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. 2019 Sep 25;7:e7763. doi: 10.7717/peerj.7763

Human disturbance caused stronger influences on global vegetation change than climate change

Xianliang Zhang 1,2,, Xuanrui Huang 1,2
Editor: Simone Vieira
PMCID: PMC6765362  PMID: 31579623

Abstract

Global vegetation distribution has been influenced by human disturbance and climate change. The past vegetation changes were studied in numerous studies while few studies had addressed the relative contributions of human disturbance and climate change on vegetation change. To separate the influences of human disturbance and climate change on the vegetation changes, we compared the existing vegetation which indicates the vegetation distribution under human influences with the potential vegetation which reflects the vegetation distribution without human influences. The results showed that climate-induced vegetation changes only occurred in a few grid cells from the period 1982–1996 to the period 1997–2013. Human-induced vegetation changes occurred worldwide, except in the polar and desert regions. About 3% of total vegetation distribution was transformed by human activities from the period 1982–1996 to the period 1997–2013. Human disturbances caused stronger damage to global vegetation change than climate change. Our results indicated that the regions where vegetation experienced both human disturbance and climate change are eco-fragile regions.

Keywords: Vegetation types, Human activity, Vegetation change, Climate change, Climatic effects

Introduction

Vegetation is the most important component of the global terrestrial ecosystem. Influenced by human disturbance and climate change, global vegetation has shifted from a semi-wild terrestrial biosphere to a mostly anthropogenic biome (Ellis et al., 2010). The changes in the global vegetation or land use in the past were addressed in numerous studies (e.g.,  Wang, Price & Arora, 2006; Li et al., 2017; Song et al., 2018). However, few studies evaluated the individual contributions of human disturbance and climate change on vegetation changes, which is crucial to know whether the past vegetation changes are mainly caused by human or nature.

Human disturbances and climate change are the two main factors that determine the changes in regional vegetation distribution. The land surface of the Earth has been modified by human activities (e.g., farming, construction and grazing) for centuries, and it has been significantly changed by human activities (Foley et al., 2005; Ellis & Ramankutty, 2008; DeFries et al., 2010). More of the land surface is being transformed as the human population continues to increase (Goldewijk et al., 2011; Ellis et al., 2013). The new geological epoch has been referred to as the Anthropocene (Lewis & Maslin, 2015) and the period in which biomes have been severely transformed by human disturbance is referred to as an anthrome (Ellis & Ramankutty, 2008). With the continuous expanding human settlements, the distribution of vegetation types across the globe has changed markedly compared with the potential distribution of vegetation types which reflect vegetation distribution in the absence of anthropogenic influences.

Climate change is another factor that causes shifts in the vegetation distribution (Kelly & Goulden, 2008). Climate change had strong influences on the transformation of the vegetation distribution. Tropical rainforest and arctic tundra have experienced boundary changes as a result of climatic change (Diaz & Eischeid, 2007; Cook & Vizy, 2008; Zhang & Yan, 2014a). Widespread forest die-off from drought and heat stress increased with climate change (Allen et al., 2010; Anderegg, Kane & Anderegg, 2013). Some dying forests are likely to be replaced by other vegetation types. For instance, boreal forests are experiencing the strongest warming among forest ecosystems, and large area of boreal forest are expected to be replaced by other biomes (Gauthier et al., 2015). Vegetation distribution change would be severe with continuous climate warming.

The anthropogenic transformation of biomes and the terrestrial biosphere has been investigated to reflect vegetation distribution changes by comparing different biomes at century intervals (Ellis et al., 2010; Ellis, 2011), without considering the contribution of climate changes on shifting the vegetation. In addition, climate data (1700–2000) used to detect the anthropogenic transformation of biomes was almost 20 years ago (Ellis et al., 2010). The updated analysis is urgently needed as climate change plays an important role in shifting the vegetation.

In this study, we delimit the influence of human disturbance and climate on the distribution of vegetation based on updated data from 1982 to 2013. Accordingly, our main goals were to (1) delineate the influence of human disturbance versus climate on the vegetation distribution, and (2) identify those regions most susceptible to human disturbance in recent period.

