A major focus of the group is to understand how species embedded in complex networks respond to environmental change. Species do not respond in isolation. Their dynamics depend on who they interact with, how strongly they interact, and whether the traits underlying those interactions can evolve. We study how ecological processes and community structure interact with evolutionary processes such as adaptation, trait shifts, and changes in genetic variance.
Core Questions
We asks questions such as:
- How do species’ roles in networks influence their responses to warming or disturbance?
- Do generalists and specialists differ in their adaptive capacity?
- How do changes in traits, such as phenology, body size, or foraging traits, alter network structure and community stability over time?
Our approach combines dynamical systems theory, quantitative genetics, and network ecology to develop general frameworks for mutualistic networks, trophic food webs, and other multispecies communities.
Selected References
Our recent work explores these dynamics in greater detail. For example, we found that network structure significantly impacts trait evolution and stability (Baruah & Lakämper, 2024). You can find more details about these frameworks in our previous studies (Baruah & Wittmann, 2025) and (Baruah, 2022).
References
2025
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Evolution of genetic variance and its consequences for eco-evolutionary responses in complex mutualistic networks
Gaurav Baruah and Meike J. Wittmann
Jun 2025
Pages: 2024.12.25.630074 Section: New Results
Rising temperatures threaten biodiversity and ecosystem resilience by disrupting phenological synchrony in plant–pollinator networks. These interactions, essential for ecosystem functioning, are sensitive to temperature shifts. We model species eco-evolutionary responses to an abrupt temperature increase, comparing evolution in mean trait alone versus evolution involving both the mean and genetic variance. We show that networks where species evolve in both their mean phenotype and genetic variance exhibit increased resilience to temperature shifts. Hyper-generalist species, engaged in extensive mutualistic interactions, accumulate greater genetic variance, thereby facilitating faster evolutionary rescue under strong selective pressures. Specialists also benefit by interacting with these hyper-generalists, which stabilize adaptation to new temperatures. We observe emergence in opposing selection pressures in complex networks that promote increases in genetic variance (“evolvability”), reducing trait lag, enabling faster adaptation, and increasing survival. Our findings highlight the role of evolving genetic variance and network architecture in mitigating plant–pollinator phenotypic mismatches under rising environmental temperatures. This study provides insights into the adaptive capacity of mutualistic networks, highlighting dynamic genetic variance changes as key to resilience amid accelerating climate warming. Our study highlights the importance of feedbacks between network topology and genetic architecture for conservation under climate change.
2024
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Stability, resilience and eco-evolutionary feedbacks of mutualistic networks to rising temperature
Gaurav Baruah and Tim Lakämper
Journal of Animal Ecology, 2024
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2656.14118
Ecological networks comprising of mutualistic interactions can suddenly transition to undesirable states, such as collapse, due to small changes in environmental conditions such as a rise in local environmental temperature. However, little is known about the capacity of such interaction networks to adapt to a rise in temperature and the occurrence of critical transitions. Here, combining quantitative genetics and mutualistic dynamics in an eco-evolutionary framework, we evaluated the stability and resilience of mutualistic networks to critical transitions as environmental temperature increases. Specifically, we modelled the dynamics of an optimum trait that determined the tolerance of species to local environmental temperature as well as to species interaction. We then evaluated the impact of individual trait variation and evolutionary dynamics on the stability of feasible equilibria, the occurrence of threshold temperatures at which community collapses, and the abruptness of such community collapses. We found that mutualistic network architecture, that is the size of the community and the arrangement of species interactions, interacted with evolutionary dynamics to impact the onset of network collapses. Some networks had more capacity to track the rise in temperatures than others and thereby increased the threshold temperature at which the networks collapsed. However, such a result was modulated by the amount of heritable trait variation species exhibited, with high trait variation in the mean optimum phenotypic trait increasing the environmental temperature at which networks collapsed. Furthermore, trait variation not only increased the onset of temperatures at which networks collapsed but also increased the local stability of feasible equilibria. Our study argued that mutualistic network architecture interacts with species evolutionary dynamics and increases the capacity of networks to adapt to changes in temperature and thereby delayed the occurrence of community collapses.
2022
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The impact of individual variation on abrupt collapses in mutualistic networks
Gaurav Baruah
Ecology Letters, 2022
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.13895
Individual variation is central to species involved in complex interactions with others in an ecological system. Such ecological systems could exhibit tipping points in response to changes in the environment, consequently leading to abrupt transitions to alternative, often less desirable states. However, little is known about how individual trait variation could influence the timing and occurrence of abrupt transitions. Using 101 empirical mutualistic networks, I model the eco-evolutionary dynamics of such networks in response to gradual changes in strength of co-evolutionary interactions. Results indicated that individual variation facilitates the timing of transition in such networks, albeit slightly. In addition, individual variation significantly increases the occurrence of large abrupt transitions. Furthermore, topological network features also positively influence the occurrence of such abrupt transitions. These findings argue for understanding tipping points using an eco-evolutionary perspective to better forecast abrupt transitions in ecological systems.