Disturbance control can effectively restore the habitat of the giant panda (Ailuropoda melanoleuca)

Biological Conservation 238 (2019) 108233

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Policy analysis

Disturbance control can effectively restore the habitat of the giant panda (Ailuropoda melanoleuca)

T

Lan Qiua,b, Han Hana, Hong Zhoua, Mingsheng Honga, Zejun Zhanga, Xuyu Yangc, Xiaodong Gud, ⁎ ⁎ Wen Zhange, Wei Weia, , Qiang Daib, a

Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, PR China Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, PR China c Sichuan Wildlife Resource Investigation and Protection Station, Chengdu, PR China d Sichuan Administrative Bureau of Giant Panda National Park, Chengdu, PR China e Sichuan Provincial Institute of Forestry Survey and Planning, Chengdu 610082, PR China b

ARTICLE INFO

ABSTRACT

Keywords: Giant panda Habitat conservation Human disturbance Restoration

Expanding human disturbances on a global scale are encroaching upon wildlife habitat. Management of human–wildlife conflict is an important issue in biodiversity conservation. Conservation decisions are supported by information on how many habitats could be recovered by controlling human disturbances, yet it has rarely been quantitatively studied. Giant pandas (Ailuropoda melanoleuca) are still confronted with threats of human disturbance even though populations have been restored in recent decades. In this study, the impact of four types of human disturbance, including livestock, infrastructure, farming and other disturbances were assessed based on a dataset that covers 75% of total giant panda population. Five scenarios where disturbances are controlled were used to evaluate the habitat area that can be restored by controlling disturbance. Results revealed that 2102 km2 of suitable habitat can be restored if all these disturbances were completely controlled. Controlling livestock alone can restore up to 830 km2 of habitat, much more than controlling farming or infrastructure. Controlling infrastructure restored more habitat than the other disturbances in the Daxiangling and Qionglai Mountains. Moreover, in the Minshan Mountains, controlling agriculture resulted in the most habitat restoration. It appears, the reserves system does work in controlling the three types of human disturbance, however, more control is needed over human disturbances in order to restore wildlife habitat outside of the reserves. These results not only calculate how much giant panda habitat can be restored by controlling disturbance, but also provide insights for other species habitat management.

1. Introduction

(Hoffmann et al., 2010). For example, China has implemented strict forest protection programs since 1999 and has become one of the few countries in the world to see a net increase in forest cover (Chen et al., 2019). China has restored forest cover in some wildlife habitats (Li et al., 2013a), but a large number of studies have demonstrated that the restoration of vegetation alone does not fully protect wild animals and that it is also necessary to effectively control human disturbances (Barber et al., 2014). Multiple human disturbances may exist in the same region at the same time, affecting wildlife habitats in different ways (Coppes et al., 2017). For instance, previous studies have shown that farm animals sharing habitat with wildlife compete for food sources (McLaren et al., 2018), while also destroying the soil environment, vegetation structure, and ecological functions (Wassie et al., 2009; Su et al., 2017). In

Since the end of the last century, increasing human activity has disturbed wildlife habitats around the world (Alamgir et al., 2019; Plante et al., 2018). Disturbances change animal behavior (Kühl et al., 2019), impair animal health (Frid and Dill, 2001), disperse animals away from their original habitat range (Johnson and Russell, 2014), and reduce reproductive rates (Strasser and Heath, 2013), which leads to population decline (Solveig Vors and Boyce, 2009), and in some cases, extirpation (Srivathsa et al., 2019). In order to protect wildlife, 196 countries signed the Convention on Biological Diversity (CBD), which was adopted in 1992. Protective actions have reduced the loss of biodiversity to a certain extent but have not completely curbed the increasing risks of extinction as a result of human disturbances

⁎

Corresponding authors. E-mail addresses: [email protected] (W. Wei), [email protected] (Q. Dai).

https://doi.org/10.1016/j.biocon.2019.108233 Received 18 June 2019; Received in revised form 24 August 2019; Accepted 30 August 2019 0006-3207/ © 2019 Elsevier Ltd. All rights reserved.