Data and Methods

Climate and vegetation data

Global gridded monthly mean temperature and total precipitation data were obtained from the CRU TS 4.01 dataset at 0.5° × 0.5° resolution (Harris et al., 2014). This dataset interpolates climate data from meteorological stations distributed throughout the world to the global land area, grid-by-grid, for the period 1901–2013 and has been used in previous climate classifications (Zhang & Yan, 2014a). We used climate data for the period 1982–2013 as this period reflected the availability of the vegetation data.

The normalized difference vegetation index (NDVI) has been used to indicate the greenness of vegetation in numerous vegetation studies (e.g.,  DeFries et al., 2000; Loveland et al., 2000; Breshears et al., 2005; Tucker et al., 2005; Zhou et al., 2014). As a coarse measure, the differences in NDVI may mask changes to vegetation species composition; however, it has limited influences on identifying vegetation types. It is defined as

NDVI=NIRREDNIR+RED,

where NIR and RED are the amounts of radiation in the near-infrared and red regions, respectively. The NIR and RED reflectances should be corrected for atmospheric effects. The NDVI values range from −1 to 1, where negative values correspond to an absence of vegetation and positive values indicate vegetated land.

Monthly mean NDVI data at 0.0833° × 0.0833° spatial resolution were retrieved from the Advanced Very High Resolution Radiometer (Pinzon & Tucker, 2014) based Global Inventory Modelling and Mapping Studies dataset (https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0/) for the period 1982–2013.

The high-resolution climate data was not available for the vegetation classification. Hence, the NDVI data was up-scaled to match the resolution of climate data. The NDVI data were up-scaled by calculating the arithmetic mean of the nearest neighbor grids over a six-by-six window because one grid cell of climate data (0.5° × 0.5°) covers six-by-six grid cells in NDVI data (0.0833° × 0.0833°).

Separation of climate- and anthropo-driven vegetation changes

How to delimit the influence of human disturbance and climate on the vegetation was outlined in the sketch of Fig. 1. The vegetation distribution in the real world could be represented by existing vegetation types which reflected the vegetation distribution under human influences (Zhang et al., 2017a). Existing vegetation types include effects of human influences while potential vegetation types exclude these effects. The real vegetation changes could be identified by the changes in existing vegetation over the two periods. The potential vegetation changes are mainly caused by climate changes. The climate-driven vegetation changes could be reflected by the changes in potential vegetation over different periods. The anthropo-driven vegetation changes can be identified by the difference between the changes in potential and existing vegetation.

Figure 1. Sketch map for delimiting individual contributions of human disturbances and climate changes on vegetation.

Figure 1

Potential vegetation distribution

The potential vegetation was generally defined based on climate variables (Köppen, 1936; Holdridge, 1947; Holdridge, 1967; Box, 1981; Box, 1996; Ramankutty & Foley, 1999; Beck et al., 2005; Baker et al., 2010; Ellis et al., 2010; Levavasseur et al., 2012); therefore, the potential vegetation types could be represented by corresponding climate types. Climate types could be objectively classified to different global climate types based on the monthly attributes using the K-means clustering method (Mahlstein, Daniel & Solomon, 2013; Zhang & Yan, 2014a; Zhang & Yan, 2014b; Zhang & Yan, 2016; Zhang, Yan & Chen, 2017b). Monthly mean temperature and monthly total precipitation were used as input multivariables that consisted of an n × 24 matrix X1:

X1=T11T1mP11P1mTn1TnmPn1Pnm

where T is monthly mean temperature, P is monthly mean precipitation, m is 12, and n is the number of all the grid cells in the global land area, except the Antarctic. The rows in X1 represent the monthly attributes, while the columns represent the number of grid cells. The names of vegetation types were designated by referring to the Koeppen classification (Kottek et al., 2006).

Existing vegetation distribution

The existing vegetation types were classified based on climate and NDVI data using the K-means method (Zhang et al., 2017a). An n × 36 matrix X2 was constituted by monthly mean temperature, monthly total precipitation and monthly mean NDVI:

X2=T11T1mP11P1mNDVI11NDVI1mTn1TnmPn1PnmNDVIn1NDVInm

where T is monthly mean temperature, P is monthly mean precipitation, NDVI is monthly mean NDVI, m is 12, and n is the number of all the grid cells in the global land area, except the Antarctic.

