An Overview of Canadian Research Activities on Diseases Caused by Phytophthora ramorum: Results, Progress, and Challenges.

International trade and travel are the driving forces behind the spread of invasive plant pathogens around the world, and human-mediated movement of plants and plant products is now generally accepted as the primary mode of their introduction, resulting in huge disturbance to ecosystems and severe socio-economic impact. These problems are exacerbated under the present conditions of rapid climatic change. We report an overview of the Canadian research activities on Phytophthora ramorum. Since the first discovery and subsequent eradication of P. ramorum on infected ornamentals in nurseries in Vancouver, British Columbia, in 2003, a research team of Canadian government scientists representing the Canadian Forest Service, Canadian Food Inspection Agency, and Agriculture and Agri-Food Canada worked together over a 10-year period and have significantly contributed to many aspects of research and risk assessment on this pathogen. The overall objectives of the Canadian research efforts were to gain a better understanding of the molecular diagnostics of P. ramorum, its biology, host-pathogen interactions, and management options. With this information, it was possible to develop pest risk assessments and evaluate the environmental and economic impact and future research needs and challenges relevant to P. ramorum and other emerging forest Phytophthora spp.

Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework.

AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.