Resilience, tipping points, hysteresis in ecological networks

understanding resilience

Ecological systems, such as plant-pollinator networks , plant-herbivore networks , or trophic food webs are inherently complex and have been shown to exhibit multiple stable states . These systems are sensitive to changes in environmental parameters and can undergo sudden transitions to undesirable states such as community collapse. Ecological and structural feedbacks can shape such ecological networks over time, thereby influencing their resilience. Our recent results suggest that incorporating eco-evolutionary feedbacks into simple mutualistic networks increases their stability to perturbation (Baruah & Lakämper 2024). However, many key aspects of network architecture (e.g., network complexity), factors impacting eco-evolutionary dynamics (e.g., genetic variance), and their relationship with resilience to critical transitions remain underexplore

We develop theory and experiments to identify when ecological communities are close to collapse, which structural or evolutionary properties buffer them against perturbation, and how interventions might improve recoverability from a collapse state. We are especially interested in whether eco-evolutionary feedbacks, phenotypic variation can help revive degraded ecological networks. This research combines mathematical modelling with experimental and field-inspired questions, including network restoration in plant-pollinator systems, and the role of hub species, structural complexity, and diversity in community recovery.

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 (Singh et al., 2025) and (Baruah, 2022), (Baruah & Wittmann, 2024) ,(Baruah et al., 2022)(Patnaik & Baruah, 2024).

References

2025

Behavioral variation affects persistence of an experimental food-chain

Pragya Singh, Gaurav Baruah, and Caroline Müller

Jul 2025

ISSN: 2692-8205 Pages: 2025.07.04.663144 Section: New Results

Abs DOI

Intraspecific behavioral variation in prey could alter predator–prey interactions, yet its effects on temporal dynamics and food-web persistence remain underexplored. Pea aphids (Acyrthosiphon pisum) exhibit dropping behavior in response to predators like the seven-spot ladybird (Coccinella septempunctata). This response could be an effective anti-predator defense but could be costly in terms of energy expenditure and time not available for feeding. To investigate the impact of behavioral variation on food-chain persistence and dynamics, we used a tri-trophic experimental system with Vicia faba (plant), pea aphids (prey), and seven-spot ladybirds (predator), implementing three aphid behavioral treatments: droppers, non-droppers, and a mix of both droppers and non-droppers. To minimize genetic differences, we used clonal aphid populations across all treatments. We then tracked predator-prey population dynamics and species persistence over 25 days. Our results showed that aphid dropping behavior reduced food-chain persistence, with extinction risk significantly higher in dropper treatments than in the mix or non-dropper treatments. Ladybirds persisted across treatments, although they showed a steeper decline in abundance in the dropper treatment. In the mixed behavioral treatment, they had an intermediate persistence, suggesting a buffering effect of behavioral variation. Trophic food-chain state transitions also differed by treatment, with tri-trophic states most stable in the non-dropper, and least frequent in the dropper treatment. Furthermore, our results showed a trend of dropper treatments becoming more stable and robust towards the end of the experiment. These results demonstrate that prey behavior influences the persistence and dynamics of food-chains, with important implications for behavior-driven community dynamics.

2024

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

Abs DOI

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.
Reviving collapsed plant–pollinator networks from a single species

Gaurav Baruah and Meike Wittmann

PLOS Biology, Oct 2024

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Mutualistic ecological networks can suddenly transition to undesirable states due to small changes in environmental conditions. Recovering from such a collapse can be difficult as restoring the original environmental conditions may be infeasible. Additionally, such networks can also exhibit a phenomenon known as hysteresis, whereby the system could exhibit multiple states under the same environmental conditions, implying that ecological networks may not recover. Here, we attempted to revive collapsed mutualistic networks to a high-functioning state from a single species, using concepts from signal propagation theory and an eco-evolutionary model based on network structures of 115 empirical plant–pollinator networks. We found that restoring the environmental conditions rarely aided in recovery of collapsed networks, but a positive relationship between recovering pollinator density and network nestedness emerged, which was qualitatively supported by empirical plant–pollinator restoration data. In contrast, network resurrection from a collapsed state in undesirable environmental conditions where restoration has minimal impacts could be readily achieved by perturbing a single species or a few species that controls the response of the dynamical networks. Additionally, nestedness in networks and a moderate amount of trait variation could aid in the revival of networks even in undesirable environmental conditions. Our work suggests that focus should be applied to a few species whose dynamics could be steered to resurrect entire networks from a collapsed state and that network architecture could play a crucial role in reviving collapsed plant–pollinator networks.
Predicting recoverability of collapsed food webs through perturbation and dimension reduction

Swastik Patnaik and Gaurav Baruah

Jul 2024

Pages: 2024.07.09.602684 Section: New Results

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Biodiversity collapse, driven by escalating environmental changes, poses significant threats to ecosystem stability and the provision of essential ecosystem services. Understanding the recoverability of collapsed food webs thus is crucial for devising effective conservation strategies. This study delves into the theoretical exploration of the recoverability of food webs from a collapsed state. Through simple tools like dimension reduction, propagation of species-specific perturbation, and dynamical simulations, we explore whether simple tri-trophic food webs can be recovered from a collapsed state. Our study examines in detail the topological features of predator-prey food webs that could either facilitate or impede their recovery. We demonstrate that the recoverability of complex food webs can be predicted by using a simple dimension-reduced model, with certain structural factors that could constrain the full recovery of collapsed food webs. Furthermore, dynamic simulations also highlighted the significance of topological features such as connectance and the number of predator links in determining recoverability. Our dimension-reduced modeling framework offers insights into the feasibility of restoring entire complex predator-prey networks through species-specific interventions. This study contributes to a deeper understanding of ecosystem resilience and aids in the development of targeted conservation strategies.

2022

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

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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.
Community structure determines the predictability of population collapse

Gaurav Baruah, Arpat Ozgul, and Christopher F. Clements

Journal of Animal Ecology, 2022

_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2656.13769

Abs DOI

Early warning signals (EWS) are phenomenological tools that have been proposed as predictors of the collapse of biological systems. Although a growing body of work has shown the utility of EWS based on either statistics derived from abundance data or shifts in phenotypic traits such as body size, so far this work has largely focused on single species populations. However, to predict reliably the future state of ecological systems, which inherently could consist of multiple species, understanding how reliable such signals are in a community context is critical. Here, reconciling quantitative trait evolution and Lotka–Volterra equations, which allow us to track both abundance and mean traits, we simulate the collapse of populations embedded in mutualistic and multi-trophic predator–prey communities. Using these simulations and warning signals derived from both population- and community-level data, we showed the utility of abundance-based EWS, as well as metrics derived from stability-landscape theory (e.g. width and depth of the basin of attraction), were fundamentally linked. Thus, the depth and width of such stability-landscape curves could be used to identify which species should exhibit the strongest EWS of collapse. The probability a species displays both trait and abundance-based EWS was dependent on its position in a community, with some species able to act as indicator species. In addition, our results also demonstrated that in general trait-based EWS were less reliable in comparison with abundance-based EWS in forecasting species collapses in our simulated communities. Furthermore, community-level abundance-based EWS were fairly reliable in comparison with their species-level counterparts in forecasting species-level collapses. Our study suggests a holistic framework that combines abundance-based EWS and metrics derived from stability-landscape theory that may help in forecasting species loss in a community context.