Temporal changes in the influences of human disturbance and climate change on vegetation distribution

Fraedrich, Gerstengarbe & Werner (2001) suggested that an interval of at least 15 years is required to detect temporal changes in the geographical distribution of climate types. Thus, the period from 1982 to 2013 was split into two periods (1982–1996 and 1997–2013), and the existing and potential vegetation types were classified over the two periods to check the temporal changes in the influences of human disturbance and climate change on vegetation distribution.

The differences between the existing vegetation distribution and the potential vegetation distribution over the period 1982–1996 reflected the vegetation changes from no human influence period to the period 1982–1996. The differences between the existing vegetation over two periods 1982–1996 and 1997–2013 reflected the existing vegetation changes from the period 1982–1996 to the period 1997–2013.

The potential vegetation changes were indicated by the changes in climate types over the two periods. However, whether or not the potential vegetation change was reflected in the real vegetation changes should be verified by checking the overlapped changes in both potential vegetation and existing vegetation. When changes were detected in both the potential and existing vegetation, they were identified as the influence of climate changes on vegetation. The impacts of human disturbance on the vegetation distribution could be identified by the differences between the existing vegetation changes and the potential vegetation changes.

Results

Global existing vegetation types were defined for the period 1982–1996 (Fig. 2A) and the period 1997–2013 (Fig. 2B). Changes in the distribution of existing vegetation were found worldwide, except in the polar and desert regions, from the period 1982–1996 to the period 1997–2013 (Fig. 2C). The largest changes in vegetation types were found in central Africa, eastern China, western America and Australia. The least changes in the vegetation were found in tropical rainforest, tropical and temperate deserts, frigid deciduous coniferous forest and polar frost.

Figure 2. Geographical distribution of climatic vegetation types for (A) the period 1982–1996 and (B) the period 1997–2013 and (C) the differences between them.

Figure 2

The black regions are those that have undergone transformations in vegetation type.

The distribution of potential vegetation over the period 1982–1996 (Fig. 3A) was similar to that over the period 1997–2013 (Fig. 3B). Changes in potential vegetation mainly occurred on the boundaries between adjacent types of vegetation from the period 1982–1996 to the period 1997–2013 (Fig. 3C). Obvious boundary changes were seen between tropical rainforests and tropical dry forests, between tropical deserts and the Sahel, and between temperate deciduous and evergreen forests. After comparison with the existing vegetation changes, actual changes in existing vegetation caused by climate variations were only detected in a limited number of grid cells (Fig. 3D). These grid cells were distributed worldwide, mainly in the ecotones.

Figure 3. Geographical distribution of potential vegetation types for (A) the period 1982–1996 and (B) the period 1997–2013 and (C) the potential and (D) actual changes between them.

Figure 3

The colors used for the potential vegetation types represent the same meanings as in Fig. 2. The black regions are those that have undergone transformations in vegetation type.

A large area of vegetation was transformed by human disturbances (Fig. 4C) by changing the potential vegetation (Fig. 4B) to the existing vegetation (Fig. 4A) from no human influence period to the period 1982–2013. The impact of human disturbance on the vegetation distribution occurred worldwide from the period 1982–1996 to the period 1997–2013 (Fig. 5). The human-induced vegetation changes were not only seen at the boundaries of vegetation types, but also within the regions of vegetation types. About 3% of total vegetation distribution was transformed by human activities from the period 1982–1996 to the period 1997–2013. The largest changes in vegetation were found in the eastern China, central Africa and western America.

Figure 4. Geographical distribution of (A) existing vegetation types and (B) potential vegetation types over the period 1982–2013, and (C) the differences between them.

Figure 4

The differences show the effects of the human activity on the distribution of vegetation types from no human influenced period to 1982–2013.

Figure 5. Impact of human activity on the distribution of vegetation types over the period 1982–2013.

Figure 5

The black regions are those that have undergone transformations in vegetation type as a result of human activity.

