Quantification of the risk of Phytophthora dieback in The Greater Blue Mountains World Heritage area

Abstract

Biological invasions exert great pressure on natural ecosystems and conservation areas, the latter of which have been established to conserve biodiversity. The presence of invasive species in natural ecosystems disrupts evolutionary processes, alters species abundance and can potentially lead to extinction (Mack et al., 2000; Crowl et al., 2008). When an invasive species is the cause of plant disease, the potential for that pathogen to survive in a new environment and the expectation of the impacts it may cause, can be estimated from locations where it already occurs. Understanding the dynamics of disease is important for management and research alike, and will hopefully make way for a proactive rather than reactive response. Disease in natural Australian ecosystems caused by the invasive species Phytophthora cinnamomi has been recognised for nearly 100 years (Newhook and Podger, 1972); its devastating impacts have lead to the disease syndrome, Phytophthora dieback, being classified as a Key Threatening Process by the Australian Federal Government (Commonwealth of Australia, 2005). Yet, the assessment of potential disease establishment, that is, disease risk, is limited. This remains true for the globally significant Greater Blue Mountains World Heritage Area (GBMWHA) in New South Wales, a centre of plant and animal conservation. Not only is the understanding of the pathogen distribution limited, so too is knowledge of the potential impacts on flora and the influence climate change may have on disease expression. Management of Phytophthora dieback in the GBMWHA is made increasingly complex by the rugged and remote nature of much of the World Heritage Area, as well as competing demands from tourism, recreation and the impacts of fire and other introduced species. This study aims to address some of these complexities by establishing the suitability of the GBMWHA to P. cinnamomi, its current distribution and the potential for disease. Additionally, with the difficulty of accessing much of the GBMWHA and the risk of disease transmission in mind, an alternate approach to disease identification is trialed. The first task of this project, was concerned with understanding the potential distribution of P. cinnamomi within the GBMWHA using mechanistic modelling and information on the pathogen’s ecology. Most of the GBMHWA was found to be suitable, leading to the acceptance of the first hypothesis that the climatic and topographic conditions of the GBMWHA are conducive to P. cinnamomi establishment. The most conducive areas were characterised by high soil wetness, high rainfall and moderate temperatures, while the areas least conducive were conversely hotter and drier. Although iv the model appeared to overpredict into areas the pathogen was not found, increasing distribution risk was associated with increasing isolations, possibly indicating that the pathogen is yet to reach its potential niche. The modelled distribution of P. cinnamomi was then used to inform a field investigation to determine the actual distribution in the GBMWHA and assess the impact of the pathogen on vegetation communities and individuals. As an invasive species, the distribution of P. cinnamomi was hypothesised to be primarily found in locations with high anthropogenic activity; however it was isolated extensively from remote areas, leading to the rejection of this hypothesis. Disease was never the less expected, albeit sporadic, as per disease expression in other vegetation communities in New South Wales (Arentz, 1974; Walsh et al., 2006; Howard, 2008). Heathland communities that often have a higher incidence of disease (McDougall and Summerell, 2003), had a high rate of pathogen isolation, as well as clear indications of disease in the GBMWHA. Additionally, freshwater wetlands, many of which are endangered ecological communities under Commonwealth and State legislation, had a high rate of pathogen isolation also. The results collected during the field work were then utilised to assess the risk of Phytophthora dieback occurring in the GBMWHA within the context of the disease triangle. The distribution of P. cinnamomi was combined with models of over 130 individual host species to produce a spatially explicit model, quantifying the risk of disease. That a large portion of the GBMWHA is at risk of Phytophthora dieback was not the case, and as such this hypothesis was rejected. Although much of the World Heritage Area had a least some level of risk, greatest risk was associated with a few small areas that occurred at higher elevations with suitable rainfall and temperature conditions. Unfortunately, many of these locations were associated with high levels of tourism and recreation, highlighting the potential for anthropogenic dispersal of P. cinnamomi into, around and out of the GBMWHA. Disease itself has a temporal element which cannot be quantified in one set of field results and as disease spreads the results become outdated quickly (O'Gara et al., 2005). Field-based assessments of disease are expensive and time consuming, and in area as vast and rugged as the GBMWHA, difficult and potentially dangerous. Real-time information on the impacts of disease are therefore needed by land managers to efficiently deploy management strategies (O'Gara et al., 2005). Remote sensing offers an alternative means of assessment not requiring site entry. Vegetation condition can be assessed remotely in all manner of plant systems including the detection and quantification of disease. As such, it was hypothesised here that infection caused by P. cinnamomi could be detected fro