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A spatially explicit empirical model of structural development processes in natural forests based on climate and topography.

Yuichi YamauraDavid B LindenmayerYusuke YamadaHao GongToshiya MatsuuraYasushi MitsudaTakashi Masaki
Published in: Conservation biology : the journal of the Society for Conservation Biology (2019)
Stand structure develops with stand age. Old-growth forests with well-developed stand structure support many species. However, development rates of stand structure likely vary with climate and topography. We modeled structural development of 4 key stand variables and a composite old-growth index as functions of climatic and topographic covariates. We used a hierarchical Bayesian method for analysis of extensive snap-shot National Forest Inventory (NFI) data in Japan (n = 9244) to account for differences in stand age. Development rates of structural variables and the old-growth index exhibited curvilinear responses to environmental covariates. Flat sites were characterized by high rates of structural development. Approximately 150 years were generally required to attain high values (approximately 0.8) of the old-growth index. However, the predicted age to achieve specific values varied depending on environmental conditions. Spatial predictions highlighted regional variation in potential structural development rates. For example, sometimes there were differences of >100 years among sites, even in the same catchment, in attainment of a medium index value (0.5) after timber harvesting. The NFI data suggested that natural forests, especially old natural forests (>150 years), remain generally on unproductive ridges, steep slopes, or areas with low temperature and deep snow, where many structural variables show slow development rates. We suggest that maintenance and restoration of old natural forests on flat sites should be prioritized for conservation due to the likely rapid development of stand structure, although remaining natural forests on low-productivity sites are still important and should be protected.
Keyphrases
  • climate change
  • human health
  • electronic health record
  • big data
  • deep learning
  • artificial intelligence
  • quality improvement
  • psychometric properties
  • data analysis