About

Hi, I’m Ming! I am a postdoc at Dunn School of Pathology at the University of Oxford. I study the eco-evolutionary dynamics of the biological world through simulations, statistics, experiments, and mathematical modeling. I recently completed my PhD with Stuart West before joining Kevin Foster’s group in late 2023. My current work focuses on the diversity-stability relationship in biological communities, a key question for understanding how biological communities respond to disturbances such as climate change and antibiotic treatments. During my postdoc, I leverage microbial community data to advance new theoretical and predictive frameworks.

Research

My PhD thesis is about the evolution of phenotypic diversity, that is, why do we see variations between individuals within the same species (e.g., behavior or moprphology)? I looked into various mechanisms that support the selection for variation, described in the following subsections. (Stay tuned for updates on my postdoc projects!)

Division of labour (DoL)

One way to get variaiton within a population is through specialisation to various tasks, and make the entire group of individual become more adaptive. Stu’s research group has a series of paper on this topic, including DoL’s formal definition, what population settings favour DoL, the mechanism to divide labour in clonal population, or nonclonal population, and how does the spatial network between individuals affects DoL.

Cheat-cooperator dynamics

Another way to get individual difference within a population is through the coexistence between cheats and cooperators, where the latter produce some public goods while the former exploit them. Two of my past projects investigated this dynamics: (1) If a ‘manipulative cheat’ can co-evolve with cheating trait, then a genetic arms-race could occur. This mechanism can help explains some genetic complication found in empirical populations (paper). (2) If the population goes through periodic bottlenecks and there are some density dependence of the cheats’ growth rate on cooperators’ abundance, then the proportion of cheats could oscillate through time (paper).

Abiotic environmental fluctuations

The third way to maintain variation within a population is through the balencing selection induced by abiotic environmental fluctuations. That is, if strategies are optimal at different times, then the fitness difference between strategies are reduced and coexistence is more likely to happen. Two of my previous projects investigated the effects of fluctuations on different time scales: (1) Long-term variations favour specialists against generalists, while short-term variations promote coexistence of both (paper). (2) Long-term variations facilitate competitive exclusions between specialists, while short-term variations still promote coexistence. By mixing the two, we recovered all possible combinations of previously proposed patterns on variability and diversity (paper).

New content under construction…

Hi, this is Ming! I am a postdoc at Dunn School of Pathology at the University of Oxford. My research focuses on biological dynamics over time, which lie at the heart of how natural selection shapes living systems. These dynamics can include changes in population size or shifts in gene frequency within a population. More broadly, they are driven by three major forces: abiotic conditions, biotic interactions within species, and interactions between species. To understand the causes and consequences of these dynamics, I combine analytical theory, numerical methods, simulations, advanced statistics, machine learning, and experiments. My research aims to understand all three in depth.

Theme 1: Abiotic conditions

Abiotic conditions such as temperature, rainfall, humidity, salinity, and pH are fundamental to biological dynamics. They help determine which species are favoured under a given set of environmental conditions. At first glance, these factors may seem straightforward, but in nature they are constantly changing across space and time. That variability makes it much harder to predict whether species will persist, decline, or adapt. Theoretical frameworks are therefore essential for uncovering the general rules that link environmental change to biological outcomes.

Considering these environmental variations, we have looked into

• The classic bet-hedging (between specialist and generalist strategies)
• Coexistence between specialists
• Environmental predictabilities
• The functional trait diversity

We found temporal scale of these fluctuations may have contrasting effects on biological systems, while environmental predictability is a potentially misleading term implying cognitive processes.

Theme 2: Biotic interactions within species

Biological interactions within species, among individuals in the same population, are another major driver of biological dynamics. These social interactions can involve cooperation, conflict, or exploitation, and they strongly influence how populations evolve over time. In particular, they shape whether helpful traits can persist, how conflict is controlled, and how more integrated forms of life emerge. These same principles also help explain major evolutionary transitions, such as the origin of genomes, multicellular organisms, and superorganisms.

Theme 3: Interactions between species

The third major driver of biological dynamics is the interaction between species. These interactions include competition, predation, cross-feeding, and facilitation, and together they create the network structure of ecological communities. Once many species are linked in this way, even simple local interactions can generate complex community-level dynamics. Understanding when such systems are stable, predictable, or fragile is therefore one of the central challenges in ecology.

If you would like to reach out, please contact: ming.liu.ac [at] gmail.com. I’m also a freelance photographer with ~15 years of experience. Feel free to explore my photographic work here!