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Monitoring and distribution modelling of invasive species along riverine habitats at very high resolution

Descombes, Patrice, Petitpierre, Blaise, Morard, Eric, Berthoud, Michael, Guisan, Antoine, Vittoz, Pascal
Biological invasions 2016 v.18 no.12 pp. 3665-3679
Impatiens glandulifera, Reynoutria japonica, biogeography, canopy, environmental factors, invasive species, inventories, models, monitoring, niches, planning, plants (botany), population distribution, population size, risk, rivers, soil texture, topography, Switzerland
Monitoring and species distribution models (SDMs) are increasingly used to support conservation planning but are rarely projected at a very high resolution for conservation management. In this study, we compared the population distribution and size of five invasive plant species along an 18 km alluvial system in Switzerland, over a period of 11 years. Exhaustive inventories of past (2001) to current (2012) populations showed a massive increase in invaded areas over the eleven years. Impatiens glandulifera and Reynoutria japonica were the species with the largest increases in population number and size. The ecological preferences of each species were then modelled at 1 m resolution, using environmental variables expressing topography, disturbances, dispersal, soil texture and light availability. SDMs successfully depicted the niches at very high resolution. Some of the important predictors (e.g., canopy density, distance to river) would have been unhelpful at a coarser resolution. From these very-high-resolution models, we predicted the potential distribution and abundance of species and derived two indices indicating the amount of habitat still available for future species colonisation, crucial information for management. Large, empty areas were predicted to be suitable for each species, suggesting that the observed increase in population size may continue in the future. The two proposed range-filling indices and abundance models may be used efficiently in future studies at very fine resolution to prioritise eradication efforts in previously invaded areas and controls in areas at high risk of invasion. To our knowledge, this is the first study investigating the efficiency of SDMs to predict invasions at such a fine resolution.