We operate an international seminar series on Theoretical Ecology via Zoom since September, 2020. With some exceptions, the hour-long events are held on every other Tuesday at 9 a.m. Pacific Time, which corresponds to 5 p.m. in London and 6 p.m. in Paris most of the time. Our invited lecturer speaks for cc. 20-30 minutes. The rest of the hour is for questions and discussions, which are often lively. The seminars are recorded and posted on our YouTube channel. We send out notifications before each lecture via email and Twitter. The webinar is organised by György Barabás (dysordys@gmail.com), Géza Meszéna (meszena.geza@ttk.elte.hu) and Chris Terry (christopher.terry@biology.ox.ac.uk). Any comment, or suggestion are welcome.
Zoom link (unless stated otherwise): https://liu-se.zoom.us/j/63158449287
YouTube channel with the lecture videos and teaching material, etc.
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Scheduled lectures
Ophélie Ronce (Montpellier): When do leading and rear edges of therange shift slower or faster than climate? Insights from a mathematical model
by Ophélie Ronce, Gaël Raoul, Matthieu Alfaro
15 October, 2024
Climatic spatial gradients often result in the evolution of locally adapted phenotypic clines. Such gradients should shift through time under climate warming. Species can then persist
(i) by tracking through space the range of climatic conditions to which they are already adapted to,
(ii) by staying put and evolving new trait values allowing adaptation to new conditions, or
(iii) by any combination of migration and evolution, with varying consequences for the position of the rear and leading edge of their range in a changing climate.
We here use previously developed mathematical results to predict the speed at which such rear and leading edge move in an asexual species adapting to an environmental gradient that shifts in space and time. We jointly model changes in the distribution of the species abundance through space and changes in the distribution of phenotypic values defining its climatic niche. As in previous studies, we find that there is a critical climate change velocity beyond which the species cannot persist. We can however define several other types of critical climate change velocities, which allow predicting when the leading edge shifts faster or slower than climate change, and when the species persists at its rear edge. We derive predictions along a one-dimensional spatial gradient and in two dimensions. In the latter case, we predict that the direction of faster spatial spread at the edge of the range is not always the direction of faster climate change. We also predict when a local disturbance in the latitudinal climatic gradient, e.g. generated by a mountain, can stop the spread of the population despite climate warming. Conversely, local improvement of population growth, as may occur in protected areas, can allow persistence at the rear edge under faster rates of climate changes.
Bo Zhang (Oklahoma State)
29 October, 2024 Warning: Becuase of the non-syncronous transition to winter time, this lecture will be 1 h earlier, than usual in Europe: 5 p.m. in Paris.
Aaron King (Michigan, Santa Fe): Exact phylodynamics via structured Markov genealogy processes
12 November, 2024
Phylodynamic inference allows us to extract information on determinants of epidemic dynamics from sampled pathogen genomes. A key problem in phylodynamics has been a mismatch between inference methodology and epidemiological models: the approximations that must be made to perform inference do not align well with the questions of greatest interest. I will describe recent work in which we have obtained exact expressions for phylodynamic likelihoods associated with compartmental models of (almost) arbitrary complexity. To derive these, we first show that to each discretely structured Markov population process there is an associated genealogy process, i.e., a time-evolving tree-valued process defined as the genealogy of all previously sampled individuals. We then deduce exact expressions for the likelihood of an observed genealogy in terms of filter equations. These filter equations can be solved numerically using standard Monte Carlo integration methodology. These results unify and extend existing approaches and broaden the scope of phylodynamic inference methods.
Jacopo Grilli (ICTP)
26 November, 2024