Eastern China was selected to verify our results (Fig. 6). The NDVI changed from −0.04 to 0.04 in the grid cells where the changes in existing vegetation occurred (Fig. 6B). The changes in vegetation type were verified by the changes in the NDVI (Fig. 6C). The vegetation changed in the regions where the changes in vegetation types were detected. The actual vegetation changes were compared with the land use change between 1990 and 2000 reported by Liu et al. (2002), Fig. 6D. The vegetation changes detected by two studies were mainly concentrated in similar regions. The areal changes in certain vegetation types were similar to those obtained using Liu’s land use data (Table 1). Larger changed areas of vegetation types were detected in this study than in Liu et al. (2002), because more detailed changes in land use could be detected using the land use data with higher resolution and more detailed vegetation types (24 types).

Figure 6. (A) Changes in the existing vegetation in eastern China, (B) changes in the NDVI in regions where existing vegetation changes were detected, (C) changes in the NDVI over the whole region, and (D) changes in land use detected using the land use data of Liu.

Figure 6

The red dot represents big city in eastern China.

Table 1. Comparison of changed area of vegetation types in eastern China detected in this study to those detected using the land use data of Liu et al. (2002).

Liu’s land use represents the land use data of Liu et al. (2002).

Changed area of vegetation types (104 km2)
This study Liu’s land use
Temperate grassland 7.5 6.4
Temperate Evergreen broadleaf forest 7.7 6.1
Temperate Deciduous forest 6.7 4.6
Sub-frigid mixed forest 6.4 4.9
Frigid evergreen coniferous forest 3.3 2.5

Discussion

The relative contributions of human disturbance and climate change on the vegetation changes was separated by comparing the differences between existing vegetation and potential vegetation. Potential vegetation distribution is the vegetation without human disturbances, and it was similar to potential natural vegetation in 1700 as defined by Ramankutty & Foley (1999) and Ellis et al. (2010). The existing vegetation types were classified based on both vegetation and climate data to reflect the connection between vegetation and climate, without using the method that defines the vegetation types based on NDVI data (DeFries & Townshend, 1994; Lu et al., 2003). Potential vegetation types and their corresponding existing vegetation types were easily compared because they were classified by the same method.

Human disturbance has influenced vegetation for several centuries. The human-induced vegetation changes from no human influenced period to the period 1982–2013 was similar to those reported human-induced land degradation (Bai et al., 2008). The transformations of vegetation caused by human activities mainly through farming, construction and grazing (Barger et al., 2018). The vegetation was mainly transformed into cropland, pasture and constructions due to human activities (Ellis, 2011). Transitions in land use before 1900 mainly occurred in China, India, Europe, North America and Australia (Ellis et al., 2010). Transformations in the distribution of vegetation accelerated when rapid growth in the human population increased the pressure to expand the amount of pasture and farmland. These regions are the key zones with respect to the anthropogenic transformation of vegetation from the no human influence period to the period 1982–2013.

The impacts of human disturbance on vegetation were seen worldwide from the period 1982–1996 to the period 1982–2013, except for the polar and desert regions. The areas that were most affected by human disturbance were those with the highest population densities, including Europe, western North America, Central Africa, southern South American, eastern Australia and eastern China. The expansion of pasture mainly took place in central Asia, Australia, southern Africa and in the tropical Sahel and subsequent overgrazing led to transformations in the vegetation cover. The amount of cropland has expanded markedly in North and South America, Europe, southern Australia, northeast China and southern Asia. Grassland has been replaced by farmland in Europe and in North and South America. Cropland has expanded into the shrublands of Australia. The main change in land use in eastern China has been the replacement of forest and grassland with cropland (Zhang et al., 2016; Zhang et al., 2017c). However, human disturbances which only influenced the vegetation structure could not be reflected by our results because no shift in vegetation types could be detected.

The boundaries of some potential vegetation types were altered by climate changes. A shifting of ecotones seems more likely the result of climate change influencing areas that are borderline based on climate, thus, a small shift in climate is likely to result in a change in ecotones rather than in central vegetation areas. The vegetation in these ecotones was under pressure and would show further changes over time. However, the influence of climate change on vegetation was limited from the period 1982–1996 to the period 1997–2013 because climate-induced vegetation shift was not viable over short periods, except when there was an abrupt climate shift. Moreover, the coarse resolution of the data restricted to detect the detail vegetation changes induced by climate change. Climate warming and hot drought would likely cause species composition of regional vegetation (Breshears et al., 2005; Allen et al., 2011), not vegetation type change (e.g., forest to grassland), which could not be visible over a short period. However, widespread increased tree mortality has been found in some forest ecosystems because of climate warming (Adams et al., 2010; Van Mantgem et al., 2009), and large area of vegetation showed greening or browning trends (De Jong et al., 2013). The impact of climate change on vegetation would be more visible in these regions over a longer period (e.g., 200 years).

