Declines in global ecological security under climate change.

Ecological security is the state when an ecosystem maintains its stability under external stress. Due to climate change and the increase in human activities since the 20th century, the rapid decline in global ecological security has threatened sustainable human development. The evaluation and projection of global ecological security is important for forming adaptation strategies to maintain sustainable development in sensitive areas. However, the current assessments of ecological security mainly focus on regional scales, and the interactions among different factors have not been considered, resulting in future projections having substantial uncertainty. Here, a new index of ecological security was developed by including biological, oxygen, carbon, thermal and hydrological cycles and the impacts to ecosystem stability from climate change and human activities at a global scale. A global distribution map of ecological security has been established that covers the past 60 years and includes projections for the future 100 years. A severe decline in ecological security has occurred in drylands that has expanded into surrounding regions over the past 60 years. The response of ecological security to global warming and human activities is projected to be stronger. By ~2100, under a high greenhouse gas emissions scenario, the amount of globally insecure land would cover more than 57% of the land in the world.


Ecological security refers to a state in which natural and semi-natural ecosystems can maintain stability, associated with the ecological environment provides ecological guarantees for the sustainable development of the whole eco-economic system (Costanza and Mageau, 1999; Fu, 2010; Ma et al., 2004; Rapport, 1989; IUCN, 2012; Jenkins et al., 2013; Newbold et al., 2015). However, ecological security has been severely threatened under on-going global warming and enhanced human activities (Walther et al., 2002; Palmer et al., 2004; Piao et al., 2010; Huang et al., 2012, 2015, 2016; Feng et al., 2016; Fu et al., 2017; Zhao et al., 2018). There are some studies indicating that ecological destruction has caused desertification, food shortages (Glover et al., 2010) and water insecurity (Vörösmarty et al., 2010; Humphrey et al., 2017), which will threaten human survival and development in the 21st century. Ecological security is closely associated with biological, oxygen, carbon, hydrological and thermal cycles under climate change and human activities. This is because, changes in vegetation structure and function linked to oxygen cycle, controls the exchange of carbon, water and energy between the land and the atmosphere (Piao et al., 2020). However, the consequences of these transformations for ecological security are poorly understood. Understanding the change in these indicators is important to determining terrestrial ecological security through the processes of respiration, photosynthesis and burning. Hence, knowledge of how climate change and human activities will affect changes in ecological security in the future is essential for protection and for adaptation strategies.

Though the concept of ecological security has been proposed as early as the 1970s (Sohn, 1973), a unified and generally accepted definition has not been formed because ecological security is a complex issue that involves many aspects (Daly, 2005; Hodson & Marvin, 2009; Wang et al., 2015; Feng et al., 2018). There are two limitations to the current assessment of ecological security. Although some indicators have been used to evaluate regional ecological security, those assessments have only considered individual regional issues, such as water security (Shinoda and Yamaguchi, 2003; Sorooshian et al., 2005; Seneviratne et al., 2010; Wang et al., 2015) and urban ecological security (Hodson & Marvin, 2009). Second, as ecological security has various definitions (Allenby 2000; Liu & Chang, 2015; Hu et al., 2019), there has been no uniform and well-recognized indicator system. Therefore, it is important to understand how the variability on climate change and human activities constrains biological, oxygen, carbon, hydrological and thermal cycles to determine the ecological security.

In fact, ecological security involves various cycles in an ecosystem. The oxygen, carbon, hydrological and thermal cycles are interconnected throughout all regions on Earth, and they are also coupled to all biological cycles (Schlesinger et al., 1990; Ciais, 1999; Jacobson et al., 2000; Dickinson, 2005; Fu & Li, 2016; Huang et al., 2017a,b, 2018; Yang et al., 2019). The process of photosynthesis not only produces vital O2 and is important for the food chain but also absorbs solar irradiation and carbon balance. Carbon accumulates in soils in large quantities, major because the high water levels that result in low biological activity and slow soil organic carbon decomposition. Carbon accumulation plays an important driving role in the carbon cycle through atmosphere, green vegetation and surface soils. Thus, these factors are the most important ecological indicator for changes in biological, oxygen, carbon, hydrological and thermal cycles. The ecological security means the survival of humans and other animals should be balanced with plant vegetation within the above five cycles.

The ecological security is identified by the combination of four essential factors, including oxygen consumption (Oc), oxygen production (Op), temperature warming magnification (Tm) and the aridity index (AI).

