Potential effect of managing connectivity to contain disease spread among free‐ranging wild boar (Sus scrofa) in disparate landscapes

Abstract Landscape connectivity is a major factor shaping the spread of pathogens in wildlife populations. By managing connectivity, transmission pathways can be broken and disease spread be contained, particularly in the early phases of an outbreak. Having witnessed recent outbreaks of African Swine Fever in free‐ranging wild boar (Sus scrofa) in Belgium, Germany and Italy, offices for disease control are on the alert also in other western European countries. This study investigates the potential effect of managing landscape connectivity to contain disease spread among free‐ranging wild boar in disparate landscapes. It involves research into (1) the ease with which wildlife corridors can be blocked for wild boar, (2) the connectivity of wild boar habitat and (3) the impact of landscape fragmentation on connectivity management. This is addressed by carrying out GIS analyses and performing graph operations on the wild boar networks in different biogeographical regions of Switzerland. The results of doing research into the three above‐mentioned objects show that, regarding the first, most wildlife corridors are hard to block for wild boar, because their features or location make fencing difficult. Regarding the second, the wild boar habitat is connected. Opening wildlife passages that are currently under construction may allow wild boar to disperse to hitherto uncolonized areas. Regarding the third, all wild boar networks could be partially decomposed by blocking the easy to block corridors and closing the passages. Network decomposition would be easiest to achieve in the region where the built infrastructure is most abundant. All over Switzerland, the potential epidemic size could be reduced by 25% when blocking the minimal set of corridors and passages that cut the networks to non‐decomposable components. This study suggests that (a) combining connectivity analysis with fragmentation analysis is key to explaining why a specific measure of disease containment is more effective in one landscape than in the other, (b) complementing the permeability model with a species distribution model is essential to identify connected habitat patches for the species of concern and (c) connectivity metrics should consider also the surface area of occupied habitat patches and relative abundance of the species of concern.