Abstract
Subtropical forest productivity is significantly affected by both natural disturbances (local and regional climate changes) and anthropogenic activities (harvesting and planting). Monthly measures of forest aboveground productivity from natural forests (primary and secondary forests) and plantations (mixed and single-species forests) were developed to explore the sensitivity of subtropical mountain productivity to the fluctuating characteristics of climate change in South China, spanning the 35-year period from 1981 to 2015. Statistical analysis showed that climate regulation differed across different forest types. The monthly average maximum temperature, precipitation, and streamflow were positively correlated with primary and mixed-forest aboveground net primary productivity (ANPP) and its components: Wood productivity (WP) and canopy productivity (CP). However, the monthly average maximum temperature, precipitation, and streamflow were negatively correlated with secondary and single-species forest ANPP and its components. The number of dry days and minimum temperature were positively associated with secondary and single-species forest productivity, but inversely associated with primary and mixed forest productivity. The multivariate ENSO (EI Niño-Southern Oscillation) index (MEI), computed based on sea level pressure, surface temperature, surface air temperature, and cloudiness over the tropical Pacific Ocean, was significantly correlated with local monthly maximum and minimum temperatures (Tmax and Tmin), precipitation (PRE), streamflow (FLO), and the number of dry days (DD), as well as the monthly means of primary and mixed forest aboveground productivity. In particular, the mean maximum temperature increased by 2.5, 0.9, 6.5, and 0.9 °C, and the total forest aboveground productivity decreased by an average of 5.7%, 3.0%, 2.4%, and 7.8% in response to the increased extreme high temperatures and drought events during the 1986/1988, 1997/1998, 2006/2007, and 2009/2010 EI Niño periods, respectively. Subsequently, the total aboveground productivity values increased by an average of 1.1%, 3.0%, 0.3%, and 8.6% because of lagged effects after the wet La Niña periods. The main conclusions of this study demonstrated that the influence of local and regional climatic fluctuations on subtropical forest productivity significantly differed across different forests, and community position and plant diversity differences among different forest types may prevent the uniform response of subtropical mountain aboveground productivity to regional climate anomalies. Therefore, these findings may be useful for forecasting climate-induced variation in forest aboveground productivity as well as for selecting tree species for planting in reforestation practices.
Highlights
Due to the influences of the Tibetan-Himalayan Highland and Mediterranean climates, the subtropical zone in the Asian and European continents is mostly dry [1]
In each of the four El Niño/Southern Oscillation (ENSO) transition periods, the relationships between annual precipitation, streamflow, mean maximum annual temperature, and the number of dry days and the multivariate ENSO index (MEI) were remarkably similar in trend (Figure 3)
Spearman correlation analysis indicated that single-species forest productivity was not significantly influenced by the MEI and dry days (DD)
Summary
Due to the influences of the Tibetan-Himalayan Highland and Mediterranean climates, the subtropical zone in the Asian and European continents is mostly dry [1]. Small coastal areas of the Western Pacific have sufficient moisture to ensure the development of subtropical forests, including South China and a chain of islands from Taiwan to Okinawa [1,2,3]. The subtropical forests in South China are invaluable assets from a phytogeographical point of view [4]. A well-developed evergreen broadleaf forest exists in the eastern Nanling Mountains, which is the most extensive mountain range in South China. It is ecologically important to explore how the growth and aboveground productivity of the different forest stands change with the local-regional climate in the Nanling area. Climate change is a main important driver of short-term forest function [5]
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