Carrying capacity for vegetation across northern China drylands

Science of the Total Environment 710 (2020) 136391

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Carrying capacity for vegetation across northern China drylands Jutao Zhang a, Yuqing Zhang a,b,⁎, Shugao Qin a,c, Bin Wu a,b, Guodong Ding a,b, Xiuqin Wu a,c, Yan Gao a, Yakun Zhu a a b c

Yanchi Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China Engineering Research Center of Forestry Ecological Engineering, Ministry of Education, Beijing Forestry University, Beijing 100083, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Dryland management requires analysis of the carrying capacity for vegetation (CCV). • We present a novel technical framework for measuring and assessing CCV. • This framework may help in planning dryland management and revegetation strategies. • There is scope for improving vegetation in most China drylands during 2015–2024.

a r t i c l e

i n f o

Article history: Received 10 August 2018 Received in revised form 24 June 2019 Accepted 26 December 2019 Available online xxxx Editor: Jay Gan Keywords: Dryland ecosystem Leaf area index Multivariate empirical dynamic modelling Vegetation threshold Vegetation sensitivity

a b s t r a c t Revegetation and afforestation across drylands for establishing sustainable ecosystems requires a comprehensive understanding of the carrying capacity for vegetation (CCV) at the regional scale. To determine the CCV across drylands in northern China, we developed a technical framework based on two measures of leaf area index (LAI): maximum LAI (Max-LAI) and safe LAI (Safe-LAI), and their thresholds, CCVmax and CCVsafe, for six drylands (Horqin, Hulun Buir, Otindag, Mu Us, Tengger, and Junggar) using remote sensing datasets from 2000 to 2014. We also predicted dynamics of CCV of the drylands over the next decade (2015–2024) by establishing optimal prediction models based on environmental factors (temperature, precipitation, potential evapotranspiration, and elevation). According to these models, the Max-LAI threshold (range: 0.36–1.03 m2/m2) and Safe-LAI threshold (0.29–0.70 m2/m2) declined from east to west with decreases in aridity index. Under current climatic variability and anthropogenic disturbances, the CCV in most drylands would have positive increments (approximately 15%), except in the Horqin (approximately −15%) and Tengger (slight changes), during the following decade. This indicates that there is scope for improving vegetation coverage in most drylands, except in the Horqin and Tengger. Our results suggest that revegetation and ecosystem management to prevent ongoing desertification should be carried out at the regional scale. Although it does not account for biocrusts, artificially introduced vegetation, underground water, and other vegetation attributes (e.g., density and biomass), our technical framework and results might nonetheless be valuable in evaluating regional ecological security and guiding vegetation restoration of drylands across northern China. © 2020 Elsevier B.V. All rights reserved.

Abbreviations: AI, aridity index; CCV, carrying capacity for vegetation; CV, coefficient of variation; DEM, digital elevation model; DOY, day of the year; EDM, empirical dynamic modelling; HL, Hulun Buir; HQ, Horqin; JG, Junggar; LAI, leaf area index; MU, Mu Us; OD, Otindag; PET, potential evapotranspiration; PRE, precipitation; TEM, temperature; TG, Tengger. ⁎ Corresponding author at: Yanchi Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR China. E-mail address: [email protected] (Y. Zhang).

https://doi.org/10.1016/j.scitotenv.2019.136391 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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J. Zhang et al. / Science of the Total Environment 710 (2020) 136391

1. Introduction Drylands cover approximately 41% of the global land surface (Huang et al., 2016; Safriel and Adeel, 2005), and support N38% of the global human population (Reynolds et al., 2007). Sustainable dryland ecosystems can maintain global ecological security and socioeconomic development (Maestre et al., 2016; Safriel and Adeel, 2005). However, dryland ecosystems are very fragile and easily impaired under the stresses caused by increasing climatic variability and human interference (Fu and Ma, 2008; Guo et al., 2014; Seddon et al., 2016), leading to the pervasive phenomena of land degradation and desertification (Daliakopoulos et al., 2017; Huang et al., 2016). To reduce land degradation, many ecological restoration practices have been implemented over the past several decades worldwide; these have been carried out, for example, though the Prairie States Forestry Project, Great Plan for the Transformation of Nature, the Three-North shelter-forest system, and at the Algerian Green Dam (Grebner et al., 2013; Qiu et al., 2017; Stavi and Lal, 2015). Although these vegetation restoration practices have substantially improved regional ecosystems (Cao, 2008; Wang et al., 2018), a series of problems have arisen simultaneously, such as the uneven degradation of sand-binding vegetation (Cao et al., 2011; Li et al., 2014; Liu et al., 2018; Wang et al., 2018), reduction of soil water and groundwater (Cao, 2008; Cao et al., 2010; Huang et al., 2016; Su et al., 2012), and new desertification (Li et al., 2017; Wang et al., 2011; Wang et al., 2018). One crucial cause of such problems is that largescale revegetation exceeds the carrying capacity for vegetation (CCV) of the local abiotic environment, causing over-consumption of resources, especially water (Feng et al., 2016; Li et al., 2014, 2017; Liu et al., 2018). Therefore, a better understanding of CCV could help to determine the potential limits to the amount of vegetation in an area, establish sustainable ecosystems, and prevent land degradation in drylands. As a sustainability indicator (Khanna et al., 1999), CCV describes the threshold of the amount of vegetation per unit area required to maintain a stable ecosystem, constrained by local abiotic conditions (Feng et al., 2016; Li et al., 2014; Shao et al., 2018; Wang, 2016; Zeide, 2004). CCV depends on local variables, such as water availability, temperature, radiation, and soil texture (Feng et al., 2016; Osem and O'Hara, 2016; Song et al., 2017). The derivative term, soil water carrying capacity for vegetation, is often used in water-limited areas to emphasize that CCV is constrained by the shortage of water resources (Guo, 2013; Li et al., 2014). CCV is usually quantified using vegetation characteristics such as density, coverage, and biomass at the local scale (Guo, 2013; Li et al., 2014, 2017; Wang et al., 2017), and net primary productivity (NPP), normalized vegetation index (NDVI), and leaf area index (LAI) at the regional scale (Feng et al., 2016; Malcolm and Pitelka, 2000; Wang, 2016). Among these, LAI has been widely applied in dryland ecosystem studies and management practices (Forzieri et al., 2017; Liu et al., 2016; Osem and O'Hara, 2016; Zeng et al., 2017; Zhang et al., 2018), because it is related to the physiology and biophysical characteristics of vegetation. Several methodologies have been used to estimate CCV. These include, for example: (a) using the relationship between carbon (or radiation) and water to estimate the potential or theoretical productivity (i.e., the maximum threshold of vegetation growth) (Milner et al., 1996; Montgomery, 2014; Yi et al., 2012); (b) using the water balance method in a catchment to calculate the ecological water demand, and then converting ecological water demand into the safe threshold of vegetation growth (Feng et al., 2016; Li et al., 2014); (c) using ecohydrological process modelling to simulate the water cycle or the empirical relationship between plant and soil water, and to predict the possible amount of vegetation under specific water conditions (Guo, 2013; Li et al., 2017; Wang et al., 2017); and (d) using bioclimatic envelope modelling or biogeographical modelling to establish the empirical relationships between the vegetation index and environmental factors and to predict the potential vegetation growth on a temporal or spatial scale

