Accurately demarcating distributions of biological taxa has long been at the core of ecology. Yet our understanding of the factors defining species range limits is incomplete, especially for tropical species in the Global South. Human-driven threats to the survival of many taxa are increasing, particularly habitat loss and climate change. Identifying distributional range limits of at-risk and data-limited species using Species Distribution Models (SDMs) can thus inform spatial conservation planning to mitigate these threats. The Madagascar Peregrine Falcon Falco peregrinus radama is the resident sub-species of the Peregrine Falcon complex distributed across Madagascar, Mayotte, and the Comoros Islands. There are currently significant knowledge gaps regarding its distribution, habitat preferences, and population size. Here, we use penalized logistic regression and environmental ordination to identify Madagascar Peregrine Falcon habitat in both geographic and environmental space and propose a population size estimate based on inferred habitat. From the penalized logistic regression model, the core habitat area of the Madagascar Peregrine Falcon extends across the central and northern upland plateau of Madagascar with patchier habitat across coastal and low-elevation areas. Range-wide habitat use in both geographic and environmental space indicated positive associations with high elevation and aridity, coupled with high vegetation heterogeneity and >95% herbaceous landcover, but general avoidance of areas >30% cultivated land and >10% mosaic forest. Based on inferred high-class habitat from the penalized logistic regression model, we estimate this habitat area could potentially support a population size ranging between 150 and 300 pairs. Following IUCN Red List guidelines, this subspecies would be classed as ‘Vulnerable’ due to its small population size. Despite its potentially large range, the Madagascar Peregrine Falcon has specialised habitat requirements and would benefit from targeted conservation measures based on spatial models to maintain viable populations.
