Dinámica y densidad espacial de la biomasa aérea en un bosque altoandino del municipio de Guasca-Cundinamarca

Abstract

The high Andean forests are considered one of the most ecologically relevant ecosystems due to endemism and diversity of species, but they are also more vulnerable due to the processes of expansion of the agricultural frontier and global climate change. Among the ecosystem services offered by this forest is the accumulation of atmospheric carbon, whose quantification and monitoring can contribute to the consolidation of strategies for its management and conservation. Based on the above, in this document the use of geographic information can be observed with the points sampled in the field of a high Andean forest ecosystem in the municipality of Guasca-Cundinamarca. The georeferenced points were taken within a permanent plot, with measurements in the years 2009, 2013, and 2018 where it was sought to identify the dynamics of the density of aerial biomass, the exploratory analysis of the data shows a tendency to a normal behavior when perform a logarithmic transformation. It was found and spatially identified that the individuals are not distributed randomly but in a grouped way in the field, which were modeled by a point process type Strauss and Poisson with the intensity of 𝛽. It was also possible to model a surface from a non-parametric kernel estimator that allowed us to see the sites of the plot where the individuals with the highest biomass are grouped with a high probability, where these processes of grouping and density on the ground can occur due to to the vegetal associations between species, which are known in the mature vegetation of the ecosystem, in turn, is considered as an indicator of health and integrity, the above due to the fact that these condition the diversity and availability of resources. It was possible to observe the variation of the aerial biomass density in the forest in the three proposed evaluation years, we observed the increase between the classes in the state transition diagram, where each class changes to the immediately superior class, but continues with the same spatial pattern initially computed. In addition to this, it allowed us to calculate a probability of change in each of the classes to the future year of the evaluated interval, in this case for the year 2017 and the year 2023.