 2002). The die-offs and recolonizations of prairie dog towns create a metapopulation structure of the host (Roach et al. 2001), which is the fleas’ resource. We know that the El Niño Southern Oscillation increases the probability of die-offs caused by plague (Stapp et al. 2004), and hence affects the metapopulation dynamics. We have a record of prairie dog town extinctions and recolonizations on the PNG that extends back to 1981 (Antolin et al. 2006)

Three specialized flea species live on prairie dogs that are separated in their seasonal and spatial abundance. Oropsylla hirsuta (Oh) is a “body” flea and is common from mid-May until November. Oropsylla tuberculata cynomuris (Otc) is a “nest” flea and is common from January until May. A third species, Pulex simulans (Ps) is occasionally collected from prairie dogs on some towns, but is primarily a flea of carnivores like foxes and coyotes.

 

Project Description:

Our main hypothesis is that abundance of the three species will be differently affected by extinction of prairie dog towns and their subsequent recolonization. The body flea Oh should rapidly become abundant again on towns shortly after recolonization, while the nest flea Otc will require longer times to establish large populations. Also, the rate of colonization of the two species will be affected by dispersal capabilities of hosts (within 5-10 km of existing towns), again with Oh colonizing towns regardless of distance, but Otc limited to shorter distances from existing towns because of its more limited time on animals. Finally, the distribution of Ps will be less predictable on the basis of extinction and recolonization of prairie dogs, but could be driven by local landscape features like dens of foxes or coyotes. Given the carnivores’ larger dispersal distances, we do not expect Ps abundance to relate to spatial arrangement of the towns themselves.

Students will explore a simulation model incorporating metapopulation structure of prairie dog towns, flea competition for the host resource, and climatic drivers. A combination of field sampling for fleas, lab experiments, long term data analysis, and literature values will be used to estimate parameters.

The proposed project offers several mathematical challenges. This complex model incorporates physical processes interacting on multiple scales, which makes obtaining accurate simulations very difficult. In addition, the complex nature and range of physical processes in the model means that experimental measurements can yield only an incomplete picture of the system. In particular, we are likely to miss important climatic signals in the literature values and our own measurements. Consequently, a sensitivity analysis has to be performed as part of developing an overall understanding of the model. Students will be exposed to cutting-edge mathematical tools for performing numerical simulations and sensitivity analysis.