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

Abstract

Deforestation and forest degradation in Andean forests is influenced by natural and social environments including a wide elevation range and anthropogenic disturbance. Tree community composition is receiving attention as a key indicator of forest degradation. However, difference in factors affecting community composition at different elevation zones remains unclear. We aimed at elucidating factors (natural and human disturbances, and forest characteristics) that influence the variations in community composition in Andean forests. We conducted a ground-based survey setting 45 plots across a wide elevation range (ca. 600 to 3500 m a.s.l.) in Cusco region, Peru. Above ground biomass (AGB) decreased with increasing elevation. The generalized linear models for multivariate abundance data suggested that a factor affecting community composition was natural disturbance (erosion) at low elevation (<1000 m), while human disturbance (infrastructure such as sheds and trails) at high elevation (≥2400 m). Within each of the different elevation zones, the AGB affected community composition only at mid elevation (1000-2400 m), whereas mean tree height showed a consistent effect on community composition across the three elevations. Our results suggest that the effects of human disturbance on community composition were more prominent at higher elevation. The results also suggest that mean tree height may have a potential to be a key measure for evaluating variations in community composition in Andean forests.

Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

“…The discretization of the plots according to the negative or positive nature of the β-diversity scores has been reported before, also relating biomass mean values to certain classes [10]. The previous study and other related [11,45] had found a clear link between community taxonomic diversity and carbon stock through patches with human disturbances. Our findings support that relation, because the greatest differences in biomass and beta diversity were found between the same areas.…”

“…Actually, even with the relatively lower correlation of the LiDAR metrics, the addition of the CHM variable to the model improved its predictive power, representing almost a third of the total explained variability. Height variables had been considered before as key measures for evaluating variations in community composition [11], and in this study, the canopy height was especially momentous for distinguishing the dissimilarity in tree species composition between the shorter areas with agriculture disturbance history, and the taller areas with selective logging disturbance history (Table 1).…”

“…This ordination technique is widely used for synthesizing the typical matrix of a large number of species by plots, into a reduced dimensional space (two metric variables in this case), that could be regressed against remotely sensing data. nMDS is a non-parametric technique based on Bray-Curtis distances that preserves the ranked dissimilarity in the compositional space [57,58], with precedent in both ecology and remote sensing [11,22,25,59]. The ordination results were evaluated using the stress function [55], which characterizes the disagreement between the rank-order of distances in the original multidimensional data, relative to the distances given by the ordination in the bidimensional space.…”

“…Tree community composition is a key indicator of forest degradation [10,11], because it is one of the last recovering variables after a disturbance [12,13], and consequently it is an excelent measure of diversity loss. Crown-level assessment of species composition has been increasingly successful for boreal and temperate forests [14][15][16], but its applicability remains challenging for the most complex tropical forests.…”

“…In some studies, it is just an intermediated step to ulterior purposes like analyze topics on vegetation ecology [33,34], predict the pertinence probability of each pixel to certain habitat types [27] or classify intact and degraded forest with multispectral imagery [10]. Some other studies have focused specifically on modeling compositional ordination, in order to propose a predictive map of the floristic gradient, which can directly give us an intuitive access to dissimilarity patterns, very difficult to discern from categorical maps [11]. Several pieces of research have been done in grasslands [21,27,29,30], and temperate [22,24,26] or tropical forested landscapes [19,23,25].…”