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A topography-based model of forest cover at the alpine tree line in the tropical Andes

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Abstract Aim 

To present a method that assesses the influence of environmental variables, including climate, substrate, topography, and anthropogenic disturbances, on the distribution of Andean forest at the tree line, and to compare this forest distribution between areas. Location 

Sangay National Park, on the eastern slopes of the Andes in central Ecuador. Methods 

A logistic regression model was built using topographical variables and environmental indices, derived from a digital elevation model, to explain forest cover, derived from a Landsat ETM image, in a zone around the average tree line altitude. Results 

The model shows that after altitude, which can explain about 80% of forest cover, wetness has the next strongest effect (areas accumulating water, but also cold air, were devoid of forest, resulting in inverted tree lines), followed by eastness (western slopes had forest to higher altitudes). Application of the model in two nearby areas showed that the real tree line was lower than the predicted tree line in both areas, probably owing mainly to macroclimatic differences in one area, and partly also to human land use in the other. The locations with the largest deviations could be the focus of further research concerning human impacts on tree line vegetation. Main conclusions 

The tree line is located at lower altitudes on east-facing slopes, which may be because high levels of radiation are received by east-facing slopes in the clear mornings, resulting in the photoinhibition of tree seedlings in the páramo. In spite of the limitations of the quality and resolution of the remote sensing data, the presented method provides indications for important ecological factors at the tree line. The method also allows the detection of differences in tree line position between areas, which may reflect climatic differences or the location of anthropogenic disturbances.
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Keywords: Andes; Ecuador; Sangay National Park; cloud forest; digital elevation model; logistic regression model; páramo; remote sensing; topography

Document Type: Research Article

Publication date: 2008-04-01

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