The vegetation would return to what kind of potential forest type if there is no human disturbance can be referred by the potential vegetation types. The potential vegetation type can restore itself over a period with either limited or no human interference. For instance, abandoned farmland in northeast China can transform back to forest cover. These transformations caused fundamental vegetation changes, and could be reflected in our results. There is a large potential in global tree restoration if human disturbance was limited, which is consistent with the results reported by Bastin et al. (2019).

The global existing vegetation was seriously transformed from the period 1982–1996 to the period 1997–2013. About 3% of global vegetation have changed their types in the past 30 years. Human disturbance caused stronger influences on vegetation changes than climate change, which is consistent with a study in northern forests (Danneyrolles et al., 2019). However, the influences of climate changes on vegetation distribution could not be ignored. The regions that were influenced by both human disturbance and climate change are vulnerable to vegetation changes in the future.

Conclusion

The effects of human disturbance and climatic change on the distribution of global vegetation types could be separated using the proposed method. A large area of vegetation was transformed by human disturbances from no human influenced period to the period 1982–2013. About 3% of total vegetation distribution was transformed by human activities from the period 1982–1996 to the period 1997–2013. However, the influence of climate change on vegetation was limited from the period 1982–1996 to the period 1997–2013. Therefore, human disturbances caused stronger damage to global vegetation change than climate change.

Funding Statement

This work was funded by the National Key Research and Development Program of China (2017YFD060040301), the National Natural Science Foundation of China (grant numbers 41601045, 31570632, 41571094) and the talents introduction program in Hebei Agricultural University (YJ201918). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Xianliang Zhang conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Xuanrui Huang contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper.

Data Availability

The following information was supplied regarding data availability: The data used in this study are public data. The NDVI data is available at: https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0/. The CRU climate data is available at: https://crudata.uea.ac.uk/cru/data/hrg/.

