Time series in analysis of yerba-mate biennial growth modified by environment

Int J Biometeorol. 2011 Mar;55(2):161-71. doi: 10.1007/s00484-010-0322-4. Epub 2010 Jun 3.

Abstract

To assess differences in the lag-effect pattern in the relationship between yerba-mate biennial growth and environmental factors, a time-series analysis was performed. A generalized Poisson regression model was used to control time trends, temperature, growing degree days (GDD), rainfalls and night length (NL). It was hypothesized that the active growth and growth pauses in yerba-mate are controlled endogenously and modified by environment, and that genders would respond differently to environmental modifications. The patterns in the lag effect from the distributed-lag models were similar to those of time-series models with meteorological data means with lag = 0. GDD and NL were principal factors affecting biennial yerba-mate shoot elongation and the number of green leaves of females grown in monoculture, besides their significant effects on metamer emission and leaf area in males grown in monoculture. NL also had a significant influence on shoot elongation and leaf area of both genders grown in forest understorey (FUS), indicating that yerba-mate growth is synchronized by an internal clock sensitive to temperature adjustments. The morphological plasticity and the adaptation efforts of yerba-mate were more pronounced in monoculture than in FUS. Sexual dimorphism was expressed-males were more sensitive to environmental changes than females, especially in monoculture. Growth modifications were much more intense when plants were grown in a cultivation system that is less like yerba-mate natural habitat (monoculture) than in one resembling its natural habitat (FUS). Our data support the ecological specialization theory.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Data Interpretation, Statistical*
  • Ecosystem*
  • Ilex paraguariensis / growth & development*
  • Models, Biological*
  • Models, Statistical*
  • Seasons*