From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics

Peng, Changhui

doi:10.1016/s0304-3800(00)00348-3

Cited by 127 publications

(91 citation statements)

References 163 publications

(211 reference statements)

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“…At an ecosystem scale, many climate-vegetation models have been used to study the relationship between vegetation and climate, such as Holdridge's life zone model [30], Kira's index model [38] and the dynamic vegetation model [55,56]. At a species scale, previous studies have normally used the peak width at half height (PWH) to study the climatic thresholds of species [45,57].…”

Section: Species Record Database and Species Modeling Toolsmentioning

confidence: 99%

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Guo

et al. 2014

Forests

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Black locust (Robinia pseudoacacia L.) is a tree species of high economic and ecological value, but is also considered to be highly invasive. Understanding the global potential distribution and ecological characteristics of this species is a prerequisite for its practical exploitation as a resource. Here, a maximum entropy modeling (MaxEnt) was used to simulate the potential distribution of this species around the world, and the dominant climatic factors affecting its distribution were selected by using a jackknife test and the regularized gain change during each iteration of the training algorithm. The results show that the MaxEnt model performs better than random, with an average test AUC value of 0.9165 (±0.0088). The coldness index, annual mean temperature and warmth index were the most important climatic factors affecting the species distribution, explaining 65.79% of the variability in the geographical distribution. Species response curves showed unimodal relationships with the annual mean temperature and warmth index, whereas there was a linear relationship with the coldness index. The dominant climatic conditions in the core of OPEN ACCESSForests 2014, 5 2774 the black locust distribution are a coldness index of −9.8 °C-0 °C, an annual mean temperature of 5.8 °C-14.5 °C, a warmth index of 66 °C-168 °C and an annual precipitation of 508-1867 mm. The potential distribution of black locust is located mainly in the United States, the United Kingdom, Germany, France, the Netherlands, Belgium, Italy, Switzerland, Australia, New Zealand, China, Japan, South Korea, South Africa, Chile and Argentina. The predictive map of black locust, climatic thresholds and species response curves can provide globally applicable guidelines and valuable information for policymakers and planners involved in the introduction, planting and invasion control of this species around the world.

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Section: Species Record Database and Species Modeling Toolsmentioning

confidence: 99%

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Guo

et al. 2014

Forests

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“…The dynamic vegetation model is an efficient tool that has reduced some of the uncertainty about the responses of vegetation to climate change. But in the early stages of development, validating and capturing disturbance-related effects become major challenges for the dynamic vegetation models (Peng 2000). Different methods of climate-vegetation classifications and different sources of data, such as plant function types, climatic data and soil data, prevented the results from being comparable with each other (Hurtt et al 1998;Kittel et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Climate‐induced changes in the vegetation pattern of China in the 21st century

Li¹,

Cao²,

Li³

2006

Ecological Research

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Quantifying climate-induced changes in vegetation patterns is essential to understanding land-climate interactions and ecosystem changes. In the present study, we estimated various distributional changes of vegetation under different climate-change scenarios in the 21st century. Both hypothetical scenarios and Hedley RCM scenarios show that the transitional vegetation types, such as shrubland and grassland, have higher sensitivity to climatic change compared to vegetation under extreme climatic conditions, such as the evergreen broadleaf forest or desert, barren lands. Mainly, the sensitive areas in China lie in the Tibetan Plateau, Yunnan-Guizhou Plateau, northeastern plain of China and eco-zones between different vegetations. As the temperature increases, mixed forests and deciduous broadleaf forests will shift towards northern China. Grassland, shrubland and wooded grassland will extend to southeastern China. The RCM-project climate changes generally have caused positive vegetation changes; vegetation cover will probably improve 19% relative to baseline, and the forest will expand to 8% relative to baseline, while the desert and bare ground will reduce by about 13%.

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“…The HLZ model (Holdridge 1947(Holdridge , 1967(Holdridge , 1971) is a scheme which utilizes the three bioclimatic variables to formulate the biome distribution (Yue et al , 2006). The HLZ model relates the distribution of major biome types (termed life zones) to bioclimatic variables, and has been widely accepted for use in projecting the impact of climate change on vegetation distribution (Post et al 1982;Belotelov et al 1996;Peng 2000Fan et al 2013b). The biome types distribution in China include nival area, alpine dry tundra, alpine moist tundra, alpine wet tundra, alpine rain tundra, boreal desert, boreal dry scrub, boreal moist forest, boreal wet forest, boreal rain forest, cool temperate desert, cool temperate scrub, cool temperate steppe, cool temperate moist forest, cool temperate wet forest, cool temperate rain forest, warm temperate desert, warm temperate desert scrub, warm temperate thorn steppe, warm temperate dry forest, warm temperate moist forest, warm temperate wet forest, subtropical dry forest, subtropical moist forest, subtropical wet forest, tropical desert and tropical moist forest.…”

Section: Datasetsmentioning

confidence: 99%

Scenarios of land cover in Karst area of Southwestern China

Fan

Yue

et al. 2015

Environ Earth Sci

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The method of surface modeling of land cover scenarios (SMLCS) has been improved to simulate the scenarios of land cover in the karst areas of southwestern China. On the basis of the observation monthly climatic data collected from 782 weather stations of China during the period from 1981 to 2010, the climatic scenarios data of RCP26, RCP45 and RCP85 scenarios released by CMIP5, and the land cover current data of China in 2010, the land cover scenarios of southwestern China were respectively simulated. The average total accuracy and Kappa index of SMLCS are 90.25 and 87.96 %, respectively. The results show that there would be a very apparent similar variety on the spatial distribution pattern of land cover in the karst areas of southwestern China under all the three scenarios during the period from 2010 to 2100, but there would have the different change rate. In general, the change rate of land cover type under RCP85 scenario would be the fastest, then under RCP45 scenario, and under RCP26 would be the slowest. From 2010 to 2100, deciduous coniferous forest, deciduous broadleaf forest, grassland, cropland, nival area, and desert and bare rock would have a gradual decrease trend, while evergreen coniferous forest, evergreen broadleaf forests, mixed forest, scrublands, wetlands, construction built-up land, and water bodies body would gradually increase in karst areas of southwestern China, in which wetland would have the fastest increase rate (5.28 % per decade on average), and desert and bare rock would decrease with the fastest rate (2.34 % per decade on average).Keywords Surface modeling of land cover scenarios (SMLCS) Á Land cover Á Scenarios Á Karst areas of Southwestern China

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From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics

Cited by 127 publications

References 163 publications

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Mapping the Global Potential Geographical Distribution of Black Locust (Robinia Pseudoacacia L.) Using Herbarium Data and a Maximum Entropy Model

Climate‐induced changes in the vegetation pattern of China in the 21st century

Scenarios of land cover in Karst area of Southwestern China

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