  • Oxygen consumption

O2 is consumed by a wide range of processes, some of which are negligible or are difficult to quantify, including the weathering of organic matter and sulfide minerals, volcanic gas oxidation and so on.Here, five main O2 consumption processes, including (1) fossil fuel combustion, (2) human respiration, (3) livestock respiration, (4) fires and (5) heterotrophic and soil respiration, are considered (Petsch, 2013). . The detailed methods and datasets of the processes (1)-(4) could be found in Huang et al. (2018) and Liu et al., (2020). Here, the heterotrophic and soil respiration (Rh + Rd) is the process that consumes oxygen when soil organisms respire, where Rh is the respiration by heterotrophs and Rd is the respiration by decomposers (microbes), which is measured by the difference between net primary productivity (NPP) and net ecosystem productivity (NEP). The simulated Rh + Rd dataset (see Table 1) is obtained from the simulation of the Fifth Coupled Model Intercomparison Project (CMIP5) (Taylor et al., 2012). The observed Rh + Rd data are obtained from the Global Fire Emissions Database (GFED,, Van et al., 2017).

  • Oxygen production

Oxygen is produced during photosynthesis, during which plants and other organisms absorb carbon dioxide (CO2) from the atmosphere and release oxygen (O2). Photosynthesis can be expressed by the following chemical equation:

6H2O + 6CO2 ———->C6H12O6 + 6O2 (1)

Gross primary production (GPP) is the total amount of CO2 fixed by a plant during photosynthesis. NPP is the net amount of gross primary productivity remaining after including the cost of plant respiration. According to Eq. (1), we can use the following equation to calculate the net amount of O2 produced during the process of photosynthesis if the known amount of carbon is fixed through photosynthesis (NPP).

O2 = NPP * 2.667

Due to the molar mass of O2 is 32 g per mole and the C is 12 g per mole, thus the ratio is 2.667. The simulated NPP dataset from 1948 to 2100 is obtained from the CMIP5 simulation (Taylor et al., 2012) and the observed NPP data based on MODIS from 2000 to 2015 were acquired from the Global Fire Emissions Database (Van et al., 2017). The simulated NPP and observed NPP data are re-gridded to a 1.0°×1.0° resolution for comparison. The detailed methods and datasets of oxygen production could be found in Huang et al. (2018) and Liu et al. (2020)

  • Temperature warming magnification

The temperature warming magnification is represented by Tm(i,n), which is defined as the ratio of the grid warming rate to the global warming rate and as follows:

Tm (i, n) = Ttrend (i, n) / TGtrend (n) (3)

where Ttrend (i, n) is defined as the linear trend of the surface temperature from 1901 to year n at grid point i, and TGtrend (n) is defined as the linear trend of the global average surface temperature from 1901 to year n for year n. The CRUTEMP4 dataset, which is developed by the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia (Morice et al., 2012), combines both surface air (over land) and sea surface (over ocean) temperature data. To fill the missing values in the CRUTEMP4, Dai and Zhao (2017) have supplemented the CRUTEMP4 with CRU TS2.3 temperature data. Therefore, we use this modified version of the CRUTEMP4 to calculate observed TGtrend and Ttrend. For simulations, the surface air temperature data used to calculate TGtrend and Ttrend are obtained from 20 CMIP5 climate models (Taylor et al., 2012). We averaged the Tm data of 20 models to calculate the ecological security index (ESI) for each model (see Table 1).

  • Aridity index

The AI (i,n) represents the aridity index at grid i for year n, which is defined as the ratio of precipitation to potential evapotranspiration (PET) and denoted as

AI (i, n) = P(i, n) / PET (i, n) (4)

For observation, the precipitation data is from the NOAA’s PRECipitation REConstruction over Land (PREC/L) dataset (Chen et al., 2002) developed by the Climatic Prediction Center (CPC), which covers for 1948 to the present on a 0.5° grid. And the PET data is from CRU TS 3.25 dataset (Harris et al., 2014), which covers the period 1901–2016 and all land areas at 0.5° resolution. In order to be consistent with the AI in the models, we keep the climatology of observed precipitation and PET data consistent with the observed precipitation and PET data provided by Feng and Fu (2013). For simulations, the precipitation and PET simulation datasets used here are provided by Feng and Fu (2013). These data are derived from the monthly mean temperature, precipitation, solar radiation, specific humidity and wind speed products obtained from the CMIP5 climate models (Taylor et al., 2012). Feng and Fu (2013) provided AI data from 20 models, which are not consistent with the models that can provide NPP and Rh + Rd. Therefore, we averaged the AI data of 20 models to calculate the ESI for each model (Table 1)

  • Land datasets

The surface energy flux data for sensible and latent heat, 0–7 cm volumetric soil water, and 0–7 cm soil temperature data were collected from the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) on a 0.25°×0.25° grid for 2000 to 2015. In addition, for verification, we used the CPC Soil Moisture dataset (Fan and Dool, 2014) on a 0.5°×0.5° grid from a model, which covers the period of 1948-present and was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, and we also explored the surface energy flux data for sensible and latent heat, and 0–10 cm soil moisture content from the NASA Global Land Data Assimilation System (GLDAS) land model simulation using the Noah Land Surface Model forced by observational data on a 1°×1° grid (GLDAS_NOAH10_M.2.0) for 1948 to 2010.

