Correlation Engine 2.0
Clear Search sequence regions


  • breast (2)
  • chile (1)
  • forest (22)
  • livestock (1)
  • native (1)
  • nutrient (1)
  • soil (3)
  • tree (4)
  • Sizes of these terms reflect their relevance to your search.

    The process of forest degradation, along with deforestation, is the second greatest producer of global greenhouse gas emissions. A key challenge that remains unresolved is how to quantify the critical threshold that distinguishes a degraded from a non-degraded forest. We determined the critical threshold of forest degradation in mature stands belonging to the temperate evergreen rain forest of southern Chile by quantifying key forest stand factors characterizing the forest degradation status. Forest degradation in this area is mainly caused by high grading, harvesting of fuelwood, and sub-canopy grazing by livestock. We established 160 500-m2 plots in forest stands that represented varied degrees of alteration (from pristine conditions to obvious forest degradation), and measured several variables related to the structure and composition of the forest stands, including exotic and native species richness, soil nutrient levels, and other landscape-scale variables. In order to identify classes of forest degradation, we applied multivariate and machine-learning analyses. We found that richness of exotic species (including invasive species) with a diameter at breast height (DBH) < 10 cm and tree density (N, DBH > 10 cm) were the two composition and structural variables that best explained the forest degradation status, e.g., forest stands with five or more exotic species were consistently found more associated with degraded forest and stands with N < 200 trees/ha represented degraded forests, while N > 1,000 trees/ha represent pristine forests. We introduced an analytical methodology, mainly based on machine learning, that successfully identified the forest degradation status that can be replicated in other scenarios. In conclusion, here by providing an extensive data set quantifying forest and site attributes, the results of this study are undoubtedly useful for managers and decision makers in classifying and mapping forests suffering various degrees of degradation. © 2021 The Ecological Society of America.

    Citation

    Alex Fajardo, Juan C Llancabure, Paulo C Moreno. Assessing forest degradation using multivariate and machine-learning methods in the Patagonian temperate rain forest. Ecological applications : a publication of the Ecological Society of America. 2022 Mar;32(2):e2495

    Expand section icon Mesh Tags

    Expand section icon Substances


    PMID: 34783406

    View Full Text