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Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China

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ABSTRACT

Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors.

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Landscape indexes of different land-use types (first seven columns from the left) and landscape resistance estimates (last column) used in the entropy coefficient method (the axis at the bottom of the figure is the value corresponding to the indexes shown at the top; the values of the indexes were calculated using Fragstats 4.2).(Created by Fragstats, version 4.2, http://www.umass.edu/landeco/research/fragstats/).
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f3: Landscape indexes of different land-use types (first seven columns from the left) and landscape resistance estimates (last column) used in the entropy coefficient method (the axis at the bottom of the figure is the value corresponding to the indexes shown at the top; the values of the indexes were calculated using Fragstats 4.2).(Created by Fragstats, version 4.2, http://www.umass.edu/landeco/research/fragstats/).

Mentions: The entropy coefficient method is an objective weighting method in which weight is determined by entropy. Entropy is the amount of additional information needed to specify the exact physical state of a system. The greater the entropy, the more information it provides34. This process involves three main steps: 1) normalizing the initial information matrix, 2) calculating entropy weight, and 3) calculating resistance. In this study, landscape indexes and ecosystem service values were used as the ecological attributes for each land-use type in the weighted calculation (Fig. 3)2939. Thus, the initial information matrix contains seven rows and five columns. To compare the different factors, the value of the factors was normalized by equations (3) and (4). i and j correspond to the number of rows and columns, respectively, in the initial information matrix. is the normalized result, xij is the original value, and and are the maximum and minimum value in ith row, respectively.


Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China
Landscape indexes of different land-use types (first seven columns from the left) and landscape resistance estimates (last column) used in the entropy coefficient method (the axis at the bottom of the figure is the value corresponding to the indexes shown at the top; the values of the indexes were calculated using Fragstats 4.2).(Created by Fragstats, version 4.2, http://www.umass.edu/landeco/research/fragstats/).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385553&req=5

f3: Landscape indexes of different land-use types (first seven columns from the left) and landscape resistance estimates (last column) used in the entropy coefficient method (the axis at the bottom of the figure is the value corresponding to the indexes shown at the top; the values of the indexes were calculated using Fragstats 4.2).(Created by Fragstats, version 4.2, http://www.umass.edu/landeco/research/fragstats/).
Mentions: The entropy coefficient method is an objective weighting method in which weight is determined by entropy. Entropy is the amount of additional information needed to specify the exact physical state of a system. The greater the entropy, the more information it provides34. This process involves three main steps: 1) normalizing the initial information matrix, 2) calculating entropy weight, and 3) calculating resistance. In this study, landscape indexes and ecosystem service values were used as the ecological attributes for each land-use type in the weighted calculation (Fig. 3)2939. Thus, the initial information matrix contains seven rows and five columns. To compare the different factors, the value of the factors was normalized by equations (3) and (4). i and j correspond to the number of rows and columns, respectively, in the initial information matrix. is the normalized result, xij is the original value, and and are the maximum and minimum value in ith row, respectively.

View Article: PubMed Central - PubMed

ABSTRACT

Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors.

No MeSH data available.


Related in: MedlinePlus