Abstract
The forest dynamics are usually explained by the precipitation and temperature through fixed effects models using ordinary least squares and geographically weighted regression methods. However, forest dynamics were found insufficiently explained by meteorological factors as the fixed effects models were not designed to account for random effects. In this study, we utilized three types of forests located in the Gulf of Mexico Coast region, including softwood, hardwood, and mixed forests to investigate the underlying forest dynamics to meteorological variations by incorporating random effects into fixed effects models. Four types of linear mixed effects models (LMMs) were developed for regressing the normalized difference of vegetation index (NDVI) against two explanatory variables: precipitation and temperature. By assuming that the intercept and slope parameters estimated from LMMs would vary randomly, we intended to explore if the amount of variation in the NDVI variables could be reduced by the use of random effects variables. The results suggested that the random intercept and random slope model fitted the data better than the random intercept model with higher R 2, lower Akaike information criterion, and Bayesian information criterion values. The R 2 value indicated that the explanatory power of the LMM varies between forest types. Moreover, this study revealed that a linear mixed effects model could significantly reduce the unexplained variance by introducing random effects variables, and forest dynamics is a synthetic result of the mixed effects of temperature and fixed effects of precipitation.
Highlights
C LIMATE change is of the fundamental importance to the changes in vegetation conditions [1]
By observing the variation of R2 values, we found that the marginal R2 values at the given season/year across four types of linear mixed effects models (LMMs) are the same and this is because of the fact that the marginal R2 only describes the proportion of variance explained by fixed effects variables alone
The random intercept and random slope model best fitted the data regarding the larger conditional R2, smaller Akaike information criterion (AIC), and smaller Bayesian information criterion (BIC) values, suggesting a significant improvement in the model fitting by accounting for the combined effects of random effects and fixed effects
Summary
C LIMATE change is of the fundamental importance to the changes in vegetation conditions [1]. The distribution of vegetation can be explained by climatic factors, such as precipitation, temperature, potential. As previous studies have noted, factors, such as precipitation and temperature, could alter vegetation patterns, and which were believed capable of explaining the climate-related variation in forests [8]. Wang et al [9] suggested that the vegetation growth in North America’s mid to high latitudes is very sensitive to the temperature changes and can be partly explained by changes in the trends of temperatures. Li and Meng [12] have examined the effects of climate change on forest dynamics across the Gulf Coast of the United States and found that the seasonality of precipitation and temperature can explain forest dynamics
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