(Iio et al., 2014; Malcolm and Pitelka, 2000; Schleppi et al., 2011). These methods usually require long-term field data on hydrology and vegetation, and such data are not available for most drylands. Consequently, it is imperative to develop a new approach based on existing data to evaluate CCV. In addition, CCV has two connotations in the above practices: it is either the maximum amount of vegetation that an ecosystem can sustainably support, or the amount of vegetation that is stable under potential stresses. However, both connotations of CCV have been poorly explored in previous studies. Recently, the terms “Max-LAI” (an upper potential LAI dictated mainly by water availability) and “Safe-LAI” (a safe range of LAI for maintaining ecosystems and to reduce stresses from climatic variability such as drought) were proposed for the assessment and management of dryland ecosystems (Long et al., 2004; Osem and O'Hara, 2016). These metrics are related to the two connotations of CCV mentioned above. Nevertheless, Max-LAI and Safe-LAI cannot be directly used to estimate CCV. This is because Max-LAI reflects only the maximum LAI in a given year under a certain resource stress (Iio et al., 2014; Palmer et al., 2010; Schleppi et al., 2011), and the previous calculations of Safe-LAI lacked practicality and were arbitrarily defined as a constant percentage of Max-LAI (O'Hara and Valappil, 1999; Osem and O'Hara, 2016). Moreover, both Max-LAI and Safe-LAI reflect potential vegetation quantities under a short-term dynamic state or at an annual scale, whereas CCV represents a long-term equilibrium state at a multiyear scale, and according to local abiotic conditions. Ecological restoration areas across drylands in northern China, where data for vegetation and hydrology are lacking, have also been troubled by land degradation (Cao, 2008; Cao et al., 2011; Liu et al., 2018), and the revegetation intensity is usually poorly matched with the local CCV (Li et al., 2014, 2017; Shao et al., 2018). The fact that the CCV of these drylands is unknown greatly constrains the development of policy development for vegetation restoration and management of the existing vegetation in these areas. To measure the CCV in the Horqin (HQ), Hulun Buir (HL), Otindag (OD), Mu Us (MU), Tengger (TG), and Junggar (JG) drylands (Fig. 1), we propose two indicators of vegetation threshold, the Max-LAI and Safe-LAI thresholds. We analysed these across the six drylands, for the period 2000 to 2014, and estimated the possible dynamics of the thresholds in the following decade (2015–2024) based on the relationships between the thresholds and local environment factors, such as temperature (TEM), precipitation (PRE), potential evapotranspiration (PET), and elevation (replaced by a digital elevation model, DEM). The specific objectives were: (a) to explore a feasible method for evaluating the regional CCV in drylands in the absence of field data; and (b) to reveal the variations in CCV and its driving factors across drylands in northern China. Ultimately, we suggest a new technical framework for estimating CCV, which might contribute to vegetation restoration across drylands in northern China.

