Abstract
A biome is a major regional ecological community characterized by distinctive life forms and principal plants. Many empirical schemes such as the Holdridge Life Zone (HLZ) system have been proposed and implemented to predict the global distribution of terrestrial biomes. Knowledge of physiological climatic limits has been employed to predict biomes, resulting in more precise simulation, however, this requires different sets of physiological limits for different vegetation classification schemes. Here, we demonstrate an accurate and practical method to construct empirical models for biome mapping: A convolutional neural network (CNN) was trained by an observation-based biome map, as well as images depicting air temperature and precipitation. The trained model accurately simulated a global map of current terrestrial biome distribution. Then, the trained model was applied to climate scenarios toward the end of the 21st century, predicting a significant shift in global biome distribution with rapid warming trends. Our results demonstrate that the proposed CNN approach can provide an efficient and objective method to generate preliminary estimations of the impact of climate change on biome distribution. Moreover, we anticipate that our approach could provide a basis for more general implementations to build empirical models of other climate-driven categorical phenomena.
Highlights
Terrestrial biomes and climate are among the earliest known ecological concerns, and many empirical schemes have been proposed to characterize their relationship (Prentice and Leemans, 1990)
Our results demonstrate that the proposed convolutional neural network (CNN) approach can provide an efficient and objective method to generate preliminary estimations of the impact of climate change on biome distribution
45 Here, we demonstrate an accurate and practical method to construct empirical models for operational global biome mapping via a convolutional neural network (CNN) approach
Summary
Terrestrial biomes and climate are among the earliest known ecological concerns, and many empirical schemes have been proposed to characterize their relationship (Prentice and Leemans, 1990). One of the best known of these schemes is the Holdridge Life Zone (HLZ) system (Holdridge, 1947), which classifies vegetation distribution using only two independent variables: the annual mean precipitation and the bio-temperature (i.e., mean of positive air temperature). The HLZ scheme accounts well for ecophysiological constraints. This scheme is based on biotemperatures, given that plant productivity becomes negligible at temperatures below 0°C. It employs logarithmic conversions to better depict the relationship between climatic parameters and life zone boundaries, in 30 quantitative recognition of the temperature control of metabolic processes.
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