The article discusses the concepts, methods, and assumptions behind ecological niche modeling (ENM) and the distributional areas of species. It emphasizes the importance of clear definitions of "niche" and "distributional area" to enhance conceptual clarity. The authors argue for restricted definitions of niche, inspired by Grinnell's early work, to operationalize fundamental concepts like fundamental, potential, and realized niches. They address niche conservatism, showing how niche change can be measured and highlighting the irregular structure of niche space, which is crucial for understanding niche evolution. The article also discusses the role of biotic interactions in ENM and the differences between presence-only and presence-absence data in niche modeling. It stresses the need for explicit ecological and mathematical assumptions in ENM to improve interpretation of results. The authors distinguish between Grinnellian niches (based on environmental variables) and Eltonian niches (involving ecological interactions). They illustrate the duality of environmental and geographic spaces, showing how environmental conditions translate to geographic areas. The article also explores the Eltonian Noise Hypothesis, which suggests that biotic factors may not always be necessary for accurate predictions. The authors highlight the challenges of modeling niche evolution and the importance of considering different types of absence data. They conclude that different niche modeling algorithms calculate different quantities and that the results of ENM should be interpreted with an understanding of the underlying assumptions. The article underscores the complexity of niche and distributional area concepts and the need for careful consideration of ecological and mathematical factors in ENM.The article discusses the concepts, methods, and assumptions behind ecological niche modeling (ENM) and the distributional areas of species. It emphasizes the importance of clear definitions of "niche" and "distributional area" to enhance conceptual clarity. The authors argue for restricted definitions of niche, inspired by Grinnell's early work, to operationalize fundamental concepts like fundamental, potential, and realized niches. They address niche conservatism, showing how niche change can be measured and highlighting the irregular structure of niche space, which is crucial for understanding niche evolution. The article also discusses the role of biotic interactions in ENM and the differences between presence-only and presence-absence data in niche modeling. It stresses the need for explicit ecological and mathematical assumptions in ENM to improve interpretation of results. The authors distinguish between Grinnellian niches (based on environmental variables) and Eltonian niches (involving ecological interactions). They illustrate the duality of environmental and geographic spaces, showing how environmental conditions translate to geographic areas. The article also explores the Eltonian Noise Hypothesis, which suggests that biotic factors may not always be necessary for accurate predictions. The authors highlight the challenges of modeling niche evolution and the importance of considering different types of absence data. They conclude that different niche modeling algorithms calculate different quantities and that the results of ENM should be interpreted with an understanding of the underlying assumptions. The article underscores the complexity of niche and distributional area concepts and the need for careful consideration of ecological and mathematical factors in ENM.