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area in Sichuan Province is in the giant panda habitat in Sichuan Province (2.027 million km2), and the sampling area is smaller than the actual habitat area. Sichuan province is home to the biggest population size of giant panda, where population is 1374, accounting for 74.4% of the total. Based on historical survey data and ecological monitoring data, this study identified possible distribution areas of giant pandas. Within the possible distribution areas of giant pandas in Sichuan Province, there were 13,635 square grids of 2 km2 established as survey samples. One Z-shaped sample line was set for each plot, and each sample line was no shorter than 1.97 km. Giant panda traces found in the sample lines were recorded (e.g., feces, food trails, footprints, and nests), as well as the location and number of human disturbances. Data quantifying the disturbances were divided into four categories: grazing, farming, infrastructure (including roads, mines, transmission lines, and hydropower stations), and other human activities (including bamboo shoot collection, trapping, fire, logging, tourism etc). Environmental factors included terrain and vegetation data. Terrain data includes elevation, slope, and aspect. This data was retrieved from the Geospatial Data Cloud Platform of the Chinese Academy of Sciences Computer Network Information Center (http://www.gscloud.cn). Slope data was sine transformed. Aspect data was subtracted by 180° then absolute values were taken for subsequent analysis (Cushman and Wallin, 2002; Sitzia et al., 2018). Vegetation data was retrieved from the Second National Forest Inventory (NFI2) and revised according to the vegetation cover reported in the Fourth National Giant Panda Survey (NGPS4; Forestry Department of Sichuan Province, 2015). According to the use of different vegetation types by giant pandas, vegetation data was divided into broadleaf forest, evergreen broadleaf forest, coniferous forest, plantation, meadow, shrub, and other vegetation. The proportion of coverage of different vegetation types in each grid was also calculated.

addition to directly occupying wildlife habitats and degrading the forest landscape (Potapov et al., 2017), infrastructure also alters the physical and chemical surroundings of forests, resulting in physiological and behavioral changes in wildlife. Besides, the increasing of agricultural activities will also occupy wildlife habitat, disturb surrounding wildlife and decrease the quality of surrounding habitats, leading to wildlife habitat fragmentation (Edwards et al., 2015). The extent to which different human disturbances affect wildlife habitats vary, and the ecological benefits obtained after disturbances that are effectively controlled also differ. As an iconic flagship species, the giant panda (Ailuropoda melanoleuca) has been effectively protected in recent decades, with its populations and habitats continuing to grow. However, the giant panda's habitats still face severe human disturbances (Forestry Department of Sichuan Province, 2015). For example, Li et al. (2017) found that farm animals compete with giant pandas for food in their native habitats. Additionally, Wei et al. (2018b) found that the vertical distribution of giant pandas has increased due to the grazing activities of farm animals. He et al. (2019) demonstrated that roads affect the surrounding forests, thereby reducing the quality of giant panda habitats and resulting in a low density of giant pandas around roads. Moreover, disturbances from mines, hydropower stations, and residential areas have also been found to affect the habitat of giant pandas (Zhao et al., 2016). The impact of human disturbances on wildlife habitats has resulted in human-animal conflict (Young et al., 2005; Struebig et al., 2018), thus, the control of human disturbances will inevitably affect locals' living conditions. Therefore, discovering how much habitat can be restored by controlling certain human disturbances is extremely valuable for protection agencies, as this information will help agencies accurately conduct trade-off analyses. However, it should be noted that previous studies have analyzed the decline of wildlife habitats caused by human disturbances and found that when there are multiple disturbances, habitat area loss due to one disturbance are not recovered by controlling another disturbance (Polfus et al., 2011; Lowrey et al., 2017). Based on the distribution data of 75% of the world's giant pandas (Forestry Department of Sichuan Province, 2015), we predicted the giant panda's habitat area by effectively controlling various types of disturbances. This study will also quantify the possible effects of controlling various types of disturbances and the spatial distribution of recoverable habitats. The results of this study will provide support for effective decision-making in the management of various giant panda regions and will also act as a reference for protecting other species.

