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In small birds, mass-dependent predation risk (MDPR) is known to make the trade-off between avoiding starvation and avoiding predation dependent on individual mass. This occurs because carrying increased fat reserves not only reduces starvation risk but also results in a higher predation risk due to reduced escape flight performance and/or the increased foraging exposure needed to maintain a higher body mass. In principle, the theory of MDPR could also apply to any animal capable of storing energy reserves to reduce starvation and whose escape performance decreases with increasing mass. We used a unique situation along certain parts of coastal Britain, where harbour porpoises ( Phocoena phocoena ) are pursued and killed but crucially not eaten by bottlenose dolphins ( Tursiops truncatus ), to investigate whether a MDPR effect can occur in non-avian species. We show that where high levels of dolphin ‘predation’ occur, porpoises carry significantly less energy reserves than would otherwise be expected and this equates to reducing by approximately 37% the length of time that a porpoise could survive without feeding. These results provide the first evidence that a mass-dependent starvation–predation risk trade-off may be a general ecological principle that can apply to widely different animal types rather than, as is currently thought, only to birds.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic.