Potential distribution model of Leontochir ovallei using remote sensing data

S. Payacán, F.D. Alfaro, W. Pérez-Martínez, I. Briceño-de-Urbaneja


Predicting the potential distribution of short-lived species with a narrow natural distribution range is a difficult task, especially when there is limited field data. The possible distribution of L. ovallei was modeled using the maximum entropy approach. This species has a very restricted distribution along the hyperarid coastal desert in northern Chile. Our results showed that local and regional environmental factors define its distribution. Changes in altitude and microhabitat related to the landforms are of critical importance at the local scale, whereas cloud cover variations associated with coastal fog was the principal factor determining the presence of L. ovallei at the regional level. This study verified the value of the maximum entropy in understanding the factors that influence the distribution of plant species with restricted distribution ranges.


Leontochir ovallei; potential distribution; machine learning techniques; maximum entropy; environmental factors

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