2.2. Resource selection model In order to evaluate the quality of giant panda habitat, this study used the resource selection function (RSF) to analyze the effects of topography, vegetation, and disturbance on the distribution of giant pandas (Table 1). RSF quantitatively predicts the relative probability of giant pandas using a particular area by integrating the actual use data of habitats and the characteristics data of alternative habitats (Boyce et al., 2002). The generalized linear mixed-effects model function (GLMM) connected through logit was used to evaluate the giant panda resource selection model. RSF uses logistic regression by assuming an exponential function:

2. Materials and methods

w (x ) = exp(

2.1. Data collection and processing

0

+

1 x1+

+ n x n),

where X1−n denotes the resource (predictor) variables, β1−n denotes the model coefficients, and β0 is the intercept. The models of variable combinations were analyzed in order to select the best-fit models based on the Akaike information criterion (AIC). Models with ΔAIC < 2 were considered to be the “best” fit, and the best fit model was selected for

The distribution and disturbance data of giant pandas were retrieved from the Fourth National Giant Panda Survey (NGPS4; Forestry Department of Sichuan Province, 2015).The fourth giant panda survey Table 1 Variables used for modeling panda resource selection. Variable

Description

Elevation Slope Aspect Livestock grazing Infrastructure Farming Other human disturbances

Average elevation (m) Sin (slope, in degrees) |Aspect, in degrees, minus 180°| The number of recorded grazing disturbances The number of recorded infrastructure disturbances, including roads, mining, transmission lines, and hydropower stations The number of recorded farming disturbances The number of recorded human disturbances that are not grazing, infrastructure, or farming disturbances, including bamboo shoot collection, trapping, fire, logging, tourism etc. Arcsin(sqrt(vegetation coverage ratio)), including meadows, coniferous forests, evergreen broadleaf forests, broadleaf forests, shrubs, plantation, and other vegetation Random variables, namely the Minshan, Qionglai, Daxiangling, Xiaoxiangling, and Liangshan mountains

Land cover Mountains

2

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subsequent analysis (Burnham and Anderson, 2003). If cumulative Akaike weights ≤0.95, a subset of the most likely models were selected for model averaging (Lukacs et al., 2010), and panda resource selection was predicted using the averaged parameters. The relative utilization probability (i.e., the RSF value) was predicted within each grid based on RSF. It was assumed that the higher the probability that an area was distributed with giant pandas, the more suitable the area was as panda habitat (Lowrey et al., 2017). These “suitable” and “unsuitable” habitats were determined with a minimum RSF value, such that 95% the grid was covered (DeVoe et al., 2015).

Predictive variables (i.e., terrain, vegetation, and disturbance) were used to calculate RSF values in each scenario through the RSF model. By setting a number of specific disturbances to 0, RSF values were calculated in a given scenario through the RSF model. By comparing RSF values and areas of suitable habitat for each grid, the restoration area of giant panda habitat was thereby determined after accounting for disturbance control. A total of four disturbance control scenarios were set up in this study: 1) the all disturbances control scenario, which assumed that all disturbance factors were controlled throughout the study area and the eco-environmental factors remained unchanged; 2) the grazing disturbance control scenario, which assumed that only grazing disturbances were controlled and the other disturbance and eco-environmental factors remained unchanged; 3) the infrastructure disturbance control scenario, which assumed that only infrastructure disturbances were controlled and the other disturbance and eco-environmental factors remained unchanged; and, 4) the farming disturbance control scenario, which assumed that only farming disturbances were controlled and the other disturbance and ecoenvironmental factors remained unchanged.

