Evaluation of the policy-driven ecological network in the Three-North Shelterbelt region of China
Introduction
Global environmental change (e.g., biodiversity loss and global warming) has broad impacts on human societies' sustainable development (Yang et al., 2020). Forest is a crucial resource for regulating the terrestrial carbon cycle and alleviating global climate change. Thus, forest resource management is important for mitigating potential environmental and climate change risks in the future (Smith et al., 2020). Over the past decades, the Chinese government has promoted relevant environmental protection policies in multiple regions (Bryan et al., 2018). With decades of effort, these policies are working with promising improvements in most regions, e.g., China has contributed about 25% of the increase of greenness globally, of which around 42% benefited from the afforestation project (Chen et al., 2019). Despite the ecological benefit of greenness increment, the vulnerability of afforested areas to climate change in the future is also a rising concern to be addressed (Watts et al., 2018).
The Three-North Shelterbelt (TNS) program is one of China's large-scale and successful ecological projects (Bryan et al., 2018, Cao et al., 2020a). The program was initiated in 1978 and is continuously promoted until 2050. The TNS plays an essential role in mitigating against damaging sand storms and desertification. The broad impacts of TNS have been extensively explored in terms of the vegetation dynamics (Deng et al., 2019, Qiu et al., 2017), land use and land cover change (Yin et al., 2018), and climate change (Zhang et al., 2016). For example, the ecological security of the TNS program was studied by optimizing the distribution of ecosystem services at the regional scale (Xiao et al., 2020). Also, the ecological network and landscape dynamics have been deeply explored at the local scale to identify vulnerable regions in the TNS area (Babí Almenar et al., 2019, Lü et al., 2015). However, most of these studies were performed at moderate and coarse resolutions, whereas analyses of ecological networks at fine resolutions and large scales were limited (Yu et al., 2017).
Although network analysis is a powerful tool to evaluate the vulnerability of the ecological environment, widely applications have not been made at a large scale (Dong et al., 2020, Lü et al., 2020). The ecological network concept characterizes the connections and interactions among elements in the landscape (Poisot et al., 2016) and emerged in the 1970s as a tool to aid nature reserve planning (Opdam et al., 2006). It has common elements comprising hubs, buffers, and corridors (Isaac et al., 2018, Schleuning et al., 2016). The structure, connectivity, and dynamics of biomes in the ecological network can be quantitatively explored (Hu et al., 2019, Pilosof et al., 2017), which is helpful to identify any vulnerability in the network from a biodiversity or conservation perspective (Delmas et al., 2019). Nevertheless, existing studies always focus on a local scale with simplified networks, limiting ecological networks' capacity in characterizing the connectivity and complexity of ecological corridors in ecosystems (Bohan et al., 2017, Yang et al., 2020).
The relationship between the impact of the human footprint and biodiversity conservation (Jones et al., 2018, Venter et al., 2016b), need simultaneous exploration in ecological networks. The human footprint map provides information about humans imposing pressure on natural systems and altering natural states (Venter et al., 2016a). Presently, the human demands on natural systems are accelerating, resulting in an unbalanced natural system with noticeable biodiversity loss (Mu et al., 2021, Tucker et al., 2018). Hence, quantifying the relationship between human footprint (and species) and the ecological network can support sustainable planning such as national parks (Xu et al., 2017, Zube, 1995). Meanwhile, the land cover can also shape the network by altering the biodiversity, ecosystem functions, and services (Felipe-Lucia et al., 2020). Combining multiple factors simultaneously (e.g., human footprint, biodiversity, and land cover data) makes it feasible to conduct an integrated assessment of the ecological network for ecological protection.
This study developed an analysis framework with the construction of the ecological network in the entire TNS region. Using the derived network and remotely sensed indicators, vulnerable regions were identified and the impacts of human footprint and biodiversity on the ecological network were also quantitatively measured. Unlike most studies about ecological networks that were made from landscapes shaped by land covers (e.g., wildland and forest) (Cao et al., 2020b, Liu et al., 2018), here we used nature reserves as primary ecological sources to reflect policy-driven impacts on ecosystems (Cumming et al., 2017, Svancara et al., 2005). In this study, we aim to answer two questions: (1) where are ecologically vulnerable areas in the TNS region? and (2) can the derived ecological network be used to quantify the impacts of human activities on biodiversity? The remainder of this paper is organized as below: Section 2 introduces the study area and relevant datasets; Section 3 describes the proposed analysis framework; Section 4 and Section 5 present our results and discussion, respectively; and a conclusion mark is ended in Section 6.
Section snippets
Study area
Administratively, the TNS includes 551 prefectures in 13 provinces in northern China. The TNS accounts for 42.4% of China's land, with a geographical extent ranging from 73°E to 128°E in longitude and 33°N to 50°N in latitude (Fig. 1a). This program will be finished by 2050 with three main phases (i.e., 1978–2000, 2001–2020, and 2021–2050). With decades of efforts, bare lands have decreased about 15% due to the implementation of the TNS program in desert areas. Carbon sequestration in the TNS
Methods
We developed an analysis framework to identify vulnerable regions in the TNS region using the ecological network (Fig. 2). First, we mapped the ecological resistance surface (ERS) using the remotely sensed ecological index (RSEI) (Fig. 2a). The RSEI considered different ecological conditions (i.e., greenness, wetness, temperature, and dryness) and was measured using satellite observations. With ancillary datasets, we generated the ERS in the TNS region at a resolution of 250 m. Second, we
Ecological condition in the TNS region
The distribution of RSEI and ERS shows opposite trends with considerable differences in their spatial patterns, mainly caused by different climate conditions (i.e., from humid to arid zones). The RSEI map derived from greenness, wetness, temperature, and dryness describes the ecological condition in the TNS region (Fig. 4a). Compared to the RSEI, the ERS has a more noticeable spatial heterogeneity, especially in the west of the TNS region. Overall, the ERS shows a decreasing trend from the west
Construction of the policy-driven ecological network
Nature reserves were regarded as crucial nodes with the explicit implication of policies for biodiversity protection (Schulze et al., 2018). It is still challenging to realize global biodiversity conservation and ecological restoration since implementing some specific goals (e.g., the 20 Aichi targets) is far from the target (Díaz et al., 2020). Realizing these goals relies on specific spatial planning with ecological networks, aiming to provide nexus of exchange of masses and species among
Conclusions
In this study, we evaluated the policy-driven ecological network in the TNS region of China. First, we calculated the ERS in the TNS region using remotely sensed ecological indicators. Then, we generated the ecological network using the resistance surface map and evaluated the network and its corridors using the space syntax approach. Finally, we applied the derived ecological network to study human activities and biodiversity in the TNS region. This study provides a paradigm for constructing
CRediT authorship contribution statement
Haowei Mu: Conceptualization, Methodology, Data curation, Writing – original draft. Xuecao Li: Conceptualization, Methodology, Data curation, Writing – original draft. Haijiao Ma: Visualization, Investigation. Xiaoping Du: Writing – review & editing. Jianxi Huang: Writing – review & editing. Wei Su: Writing – review & editing. Zhen Yu: Writing – review & editing. Chen Xu: Visualization, Investigation. Hualiang Liu: Visualization, Investigation. Dongqin Yin: Writing – review & editing. Baoguo Li:
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This research was funded by the Chinese University Scientific Fund (1191-15051001).
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