The article discusses the use of species distribution models (SDMs) to project future species distributions under climate change, highlighting the assumptions and uncertainties involved. SDMs are based on ecological niche theory, which describes the environmental conditions a species requires to survive and reproduce. These models use current data on species occurrences and environmental variables to predict future distributions. However, the accuracy of these projections depends on several factors, including the quality and quantity of data, the algorithms used, and the scale at which the models are applied. The study focuses on 60 California landbird species, projecting their future distributions under different climate scenarios. The results indicate that most species are expected to experience a decline in distribution by 2070, with significant losses in certain "hotspots" of vulnerability. Changes in species richness vary across the state, with some areas showing greater projected losses than others. The study also highlights the potential for changes in species co-occurrences, creating spatial variation in similarities between current and future assemblages. The authors emphasize the importance of addressing the assumptions and uncertainties in SDMs to improve their reliability. They note that while SDMs can provide valuable insights for conservation and management, the uncertainties in model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. The study concludes that SDMs can be a useful tool for incorporating future conditions into conservation and management practices, but their effectiveness depends on accurately documenting the sources and magnitudes of uncertainty and being willing to act despite these uncertainties. The article also discusses the limitations of SDMs, including the potential for omission and commission errors, the role of dispersal and landscape fragmentation, and the influence of biotic interactions. It highlights the need for further research to improve the accuracy of SDMs, particularly in incorporating demographic, dispersal, and landscape effects, as well as biotic interactions. The study underscores the importance of considering uncertainties in conservation and management decisions, emphasizing the need for adaptive management strategies in an increasingly uncertain world.The article discusses the use of species distribution models (SDMs) to project future species distributions under climate change, highlighting the assumptions and uncertainties involved. SDMs are based on ecological niche theory, which describes the environmental conditions a species requires to survive and reproduce. These models use current data on species occurrences and environmental variables to predict future distributions. However, the accuracy of these projections depends on several factors, including the quality and quantity of data, the algorithms used, and the scale at which the models are applied. The study focuses on 60 California landbird species, projecting their future distributions under different climate scenarios. The results indicate that most species are expected to experience a decline in distribution by 2070, with significant losses in certain "hotspots" of vulnerability. Changes in species richness vary across the state, with some areas showing greater projected losses than others. The study also highlights the potential for changes in species co-occurrences, creating spatial variation in similarities between current and future assemblages. The authors emphasize the importance of addressing the assumptions and uncertainties in SDMs to improve their reliability. They note that while SDMs can provide valuable insights for conservation and management, the uncertainties in model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. The study concludes that SDMs can be a useful tool for incorporating future conditions into conservation and management practices, but their effectiveness depends on accurately documenting the sources and magnitudes of uncertainty and being willing to act despite these uncertainties. The article also discusses the limitations of SDMs, including the potential for omission and commission errors, the role of dispersal and landscape fragmentation, and the influence of biotic interactions. It highlights the need for further research to improve the accuracy of SDMs, particularly in incorporating demographic, dispersal, and landscape effects, as well as biotic interactions. The study underscores the importance of considering uncertainties in conservation and management decisions, emphasizing the need for adaptive management strategies in an increasingly uncertain world.