The study area was found to have a suitable habitat consisting of 20,580 km2 and an unsuitable habitat consisting of 6690 km2. Disturbance control scenarios were inferred based on the averaged model. In all disturbance control scenarios, the suitable habitat area was 22,682 km2 (Fig. 1; Table 4). This implies that, if all disturbances were controlled, there could up to be 2102 km2 of restorable habitat suitable for giant pandas, which accounts for 10.21% of the total suitable habitat area. Moreover, the Xiaoxiangling and Liangshan mountains were found to be the most affected by human disturbances. If all disturbances were controlled, roughly 20.36% of the suitable habitat could be restored in the Xiaoxiangling mountains and roughly 19.54% in the Liangshan mountains (Table 5). Controlling grazing disturbances alone could restore up to 830 km2 of suitable habitat (Table 4). Grazing disturbances were found mainly in the Xiaoxiangling and Liangshan mountains. If grazing disturbances were controlled, it could restore up to 16.29% of the suitable habitat in the Xiaoxiangling mountains and 14.79% in the Liangshan mountains. Infrastructure disturbances were found mainly in the Daxiangling and Xiaoxiangling mountains. Controlling infrastructure disturbances could restore up to 9.17% of the suitable habitat in the Daxiangling mountains and 7.87% in the Xiaoxiangling mountains. Agricultural disturbances were mainly found in the Minshan and Qionglai mountains. Controlling farming disturbances could restore up to 2.54% of the suitable habitat in the Minshan mountains and 2.36% in the Qionglai mountains. When various disturbances are controlled, the amount of habitat recovered in the protected area was far smaller compared to the area outside of the reserve. The protected area could recover up to 532 km2 of giant panda habitat, accounting for 4.59% of the total protected area (11,578 km2). Outside the protected area, however, up to 1570 km2 of giant panda habitat could be recovered, accounting for 10.01% of the total area outside of the protected area (15,692 km2) (Fig. 2).

2.4. Statistical analyses

4. Discussion

All analyses were conducted in R v3.5.1 (R Core Team, 2018). RSF was analyzed using the lme4 package (https://CRAN.R-project.org/ package=lme4) (Bates et al., 2015). Model selection and averaging was conducted using the MuMIn package (https://CRAN.R-project.org/ package=MuMIn) (Bartón, 2013).

These results demonstrate that if human disturbances were effectively controlled, much of the suitable habitat areas for giant pandas could be restored. Numerous studies have revealed that giant pandas are indeed very sensitive to human disturbances and clearly avoid areas with intense human activity (Wei et al., 2014; Wei et al., 2019; Zhao et al., 2016). This study did not directly assess habitat area loss due to giant pandas avoiding human disturbances, rather, it compared the RSF model results of real-world scenarios and disturbance control scenarios as an effort to estimate suitable habitats that may be recovered by the control of various types of human disturbances. Collectively, results revealed that if all human disturbances were controlled, a suitable habitat of roughly 2102 km2 could be restored within the most important distribution areas of giant pandas. Moreover, grazing disturbance control alone could restore the largest habitat area. However, in other areas like the Daxiangling and Qionglai Mountains, infrastructure disturbance control was found to be the most beneficial for habitat restoration. This is likely because the Daxiangling and Qionglai mountains have a large number of roads, hydropower stations, mines, and other facilities. In the Minshan Mountains, the control of agricultural disturbances would have the largest payoff, which is likely due to the wide distribution of farmland in the southern part of the mountains. These results suggest that the types of disturbances that need to be controlled will differ based on the area and are not the same. Since 1998, China has successively launched a series of forest protection policies, such as the “Natural Forest Protection Project,” “Grain to Green,” and the “Nature Reserve Construction Project.” Through the active implementation of these policies, forest coverage in giant panda habitats has continued to rise (Li et al., 2013b). However, the results of this study revealed that, for wild animals like the giant panda that are sensitive to human activities, it is not enough to rely solely on the protection and restoration of forest vegetation. More habitats may be