2. Materials and methods 2.1. Study areas The six drylands examined here are located in the northern temperate zone (Fig. 1). The eastern drylands (HQ, HL, and OD) are characterized by a temperate monsoon climate, and the western drylands (MU, TG, and JG) by a temperate continental climate. The average annual temperature (2000–2014) of the six drylands ranged from −2 °C to 9 °C. The coldest dryland was HL, located at the highest latitude, and the warmest were TG and MU. The average annual precipitation (2000–2014) ranged from 70 mm to 500 mm, and declined from east to west. Based on the aridity index (AI), defined as the ratio of average annual precipitation to average annual potential evapotranspiration (Middleton and Thomas, 1992), these drylands are located in a dry sub-humid zone (HQ, and eastern parts of the HL and OD drylands: 0.5 b AI b 0.65), semi-arid zone (western parts of the HL, central part

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Fig. 1. Study areas in northern China and the local eco-environmental conditions. (a) Six drylands, including Horqin (HQ), Hulun Buir (HL), Otindag (OD), Mu Us (MU), Tengger (TG), and Junggar (JG), were selected as study areas. The image of northern China was obtained from Google Maps. (b), (c), and (d) are the annual temperature (TEM), annual precipitation (PRE), and annual potential evapotranspiration (PET) in the six drylands, respectively.

of the OD, and in the TG and MU drylands: 0.2 b AI b 0.5), and arid zone (JG dryland: 0.05 b AI b 0.2). From east to west, the six drylands belong to a steppe mainly dominated by perennial herbs (Stipa spp. and Achnatherum spp. in the HQ, HL, OD) and shrubs (Caragana spp. in the HQ, HL, OD, and MU), desert steppe dominated by shrubs (Artemisia spp. and Reaumuria spp. in the western MU and TG), and desert dominated by shrubs (Reaumuria spp. and Haloxylon spp. in the JG) (Wang et al., 2011; Zhang et al., 2018). Some tree species, such as Ulmus pumila, Salix gordejevii, and Pinus sylvestris var. mongolica are sparsely distributed in the eastern drylands (the HQ, HL, and eastern OD).

firstly partitioned each dryland into several geographical units according to the terrain (DEM). To reduce the influence of bare sand on our estimates, we excluded the units in which sandy land cover was b60%, based on the specific land-use data (Wang et al., 2005b). To reduce the impact of other factors on the estimates, we then excluded the units in which the total proportion of urban land, farmland, and water bodies was N10%, using the IGBP land-use data (Friedl, 2013). Details of these exclusions can be found in the Supporting Information of Zhang et al. (2018). Finally, 332 units in the six drylands were retained for analysis of CCV. The values of LAI and of the environmental variables that we used (TEM, PRE, PET, and DEM) are averaged over each eightday interval within each geographical unit.

2.2. Datasets and geographical units 2.3. Calculation of the Max-LAI threshold The LAI data for the six drylands from 2000 to 2014, with eight-day intervals and a spatial resolution of 1 km × 1 km, were estimated based on an empirical relationship between LAI and enhanced vegetation index (EVI) (Zhang et al., 2018), using Moderate Resolution Imaging Spectroradiometer (MODIS) sensing datasets (Didan, 2015). The TEM and PRE data were interpolated using the geographically weighted regression method (Jin et al., 2016) using DEM data as a predictor, based on the daily meteorological data of 421 stations in northern China provided by the China Meteorological Data Service Center (Ren et al., 2012). The reference crop evapotranspiration (i.e., potential evapotranspiration, PET), was calculated using the Food and Agriculture Organization's Penman-Monteith equation, with a temporal resolution of eight days and a spatial resolution of 1 km × 1 km (Allen et al., 1998; Milly and Dunne, 2016). The elevation was replaced by DEM data obtained from the Consultative Group on International Agricultural Research – Consortium for Spatial Information Shuttle Radar Topography Mission 1 km database (Reuter et al., 2007). To minimize the influence of anthropogenic disturbances, uneven surfaces, and heterogeneity of water resources on our estimates, we

To measure the maximum attainable amount of vegetation, the Max-LAI threshold (CCVmax) was used as a proxy for CCV. CCVmax is defined as the average annual Max-LAI (Fig. 2a), and can be calculated under stable ecosystems as follows: CCVmax ¼

n  1X ½ Max‐LAIi w n i¼1

ði ¼ 1; 2; …; nÞ

ð1Þ

where n is the length of time series; i is the sequential year; [Max-LAI]i is a maximum LAI for year i; and |w means ‘given the local abiotic conditions (e.g., climate and terrain)’. This threshold characterizes the equilibrium position of the Max-LAI indices in different years, and represents the maximum amount of vegetation growth to maintain a sustainable ecosystem under the limitation of local abiotic conditions (Osem and O'Hara, 2016). The interannual trends for yearly and monthly LAI across the drylands in northern China were not statistically significant (Mann-Kendall test; Figs. S1 and S2). Therefore, Eq. (1) can be used to calculate the Max-

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LAI threshold for all geographical units in each dryland. More details on the vegetation dynamics of the six drylands are provided in Fig. S3. Furthermore, because biocrusts on the dryland surface influence the vegetation index (Chen et al., 2005; Rodríguez-Caballero et al., 2015), the Max-LAI in a given year should be validated at all pixels in each geographical unit to obtain a more precise Max-LAI threshold for a dryland. That is, the Max-LAI at a pixel should be calculated as: Max‐LAI ¼ λ  Max‐LAIobs

