Spatial structure of the abiotic environment and its association with sapling community structure and dynamics in a cloud forest

Int J Biometeorol. 2012 Mar;56(2):305-18. doi: 10.1007/s00484-011-0434-5. Epub 2011 May 8.

Abstract

Analyzing the relationship between the spatial structures of environmental variables and of the associated seedling and sapling communities is crucial to understanding the regeneration processes in forest communities. The degree of spatial structuring (i.e., spatial autocorrelation) of environmental and sapling community variables in the cloud forest of Teipan, S Mexico, were analyzed at a 1-ha scale using geostatistical analysis; after fitting semivariogram models for each set of variables, the association between the two sets was examined through cross-variograms. Kriging maps of the sapling community variables (density, cover, species richness, and mortality and recruitment rates) were obtained through conditional simulation method. Canopy openness, total solar radiation, litter depth, soil temperature and soil moisture were spatially structured, as were sapling density, species richness and sapling mortality rate. Mean range in semivariograms for environmental and sapling community variables were 13.14 ± 3.67 and 12.68 ± 5.71 m (±SE), respectively. The spatial structure of litter depth was negatively associated with the spatial structures of sapling density, species richness, and sapling community cover; in turn, the spatial structure of soil moisture was positively associated with the spatial structure of recruitment rate. These associations of the spatial structures of abiotic and sapling community variables suggest that the regeneration processes in this cloud forest is driven by the existence of different microsites, largely characterized by litter depth variations, across which saplings of tree species encounter a range of opportunities for successful establishment and survival.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biodiversity*
  • Data Interpretation, Statistical
  • Linear Models
  • Mexico
  • Seedlings*
  • Soil / analysis
  • Solar Energy
  • Temperature
  • Trees*
  • Water / analysis

Substances

  • Soil
  • Water