References

  • Adams et al. (2010).Adams HD, Macalady AK, Breshears DD, Allen CD, Stephenson NL, Saleska SR, Huxman TE, McDowell NG. Climate-induced tree mortality: Earth system consequences, Eos. Transactions American Geophysical Union. 2010;91:153–154. [Google Scholar]
  • Allen et al. (2011).Allen CR, Fontaine J, Pope KL, Garmestani AS. Adaptive management for a turbulent future. Journal of Environmental Management. 2011;92(5):1339–1345. doi: 10.1016/j.jenvman.2010.11.019. [DOI] [PubMed] [Google Scholar]
  • Allen et al. (2010).Allen CD, Macalady AK, Chenchouni H, Bachelet D, Mcdowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running S, Semerci A, Cobb N. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management. 2010;259:660–684. doi: 10.1016/j.foreco.2009.09.001. [DOI] [Google Scholar]
  • Anderegg, Kane & Anderegg (2013).Anderegg WRL, Kane JM, Anderegg LDL. Consequences of widespread tree mortality triggered by drought and temperature stress. Nature Climate Change. 2013;3:30–36. doi: 10.1038/nclimate1635. [DOI] [Google Scholar]
  • Bai et al. (2008).Bai Z, Dent DL, Olsson L, Schaepman ME. Proxy global assessment of land degradation. Soil Use Manage. 2008;24(3):223–234. doi: 10.1111/j.1475-2743.2008.00169.x. [DOI] [Google Scholar]
  • Baker et al. (2010).Baker B, Diaz H, Hargrove W, Hoffman F. Use of the KoppenTrewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People’s Republic of China. Climatic Change. 2010;98(98):113–131. doi: 10.1007/s10584-009-9622-2. [DOI] [Google Scholar]
  • Barger et al. (2018).Barger NN, Gardner TA, Sankaran M, Belnap J, Broadhurst L, Brochier V, Isbell F, Meyfroidt P, Moreira F, Nieminen TM, Okuro T, Rodrgiues RR, Saxena V, Ross M. Chapter 3: direct and indirect drivers of land degradation and restoration. In: Montanarella L, Scholes R, Brainich A, editors. IPBES: the IPBES assessment report on land degradation and restoration. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; Bonn: 2018. pp. 137–218. [Google Scholar]
  • Bastin et al. (2019).Bastin J, Finegold Y, Garcia C, Mollicone D, Rezende M, Routh D, Zohner CM, Crowther TW. The global tree restoration potential. Science. 2019;365:76–79. doi: 10.1126/science.aax0848. [DOI] [PubMed] [Google Scholar]
  • Beck et al. (2005).Beck C, Grieser J, Kottek M, Rubel F, Rudolf B. Beitrage zur globalen Klimatologie von Wolken und Niederschlag. Vol. 51. Dtsch. Wetterdienst; Berlin: 2005. Characterizing global climate change by means of Koeppen climate classification; pp. 139–149. [Google Scholar]
  • Box (1981).Box EO. Macroclimate and plant form. Junk; The Hague: 1981. [DOI] [Google Scholar]
  • Box (1996).Box EO. Plant functional types and climate at the global scale. Journal of Vegetation Science. 1996;7(3):309–320. doi: 10.2307/3236274. [DOI] [Google Scholar]
  • Breshears et al. (2005).Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW. Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(42):15144–15148. doi: 10.1073/pnas.0505734102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Cook & Vizy (2008).Cook KH, Vizy EK. Effects of twenty-first-century climate change on the Amazon rain forest. Journal of Climate. 2008;21(3):542–560. [Google Scholar]
  • Danneyrolles et al. (2019).Danneyrolles V, Dupuis S, Fortin G, Leroyer M, De Romer A, Terrail R, Vellend M, Boucher Y, Laflamme J, Bergeron Y, Arseneault D. Stronger influence of anthropogenic disturbance than climate change on century-scale compositional changes in northern forests. Nature Communications. 2019;10 doi: 10.1038/s41467-019-09265-z. Article 1265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Diaz & Eischeid (2007).Diaz HF, Eischeid JK. Disappearing “alpine tundra” Köppen climatic type in the western United States. Geophysical Research Letters. 2007;34:L18707. doi: 10.1029/2007GL031253. [DOI] [Google Scholar]
  • De Jong et al. (2013).De Jong R, Schaepman ME, Furrer R, De Bruin S, Verburg PH. Spatial relationship between climatologies and changes in global vegetation activity. Global Change Biology. 2013;19:1953–1964. doi: 10.1111/gcb.12193. [DOI] [PubMed] [Google Scholar]
  • DeFries et al. (2000).DeFries RS, Hansen MC, Townshend J, Janetos AC, Loveland TR. A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology. 2000;6:247–254. doi: 10.1046/j.1365-2486.2000.00296.x. [DOI] [Google Scholar]
