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Abstract
This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions.
Highlights
Vegetation is one of the most important components of an ecosystem and is affected by interaction between terrestrial and atmospheric systems through carbon and hydrologic cycles [1,2]
Our results revealed that the Redundancy Analysis (RDA) model based on the Empirical Mode Decomposition (EMD) filtered data explained 96.2% of the variation of Enhanced Vegetation Index (EVI)
Our findings reinforced that precipitation had strong and positive effect on EVI compared to temperature, indicating that precipitation was the dominant climate factor affecting EVI in the study region
Summary
Vegetation is one of the most important components of an ecosystem and is affected by interaction between terrestrial and atmospheric systems through carbon and hydrologic cycles [1,2]. In ecological and geographical sciences, there is a long tradition to study spatial variability of climate change, its impact on vegetation growth and varied response of vegetation to climate change [20]. It is relatively easy to find long records of climate change, it is difficult to find long time-series data about vegetation growth and evolution [9] Both climate change and vegetation growth display distinctive seasonal and annual fluctuations as well as abrupt variations. Current studies of vegetation growth and its response to climate change are often using time-averaged or cumulative data to develop ecological or environmental models. The overall trends of EVI, precipitation, and temperature are extracted via EMD and, redundancy analysis is integrated to examine partial and joint effects of precipitation and temperature on primary productivity at regional, local and plant community scales
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