Elevational Shifts in the Topographic Position of Polylepis Forest Stands in the Andes of Southern Peru

Abstract

Abstract:The patchy distribution of high-Andean treeline forests has provoked discussion about the relative importance of anthropogenic and climatic causes of this pattern, both of which vary with topography. We aimed to understand the topographic controls on the distribution of Polylepis subsericans treeline forests in the Andes of southern Peru, and the changes in these controls along an elevational gradient. We mapped Polylepis forests in the Cordillera Urubamba, Cusco, using high-resolution aerial images and related forest cover to topographic variables extracted from a digital terrain model (30-m resolution). The variables were selected based on their expected biological relevance for tree growth at high elevations. We constructed logistic regression models of forest cover, separately for each of five 100-m elevational belts. To deal with spatial autocorrelation, models were based on randomized 10% subsampling of the data with 1000 repetitions. The results suggest a consistent shift in topographic preference with elevation, with forests at lower elevations showing a preference for topographically protected sites near rivers and forests at higher elevations being increasingly restricted to north-facing and well-drained sites. Our study offers the first indication of the ability of Andean treeline forests to benefit from the topographic heterogeneity of the high-Andes. Providing that dispersal and establishment are possible, local relocation between microsites could help these forests to persist regionally in spite of changing climatic conditions.

Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data

“…In order to analyse the relationship of the complex topography, the forest structure and the AGB distribution in the study area, the Topographic Position Index (TPI), which identifies ridges and depressions [30,67] and the Morphometric Protection Index (MPI), which illustrates the sheltering effect of the surrounding terrain or the exposure of the cell [29], were calculated by means of the DTM (0.25 m × 0.25 m resolution), using the software SAGA GIS [99,100]. The TPI compares the elevation of each grid cell to the mean elevation within a specific distance to determine whether the position of the cell is situated on a ridge (positive values) or in a valley (negative values), which permits the separation into ravine and ridge forest [101].…”

“…The TPI compares the elevation of each grid cell to the mean elevation within a specific distance to determine whether the position of the cell is situated on a ridge (positive values) or in a valley (negative values), which permits the separation into ravine and ridge forest [101]. The MPI evaluates, up to a given distance, whether the respective cell is protected by the surrounding relief to illustrate the topographic preference for bigger trees within the forest stands [29]. MPI-values near zero indicate unprotected locations, whereas higher positive values indicate the degree of protection of a grid cell within the catchment.…”

“…In consequence, field plot measurements generally under-or overestimate the AGB in dense natural forest stands [3]. As Gourlet-Fleury et al [27] clarified, for a reliable AGB estimation, the field plot distribution as well as the spatial scale must be fine enough to capture the landscape variability, which is especially complicated in tropical high mountains, because of the relation between forest structure, topography and morphometric protection of the site [16,[28][29][30].…”

“…From the LiDAR data the H of the individual trees as well as the forest, terrain and land cover structure can be derived, whereas from the field plot measurements the local mean WD and the specific height-diameter relationship are obtained. The AGB stock and its distribution are analysed by means of the Topographic Position Index (TPI), which identifies ridges and depressions [30,67] and the Morphometric Protection Index (MPI), which illustrates the sheltering effect of the surrounding terrain [29]. The results are compared to AGB values calculated in previous plot-based investigations within the same area.…”

Variation in Tree Community Composition and Carbon Stock under Natural and Human Disturbances in Andean Forests, Peru

“…Also, the linear relationship between community composition and AGB was a key to evaluate the validity of community composition as an indicator of change in biodiversity along the degree of forest degradation. In a region covering a wide elevation range, however, AGB might not function as a surrogate of forest degradation, because AGB decreases not only by forest degradation derived from anthropogenic disturbances (e.g., land use history including selective logging [8], logging road network [13] and fire related to agricultural frontiers [14]), but also by increased elevation with changes in climatic conditions [15] and topography [16]. Thus, the relationship between community composition and AGB can be masked by various factors.…”