Applying fire connectivity and centrality measures to mitigate the cheatgrass-fire cycle in the arid West, USA

Context: Strategic placement of fuel treatments across large landscapes is an important step to mitigate the collective effects of fires interacting over broad spatial and temporal extents. On landscapes where highly invasive cheatgrass (Bromus tectorum) is increasing fire activity, such an approach could help maintain landscape resilience. Objectives: Our objectives are to 1) model and map fire connectivity on a cheatgrass-invaded landscape, as well as the centrality of large cheatgrass patches, in order to inform a landscape fuel treatment (i.e., a network of greenstrips); and 2) evaluate the modeled greenstrip network based on changes to cheatgrass patch centrality. Methods: Our analysis covers 485-km² on the Kaibab National Forest in Northern Arizona. We apply a circuit-theoretic model of fire connectivity between all pairs of large cheatgrass patches. Based on these results, we calculate a measure of centrality for each patch to inform fuel treatment placement. We evaluate the modeled greenstrip network by comparing the pre- and post-treatment centrality of each patch. Results: After modeling fire connectivity across the landscape, we identify 25 of 68 large cheatgrass patches with relatively high centrality. When we simulate greenstrips around these focal patches, model results suggest that they are effective in reducing the centrality for at least 19 of the 25 patches. Conclusions: Fire connectivity models provide robust network centrality measures, which can help generate multiple, landscape fuel treatment alternatives and facilitate on-the-ground decisions. The extension of these methods is well suited for landscape fuels management in other vegetation communities and ecosystems.