Supervising team: Stefanie Kramer-Schadt, Niels Blaum, Volker Grimm
Workplace: IZW Berlin
0. Brief expected profile of PhD student
Candidates must have a completed MSc in Ecology or related fields, e.g. epidemiology. Candidates with a modelling background from other natural sciences will also be considered. Sound experience in programming, statistical analysis of data, and, preferably, in the design and analysis of process-based mathematical or simulation models is required. A solid background in disease ecology and movement ecology is desirable.
1. Short Abstract
This project aims to shed light on the evolutionary dimension of movement on disease pattern under anthropogenic land use changes. Extending an existing individual-based spatially explicit host-pathogen model with evolutionary algorithms will allow studying pathogen virulence levels as well as host-specific movement and life history traits to evolve in changing landscapes. This extended model will allow exploring the role of (i) dynamic resource landscapes in shaping disease patterns, (ii) the role of dispersal in the evolution of pathogenic virulence as well as the feedbacks of disease evolution on the evolution of movement strategies, and (iii) the role of life-history trade-offs between movement strategies and infectivity as equalizing mechanism allowing for coexistence of host and pathogen types.
2. Background and previous work
The movement trajectory of animals is determined by decisions depending on spatial structure and quality of the underlying habitat. This landscape heterogeneity and hence host population structure can have a huge impact on the evolution of pathogenic virulence, with the general finding that lower dispersal rates of hosts due to spatial structure select for lower pathogen virulence [1, 2]. However, recent research states that the evolution of parasites in spatially structured host populations is determined by the interplay of genetic and demographic spatial structuring, which in turn depends on the details of the ecological dynamics , including movement processes. This spatial structuring can induce pathogen phenotypic differentiation between the front and the rear of the epidemic, because in an expanding epidemic the availability of susceptible hosts and thus transmission events are higher at the front. This leads to the apparent coexistence of pathogenic strains of different virulence in the invasive, epidemic phase. This trend should, however, be reversed in the endemic phase towards lower virulence . Then, the evolution towards lower pathogenicity might be again reversed by mediating, yet rare, long-distance movements of hosts that transport pathogenic agents to fully naïve host populations after population turnover, increasing highly virulent strains that have become rare in the endemic phase.
An individual-based spatially-explicit host-pathogen model has been developed to investigate individual differences in infectivity of hosts on disease persistence .
In the first PhD-cohort of BioMove project 2 this modeling approach was adapted to investigate the effect of different host movement strategies on disease persistence in heterogeneous landscapes. To
this end, we investigated static pathogen types of different virulence. Our preliminary findings reveal that movement can reverse the effect of landscape heterogeneity, i.e. lead to higher disease
persistence, especially in host infections with virulent pathogens (Scherer et al. in prep.).
Stabilizing effects that allow less competitive, less virulent pathogenic strains to coexist with virulent ones can be evoked by habitat spatial structuring, i.e. when the rare pathogenic strain is spatially separated from the virulent one and can increase locally. Movement of hosts, by transporting infective agents, can enforce this effect by distributing pathogenic strains to other habitat patches. Thereby, the movement strategy (like rare long distance dispersers versus local explorers) might play an important role. In this context, we also want to explore the reciprocal effect of how different host movement strategies can coexist, i.e. whether pathogens can have equalizing effects on the evolution of movement strategies. Equalizing effects reduce large average fitness differences between competing host movement strategies, and it might be that a host with a movement strategy leading to higher reproductive fitness would be eliminated by virulent pathogens first (‚killing-the-winner‘). Thus, equalizing-stabilizing effects strongly depend on the explicit consideration of a dynamic environment and adaptive movement and virulence dynamics.
The proposed new study will now investigate disease dynamics under dynamic environmental conditions and adaptive movement dynamics of the host and adaptive virulence dynamics of the pathogen. In particular, this project aims at following objectives:
Revealing possible stabilizing effects of different movement strategies in changing landscapes, i.e. when hosts act as movement process links distributing different
Highlighting possible stabilizing effects of different movement strategies on the coexistence of pathogenic strains.
Deciphering equalizing effects of adaptive virulence (‚killing-the-winner‘) on movement strategies, allowing to identify the most promising movement strategy under different viral constraints.
4. Outline work program
In a first step the doctoral student will get acquainted with the NetLogo programming environment and expand the existing model developed in cohort I containing different movement strategies. Previously static landscapes will be substituted by dynamic landscape changes. The challenges will be to make landscape quality, i.e. cells of breeding capacity, dynamic over time. That means, the landscape quality values have to be either increased or decreased synchronously at every time step, reflecting annual seasons and corresponding food availability. This seasonal scale has to match the biological scale of the host species, i.e. the timing of host life-history strategies like reproduction and dispersal. The new model will be systematically evaluated in a thorough sensitivity analysis considering movement types and strength of landscape dynamics to explore the stabilizing effect of movement on disease persistence in dynamic landscapes.
In a second step, we will program genetic or evolutionary algorithms (EA). EAs are well suited to investigate problems of overlapping ecological and evolutionary time scales by representing a potential solution to the problem as an artificial trait: a string of parameter values analogous to a string of DNA. The different host movement strategies will lead to different transmission events allowing for pathogenic mutation that result in different fitness outcomes for pathogenic traits: only pathogenic traits with certain combinations of infectivity and induced host mortality will be transmitted. By representing pathogens through a few key traits like infectivity and infected host case mortality, the movement strategies can be evaluated under evolving infections. In a subsequent step, we will also let the movement strategy evolve, i.e. distance and frequency, and ask how pathogens influence the evolution of movement modes and rates, and reciprocally, what influence evolving movement tactics have on virulence evolution.
Third, we will let two movement strategies represent different species competing for a resource in space and define a trade-off between higher reproductive success for higher resource gain, e.g. through larger movement ranges, versus lower resource allocation into immune defenses, e.g. induced by large home range defense. We will investigate which strategy is best to survive under different fixed and evolving viral conditions and landscape configurations and how diseases equalize between the different movement tactics or competing species, respectively. Hence we will explore what is the optimal host community structure and composition, allowing answering the questions of disease effects for coexistence of host movement strategies and pathogen strains.
5. Linkage to ‘BioMove’ hypotheses, objectives and concepts
This project connects the ‘bottom-up’ and the ‘top-down’ clusters of BioMove and addresses two of its focal hypotheses (H1, H3). It integrates relevant topics of genetics, evolution, behavioural and landscape ecology. We will test the hypotheses that (i) movement stabilizes pathogen diversity in changing landscapes, and (ii) evolution of pathogenic virulence equalizes host coexistence through a killing-the-winner process. This project is strongly linked to P01 and P11.
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 Kramer-Schadt et al. 2009, Oikos 118:199-208.