2.3. Disturbance control scenarios

3. Results 3.1. Summary of samples There were 2350 grids identified with traces of giant pandas in the study area with 13,725 disturbance points, including 5615 grazing, 2833 infrastructure, 939 agricultural, and 4098 other human-related activity disturbance points. All trace and disturbance points were applied to the RSF function. 3.2. Resource selection model Model averaging was performed as there was no single model found to be significantly better than another (ΔAIC ≤2) (Table 2). Results of the averaged model revealed that the variables had significant effects on the quality of all types of giant panda habitat (Table 3), with a high importance level (≥0.99). In general, it was found that giant pandas prefer coniferous forests and broadleaf forests compared to meadows, shrubs, and evergreen broadleaf forests. Additionally, giant pandas were found to avoid areas that have grazing, infrastructure, or farming disturbances. 3.3. Quality of giant panda habitat in different scenarios The cutoff point used to define a “suitable” habitat was 0.06596. 3

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Table 2 Set of best-ranked panda resource selection models (model whose cumulative Akaike weight ≤0.99). Best candidate models (cumulative Akaike weight ≤0.95) used for model averaging are highlighted in bold. Model

df

LL

AIC

∆AIC

wi

1. Elevation + slope + grazing + infrastructure + farming + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + meadow + shrub + other 2. Elevation + slope + grazing + infrastructure + farming + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + plantation + meadow + shrub + other 3. Elevation + slope + grazing + infrastructure + farming + other human activities + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + meadow + shrub + other 4. Elevation + slope + aspect + grazing + infrastructure + farming + coniferous forest + evergreen broad-leaved forest + broadleaved forest + meadow + shrub + other 5. Elevation + slope + grazing + infrastructure + farming + other human activities + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + plantation + meadow + shrub + other 6. Elevation + slope + aspect + Grazing + infrastructure + farming + coniferous forest + evergreen broad-leaved forest + broadleaved forest + plantation + meadow + shrub + other 7. Elevation + slope + aspect + grazing + infrastructure + farming + other human activities + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + meadow + shrub + other 8. Elevation + slope + aspect + grazing + infrastructure + farming + other human activities + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + plantation + meadow + shrub + other 9. Elevation + slope + grazing + infrastructure + farming + coniferous forest + evergreen broad-leaved forest + broad-leaved forest + meadow + other

13

−5495.34

11,016.67

0.00

0.29

14

−5494.71

11,017.41

0.74

0.20

14

−5495.23

11,018.45

1.78

0.12

14

−5495.28

11,018.56

1.89

0.11

15

−5494.60

11,019.19

2.52

0.08

15

−5494.65

11,019.31

2.63

0.08

15

−5495.17

11,020.33

3.66

0.05

16

−5494.54

11,021.07

4.40

0.03

12

−5498.77

11,021.53

4.86

0.03

df: Degree of freedom; LL: log-likelihood; AIC: the Akaike information criterion values; ∆AIC: the difference in AIC ranks relative to the top model; wi: Akaike weight.

for communities surrounding protected areas of giant pandas (Uchida et al., 2007). As a result, grazing disturbances have increased tremendously in giant panda habitats (Hull et al., 2014). Studies on giant panda habitats have shown that farm animals eat and trample on bamboo, causing considerable damage to panda habitat and food sources (Li et al., 2017), which has forced giant pandas to migrate to high-elevation areas with less grazing disturbances (Wei et al., 2018b). Although this issue has received considerable attention (Namgail et al., 2006;), there are still problems in the management of grazing disturbance. On the one hand, it is related to people's livelihood; on the other hand, grazing activities often extend cross administrative boundaries and require a joint effort involving multiple communities, which is difficult to establish and manage (Huang et al., 2017). But the protected areas assist local communities to improve economic income through developing beekeeping, Chinese herbal medicine cultivation and processing, and ecotourism instead of grazing. In China, protected areas of giant pandas and their buffer zones strictly prohibit infrastructure construction, but illegal construction still occurs from time to time. Since 2016, a large number of illegal infrastructure facilities in protected areas have been shut down and demolished, including mines, hydropower stations, and roads, as a result of central environmental inspections and “Green Shield” nature reserve inspections. The Green Shield was led by the Ministry of Environmental Protection, including comprehensive investigations into violations within the reserves and into rectification progress on existing issues. Collectively, these efforts have helped restore the habitats of giant pandas considerably (Zhang, 2018). However, outside of the protected areas, infrastructure construction is allowed (Gao et al., 2012). This is of great concern, as the impact of infrastructure on wildlife habitats extends far beyond the area it occupies (White and Gregovich, 2017). Additionally, He et al. (2019) demonstrated that the impact of highways on giant panda distribution can reach a distance of 1500–5000 m beyond their infrastructure range. Since a large area of giant panda habitats exist outside of the protected area, it is recommended that the approval process for infrastructure projects in and around giant panda habitats that exist outside of the protected area be more stringent. As for infrastructure that already exists, ecological engineering can and should be utilized in order to mitigate the impact of infrastructure on giant panda habitats. In recent years with the development of “Grain to Green,” also referred to as the “Returning Farmland to Forests” project, large areas of farmland have been reverted to forest vegetation, which has alleviated some of the impact of agricultural production on wildlife (Uchida et al.,