Results analysis

Fig. 3 shows the spatial distribution of ESI values determined by observations obtained between 60°S-60°N from 2000 to 2015. The area coverage of the insecure land represents 53.0% of the global land are (60°S-60°N), and the proportions of the different land surface types (60°S-60°N) of the semi-secure, light-dangerous and severe-dangerous regions are 13.3%, 16.4% and 23.3%, respectively (Fig. 3b). Here, desert covers 15.9% of the global land, representing deserts such as Sahara and Taklimakan (Fig. 3a). The severe-dangerous regions are mainly distributed at the edges of deserts and regions highly affected by climate change and some other places with high human activities, including industrial and densely populated regions (Fig. 3a). The lightdangerous regions include some drylands and mountainous plateau
regions without vegetation and snow cover (Fig. 3a), which are located in Australia, North America, Europe, southern Africa and Central Asia. The semi-secure regions are transition zones between secure and dangerous land, which are mainly distributed in semi-arid and cattle-producing regions (Fig. 3a), where the amount of oxygen generated via photosynthesis cannot compensate for the oxygen consumption. Only 31.2% of the global land area is covered by land that is defined as secure, which is mainly distributed in South America, Siberia, Central
Africa and the Tibetan Plateau; these areas have strong ecological resilience and have not been disturbed by humans. In addition, the cryosphere (polar regions and Greenland), which is not discussed in this study, can also be classified as secure land that stores plenty of freshwater without human disturbances.

Based on the above climatology, it is essential to investigate how ecological security will change in the future. The CMIP5 has generated projections using several emissions scenarios and has provided a crucial reference for the spatial and temporal evolution of ecological security in the future. The model members used are shown in Table 1 and the ensemble mean of these CMIP5 models (CMIP5-EM), which filters the uncertainty from the inter-model variability and better reflects the changes in ESI, is used in this study. To ensure the reliability of future projections, the CMIP5-EM simulations of ESI over 2000–2015 (Fig. 3cd) are compared to observations. The results show that the spatial distribution and the area coverage of insecure land obtained by CMIP5 are consistent with those indicated by observations, indicating that CMIP5 is adequate for projecting future ESI changes. For the CMIP5-EM historical and RCP8.5 simulated ESI values from 2000 to 2015, the area coverage of insecure land represents 51.2% of the global land area, and those of the different land surface types (60°S-60°N) of secure regions, semi-secure regions, light-dangerous regions and severe-dangerous regions are 32.8%, 14.7%, 17.3% and 19.2%, respectively. Desert covers 15.9% of the global land area, which is mainly located Sahara and Taklimakan; this result is similar to that indicated by observations (Fig. 3c-d). The light-dangerous and severe-dangerous regions are mainly distributed in the middle and high latitudes of the Northern Hemisphere, such as in western North America, Southwest Russia and Northeast China. Generally, the CMIP5 models can capture the spatial distribution of the ESI values from 2000 to 2015, indicating that the
CMIP5-EM is adequate for projecting future changes in ESI.

Fig. 4a present the time series of the global mean ESI during the future periods. For future projections, the ESI increases to ~ 1.8 by ~ 2100 under RCP8.5 scenario and decreases from ~ 1.5 (~2020) to ~ 1.4 by 2100 under RCP4.5. These opposite trends demonstrate that the emission of greenhouse gases is the main factor controlling the variations in ESI and that the restriction of greenhouse gas emissions under RCP4.5 could mitigate this disease. Fig. 4b-f presents the time series of the changes in area of the four subtypes during the historical and future periods. Clearly, the expansion of severe-dangerous regions in the future is significant under the RCP8.5 scenario. In addition, the area coverage of semi-secure and secure regions will decrease after 2005 under the RCP8.5 scenario. However, by reducing oxygen consumption through limiting carbon dioxide emissions under the RCP4.5 scenario, the global expansion of total insecure land will be inhibited. The area coverage of severe-dangerous regions will fist increase and then decrease; simultaneously, the areas of secure regions will first decrease and then increase. Under the RCP4.5 scenario, the areas of the four land surface types at the end of the century will change little compared with those at the beginning of this century. Among them, the areas of severe-dangerous regions and light-dangerous regions will decrease slightly; conversely, the areas of secure regions will increase slightly.

