Biology subjects

Baruah, G.

Publications and source records attributed to Baruah, G..

14 recordsLinked to original sources

bioRxiv2025

Adaptive rewiring and temperature tolerance shape the architecture of plant-pollinator networks globally

Rising environmental temperatures are rapidly reshaping plant-pollinator communities by altering species traits and interaction patterns. We develop a simple eco-evolutionary model that integrates species-specific temperature tolerance curves with phenotype-based interaction dynamics. Across temperature gradients, species adaptively rewire, that is, they change their interaction partners. This rewiring is an emergent property of our model, driven by temperature-mediated selection and co-evolutionary trait matching. As temperature increases, our model predicts a consistent decline in network-level specialization, alongside increasing connectance and nestedness which are signatures of structural re-organization. These predictions are supported by empirical patterns from 165 plant-pollinator networks worldwide, where mean annual temperature correlates positively with connectance and nestedness, and negatively with network specialisation. Our findings suggest that temperature-driven trait evolution and emergent adaptive rewiring govern the assembly and architecture of mutualistic networks. By bridging dynamical eco-evolutionary theory with global empirical data, this work reveals the central role of trait-based processes in structuring biodiversity under ongoing and accelerating climate warming.

ecology↗

bioRxiv2025

Behavioral variation affects persistence of an experimental food-chain

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.

ecology↗

bioRxiv2024

Evolution of genetic variance and its consequences oneco-evolutionary response in complex mutualistic networks

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.

evolutionary biology↗

bioRxiv2024

Predicting recoverability of collapsed food webs through perturbation and dimension reduction

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.

ecology↗

bioRxiv2023

Plant metabolites modulate animal social networks and lifespan

Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, Athalia rosae. Adult sawflies acquire neo-clerodane diterpenoids (clerodanoids) from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions in sawfly groups where some or all individuals had prior access to clerodanoids. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.

animal behavior and cognition↗

bioRxiv2023

Mutualistic network architecture and eco-evolutionary feedbacks modulate the occurrence of transitions and stability in response to rising temperature

1. 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. 2. However, little is known about the capacity of such interaction networks to adapt to changing temperatures and thereby impact the occurrence of critical transitions. 3. Here, combining quantitative genetics and mutualistic dynamics in an eco-evolutionary framework, we evaluate the resilience of mutualistic networks to critical transitions as environmental temperature increases. Specifically, we model the dynamics of a phenological optimum trait that determines the tolerance to local environmental temperature as well as temperature-dependent species interaction and evaluate the impact of trait variation and evolutionary dynamics in the occurrence of tipping points and community collapses. 4. We found that mutualistic network architecture, i.e., community size and the arrangement of species interactions, interacted with evolutionary dynamics to impact the onset of network collapses. In addition, some networks had more capacity to track the rise in temperatures than others and thereby delay the occurrence of threshold temperatures at which the networks collapsed. 5. However, such a result was modulated by the amount of heritable trait variation species exhibited, with high trait variation in the mean optimum trait value delaying the environmental temperature at which the network collapses. 6. Our study argues that mutualistic network architecture modulates the capacity of networks to adapt to changes in temperature and thereby impact the occurrence of community collapses.

evolutionary biology↗

bioRxiv2023

Reviving collapsed networks from a single species: the importance of trait variation and network architecture

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 reversing the original environmental conditions may be infeasible. Additionally, such networks can also exhibit hysteresis, implying that ecological networks may not recover. Here, using a dynamical eco-evolutionary framework, we try to resurrect mutualistic networks from an undesirable low-functional collapse state to a high-functioning state. We found that restoring the original environmental conditions rarely aided in recovering the original network due to the presence of hysteresis. By combining concepts from signal propagation theory and eco-evolutionary dynamical modeling, we show that network resurrection could be readily achieved by perturbing a single species that controls the response of the dynamical networks. We show that during the resurrection of collapsed networks, the historical network architecture, levels of trait variation, and eco-evolutionary dynamics could aid in the revival of the network even in undesirable environmental conditions. Our study argues that focus should be applied to a few species whose dynamics one could steer to resurrect the entire network from a collapsed state.