ð2Þ

where Max-LAIobs is the Max-LAI in a given year based on remote sensing satellite data (i.e., based on direct observation), and λ is the coefficient (range: 0–1) for the effect of biocrusts on LAI; λ varies with biocrust type, vegetation cover, water status, and other ecosystem factors (Chen et al., 2005; Rodríguez-Caballero et al., 2015). In this study, λ was set to 1.0, because we could not distinguish biocrust from vegetation when using the MODIS images, and because the effects of biocrust are insignificant in summer, when there is high vegetation cover (Rodríguez-Caballero et al., 2015; Weber and Hill, 2016). 2.4. Calculation of the Safe-LAI threshold To determine the safe upper limit of the amount of vegetation needed to maintain a stable ecosystem under potential stresses from climatic factors, the Safe-LAI threshold (CCVsafe) was used as a proxy for CCV. CCVsafe is defined as the average annual Safe-LAI (Fig. 2b) and is calculated as follows: CCVsafe ¼

n 1X ½Safe‐LAIi n i¼1

ði ¼ 1; 2; …; nÞ

ð3Þ

where n is the length of time series; i is the sequential year; and [SafeLAI]i is the Safe-LAI for year i. When considering the effect of biocrusts

on LAI, the Safe-LAI for a given pixel within a geographical unit can be estimated by: Safe‐LAI ¼ λ  Safe‐LAIobs

ð4Þ

where Safe-LAIobs is the Safe-LAI in a given year based on remote sensing satellite data, and λ = 1.0. The Safe-LAI is an upper limit for LAI that protects the ecosystem from the stresses of yearly climatic variability (Osem and O'Hara, 2016). We suggest that the Safe-LAI can be represented by the LAI on the day of year (DOY) when the vegetation has the highest sensitivity to climatic variations. Vegetation sensitivity is the change in the responses of vegetation relative to the change in climatic factors. To set a uniform criterion for calculating Safe-LAI in different years, we defined the “potentially dangerous period” as the period during the year in which the vegetation has the highest sensitivity. The potentially dangerous period for each dryland was calculated as the median DOY when vegetation reached the highest sensitivity to all climatic factors per year (that is, the period during which the vegetation was statistically most sensitive to climatic variability). Because we used eight-day intervals, the potentially dangerous period extends from this median DOY to DOY + 8. The vegetation in these drylands responds rapidly to climatic variation (Maestre et al., 2016), particularly with positive responses early in the growing season (Zhang et al., 2018), and the response time is ≤8 days (Zhang et al., 2018). Therefore, the potentially dangerous period could reflect variations in climatic conditions that the vegetation has to contend with. Further, the LAI during the potentially dangerous period can be used to determine the Safe-LAI for a given year. Thus, the SafeLAI threshold is the multiyear average of LAI in the potentially dangerous period. Overall, the Safe-LAI threshold in each geographical unit was determined as follows: (a) estimate the sensitivity of the vegetation to the variations in climatic factors across seasons; (b) determine the potentially dangerous period based on the vegetation sensitivity; (c) calculate LAI during the potentially dangerous period (i.e., SafeLAI) in each year; and (d) calculate the Safe-LAI threshold based on Eq. (3). We estimated vegetation sensitivity to climatic variation by scenario exploration, using multivariate empirical dynamic modelling (EDM) (Deyle et al., 2016; Zhang et al., 2018). 2.5. Prediction of the dynamics of the Max-LAI and Safe-LAI thresholds

Fig. 2. The Max-LAI and Safe-LAI thresholds. Under the multiyear average seasonal vegetation dynamics, (a) the Max-LAI threshold (CCVmax) represents the crest of the LAI dynamics (unimodal curve), and (b) the Safe-LAI threshold (CCVsafe) is the peak of LAI sensitivity to climatic factor variability (sinusoidal curve, here using the relative increment to represent the sensitivity). The types of curve of the dynamics and sensitivity of LAI follow Zhang et al. (2018). t0: time at start of the growing season; t1: time of the highest sensitivity of ecosystem LAI to the variability of climatic factors (potentially dangerous period); t2: time of the peak value of ecosystem LAI; ΔLAI: relative increment of LAI; ΔLAImax: maximum increment of LAI.

To predict the dynamics of the Max-LAI and Safe-LAI thresholds, we first explored the relationships among the vegetation thresholds and environmental factors, using Pearson's correlation analysis and bioclimatic space analysis. To do this, we first classed the values of each environmental factor into 10 equally-spaced classes, categorized the observations according to these classes, and obtained the mean value for each class (Forzieri et al., 2017). We also explored the relationships between the Safe-LAI threshold and inter-annual variability of climatic factors, using the coefficient of variation (CV). Second, based on the above relationships, we established the MaxLAI threshold model as follows: (a) the predictors (average annual TEM, average annual PRE, average annual PET, and DEM) were selected (Table S1); (b) linear, polynomial, and logarithmic equations with different predictors were established; and (c) all equations were fitted using data from all geographical units in each dryland, to select the optimal model with the highest predictive accuracy and best-fitting parameters. The Max-LAI threshold in the following decade (2015–2024) in each dryland was predicted based on its optimal prediction model using spatial repetition instead of time series data. In addition to the optimal model, the prediction of the Max-LAI threshold requires the prediction of future environmental factors. The future climatic factors for each geographical unit were calculated based on their current means and trends,

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estimated using the Theil-Sen slope method, over the selected period (2000–2014). Finally, to predict the Safe-LAI threshold, the relationship between the Safe-LAI threshold and Max-LAI threshold was characterized by calculating their ratio, β:

registered in the western MU, TG, and JG. The threshold in OD also showed a notable difference, and in JG it was more homogeneous than the Max-LAI threshold.

