Downscaling Pest Risk Analyses: Identifying Current and Future Potentially Suitable Habitats for Parthenium hysterophorus with Particular Reference to Europe and North Africa.
Bottom Line: We also consider the effects of climate change on the modelled risks.Downscaling the climate model using habitat factors resulted in substantial (approximately 22-53%) reductions in the areas estimated to be endangered.Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area.
Affiliation: CSIRO, GPO Box 1700, Canberra, ACT, Australia.
Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the 'Swiss Cheese' nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22-53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types.
No MeSH data available.
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Mentions: Under the inferential FAO habitat model 29 land use classes were identified as being at risk in Europe, including cropping and pasture areas (Table 3, Fig 6). However, grazed forests and shrublands were also identified as being at risk (Table 3). The total area of suitable habitat in Europe modelled as at risk using the FAO dataset and the inferred habitat suitability classes was 1.6 million ha, nearly twice that from the CORINE dataset based on the expert opinion.
No MeSH data available.