ecology↗

bioRxiv2022

Evolutionary effects of individual variation and dimensionality of higher-order interactions on the robustness of species coexistence

Although the eco-evolutionary effects of individual variation for species coexistence are still widely debated, theoretical evidence appears to support a negative impact on coexistence. Mechanistic models of eco-evolutionary effects of individual variation focus largely on pairwise interactions, while the dynamics of communities where both pairwise and higher-order interactions (HOIs) are pervasive are not known. In addition, most studies have focused on effects of high dimensional HOIs on species coexistence when in reality such HOIs could be highly structured and low-dimensional, as species interactions could primarily be mediated through phenotypic traits. Here, combining quantitative genetics and Lotka-Volterra equations, we explored the eco-evolutionary effects of individual variation on the patterns of species coexistence in a competitive community dictated by pairwise interactions and HOIs. Specifically, we compare six different models in which HOIs were modelled to be trait-mediated (low-dimensional) or random (high-dimensional) and evaluated its impact on robustness of species coexistence in the presence of different levels of individual variation. Across the six different models, we found that individual variation did not promote species coexistence, irrespective of whether interactions were pairwise or were of higher-order. However, individual trait variation could stabilize communities to external perturbation more so when interactions were of higher order. When compared across models, species coexistence is promoted when HOIs strengthen pairwise intraspecific competition more so than interspecific competition, and when HOIs act in a hierarchical manner. Additionally, across the models, we found that species traits tend to cluster together when individual variation in the community was low. We argue that, while individual variation can influence community patterns in many different ways, they more often lead to fewer species coexisting together.

ecology↗

bioRxiv2022

Abrupt transitions and its indicators in mutualistic meta-networks: effects of network topology, size of metacommunities and species dispersal

Gradual changes in the environment could cause dynamical ecological networks to abruptly shift from one state to an alternative state. When this happens ecosystem functions and services provided by ecological networks get disrupted. We, however, know very little about how the topology of such interaction networks can play a role in the transition of ecological networks at spatial scales. In the event of such unwanted transitions, little is known about how statistical metrics used to inform such impending transitions, measured at the species-level or at the community-level could relate to network architecture and the scale of spatial interactions such as the size of the metacommunity. Here, using hundred and one empirical plant-pollinator networks in a spatial setting, I evaluated the impact of network topology and spatial scale of species interactions on abrupt transitions, and on statistical metrics used as predictors to forecast such abrupt transitions. Using generalized Lotka-Volterra equations in a meta-network framework, I show that species dispersal rate and the size of the metacommunity can impact when an abrupt transition can occur. In addition, forecasting such unwanted abrupt transitions of meta-networks using statistical metrics of instability was also consequently dependent on the topology of the network, species dispersal rate, and the size of the metacommunity. The results indicated that the plant-pollinator meta-networks that could exhibit stronger statistical signals before collapse than others were dependent on their network architecture and on the spatial scale of species interactions.

ecology↗

bioRxiv2022

Community structure determines the predictability of population collapse

O_LIEarly warning signals (EWS) are phenomenological tools that have been proposed as predictors of the collapse of biological systems. Whilst a growing body of work has shown the utility of EWS based on either statistic derived from abundance data or shifts in phenotypic traits such as body size, so far this work has largely focused on single species populations. C_LIO_LIHowever, in order 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. C_LIO_LIHere, reconciling quantitative genetics 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 show that 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) are fundamentally linked, and thus the depth and width of the stability landscape could be used to identify which species should exhibit the strongest EWS of collapse. C_LIO_LIThe probability a species displays both trait and abundance based EWS is dependent on its position in a community, with some species able to act as indicators species. In addition, our results also demonstrate that in general trait-based EWS appear less reliable in comparison to abundance-based EWS in forecasting species collapses in our simulated communities. Furthermore, community-level abundance-based EWS were fairly reliable in comparison to their species-level counterparts in forecasting species level collapses. C_LIO_LIOur 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. C_LI

ecology↗

bioRxiv2019

Effect of time-series length and resolution on abundance- and trait-based early warning signals of population declines

Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS performance decreases with decreasing length and resolution. Our simulations suggest a relative time-series length between ten and five generations and a resolution of half a generation are the minimum requirements for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths and resolutions half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

ecology↗

bioRxiv2018

Intraspecific variation promotes trait clustering and species coexistence through higher-order interactions.

