How biological communities respond to perturbations is one of the pressing questions of modern ecology. One potentially large driver of network perturbation under current and future climate change is asynchronous changes to the phenology (the timing of key life events) of different species. For networks of interacting species, changes to phenology may cascade through networks if phenological change is uneven across interaction partners. Therefore, phenological mismatch (e.g. plant bloom no longer timed with pollinator emergence) has the potential to rearrange wholescale networks, yet this has not been previously assessed. We tested whether plant-pollinator network structure changes when altering the phenology of an entire community. To test the impact of community level perturbations, we manipulated the timing of plant flowering by accelerating snowmelt in high elevation subalpine meadows. Our manipulation shifted flowering timing, while leaving the phenology of pollinator communities—which are drawn from a larger area—essentially unchanged. We accelerated snowmelt by roughly two weeks in 8 spatially replicated plots, each with a paired control. We calculated a series of common network structural metrics thought to be important for network stability and functioning and tested if these differed statistically across the manipulated and control plots.
Results/Conclusions
Though we continue to collect and analyze data, preliminary results indicate no major shifts in network structure induced by the snowmelt acceleration. Manipulated networks had similar levels of nestedness (NODF), complementarity (H2`) as their paired controls. We continue to work to identify the pollinators in our networks to finer taxonomic resolution which may influence results. Overall, these results indicate that network structure may be robust even to potentially dramatic perturbations such as phenological mismatch of entire communities.