Table 3 Estimates of variable coefficients that had a significant influence on panda resource selection. Standard errors (SE) and their z-value were derived from model averaging. For all variables, positive estimates indicate preference, while negative estimates indicate avoidance. Significance levels are indicated as: *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001.

Broadleaf forest Coniferous forest Elevation Other human activities Aspect Plantation Grazing Shrub Infrastructure Evergreen broad-leaved forest Farming Slope Meadow Other vegetation

Estimate

Std. error

z value

Sig.

0.729 0.712 0.202 0.017 −0.008 −0.177 −0.298 −0.367 −0.447 −0.517 −0.803 −2.549 −2.924 −3.105

0.148 0.161 0.040 0.037 0.024 0.158 0.039 0.156 0.071 0.156 0.165 0.369 0.268 0.513

4.912 4.425 5.067 0.474 0.334 1.122 7.657 2.348 6.306 3.319 4.854 6.905 10.913 6.056

⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎

⁎⁎⁎ ⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎

SE: standard errors. For all variables positive estimates indicate preference, negative estimates indicate avoidance. Significance levels are indicated with: * p≤0.05, ***p≤0.001.

restored by eliminating disturbances caused by human activities. The Fourth National Giant Panda Survey (NGPS4) revealed that grazing has replaced deforesting in terms of habitat degradation, becoming the most detrimental human disturbance for giant panda habitats (Forestry Department of Sichuan Province, 2015). Grazing is less common in nature reserves than that outside. Whereas, grazing is limited but not eliminated in nature reserves, even though it is illegal according to the Regulations of the People's Republic of China on Nature Reserves. Mostly, it is due to animal farming is one of the important traditional industries that local family depended on in those montane ranges, and some habitat in nature reserve is also traditional grazing areas. Since 1999, as a result of the ban on deforestation, communities that relied on logging for their livelihood have lost their main source of income and have had to search for new sources of income to sustain themselves (Xu et al., 2006). Additionally, with changes in the dietary structure of Chinese residents, there has been a rapid increase in the demand for highly nutritional red meat, such as beef and lamb, which has led to a spike in sales of these meat products. In this context, animal farming has become one of the most lucrative industries 4

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Fig. 1. Distribution pattern of giant panda habitat quality in two scenarios (a, b), and the difference in the quality of the two scenarios (c). The green to orange gradient indicates panda habitat quality (RSF value) from high to low; dark orange indicates lower quality that is less suitable. Light to dark red indicates when the difference in the two scenarios is increasing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

