The effect of seasonal strength and abruptness on predator-prey dynamics

Coupled dynamical systems in ecology are known to respond to the seasonal forcing of their parameters with multiple dynamical behaviours, ranging from seasonal cycles to chaos. Seasonal forcing is predominantly modelled as a sine wave. However, the transition between seasons is often more sudden as illustrated by the effect of snow cover on predation success. A handful of studies have mentioned the robustness of their results to the shape of the forcing signal but did not report any detailed analyses. Therefore, whether and how the shape of seasonal forcing could affect the dynamics of coupled dynamical systems remains unclear, while abrupt seasonal transitions are widespread in ecological systems. To provide some answers, we conduct a numerical analysis of the dynamical response of predator-prey communities to the shape of the forcing signal by exploring the joint effect of two features of seasonal forcing: the magnitude of the signal, which is classically the only one studied, and the shape of the signal, abrupt or sinusoidal. We consider both linear and saturating functional responses, and focus on seasonal forcing of the predator's discovery rate, which fluctuates with changing environmental conditions and prey's ability to escape predation. Our numerical results highlight that a more abrupt seasonal forcing mostly alters the magnitude of population fluctuations and triggers period-doubling bifurcations, as well as the emergence of chaos, at lower forcing strength than for sine waves. Controlling the variance of the forcing signal mitigates this trend but does not fully suppress it, which suggests that the variance is not the only feature of the shape of seasonal forcing that acts on community dynamics. Although theoretical studies may predict correctly the sequence of bifurcations using sine waves as a representation of seasonality, there is a rationale for applied studies to implement as realistic seasonal forcing as possible to make precise predictions of community dynamics.

From winter to summer and back: Lessons from the parameterization of a seasonal food web model for the Białowieża forest

Dynamic food web models describe how species abundances change over time as a function of trophic and life-history parameters. They are essential to predicting the response of ecosystems to perturbations. However, they are notoriously difficult to parameterize, so that most models rely heavily either on allometric scaling of parameters or inverse estimation of biomass flows. The allometric approach makes species of comparable body mass have near-identical parameters which can generate extinctions within a trophic level. The biomass flow approach is more precise, but is restricted to steady-states, which is not appropriate for time-varying environments. Adequately parameterizing large food webs of temperate and arctic environments requires dealing both with many species of similar sizes and a strongly seasonal environment. Inspired by the rich empirical knowledge on the vertebrate food web of the Białowieża forest, we parameterize a bipartite food web model comprising 21 predators and 124 prey species. Our model is a non-autonomous coupled ordinary differential equations system that allows for seasonality in life-history and predation parameters. Birth and death rates, seasonal descriptions of diet for each species, food requirements and biomass information are combined into a seasonal parameterization of a dynamic food web model. Food web seasonality is implemented with time-varying intrinsic growth rate and interaction parameters, while predation is modelled with both type I and type II functional responses. All our model variants allow for >80% persistence in spite of massive apparent competition, and a quantitative match to observed (seasonal) biomasses. We also identify trade-offs between maximizing persistence, reproducing observed biomasses, and ensuring model robustness to sampling errors. Although multi-annual cycles are expected with type II functional responses, they are here prevented by a strong predator self-regulation. We discuss these results and possible improvements on the model. We provide a general workflow to parameterize dynamic food web models in seasonal environments, based on a real case study. This may help to better predict how biodiverse food webs respond to changing environments.

Reshaping our understanding of species' roles in landscape-scale networks

In network ecology, landscape-scale processes are often overlooked, yet there is increasing evidence that species and interactions spill over between habitats, calling for further study of interhabitat dependencies. Here, we investigate how species connect a mosaic of habitats based on the spatial variation of their mutualistic and antagonistic interactions using two multilayer networks, combining pollination, herbivory and parasitism in the UK and New Zealand. Developing novel methods of network analysis for landscape-scale ecological networks, we discovered that few plant and pollinator species acted as connectors or hubs, both within and among habitats, whereas herbivores and parasitoids typically have more peripheral network roles. Insect species' roles depend on factors other than just the abundance of taxa in the lower trophic level, exemplified by larger Hymenoptera connecting networks of different habitats and insects relying on different resources across different habitats. Our findings provide a broader perspective for landscape-scale management and ecological community conservation.

How plants connect pollination and herbivory networks and their contribution to community stability

Pollination and herbivory networks have mainly been studied separately, highlighting their distinct structural characteristics and the related processes and dynamics. However, most plants interact with both pollinators and herbivores, and there is evidence that both types of interaction affect each other. Here we investigated the way plants connect these mutualistic and antagonistic networks together, and the consequences for community stability. Using an empirical data set, we show that the way plants connect pollination and herbivory networks is not random and promotes community stability. Analyses of the structure of binary and quantitative networks show different results: the plants' generalism with regard to pollinators is positively correlated to their generalism with regard to herbivores when considering binary interactions, but not when considering quantitative interactions. We also show that plants that share the same pollinators do not share the same herbivores. However, the way plants connect pollination and herbivory networks promotes stability for both binary and quantitative networks. Our results highlight the relevance of considering the diversity of interaction types in ecological communities, and stress the need to better quantify the costs and benefits of interactions, as well as to develop new metrics characterizing the way different interaction types are combined within ecological networks.