The impact of individual variation on abrupt collapses in mutualistic networks

When Do Trait-Based Higher Order Interactions and Individual Variation Promote Robust Species Coexistence?

Models on the effects of individual variation often focus on pairwise interactions, but communities could harbor both pairwise and higher order interactions (HOIs). Theoretical studies on HOIs, where a third species modulates pairwise species competition, tend to assign them at random, even though they could be mediated and structured by one-dimensional traits. Here, we consider two different classes of models of both pairwise and higher order trait-mediated interactions: competition alleviated by increasing trait distance, and hierarchical competition where species higher in the hierarchy exert more competition on those lower and vice versa. Combining these models with evolutionary dynamics based on quantitative genetics, we compare their impact on species diversity, community pattern, and robustness of coexistence. Regardless of individual variation, trait-mediated HOIs generally do not promote and often hinder species coexistence, but there are some notable exceptions to this. We present an analytical argument to make sense of these results and argue that while the effects of trait-based HOIs on diversity may appear confusing on the surface, we can understand what outcome to expect in any given scenario by looking at the shape of the effective interaction kernel that arises from the joint action of pairwise and HOI terms. In addition, we find that (i) communities structured by competitive trait hierarchies are highly vulnerable to external perturbations, regardless of HOIs, and (ii) trait-based HOIs with distance-dependent competition create the most robust communities, with minimal impact from individual variation, and (iii) both individual variation and HOIs consistently lead to a more even distribution of species traits than would occur by chance. These findings suggest that trait-mediated HOIs foster coexistence only under special conditions, raising the question of whether HOIs must involve multiple traits to positively affect coexistence in competitive communities.

Understanding pesticide-induced tipping in plant-pollinator networks across geographical scales: Prioritizing richness and modularity over nestedness

Mutually beneficial interactions between plants and pollinators are crucial for biodiversity, ecosystem stability, and crop production. A threat to a mutualistic network is the occurrence of a tipping point at which the species abundances collapse to a near zero level. In modern agriculture, there is widespread use of pesticides. What are the effects of extensive pesticide use on mutualistic networks? We develop a plant-pollinator-pesticide model and study its dynamics using 123 mutualistic networks across the globe. We demonstrate that pesticide exposure can lead to a tipping point. Furthermore, while the network characteristics such as richness and modularity exhibit a strong association with pesticide-induced tipping, nestedness shows a weak association. A surprising finding is that the mutualistic networks in the African continent are less pesticide tolerant than those in Europe. We articulate and test a pragmatic intervention strategy through targeted management of pesticide levels within specific plant species to delay or avert the tipping point. Our study provides quantitative insights into the phenomenon of pesticide-induced tipping for safeguarding mutualistic networks that are fundamental to agriculture and ecosystems.

Reviving collapsed plant-pollinator networks from a single species

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 control 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.

Stability, resilience and eco-evolutionary feedbacks of mutualistic networks to rising temperature

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.

Using individual-based trait frequency distributions to forecast plant-pollinator network responses to environmental change

Determining how and why organisms interact is fundamental to understanding ecosystem responses to future environmental change. To assess the impact on plant-pollinator interactions, recent studies have examined how the effects of environmental change on individual interactions accumulate to generate species-level responses. Here, we review recent developments in using plant-pollinator networks of interacting individuals along with their functional traits, where individuals are nested within species nodes. We highlight how these individual-level, trait-based networks connect intraspecific trait variation (as frequency distributions of multiple traits) with dynamic responses within plant-pollinator communities. This approach can better explain interaction plasticity, and changes to interaction probabilities and network structure over spatiotemporal or other environmental gradients. We argue that only through appreciating such trait-based interaction plasticity can we accurately forecast the potential vulnerability of interactions to future environmental change. We follow this with general guidance on how future studies can collect and analyse high-resolution interaction and trait data, with the hope of improving predictions of future plant-pollinator network responses for targeted and effective conservation.

The role of phenotypic plasticity in shaping ecological networks

Plasticity-mediated changes in interaction dynamics and structure may scale up and affect the ecological network in which the plastic species are embedded. Despite their potential relevance for understanding the effects of plasticity on ecological communities, these effects have seldom been analysed. We argue here that, by boosting the magnitude of intra-individual phenotypic variation, plasticity may have three possible direct effects on the interactions that the plastic species maintains with other species in the community: may expand the interaction niche, may cause a shift from one interaction niche to another or may even cause the colonization of a new niche. The combined action of these three factors can scale to the community level and eventually expresses itself as a modification in the topology and functionality of the entire ecological network. We propose that this causal pathway can be more widespread than previously thought and may explain how interaction niches evolve quickly in response to rapid changes in environmental conditions. The implication of this idea is not solely eco-evolutionary but may also help to understand how ecological interactions rewire and evolve in response to global change.

Rising temperature drives tipping points in mutualistic networks

The effect of climate warming on species' physiological parameters, including growth rate, mortality rate and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, predicting the interactive influence of these changes on mutualistic communities remains uncertain. Using 139 real plant-pollinator networks sampled across the globe and a modelling approach, we study the impact of species' individual thermal responses on mutualistic communities. We show that at low mutualistic strength plant-pollinator networks are at potential risk of rapid transitions at higher temperatures. Evidently, generalist species play a critical role in guiding tipping points in mutualistic networks. Further, we derive stability criteria for the networks in a range of temperatures using a two-dimensional reduced model. We identify network structures that can ascertain the delay of a community collapse. Until the end of this century, on account of increasing climate warming many real mutualistic networks are likely to be under the threat of sudden collapse, and we frame strategies to mitigate this. Together, our results indicate that knowing individual species' thermal responses and network structure can improve predictions for communities facing rapid transitions.

Community structure determines the predictability of population collapse

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.