  • DeFries et al. (2010).DeFries RS, Rudel T, Uriarte M, Hansen M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geoscience. 2010;3(3):178–181. doi: 10.1038/ngeo756. [DOI] [Google Scholar]
  • DeFries & Townshend (1994).DeFries RS, Townshend J. NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing. 1994;15(17):3567–3586. doi: 10.1080/01431169408954345. [DOI] [Google Scholar]
  • Ellis (2011).Ellis EC. Anthropogenic transformation of the terrestrial biosphere. Philosophical Transactions of the Royal Society A. 2011;369(1938):1010–1035. doi: 10.1098/rsta.2010.0331. [DOI] [PubMed] [Google Scholar]
  • Ellis et al. (2013).Ellis EC, Kaplan JO, Fuller DQ, Vavrus S, Goldewijk KK, Verburg PH. Used planet: a global history. Proceedings of the National Academy of Sciences of the United States of America. 2013;110(20):7978–7985. doi: 10.1073/pnas.1217241110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Ellis et al. (2010).Ellis EC, Klein Goldewijk K, Siebert S, Lightman D, Ramankutty N. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography. 2010;19(5):589–606. doi: 10.1111/j.1466-8238.2010.00540.x. [DOI] [Google Scholar]
  • Ellis & Ramankutty (2008).Ellis EC, Ramankutty N. Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment. 2008;6(8):439–447. doi: 10.1890/070062. [DOI] [Google Scholar]
  • Foley et al. (2005).Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice IC, Ramankutty N, Snyder PK. Global consequences of land use. Science. 2005;309(5734):570–574. doi: 10.1126/science.1111772. [DOI] [PubMed] [Google Scholar]
  • Fraedrich, Gerstengarbe & Werner (2001).Fraedrich K, Gerstengarbe FW, Werner PC. Climate shifts during the last century. Climatic Change. 2001;50(4):405–417. doi: 10.1023/A:1010699428863. [DOI] [Google Scholar]
  • Gauthier et al. (2015).Gauthier S, Bernier P, Kuuluvainen T, Shvidenko AZ, Schepaschenko DG. Boreal forest health and global change. Science. 2015;349:819–822. doi: 10.1126/science.aaa9092. [DOI] [PubMed] [Google Scholar]
  • Goldewijk et al. (2011).Goldewijk KK, Beusen A, Van Drecht G, De Vos M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Global Ecology and Biogeography. 2011;20(1):73–86. doi: 10.1111/j.1466-8238.2010.00587.x. [DOI] [Google Scholar]
  • Harris et al. (2014).Harris I, Jones PD, Osborn TJ, Lister DH. Updated high-resolution grids of monthly climatic observations-the CRU TS3. 10 Dataset. International Journal of Climatology. 2014;34:623–642. doi: 10.1002/joc.3711. [DOI] [Google Scholar]
  • Holdridge (1947).Holdridge LR. Determination of world plant formations from simple climatic data. Science. 1947;105:267–268. doi: 10.1126/science.105.2723.267. [DOI] [PubMed] [Google Scholar]
  • Holdridge (1967).Holdridge LR. Life zone ecology. Tropical Science Center; San Jose: 1967. [Google Scholar]
  • Kelly & Goulden (2008).Kelly AE, Goulden ML. Rapid shifts in plant distribution with recent climate change. Proceedings of the National Academy of Sciences of the United States of America. 2008;105(33):11823–11826. doi: 10.1073/pnas.0802891105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Köppen (1936).Köppen W. Das geographische system der klimate. Berlin: Walter de Gruyter & Company; 1936. [Google Scholar]
  • Kottek et al. (2006).Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift. 2006;15:259–263. doi: 10.1127/0941-2948/2006/0130. [DOI] [Google Scholar]
  • Levavasseur et al. (2012).Levavasseur G, Vrac M, Roche DM, Paillard D. Statistical modelling of a new global potential vegetation distribution. Environmental Research Letters. 2012;7 doi: 10.1088/1748-9326/7/4/044019. Article 044019. [DOI] [Google Scholar]
  • Lewis & Maslin (2015).Lewis SL, Maslin MA. Defining the anthropocene. Nature. 2015;519(7542):171–180. doi: 10.1038/nature14258. [DOI] [PubMed] [Google Scholar]
  • Li et al. (2017).Li X, Chen G, Liu X, Liang X, Wang S, Chen Y, Pei F, Xu X. A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human–environment interactions. Annals of the American Association of Geographers. 2017;107:1040–1059. doi: 10.1080/24694452.2017.1303357. [DOI] [Google Scholar]
  • Liu et al. (2002).Liu J, Liu M, Deng X, Zhuang D, Zhang Z, Luo D. The land use and land cover change database and its relative studies in China. Journal of Geographical Sciences. 2002;12(3):275–282. doi: 10.1007/BF02837545. [DOI] [Google Scholar]