β ¼ CCVsafe =CCVmax

Multivariate linear regression equations were selected as the optimal prediction models for the two thresholds in each dryland:

ð5Þ

If the surface conditions of each geographical unit were unaltered in the next decade, β would be constant. Based on this assumption, the Safe-LAI threshold in the next decade was predicted using β and the Max-LAI threshold. 3. Results 3.1. Max-LAI threshold across the drylands The Max-LAI threshold across the six drylands ranged from 0.36 m2/ m to 1.03 m2/m2, and presented a gradually declining trend from east to west (Fig. 3). High values (N0.70 m2/m2) occurred in HQ, HL, and eastern OD, while the western MU, TG, and JG showed low values (b0.45 m2/m2). There was a notable disparity within OD, where the threshold gradually decreased from east to west, and a weak disparity within MU and TG. In HQ and HL, the threshold showed high values in the northeast and low values in the southwest. In JG, the threshold varied widely and was unevenly distributed. 2

3.2. Safe-LAI threshold across the drylands There were different potentially dangerous periods for vegetation in response to climatic variability in the different drylands (Fig. 4). The potentially dangerous periods in the six drylands were in the early growing season (100th b DOY b 180th). The earliest potentially dangerous period occurred in JG (113th day), followed by MU (137th day), and TG (169th day). In the eastern drylands (HQ, HL, and OD), these periods occurred at similar times (roughly the 153th–161th day). During the potentially dangerous periods in the six drylands, the Safe-LAI threshold varied from 0.29 m2/m2 to 0.70 m2/m2 (Fig. 5), and its spatial pattern was similar to that of the Max-LAI threshold (i.e., declining from east to west). High values (N0.50 m2/m2) occurred in HQ, HL, and eastern OD, while low values (b0.40 m2/m2) were

3.3. Optimal prediction models for the Max-LAI and Safe-LAI thresholds

CCVmax ¼ a  TEMAA þ b  PREAA þ c  PETAA þ d  DEM þ f

ð6Þ

CCVsafe ¼ β  CCVmax

ð7Þ

where CCVmax represents the Max-LAI threshold; CCVsafe is the Safe-LAI threshold; β is the ratio of the Safe-LAI threshold to the Max-LAI threshold; TEMAA is average annual temperature, °C/yr; PREAA is average annual precipitation, mm/yr; PETAA is average annual potential evapotranspiration, mm/yr; DEM represents mean elevation, m; a, b, c, and d are the coefficients of the predictors; and f is the intercept of the model. The regression coefficients for each dryland are shown in Table 1. Most models significantly explained a substantial proportion of the variance (R2 N 0.50, P b 0.001); the model for TG was the exception (R2 = 0.167, 0.05 b P b 0.1). The relative prediction errors in most drylands were below 15%, except in parts of the JG where the errors were approximately 30% (Fig. S4). In the six drylands, β ranged from 0.44 to 0.94 (Fig. 6). The spatial pattern of β was generally contrary to that of the two thresholds. High values (β N 0.80) occurred in the western OD, TG, and parts of JG, while low values (β b 0.60) occurred in HQ, HL, and parts of JG. Similar to the Max-LAI threshold, β showed a remarkable disparity in OD, and varied unevenly in JG. 3.4. Correlations among Max-LAI threshold, Safe-LAI threshold, and environmental factors Among all geographical units, the Max-LAI and Safe-LAI thresholds displayed similar relationships to climatic factors (Fig. 7a–f). Higher PRE was associated with larger Max-LAI and Safe-LAI thresholds. Under the same magnitude of PRE, regions with high TEM, large PET, and high DEM had low Max-LAI and Safe-LAI thresholds (Fig. 7a–f). The Max-LAI threshold had a positive relationship with the Safe-LAI

Fig. 3. Max-LAI threshold (CCVmax) in the six northern China drylands. HL, Hulun Buir; HQ, Horqin; JG, Junggar; MU, Mu Us; OD, Otindag; TG, Tengger.

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J. Zhang et al. / Science of the Total Environment 710 (2020) 136391

Max-LAI and Safe-LAI thresholds in 2015–2024 could be predicted using Eqs. (6) and (7). In fact, the Max-LAI and Safe-LAI thresholds would show increases (approximately 15%) in most drylands, decreases (approximately −15%) in HQ, and non-significant changes in TG (Fig. 9). Specifically, the Max-LAI threshold or Safe-LAI threshold would increase substantially (approximately 25%) in western HL, central OD, and central MU; increase slightly (approximately 10%) in eastern HL and south western MU; decrease distinctly (about −15%) in most of HQ; and change slightly (approximately ±7%) in south eastern OD, north western MU, and TG. The changes of these two thresholds in JG would range unevenly from notable decreases (−25%) to notable increases (45%). 4. Discussion Fig. 4. Potentially dangerous periods for vegetation relative to climatic variability in the six drylands examined here. DOY, day of the year; HL, Hulun Buir; HQ, Horqin; JG, Junggar; MU, Mu Us; OD, Otindag; TG, Tengger.

