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This paper introduces a novel spinal decomposition and time reversal method for analyzing non-stationary, measure-valued branching processes arising from individual-based population models with complex, nonlinear trait dynamics. By generalizing existing stationary techniques, the approach allows for the reconstruction of ancestral lineages even under asymmetric and deleterious horizontal trait transfer. The key result is a more direct and general framework for understanding evolutionary histories without relying on restrictive assumptions of stationarity or specific jump structures.
Uncovering the full ancestral history of evolving populations is now possible even with non-stationary, asymmetric trait transfer.
We investigate a stochastic individual-based population model with trait-dependent reproduction, death, competition, variation, and deleterious transfer. Nonlinearities appear in the competition term and the transfer form, which moreover is asymmetric. In the large-population limit, the process converges to a deterministic measure-valued equation. The latter helps us to uncover the past history of living individuals. We replace in the interactions the original stochastic process with the deterministic large population approximation obtaining a nonhomogeneous measure-valued branching process, for which we use a spinal decomposition, generalizing a method that existed only for the stationary case. Reversing time, we identify the law of typical ancestral lineages. This approach encompasses a variety of settings and requires delicate functional analysis. In contrast to earlier approaches in the literature, which relied either on stochastic calculus or on favourable jump structures under stationarity assumptions, our method provides a more direct and more general point of view.