Aim Megafires are increasing in intensity and frequency globally. The impacts of megafires on biodiversity can be severe, so conservation managers must be able to respond rapidly to quantify their impacts, initiate recovery efforts and consider conservation options within and beyond the burned extent. We outline a framework that can be used to guide conservation responses to megafires, using the 1.5 million hectare 2019/2020 megafires in Victoria, Australia, as a case study. Location Victoria, Australia. Methods Our framework uses a suite of decision support tools, including species attribute databases, ~4,200 species distribution models and a spatially explicit conservation action planning tool to quantify the potential effects of megafires on biodiversity, and identify species‐specific and landscape‐scale conservation actions that can assist recovery. Results Our approach identified 346 species in Victoria that had >40% of their modelled habitat affected by the megafire, including 45 threatened species, and 102 species with >40% of their modelled habitat affected by high severity fire. We then identified 21 candidate recovery actions that are expected to assist the recovery of biodiversity. For relevant landscape‐scale actions, we identified locations within and adjacent to the megafire extent that are expected to deliver cost‐effective conservation gains. Main conclusion The 2019/2020 megafires in south‐eastern Australia affected the habitat of many species and plant communities. Our framework identified a range of single‐species (e.g., supplementary feeding, translocation) and landscape‐scale actions (e.g., protection of refuges, invasive species management) that can help biodiversity recover from megafires. Conservation managers will be increasingly required to rapidly identify conservation actions that can help species recover from megafires, especially under a changing climate. Our approach brings together commonly used datasets (e.g., species distribution maps, trait databases, fire severity mapping) to help guide conservation responses and can be used to help biodiversity recover from future megafires across the world.

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10-20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of modelbased uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.