Global climate change is known to result in the emergence or re-emergence of some infectious diseases. Reliable methods to identify the infectious diseases of humans and animals and that are most likely to be influenced by climate are therefore required. Since different priorities will affect the decision to address a particular pathogen threat, decision makers need a standardised method of prioritisation. Ranking methods and Multi-Criteria Decision approaches provide such a standardised method and were employed here to design two different pathogen prioritisation tools. The opinion of 64 experts was elicited to assess the importance of 40 criteria that could be used to prioritise emerging infectious diseases of humans and animals in Canada. A weight was calculated for each criterion according to the expert opinion. Attributes were defined for each criterion as a transparent and repeatable method of measurement. Two different Multi-Criteria Decision Analysis tools were tested, both of which used an additive aggregation approach. These were an Excel spreadsheet tool and a tool developed in software ‘M-MACBETH’. The tools were trialed on nine ‘test’ pathogens. Two different methods of criteria weighting were compared, one using fixed weighting values, the other using probability distributions to account for uncertainty and variation in expert opinion. The ranking of the nine pathogens varied according to the weighting method that was used. In both tools, using both weighting methods, the diseases that tended to rank the highest were West Nile virus, Giardiasis and Chagas, while Coccidioidomycosis tended to rank the lowest. Both tools are a simple and user friendly approach to prioritising pathogens according to climate change by including explicit scoring of 40 criteria and incorporating weighting methods based on expert opinion. They provide a dynamic interactive method that can help to identify pathogens for which a full risk assessment should be pursued.

Information taken from two long-term demographic studies on Orchis morio L. and Heminiurn monorchis (L.) R.Br. is used to explore some of the factors which influence flowering. The proportion of plants which flowered each year varied considerably between species, flowering in 0. morio exceeding 40% in all years except one over an 18 year period; over a 30 year period , the number of plants of Herminium in flower never exceeded 36% of the population and no inflorescences were produced in 1977 and 1991. The relationship between flowering in Heminiurn in a given year and the monthly rainfall and temperature for the current and 3 previous years was analysed using logistic regression. Best fits were obtained using data for the summer months in the previous year, with an increasing flowering rate with rainfall and a decline with temperature. It is hypothesized that drought and high temperatures in the summer reduce leaf area and cause premature senescence and the death of leaves, with the result that not enough carbohydrates are stored to enable plants to support or initiate inflorescences the following year. For species such as Orchis morio which produce leaves in the autumn and remain green, summer drought causes no problems as they have no above ground organs. Factors which influence flowering in this species are as yet unknown. 0 1998 The Linnean Society of London ADDITIONAL KEY WORDS:--climatic factorsdroughtinflorescence initiation logistic regressionorchidsplant performance.

Temperature is hypothesized to contribute to increased pathogenicity and virulence of many marine diseases. The sea louse (Lepeophtheirus salmonis) is an ectoparasite of salmonids that exhibits strong life-history plasticity in response to temperature; however, the effect of temperature on the epidemiology of this parasite has not been rigorously examined. We used matrix population modelling to examine the influence of temperature on demographic parameters of sea lice parasitizing farmed salmon. Demographically-stochastic population projection matrices were created using parameters from the existing literature on vital rates of sea lice at different fixed temperatures and yearly temperature profiles. In addition, we quantified the effectiveness of a single stage-specific control applied at different times during a year with seasonal temperature changes. We found that the epidemic potential of sea lice increased with temperature due to a decrease in generation time and an increase in the net reproductive rate. In addition, mate limitation constrained population growth more at low temperatures than at high temperatures. Our model predicts that control measures targeting preadults and chalimus are most effective regardless of the temperature. The predictions from this model suggest that temperature can dramatically change vital rates of sea lice and can increase population growth. The results of this study suggest that sea surface temperatures should be considered when choosing salmon farm sites and designing management plans to control sea louse infestations. More broadly, this study demonstrates the utility of matrix population modelling for epidemiological studies.

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Sea lice, Lepeophtheirus salmonis, are ectoparasites of farmed and wild salmonids. Infestations can result in significant morbidity and mortality of hosts in addition to being costly to control. Integrated pest management programmes have been developed to manage infestations, and in some salmon farming areas, these programmes include the use of wrasse. Wrasse prey upon the parasitic life stages of L. salmonis and can be stocked on farms at varying densities. Despite considerable variation in the usage of wrasse, there are few quantitative estimates of how well they can control sea lice and how best to optimize their use. To explore at what densities wrasse should be stocked in order to meet specific control targets, we built an individual-based model that simulates sea lice infestation patterns on a representative salmonid host. Sea lice can be controlled through the use of chemical treatments as well as by wrasse predators. We found that the wrasse can effectively control sea lice, and the densities of wrasse needed for effective control depend upon the source of the infestation and the targeted level of control. Effective usage of wrasse can result in decreased use of chemical treatments and improved control of sea lice.

Global climate change is predicted to lead to an increase in infectious disease outbreaks. Reliable surveillance for diseases that are most likely to emerge is required, and given limited resources, policy decision makers need rational methods with which to prioritise pathogen threats. Here expert opinion was collected to determine what criteria could be used to prioritise diseases according to the likelihood of emergence in response to climate change and according to their impact. We identified a total of 40 criteria that might be used for this purpose in the Canadian context. The opinion of 64 experts from academic, government and independent backgrounds was collected to determine the importance of the criteria. A weight was calculated for each criterion based on the expert opinion. The five that were considered most influential on disease emergence or impact were: potential economic impact, severity of disease in the general human population, human case fatality rate, the type of climate that the pathogen can tolerate and the current climatic conditions in Canada. There was effective consensus about the influence of some criteria among participants, while for others there was considerable variation. The specific climate criteria that were most likely to influence disease emergence were: an annual increase in temperature, an increase in summer temperature, an increase in summer precipitation and to a lesser extent an increase in winter temperature. These climate variables were considered to be most influential on vector-borne diseases and on food and water-borne diseases. Opinion about the influence of climate on air-borne diseases and diseases spread by direct/indirect contact were more variable. The impact of emerging diseases on the human population was deemed more important than the impact on animal populations.

Wildlife conservation and the management of human–wildlife conflicts require cost‐effective methods of monitoring wild animal behavior. Still and video camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. In the present study, we describe a state‐of‐the‐art, deep learning approach for automatically identifying and isolating species‐specific activity from still images and video data. We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species (binary classification) and secondly to distinguish each of six animal species (multiclassification). We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage Mycobacterium bovis (the cause of bovine tuberculosis) transmission between badgers and cattle. We used two deep learning frameworks for automatic image recognition. They achieved high accuracies, in the order of 98.05% for binary classification and 90.32% for multiclassification. Based on the deep learning framework, a detection process was also developed for identifying animals of interest in video footage, which to our knowledge is the first application for this purpose. The algorithms developed here have wide applications in wildlife monitoring where large quantities of visual data require screening for certain species.

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