threshold (r = 0.913, P b 0.001; Fig. 8a). The Max-LAI and Safe-LAI thresholds had no obvious relationships with DEM (Fig. 8b and c). In addition, the Safe-LAI threshold had a weak relationship with the interannual variability of TEM in the six drylands (Fig. S5a), and a negative relationship with the inter-annual variability of PRE in most drylands, except in HQ and JG (Fig. S5b). There was a positive relationship between the Safe-LAI threshold and the inter-annual variability of PET across the six drylands, although within each dryland except HQ, this relationship was negative (Fig. S5c). Although β had a negative relationship with the Max-LAI threshold (r = −0.628, P b 0.001; Fig. 8d), it had a weak relationship with the Safe-LAI threshold. With increasing Max-LAI, the variance of the relationship between β and the Max-LAI threshold also increased. However, β had no significant relationships to environmental factors (Fig. 8e–i) and showed no marked patterns across climatological gradients (Fig. 7g–i). 3.5. Dynamics of the Max-LAI and Safe-LAI thresholds in 2015–2024 Annual TEM and PET had slightly decreasing trends in most drylands over the 2000–2014 period, but only TEM in HL and PET in HQ had significant decreasing trends (P b 0.05) (Table S2). Annual PRE showed increasing trends in the six drylands, but it varied significantly only in HL (P b 0.05). If the current trends of climatic factors were maintained, the

4.1. Technical framework for estimating CCV Previous studies have described CCV as the vegetation population threshold constrained by local environmental factors (Feng et al., 2016; Guo, 2013; Li et al., 2014, 2017; Shao et al., 2018; Wang, 2016). Based on this concept, we defined CCV as the vegetation threshold, under abiotic stresses, that can support sustainable dryland ecosystems. The CCV reflects multiyear vegetation growth, and varies with different environmental factors and climate change (Feng et al., 2016; Shao et al., 2018). We further clarified that CCV has two connotations, and can be characterized by the Max-LAI and Safe-LAI thresholds. Under environmental limitations, the Max-LAI threshold reflects the maximum attainable amount of vegetation and the Safe-LAI threshold reflects the safe range of the amount of vegetation that is required to maintain a stable ecosystem. These thresholds have definite physical meanings (Fig. 2) (i.e., the peak of the dynamic LAI, for Max-LAI, and of the sensitivity of LAI to multiyear climatic variability, for Safe-LAI). Previous methods for calculating CCV required hydrological or meteorological data at small spatial scales (Feng et al., 2016; Li et al., 2014; Montgomery, 2014; Yi et al., 2012). Here, we developed a technical framework for evaluating CCV at the regional scale based on Max-LAI and Safe-LAI thresholds. Our technical framework can be easily used to estimate the CCV of stable ecosystems using remote sensing data in regions without field observation data. Nevertheless, it should be noted that the vegetation should be in a stable state (in equilibrium) when using our method to estimate the Max-LAI threshold. Moreover, we propose a method for calculating the Safe-LAI threshold during

Fig. 5. Safe-LAI threshold (CCVsafe) in the six northern China drylands examined here. HL, Hulun Buir; HQ, Horqin; JG, Junggar; MU, Mu Us; OD, Otindag; TG, Tengger.

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Table 1 Optimal prediction models for the Max-LAI thresholds in six drylands in northern China. Dryland

a

b

HQ HL OD MU TG JG

−9.103 × 10−2 9.437 × 10−3 4.731 × 10−2 2.020 × 10−2 – 6.326 × 10−1

1.186 1.587 1.922 8.670 5.890 1.241

× × × × × ×

10−3 10−3 10−3 10−6 10−4 10−2

c

d

f

R2

F

P

2.504 × 10−3 – – 3.480 × 10−4 1.790 × 10−4 –

−1.164 × 10−3 1.400 × 10−4 −1.040 × 10−4 7.900 × 10−5 – 1.595 × 10−3

−4.253 × 10−1 2.339 × 10−1 4.522 × 10−1 7.201 × 10−1 9.579 × 10−2 −6.932

0.756 0.892 0.924 0.511 0.167 0.701

28.699 134.982 338.288 18.005 2.708 41.512

b0.001 b0.001 b0.001 b0.001 0.085 b0.001

Note: a, b, c, and d are the coefficients of TEM, PRE, PET, and DEM in Eq. (6), respectively; R2 is the discriminant coefficient; F is the statistic of the significance test; P is the significance level; and ‘–’ indicates that the model of the corresponding dryland does not contain the corresponding coefficient. HL, Hulun Buir; HQ, Horqin; JG, Junggar; MU, Mu Us; OD, Otindag; TG, Tengger.

potentially dangerous periods, which were effectively detected using a multivariate EDM. The potentially dangerous periods in the six drylands occurred during the early growing season (Fig. 4), and increased climatic variation could result in increased vegetation responses during the early growing season (Zhang et al., 2018), and in the need for larger safety margins of LAI. Therefore, the potentially dangerous period could reflect variations in climatic conditions that the vegetation experiences. The method for estimating the Safe-LAI threshold was more accurate than artificially setting the ratio of Safe-LAI to Max-LAI, as in previous studies (O'Hara, 1996; O'Hara and Valappil, 1999). In addition, because the spatial patterns of Max-LAI and Safe-LAI thresholds mirrored the effects of these influencing factors, our technical framework can achieve a unified evaluation of CCV in different drylands, and provides a practical approach for the sustainable management of dryland ecosystems. In spite of the above-mentioned advantages, our framework had some limitations, such as insufficiently accounting for the effects of biocrust and artificially introduced vegetation. In order to calibrate the effect of biocrust on LAI, we introduced a correction coefficient λ into Eqs. (2) and (4). According to previous studies (Chen et al., 2005; Rodríguez-Caballero et al., 2015; Weber and Hill, 2016), the greater the effect of biocrust, the smaller the value of λ, and this relationship differs between biocrust types in different drylands. A large effect of biocrust usually occurs in places with sparse vegetation cover, or after the strong pulse effect of a precipitation event. In this study, we were able to ensure that most units had abundant vegetation cover and correspondingly low biocrust cover, by selecting geographical units that matched those criteria. Max-LAI usually occurs in the middle growing season, which suggests that the effect of biocrust on Max-LAI will be

