Identifying local-scale wilderness for on-ground conservation actions within a global biodiversity hotspot

Shu, Lin; Wu, Ruidong; Cai, Hua; Ma, Jianchao; Wang, Wenli; Yang, Feiling; Wang, Junjun

doi:10.1038/srep25898

Cited by 28 publications

(26 citation statements)

References 36 publications

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“…The long‐term high intensities of development activities, such as agriculture, industry, urbanization, and road construction, have greatly decreased the biodiversity conservation values of these areas (Lin et al. ). Therefore, these gorge regions are usually excluded from PCAs.…”

Section: Discussionmentioning

confidence: 99%

“…b) because the large gorges with higher VCU levels have undergone substantial long‐term human disturbances (Lin et al. ) and are usually unsuitable for establishing nature reserves. However, reserve coverage increased as the levels of RCU, ECU, and CICD increased (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…To describe the spatial patterns of the indicators and explain the analyses in a more general and clearer way, we further grouped the 10 levels of values for each indicator into 3 broad categories: high (levels 8–10), intermediate (levels 4–7), and low (levels 1–3) (Lin et al. ).…”

Section: Methodsmentioning

confidence: 99%

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Spatial relationship between climatic diversity and biodiversity conservation value

Wang

et al. 2018

Conservation Biology

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Capturing the full range of climatic diversity in a reserve network is expected to improve the resilience of biodiversity to climate change. Therefore, a study on systematic conservation planning for climatic diversity that explicitly or implicitly hypothesizes that regions with higher climatic diversity support greater biodiversity is needed. However, little is known about the extent and generality of this hypothesis. We used the case of Yunnan, southwest China, to quantitatively classify climatic units and modeled 4 climatic diversity indicators, including the variety (VCU), rarity (RCU), endemism (ECU) of climatic units, and a composite index of climatic diversity (CICD). We used 5 schemes that reliably identify priority conservation areas (PCAs) to identify areas with high biodiversity conservation value. We then investigated the spatial relationships between the 4 climatic diversity indicators and the results of the 5 PCA schemes and assessed the representation of climatic diversity within the existing nature reserves. The CICD was the best indicator of areas with high conservation value, followed by ECU and RCU. Contrary to conventional knowledge, VCU was not positively associated with biodiversity conservation value. The rarer or more endemic climatic units tended to have higher reserve coverage than the more common units. However, only 28 units, covering 10.5% of the land in Yunnan, had >17% of their areas protected. In addition to climatic factors, topography and human disturbances also significantly affected the relationship between climatic diversity and biodiversity conservation value. Our results suggest that climatic diversity can be an effective surrogate for establishing a more robust reserve network under climate change in Yunnan. Our study improves understanding of the relationship between climatic diversity and biodiversity and helps build an evidence-based foundation for systematic conservation planning that targets climatic diversity in response to climate change.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Spatial relationship between climatic diversity and biodiversity conservation value

Wang

et al. 2018

Conservation Biology

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“…The northern and southern mountains had the largest supply of sand fixation due to the steep terrain and higher humidity, which is critical in northwestern China (Li & Wang, 2018). It also maintains large areas of native vegetation and intact natural environment with high forest biomass, high biodiversity, valuable scenery (Lin et al, 2016), and high carbon density (Buczko et al, 2017) and has moderate potential for recreational opportunity and carbon storage, which is consistent with the results in an Alpine region (Zoderer, Tasser, Erb, Lupo Stanghellini, & Tappeiner, 2016), and in the Three Parallel Rivers Region (Lin et al, 2018). Meanwhile, this area was limited in the ESs of nutrient retention, carbon sequestration, and food production.…”

Section: Es Bundles Corresponding To Socio-ecological Clustersmentioning

confidence: 99%

Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia, China

et al. 2019

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A B S T R A C TUnderstanding the spatial distributions of multiple ecosystem services (ESs), their associations, and their underlying socio-ecological contributing factors is critical for ES management. Using the city belt along the Yellow River in Ningxia, northwestern China, as a case study, this study quantified the spatial distribution of six ESs (food production, carbon sequestration, carbon storage, nutrient retention, sand fixation and recreational opportunity), analyzed the synergy and trade-off relations among them through correlation analysis, classified ES bundles through a self-organizing map method (SOM), explored the impacts of socio-ecological factors on the ESs through Ordinary Least Square regression (OLS) and Geo-detector analysis, delineated socio-ecological clusters using the SOM, and characterized the relationship between ES bundles and driver clusters through overlap analysis. The results suggest that spatial associations among ESs can be predicted by their driving mechanisms. Synergy relations existed among crop production, carbon sequestration, carbon storage and nutrient retention, and these were impacted by similar driving mechanisms. Synergy also existed between sand fixation and recreational opportunity, but significant differences existed in their driving mechanisms. Trade-off relations were shown between ESs in these two groups at the whole region scale. Three bundles were detected among the six ESs: bundle 1, characterized by recreational opportunity of high supply and other services of limited supply, was located in the transitional region between the central plain and the fringe mountains, and mainly driven by climate and proximity factors; bundle 2, characterized by high sand fixation, medium carbon storage and limited other services, was located in the northwestern and southern mountains and driven by climate and geography factors; bundle 3, characterized by high food production, carbon sequestration, carbon storage and nutrient retention of medium supply and other two services of limited supply, was located in the central plain and driven by vegetation coverage and proximity factors. Human activities can partly overcome the limitations of ecological conditions, thus specific strategies for different regions are proposed to maintain and improve ESs under global climate change.

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“…Spatial patterns of wilderness are strongly influenced by human activities [7], while conserved habitats can, to some extent, buffer anthropogenic pressures and provide refuge for some species. However, PAs are only effective when they stop habitat loss within their boundaries and when they are connected to other wild areas via corridors [8].…”

Section: Introductionmentioning

confidence: 99%

Changes in Light Pollution and the Causing Factors in China’s Protected Areas, 1992–2012

Xiang

Tan

2017

Remote Sensing

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Abstract:The natural nighttime light environment of the earth has been significantly transformed by human activities. Such "light pollution" has a profound influence on ecosystems. Protected areas (PAs) play key ecological functions and are only effective at low light pollution levels or without any light pollution. In China, with rapid population growth and high urbanization rates, light pollution in PAs continues to aggravate and threaten a number of ecosystems. We used calibrated nighttime light images to study spatial-temporal changes in light pollution in China's PAs from 1992 to 2012 by classifying light pollution into three levels (moderate, medium, and strong). The results showed that in China's PAs, the area subject to light pollution increased by about 1.79 times, with a significant increase in the intensity of artificial light. The changes in light pollution exhibited significant regional differences. In the eastern developed regions, light pollution was more significant than that in other regions and the situation in East China was the most severe. In the Qinghai-Tibet, although light pollution was less significant, the area subject to light pollution increased significantly over the evaluated period. Factors influencing light pollution were also analyzed. Light pollution in a PA is influenced by both human activities and its own characteristics.

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Identifying local-scale wilderness for on-ground conservation actions within a global biodiversity hotspot

Cited by 28 publications

References 36 publications

Spatial relationship between climatic diversity and biodiversity conservation value

Spatial relationship between climatic diversity and biodiversity conservation value

Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia, China

Changes in Light Pollution and the Causing Factors in China’s Protected Areas, 1992–2012

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