No abstract

Summary Rust fungi are important components of ecological communities and in ecosystem function. Their unique life strategies as biotrophic pathogens with complicated life cycles could make them vulnerable to global environmental change. While there are gaps in our knowledge, especially in natural plant–rust systems, this review of the exposure of rust fungi to global change parameters revealed that some host–rust relationships would decline under predicted environmental change scenarios, whereas others would either remain unchanged or become more prevalent. Notably, some graminicolous rusts are negatively affected by higher temperatures and increased concentrations of atmospheric CO2. An increase of atmospheric O3 appears to favour rust diseases on trees but not those on grasses. Combined effects of CO2 and O3 are intermediary. The most important global drivers for the geographical and host plant range expansion and prevalence of rusts, however, are global plant trade, host plant genetic homogenization and the regular occurrence of conducive environmental conditions, especially the availability of moisture. However, while rusts thrive in high‐humidity environments, they can also survive in desert habitats, and as a group their environmental tolerance is large, with no conclusive change in their overall prevalence predictable to date.

No abstract

Phenology is a key indicator and mediator of the ecological impacts of climate change. However, studies monitoring the phenology of individual species are moderate in number, taxonomically and geographically restricted, and mainly focused on spring events. As such, attention is being given to nonstandard sources of phenology data, such as the dates of species' biological records. Here, we present a conceptual framework for deriving phenological metrics from biological recording data, while accounting for seasonal variation in the level of activity by recorders. We develop a new Bayesian statistical model to infer the seasonal pattern of plant 'recordability'. The modelled dates of maximum recordability are strongly indicative of the flowering peaks of 29 insect-pollinated species monitored in two botanic gardens in Great Britain. Conversely, not accounting for the seasonality in recording activity results in biased estimates of the observed flowering peaks. However, observed first and last flowering dates were less reliably explained by the model, which probably reflects greater interspecific variation in levels of recording before and after flowering. We conclude that our method provides new potential for gaining useful insights into large-scale variation in peak phenology across a much broader range of plant species than have previously been studied.