Ecological and evolutionary effects of individual variation on species coexistence remains unclear. Competition models for coexistence have emphasized species-level differences in pairwise interactions, and invoked no role for intraspecific variation. These models show that stronger competitive interactions result in smaller numbers of coexisting species. However, the presence of higher-order interactions (HOIs) among species appears to have a stabilizing influence on communities. How species coexistence is affected in a community where both pairwise and higher-order interactions are pervasive is not known. Furthermore, the effect of individual variation on species coexistence in complex communities with pairwise and HOIs remains untested. Using a Lotka-Volterra model, we explore the effects of intraspecific variation on the patterns of species coexistence in a competitive community dictated by pairwise and HOIs. We found that HOIs greatly stabilize species coexistence across different levels of strength in competition. Notably, high intraspecific variation promoted species coexistence, particularly when competitive interactions were strong. However, species coexistence promoted by higher levels of variation was less robust to environmental perturbation. Additionally, species traits tend to cluster together when individual variation in the community increased. We argue that individual variation can promote species coexistence by reducing trait divergence and attenuating the inhibitory effects of dominant species through HOIs

ecology↗

bioRxiv2018

When do shifts in trait dynamics precede population declines?

Predicting population responses to environmental change is an on-going challenge in ecology. Studies investigating the links between fitness-related phenotypic traits and demography have shown that trait dynamic responses to environmental change can sometimes precede population dynamic responses, and thus, can be used as an early warning signal. However, it is still unknown under which ecological and evolutionary circumstances, shifts in fitness-related traits can precede population responses to environmental perturbation. Here, we take a trait-based demographic approach and investigate both trait and population dynamics in a density-regulated population in response to a gradual change in the environment. We explore the ecological and evolutionary constraints under which shifts in a fitness-related trait precedes a decline in population size. We show both analytically and with experimental data that under medium-to-slow rate of environmental change, shifts in trait value can precede population decline. We further show the positive influence of environmental predictability, average reproductive rate, plasticity, and genetic variation on shifts in trait dynamics preceding potential population declines. These results still hold under non-constant genetic variation and environmental stochasticity. Our study highlights ecological and evolutionary circumstances under which a fitness-related trait can be used as an early warning signal of an impending population decline.

ecology↗

bioRxiv2018

Eco-evolutionary processes underlying early warning signals of population declines

O_LIEnvironmental change can impact the stability of populations and can cause rapid declines in abundance. Abundance-based warning signals have been proposed to predict such declines, but these have been shown to have limited success, leading to the development of warning signals based on the distribution of fitness-related traits such as body size. C_LIO_LIThe dynamics of such traits in response to external environmental perturbations are controlled by a range of underlying factors such as reproductive rate, genetic variation, and plasticity. However, it remains unknown how such ecological and evolutionary factors affect the stability landscape of populations and the detectability of abundance and trait-based warning signals of population decline. C_LIO_LIHere, we apply a trait-based demographic approach and investigate both trait and population dynamics in response to gradual changes in the environment. We explore a range of ecological and evolutionary constraints under which the stability of a population may be affected. C_LIO_LIWe show both analytically and with model-based simulations that strength of abundance-based early warning signals is significantly affected by ecological and evolutionary factors. C_LIO_LIFinally, we show that a unified approach, combining trait- and abundance-based information, significantly improves our ability to predict population declines. Our study suggests that the inclusion of trait dynamic information alongside generic warning signals should provide more accurate forecasts of the future state of biological systems. C_LI

ecology↗