  • Loveland et al. (2000).Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L, Merchant JW. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing. 2000;21:1303–1330. doi: 10.1080/014311600210191. [DOI] [Google Scholar]
  • Lu et al. (2003).Lu H, Raupach MR, McVicar TR, Barrett DJ. Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sensing of the Environment. 2003;86(1):1–18. doi: 10.1016/S0034-4257(03)00054-3. [DOI] [Google Scholar]
  • Mahlstein, Daniel & Solomon (2013).Mahlstein I, Daniel JS, Solomon S. Pace of shifts in climate regions increases with global temperature. Nature Climate Change. 2013;3(8):739–743. doi: 10.1038/nclimate1876. [DOI] [Google Scholar]
  • Pinzon & Tucker (2014).Pinzon JE, Tucker CJ. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens-Basel. 2014;6:6929–6960. doi: 10.3390/rs6086929. [DOI] [Google Scholar]
  • Ramankutty & Foley (1999).Ramankutty N, Foley JA. Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochemical Cycles. 1999;13(4):997–1027. doi: 10.1029/1999GB900046. [DOI] [Google Scholar]
  • Song et al. (2018).Song X, Hansen MC, Stehman SV, Potapov PV, Tyukavina A, Vermote EF, Townshend JR. Global land change from 1982 to 2016. Nature. 2018;560:639–643. doi: 10.1038/s41586-018-0411-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Tucker et al. (2005).Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing. 2005;26(20):4485–4498. doi: 10.1080/01431160500168686. [DOI] [Google Scholar]
  • Van Mantgem et al. (2009).Van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, Fulé PZ, Harmon ME, Larson AJ, Smith JM, Taylor AH, Veblen TT. Widespread increase of tree mortality rates in the western United States. Science. 2009;323:521–524. doi: 10.1126/science.1165000. [DOI] [PubMed] [Google Scholar]
  • Wang, Price & Arora (2006).Wang A, Price DT, Arora V. Estimating changes in global vegetation cover (1850–2100) for use in climate models. Global Biogeochemical Cycles. 2006;20:GB3028. doi: 10.1029/2005GB002514. [DOI] [Google Scholar]
  • Zhang et al. (2017a).Zhang X, Wu S, Yan X, Chen Z. A global classification of vegetation based on NDVI, rainfall and temperature. International Journal of Climatology. 2017a;37:2318–2324. doi: 10.1002/joc.4847. [DOI] [Google Scholar]
  • Zhang et al. (2016).Zhang X, Xiong Z, Zhang X, Shi Y, Liu J, Shao Q, Yan X. Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: a case study over northeast China. Climate Dynamics. 2016;46(3):765–778. doi: 10.1007/s00382-015-2611-4. [DOI] [Google Scholar]
  • Zhang et al. (2017c).Zhang X, Xiong Z, Zhang X, Shi Y, Liu J, Shao Q, Yan X. Simulation of the climatic effects of land use/land cover changes in eastern China using multi-model ensembles. Global and Planetary Change. 2017c;154:1–9. doi: 10.1016/j.gloplacha.2017.05.003. [DOI] [Google Scholar]
  • Zhang & Yan (2014a).Zhang X, Yan X. Spatiotemporal change in geographical distribution of global climate types in the context of climate warming. Climate Dynamics. 2014a;43(3–4):595–605. doi: 10.1007/s00382-013-2019-y. [DOI] [Google Scholar]
  • Zhang & Yan (2014b).Zhang X, Yan X. Temporal change of climate zones in China in the context of climate warming. Theoretical and Applied Climatology. 2014b;115(1–2):167–175. doi: 10.1007/s00704-013-0887-z. [DOI] [Google Scholar]
  • Zhang & Yan (2016).Zhang X, Yan X. Deficiencies in the simulation of the geographic distribution of climate types by global climate models. Climate Dynamics. 2016;46:2749–2757. doi: 10.1007/s00382-015-2727-6. [DOI] [Google Scholar]
  • Zhang, Yan & Chen (2017b).Zhang X, Yan X, Chen Z. Geographic distribution of global climate zones under future scenarios. International Journal of Climatology. 2017b;37:4327–4334. doi: 10.1002/joc.5089. [DOI] [Google Scholar]
  • Zhou et al. (2014).Zhou L, Tian Y, Myneni RB, Ciais P, Saatchi S, Liu YY, Piao S, Chen H, Vermote EF, Song C, Hwang T. Widespread decline of Congo rainforest greenness in the past decade. Nature. 2014;509(7498):86–90. doi: 10.1038/nature13265. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The following information was supplied regarding data availability: The data used in this study are public data. The NDVI data is available at: https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0/. The CRU climate data is available at: https://crudata.uea.ac.uk/cru/data/hrg/.


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