small. Therefore, we set λ = 1.0 to calibrate the Max-LAI value. SafeLAI often occurs in the early growing season (Fig. 4), when there is sparse vegetation cover and a lack of available water, hence biocrust is likely to affect Safe-LAI more strongly than it affects Max-LAI. Unfortunately, due to the lack of studies on the effect of biocrust during our study period, it is not easy to determine the value of λ in different pixels or units. Hence, we also set λ = 1.0 to estimate the potential maximum Safe-LAI. Furthermore, much vegetation has been planted during revegetation programs in northern China; this vegetation has a different composition and structure from the naturally occurring vegetation. Although we excluded areas which may have been influenced by human activity, to reduce the effect of artificially introduced vegetation, it may nonetheless reduce the accuracy of our technical framework. Our results indicate that the different increments in CCV were caused by the different trends of climatic factors in the six drylands. The positive increments in CCV were mostly caused by the increasing trend of PRE and the decreasing trend of TEM (e.g., in western HL, central OD, and eastern MU). Previous studies (Feng et al., 2016; Guo, 2013; Xu et al., 2018) also concluded that the vegetation of eastern MU would be at CCV. Our predictions suggest that the weakest trends observed in TG would lead to non-significant changes in CCV in 2015–2024, and Li et al. (2014) achieved a similar result. However, Wang et al. (2017) found that the CCV in TG could increase by 20%. These differences in predictions might reflect the fact that the simulations of Wang et al. (2017) were derived under ideal climatic conditions rather than current climatic variations. In addition, the significant decrease in PET (Tables S1 and S2) would mainly result in reduced CCV in HQ, because of the positive relationships between PET and CCV in this dryland. A previous

Fig. 6. Ratio of the Safe-LAI threshold to the Max-LAI threshold (β) in the six northern China drylands examined here. HL, Hulun Buir; HQ, Horqin; JG, Junggar; MU, Mu Us; OD, Otindag; TG, Tengger.

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Fig. 7. Safe-LAI and Max-LAI thresholds (or the ratio of the Safe-LAI threshold to the Max-LAI threshold β) across environmental gradients (in different bioclimatic spaces) at the interannual scale.

Fig. 8. Relationships among vegetation thresholds, ratio of the Safe-LAI threshold to the Max-LAI threshold (β), and environmental factors: (a) the relationship between CCVmax and CCVsafe; (b, c) the relationships of CCVmax and CCVsafe to DEM; (d–i) the relationships of β to CCVmax, CCVsafe, and environmental factors.

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Fig. 9. Predicted relative increments in CCV over the decade 2015–2024 compared to 2000–2014, in six northern China drylands. HL, Hulun Buir; HQ, Horqin; JG, Junggar; MU, Mu Us; OD, Otindag; TG, Tengger.

study also showed that the vegetation of HQ has already been degraded by the severe water deficit and intensive human interference (Liu et al., 2018). All of these results suggest that the vegetation of HQ may suffer from further stresses in the future. When predicting the dynamics of CCV, we assumed that the trends of climatic factors observed for 2000–2014 would not shift during the next decade. However, in terms of long-term climate change, warming is inevitable but the change of PRE is still unclear (Huang et al., 2016; Li and Zha, 2019; Xu et al., 2017). If TEM increases but PRE does not, the trend in CCV will not follow the current relationship with climatic factors, and CCV will not continue to increase. Therefore, we have limited our prediction of CCV to 2015–2024, rather than over a longer period. In addition, it should be noted that, for the regions where the current CCV had weak relationships with climatic factors, the predictive model of CCV should include more influential factors (e.g., underground water). Our results indicate that β did not change with environmental factors (Figs. 7 and 8), meaning that other factors (e.g., vegetation types and geographical location) rather than environmental factors might be influencing β in each geographic unit. In 2015–2024, if there is no drastic human interference within each geographic unit and the vegetation types remain unchanged, β will be maintained. Based on this hypothesis, the dynamics of the Safe-LAI threshold in each unit can be predicted based on β and on the predicted Max-LAI threshold. If the local conditions shift due to anthropogenic disturbances, the Safe-LAI threshold will not be easy to predict. 4.2. Factors influencing CCV We found that the regional CCV across drylands in northern China was influenced by aridity, water resources, and TEM. The Max-LAI and Safe-LAI thresholds were equally sensitive to aridity (Fig. 7b and e), and with the decrease in AI, the Max-LAI and Safe-LAI thresholds declined (Figs. 3 and 5). These patterns occurred in the areas where the annual PRE was lower than 500 mm; however, when the annual PRE was N500 mm, there were no clear relationships between LAI and PRE or AI (Leuschner et al., 2006). These complex relationships suggest that other factors, such as TEM, also affect the Max-LAI threshold (Hebert and Jack, 1998; Iio et al., 2014; Kergoat, 1998; O'Hara and York, 2014; Osem and O'Hara, 2016). However, the effects of TEM on LAI in the six drylands were indistinguishable from those of PRE or AI, because the precipitation of the six drylands was lower than 500 mm.

