An Annual basal area growth model with multiplicative climate modifier fitted to longitudinal data for shortleaf pine

Abstract

Abstract Understanding climatic influences on annual basal area growth (ABAG) rates of individual trees is necessary to predict future stand dynamics. We fitted nonlinear ABAG models for shortleaf pine (Pinus echinata Mill.) with climate variables linearly added to the arguments of logistic and exponential multiplicative functions of climate variables as climate modifiers to incorporate 14 growing seasons and 30 month-specific climate variables including standardized precipitation index. Data were collected from permanently established plots in Arkansas and Oklahoma. Six re-measurement events collected between 1985 and 2014 provided five growth periods (GPs) and ABAG models were fitted using a mixed-effects approach. Model performance was evaluated using likelihood ratio tests and fit statistics. Climate variables from GPs expressed as deviations from long-term means that performed better than other candidate variables included (1) month-specific: June mean maximum air temperature (°C) (DTMAX6), and September precipitation (mm) (DPPT9); and (2) growing seasons: mean maximum air temperature (°C) (DGTMAX) and precipitation (mm) (DGPPT). ABAG models fitted with multiplicative climate modifiers provided improved growth predictions compared with models fitted with climate variables linearly added to the argument of a logistic function. There was positive correlation with DGTMAX and negative correlation with DMPPT. In addition, 1°C increase in mean maximum temperature had a greater cumulative effect on ABAG rates of young versus old trees. Fitting ABAG models with climate modifiers are useful for assessing variations in productivity due to climate change in the future.