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Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches. 

Disturbances can facilitate biological invasions, with the associated increase in resource availability being a proposed cause. Here, we experimentally tested the interactive effects of disturbance regime (different frequencies of biomass removal at equal intensities) and resource abundance on invasion success using a factorial design containing five disturbance frequencies and three resource levels. We invaded populations of the bacteriumPseudomonas fluorescenswith two ecologically different invader morphotypes: a fast‐growing “colonizer” type and a slower growing “competitor” type. As resident populations were altered by the treatments, we additionally tested their effect on invader success. Disturbance frequency and resource abundance interacted to affect the success of both invaders, but this interaction differed between the invader types. The success of the colonizer type was positively affected by disturbance under high resources but negatively under low. However, disturbance negatively affected the success of the competitor type under high resource abundance but not under low or medium. Resident population changes did not alter invader success beyond direct treatment effects. We therefore demonstrate that the same disturbance regime can either be beneficial or detrimental for an invader depending on both community resource abundance and its life history. These results may help to explain some of the inconsistencies found in the disturbance‐invasion literature.

 

Disturbance is a key factor shaping ecological communities, but little is understood about how the effects of disturbance processes accumulate over time. When disturbance regimes change, historical processes may influence future community structure, for example, by altering invasibility compared to communities with stable regimes. Here, we use an annual plant model to investigate how the history of disturbance alters invasion success. In particular, we show how two communities can have different outcomes from species introduction, solely due to past differences in disturbance regimes that generated different biotic legacies. We demonstrate that historical differences can enhance or suppress the persistence of introduced species, and that biotic legacies generated by stable disturbance history decay over time, though legacies can persist for unexpectedly long durations. This establishes a formal theoretical foundation for disturbance legacies having profound effects on communities, and highlights the value of further research on the biotic legacies of disturbance.