Fig. 5 shows the spatial distribution of the mean ESI in 2085–2100 and the areal changes of insecure land indicated from RCP8.5 scenari by CMIP5-EM. By the end of this century, the area coverage of insecure land will be 57.1% of the global land area, which will mainly be distributed in Africa, India, Europe, West and East Asia, and the different land surface types (60°S-60°N) of secure regions and semi-secure regions will be 27.1% and 13.1%, respectively. Relative to the beginning of this century (Fig. 3), the area of secure, semi-secure and light-dangerous land will decrease 5.6% of the global land area by ~ 2100 under RCP8.5 scenario. The greatest increase in area will be in severe-dangerous regions, which will increase from 23.3% to 28.9% of the global land area by ~ 2100 under RCP8.5 scenario. By comparing the areal changes of insecure land from 2000 to 2015 to those from 2085 to 2100 under RCP8.5 scenario, the increased areas of the semi-secure, light dangerous and severe-dangerous regions are 4.6%, 5.6% and 7.8%, respectively, and the net areal changes of secure land and severe-dangerous regions represent −4.1% and 5.6% of global land, respectively. The increase in severe-dangerous regions (reaching up to 28.9% of the global land area by 2100) is the most significant, which is mainly attributed to the remarkable decrease in secure land and the slight decrease in semi-secure and light-dangerous regions.


Achieving the Sustainable Development Goals requires maintaining a balance between the environment, society and the economy Environmental sustainability has always been an important part of sustainable development and a necessary condition for achieving strong
sustainability (Wu, 2013). In order to achieve environmental sustainability, building a reasonable index can guide policymaker in the right direction. There are many indexes available for assessing environmental sustainability, such as Ecological Footprint (Wackernagel and Rees, 1996), Environmental Sustainability Index and Environmental
Performance Index (, which are only published on the national scale. Of course, ecological security is also an important indicator. Ecological security has emerged as a critical policy focus across the world. Governments are increasingly being asked to explain their performance on a range of environment protection and natural resource management challenges with reference to quantitative metrics. Ecological security index can make us easier to spot problems, track trends, identify best practices, and optimize the gains from constructions of ecological civilization.

As the oxygen is the most important survival factor of all animals on the earth, its content directly determines the biomass of the earth. Several historical mass extinctions have been associated with reduced oxygen concentration during historical periods. In the past 100 years,
under the frequent intervention of human activities, many resources on the earth have been in an over-used state, including fresh water resources, and we have crossed the threshold of one-third of the 9 planetary boundaries proposed by Rockström et al. (2009). Also, the overexploitation and use of fossil fuels has also led to a record high rate of human oxygen consumption and carbon dioxide emissions, and its greenhouse effect has led to the overall warming of the earth. During the Anthropocene, due to the expansion of urban area and the acceleration of desertification, the oxygen produced by the earth’s vegetation also gradually decreased. Increased oxygen consumption and decreased oxygen production have put the planet which we live in on an unsustainable state. In this article, we combined the oxygen consumption, the oxygen production, the global warming and the extent of land aridity to construct an ESI and make future estimates.

Obviously, geoscientists can apply the newly proposed ESI to investigate and reanalyze the vitality of land with different ecological statuses, especially the formation and expansion of insecure land. The systematic classification of land using ESI can not only alert us to climate crises due to natural insecure land but can also warn us about the hidden threat from anthropogenic insecure land, which has mostly been ignored worldwide due to greed-driven human activities. Under global change, extreme weathers and human disturbances, such as rapid population growth and accelerate urbanization, have an increasing impact on the ecosystem stability. The above results highlight that when the external forcing are beyond the environmental capacity, the secure land would become unsustainable and the risk of ecological security would occur. Based on the population projections under SSP5 (RCP8.5), the global population will increase to 12.7 billion by 2100, indicating that more people will be threatened by severe ecological security. Thus, detecting the starting point of insecure land at an earlier stage and preventing its invasion is of particular importance and a high priority
because it can further avoid insecure land from connecting altogether and decelerate the expected losses of biological species richness (Pounds et al., 2006). Thus, more urgent actions must be taken to promote oxygen production and preserve water resources, including
planting more trees, slowing down the expansion of deserts and drylands to stop dust from spreading (Shugart et al., 2003) and cultivating new green land among large regions of insecure land. In addition, we must avoid extra activities that consume more oxygen and water, especially by limiting fossil fuel combustion, forbidding ocean and lake trash, properly disposing of municipal and industrial waste and evenly distributing industrial zones. If we continue to take land resources for granted, more land will definitely develop into insecure land and even desert; this final step is irreversible and can cause humans to permanently lose their habitat (Prentice et al., 2007). Therefore, it is unwise and lacking in foresight to trade actual land resources for rapid economic development. We must establish new policies to stop the environment from worsening any further using global cooperation without hesitation.

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