“Grain to Green” project. The results of this study also revealed that if human disturbances are controlled, the habitat outside the protected area can be largely restored, while the habitat restored in the protected area will be small. This is because human disturbances, such as grazing, will not be completely eliminated in the protected area and existing disturbances within the protected area are fewer compared to those outside of the protected area (Western et al., 2009). This also indicates that protected areas have played a positive role in habitat protection. For instance, Wei et al. (2018a) discovered that the ecosystem services as a result of giant panda habitat conservation outweigh conservation inputs. However, many human disturbances actually result from the production and daily activities of local people (Su et al., 2017). Therefore, disturbance control as an effort to protect wildlife animals will inevitably affect local residents (Pooley et al., 2017). Currently, the Giant Panda National Park has been established in China and covers the existing protected habitats and surrounding areas, and habitats outside the protected areas are expected to be further restored.The national park protection and management system is still in the trial process (General Offices of the Communist Party of China Central Committee and the State Council of PRC, 2017). The Giant Panda National Park implements district control, which divides 74.25% of the area into core protected areas, strictly prohibits human activities; divides 27.75% of the areas into general control areas, clearly limits and prohibits commercial development activities, such as: infrastructure, hydropower station, mine, road etc. Moreover, by transforming traditional community production and lifestyles(grazing and farming), it effectively reduces the disturbance and destruction of natural resources and natural ecosystems by human activities, reduces pollution of soil, water and the atmosphere, and improves the ecological environment. Such effors will require further investigation regarding how to balance wildlife protection and the activities of local residents' through national park protection mechanisms. In conclusion, this study estimated the benefits of habitat restoration as a result of various human disturbance controls and provides a foundation for effective decision-making. It should be noted that the study area is limited to the area covered by the Fourth National Giant Panda Survey in Sichuan Province, which is much smaller than the official giant panda habitat as defined by the Forestry Department of

Table 4 Restoration of giant panda habitats under different types of disturbance control scenarios. Control scenario

Suitable habitat area (km2)

Restored habitat area (km2)

Proportion of restored habitat area (%)

No No No No

21,410 21,316 21,038 22,682

830 736 458 2102

4.03 3.58 2.23 10.21

grazing infrastructure farming disturbances

Table 5 Restoration of giant panda habitats in the all human disturbances controlled scenario in the different mountain ranges. Mountain ranges

Suitable habitat area (km2)

Restored habitat area (km2)

Proportion of restored habitat area (%)

Daxiangling Liangshan Minshan Qionglai Xiaoxiangling

1064 2958 9216 8550 894

126 578 544 672 182

11.84 19.54 5.90 7.86 20.36

2007). The “Grain to Green” policy encourages farmers to restore farmland to ecological or economic forests. Compensation for restoration to ecological forest is slightly higher than for economic forest, although the difference is not large. Therefore, there are farmers who are willing to restore farmland to economic forest with lower ecological value (Chen et al., 2019). However, such restoration efforts only reduce disturbances caused by frequent human activities in farmland and do not truly restore the farmland to the habitat it once was (Xu et al., 2006). Similarly, the National Forest Conservation Program is another large-scale forestry project in China that is focused more on the restoration and protection of natural forests (Schomers and Matzdorf, 2013). In the long term, however, this project's contribution to giant panda habitats may have a greater impact. Taking into account the results of this study and the existing “Grain to Green” policy, we propose that the compensation for the restoration of ecological forests should be increased to further improve the ecological benefits of the 5

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Fig. 2. Changes in giant panda habitats in all mountain ranges when all disturbances were effectively controlled. Light brown indicates suitable habitats for giant pandas; dark brown indicates unsuitable habitats; orange indicates giant panda habitats that may be restored under effective disturbance control. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Sichuan Province (2015). If the assessment were carried out throughout the entire panda habitat, the area of restored habitat could be much larger. Our research focuses on restoring the actual habitat for giant panda, but in future research, by evaluating the quality of habitats, it will be of great help in assessing the restoration of potential habitats and releasing the giant panda to its recent lost habitats. There is hope for effective species conservation, as long as there is continued investment in the protection of rare species, such as the giant panda, as well as the scientific development and implementation of effective forest protection policies that strengthen the control of human disturbances based on scientific investigation. Declaration of competing interest We declare our manuscript has not been submitted elsewhere for publication, in whole or in part. All the authors listed have contributed significantly, and approved the manuscript submitted here. There is no actual or potential conflict of interest including financial, personal or other relationships with other people or organizations. Acknowledgement Thanks to the team behind the Fourth National Giant Panda Survey (Sichuan). This study was funded by the National Key Programme of 6

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