Desenvolvimento de um sistema de gestão adaptativa para prever e mitigar os danos causados pelo nematode do pinheiro Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) em Portugal

The pinewood nematode (PWN) Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) is the causal agent of pine wilt disease (PWD), being considered the most serious pine pest in Far East Asia where it was introduced in the early 1900`s (Togashi & Jikumaru 2007). This species is native to North America, where it does not cause any serious damage in native conifer trees, being just considered secondary pathogen in stressed or recently dead trees (Wingfield et al. 1984). The PWN was found in Portugal in 1999 (Mota et al. 1999), and in spite of exceptional measures to constrain and eradicate the PWN in its initial area of introduction, the pathogen was found much further north of this initial area, and was considered to be spread to all the National Territory in 2008. At this moment urgent measurements are needed to a long term management of this pathogen at tolerably low densities, in Portugal, and possibly in the rest of Europe. The PWD is complex and until know, after decades of intensive research, there is no agreement on one single factor that determines its development. Instead, several competing models or hypothesis are proposed. Most of the information concerning these models was obtained in Far East Asia, where the PWN has a major economical impact. The lack of economical impact of the PWN in its native habitat made investigation in North America scarce. However, information on what keeps the nematode as relatively innocuous in its native area would be extremely useful to predict and promote the occurrence of those factors on areas where the PWN was introduced. In the first part of this project we will collect information in three sites in the USA, representing different climatic areas (New Hampshire, Missouri, and Louisiana), with the cooperation of three US experts which are part of the project team. We will collect data that will allow us to understand why North American pine species are resistant to the PWN, while studying the production of phytochemicals by inoculated trees (Task 2), and mutualistic and antagonistic relations (Task 3), and its relation with the different climatic patterns. The same work will be repeated in Portugal. Furthermore, in Portugal we will extend the study of the PWN to all the territory, to assess the relation between PWN virulence and prevalence of the PWD (Task 1); dynamics of the PWN and its vector (Task 4); environmental thresholds for the establishment of the PWD in the most important Portuguese pine species (Task 5). By the end of the project we expect to have solid data to complete the different models that most commonly are used to explain the PWD. These models are: 1) evolution of the epidemics at landscape level (spatio-temporal changes in the mean and variance of the virulence of PWN populations, and tree resistance) (Togashi & Jikumaru 2007); 2) endogenous tree resistance (morphology and production of phytochemicals) (Suga et al. 1993, Kuroda 2004); 3) environmental factors (temperature, moisture, radiation) (Rutherford and Webster 1987); 4) community interactions (mutualistics and antagonistic bacteria and fungi species) (Maehara & Futai 2002, Maehara 2008); 5) population dynamics of the vector (a specific Monochamus spp.) (Togashi & Jikumaru 2007). Due to the major economical impact of the PWN, at this moment it is of major importance that information obtained by researchers is quickly used by forest managers. It is urgent to develop a management plan that can include known information, and the uncertainty that is inherent to the process of decision making, and that can be updated while new information will come to light. This becomes even more important taking into account that control measures in affected areas in Asia where the pest leads to important damages, greatly affect the course of the epidemics (Togashi & Jikumaru 2007). The concept of adaptive management provides the tools to achieve these objectives in a changing decision making environment (Borges et al. 2003), favoring advice to management, instead of simply testing scientific hypotheses. We should be able to select the best models to predict the epidemics, simplifying the system of decision as much as possible, including uncertainty and management strategies. The ultimate goal of this project will be to develop a general model for optimizing these pest management schemes, which is of high priority in Portugal. For this Bayesian modelling techniques will be used, in order to include the several competing predictive models, risk and uncertainty, and the impact of management tactics. This model will be updated as new information is obtained through monitoring programmes.