The paper introduces a multivariate approach called Ecological-Niche Factor Analysis (ENFA) for studying geographic species distribution without requiring absence data. Building on Hutchinson’s concept of the ecological niche, ENFA compares the distribution of observed localities of a focal species with a reference set of the study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species' optimum and the mean habitat within the reference area. Other factors maximize the specialization of the focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. The eigenvectors and eigenvalues are interpretable and can be used to build habitat-suitability maps. This method is particularly useful when absence data are unavailable, unreliable, or meaningless. The authors illustrate the method with an application to the Alpine ibex in Switzerland, showing that ibexes prefer high-altitude, steep, and rocky slopes with rich pastures but avoid forests and human activities. The habitat-suitability map generated by ENFA provides insights into the species' ecological requirements and can be used for conservation planning.The paper introduces a multivariate approach called Ecological-Niche Factor Analysis (ENFA) for studying geographic species distribution without requiring absence data. Building on Hutchinson’s concept of the ecological niche, ENFA compares the distribution of observed localities of a focal species with a reference set of the study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species' optimum and the mean habitat within the reference area. Other factors maximize the specialization of the focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. The eigenvectors and eigenvalues are interpretable and can be used to build habitat-suitability maps. This method is particularly useful when absence data are unavailable, unreliable, or meaningless. The authors illustrate the method with an application to the Alpine ibex in Switzerland, showing that ibexes prefer high-altitude, steep, and rocky slopes with rich pastures but avoid forests and human activities. The habitat-suitability map generated by ENFA provides insights into the species' ecological requirements and can be used for conservation planning.