TEM did not alter the relationships between LAI and PRE or LAI and AI; this is evident from the fact that warm-dry regions had lower Max-LAI and Safe-LAI thresholds than the cold-wet regions (Fig. 7a and d). In addition, the LAI of vegetation utilizing groundwater is significantly higher than that of vegetation not using groundwater (O'Grady et al., 2011). For example, some plant communities (e.g., Haloxylon spp.) in JG utilize groundwater (Zhu et al., 2012), potentially explaining the high Max-LAI threshold that we estimated for JG (Fig. 3). We found that different vegetation types were associated with different relationships between β and the Max-LAI threshold across the six drylands; these relationships were more sensitive to the Max-LAI threshold in western than in eastern drylands. This geographic difference was not caused by differences in environmental factors, because β was not influenced by environmental factors. Therefore, vegetation types might influence the relationship between β and Max-LAI threshold. The LAI of trees usually changes more than that of shrubs and herbs in response to climatic stress, because trees lose their leaves in response to climatic stress (Osem and O'Hara, 2016). Further, trees require a large safety margin (low β values) to maintain ecosystem stability (Figs. 2 and 5). In addition, we found that the Safe-LAI threshold varied significantly with climatic factors, especially with PRE (Fig. S5). Drylands with large variability in PRE also had low β values and low Safe-LAI thresholds. Our results also indicate that the dynamics of CCV in each dryland were influenced by different environmental factors. This was confirmed by the differences between the predictors of the Max-LAI threshold and Safe-LAI threshold in the different drylands, based on the optimal prediction models (Table 1). While PET was not involved in the models for HL, OD, and JG, which indicated that PET had a very weak impact on vegetation in the corresponding drylands, TEM and DEM showed poor predictive value in the model for TG (Zhang et al., 2018), suggesting that the relationship between vegetation and environmental factors differed among drylands. Furthermore, we found that climate had a greater influence on vegetation than other factors such as anthropogenic disturbances, based on the absolute residuals of the optimal prediction models; this is consistent with other studies (Qiu et al., 2018; Wang et al., 2005a; Xu et al., 2018). 4.3. Implications and limitations Because CCV reflects the thresholds of vegetation constrained by local environmental factors, it can be used to help ecological restoration

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practices in dryland ecosystems, to relieve the stresses from environmental factors. For example, in the design of the scale of ecological restoration, the Max-LAI and Safe-LAI thresholds can be used as the maximum and optimal attainable LAI, respectively. In the management of dryland ecosystems, Safe-LAI can be used as a warning indicator to identify whether an ecosystem can cope with the possible stress from climatic variability, and as the criterion for reducing LAI to relieve the stress induced by climatic variability, using measures like livestock grazing or harvesting of woody and herbaceous vegetation (Osem and O'Hara, 2016). Furthermore, our results show that, under current stresses in the context of anthropogenic activities, there is scope for improving vegetation coverage over the next decade in most drylands, except in TG and HQ. However, this does not imply that non-native species can be introduced for local vegetation restoration. Management practices such as enclosure protection and grazing prohibition should be preferentially adopted to promote native species. We have presented a technical framework for estimating dryland CCV, using satellite data. It remains to be verified whether this framework can be applied at other scales and in other regions. For the regions where the current CCV had weak relationships with climatic factors, other influencing factors such as underground water need to be considered as predictors to improve model accuracy. Although we excluded the effect of anthropogenic activities as much as possible, CCV is affected by the fact that environmental changes are compounded by anthropogenic activities; thus, these effects should be incorporated in future research. Moreover, CCV reflects not only the influence of LAI but also of other vegetation attributes, such as structure, species, density, and biomass (Maestre et al., 2016; Shao et al., 2018). A better understanding of the effects of the above attributes on CCV can help to improve the precision of local vegetation restoration and ecosystem-specific management practices, and to mitigate land degradation. 5. Conclusions Our study proposed two indicators (Max-LAI threshold and Safe-LAI threshold) to represent the different connotations of CCV, and established quantitative relationships between these indicators and environmental factors (TEM, PRE, PET, and DEM) to identify the dynamics of CCV for six drylands in northern China. The methods to calculate these indicators, including estimating vegetation sensitivity using multivariate EDM, offer a new technical framework for estimating CCV at the regional scale. Based on our technical framework, the Max-LAI and Safe-LAI thresholds across the six drylands varied from 0.36 to 1.03 m2/m2 and 0.29 to 0.70 m2/m2, respectively, decreasing from east to west, from 2000 to 2014. For the period 2015 to 2024, our predictions are that most of the drylands, except HQ and TG, would show an increasing trend in CCV. This suggests that, under current climatic variability and anthropogenic disturbances, there is scope for improving vegetation coverage in most drylands. Our results might help in planning region-specific revegetation programs across these drylands, and guiding sustainable ecosystem management to achieve land degradation neutrality by 2030. Future research should focus on including other factors that may affect CCV, such as biocrusts, artificially introduced vegetation, underground water, vegetation attributes (e.g., density and biomass), and anthropogenic activities. Declaration of competing interest The authors declare no financial/commercial conflicts of interest. Acknowledgements This study was jointly funded by the National Key Research and Development Program of China (2016YFC0500905) and the National Basic Research Program of China (2013CB429901). We thank Guanglei Gao and Hongjie Guan for their suggestions; Junguo Liu, Changzhen Yan,

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