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Evaluation of ecosystem health in Futian mangrove wetland based on the PSR-AHP model

Published Online: 24 Aug 2022
Volume & Issue: AHEAD OF PRINT
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Received: 03 Mar 2022
Accepted: 25 May 2022
Journal Details
License
Format
Journal
eISSN
2444-8656
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Introduction

Shenzhen Futian Mangrove Nature Reserve is located on the north bank of Shenzhen Bay. The area ranges from 22°30′–22°32′ north latitude to 113°56′–114°3′ longitude east, with a total area of 3.68 km2 (as shown in Figure 1). The region belongs to the East Asian monsoon region and the South Asian subtropical monsoon maritime climate region. The average annual temperature is 22.4°C. January is the coldest month. The lowest extreme temperature is 0.2°C. July is the hottest month. The highest extreme temperature is 38.7°C. The average annual rainfall is 1,700–1,900 mm, and rainfall is received mainly from April to September. The annual evaporation capacity is 1,500–1,800 mm. The average annual relative humidity is 80%. The annual sunshine hours are about 2000 d [1, 2]. It is the only mangrove wetland in the world located in the centre of the city, which is also an important ‘transit station’ and ‘gas station’ on the international migratory bird passage of the Eastern Hemisphere [3]. It is an important object of biodiversity and wetland ecological protection in the world [4].

Fig. 1

Map of Futian Reserve.

Futian Nature Reserve is mainly composed of mangroves, fish ponds surrounded by dam, tidal flats, rivers, land, sea area and so on. Mangrove accounts for the largest area. The area of mangroves is mostly strip-shaped, the community appearance is relatively simple, and the region of mangroves mainly contains shrubs and small arbour trees. The canopy is relatively neat and generally 4–6 m high [5]. Mangrove plants mainly include Kandelia candel, Bruguiera gymnorhiza, egicerascorniculatum, Avicennia marina, Acanthus ilicifolius and so on [6]. Phytoplankton and zooplankton are mainly concentrated in the fish ponds surrounded by dams. Phytoplankton has a high proportion of algae such as Bacillariophyta and Chlorophyta. Major species of zooplankton are Protozoa, Rotifera, Cladocera and Copepoda [7]. Benthic animals mainly live in debris and tidal flats at the bottom of mangrove forests, and such animals are comprised mainly of crustaceans, molluscs and mudskippers [8]. Among birds, the majority are land birds. For the most common land birds, there are 5 orders, 19 families and 55 species here [9].

During the 30 years of rapid economic development in Shenzhen, the Futian mangrove wetland ecosystem was seriously affected by urban expansion and environmental pollution. Mangrove wetlands were shrinking, and mud deposits of the tidal flats were increasing the level of the seabed. A forest of tall buildings blocked the passage of birds. Ecological problems such as the death of fish and shrimp caused by water pollution became rampant. The wide spread of alien species such as Sonneratia caseolaris has already led to a biological invasion. The frequent outbreak of pests resulted in the failure of Avicennia marina, a kind of mangrove plant, to reproduce naturally. Ecological problems are becoming increasingly serious. The whole ecosystem shows signs of instability and unsustainability in spatial structure and ecological process, self-regulation and renewal ability and resilience to external stress [10, 11]. Due to the small area of the Futian Mangrove Nature Reserve, the ecological health of the wetland ecosystem is more fragile; so, there is an urgent need to construct the ecological health assessment of wetland to support its protection and management. Wang Chonghuan of Jilin Normal University evaluated the health of the Futian mangrove wetland ecosystem from the perspective of early health warning, established a linear programming model by using the fitting function, qualitatively analysed the number of local species and invasive species in the mangrove wetland, fitted the functions of the bird number, phytoplankton, insects and pests and calculated the number trend of local species. The stability of the mangrove ecosystem is analysed qualitatively, and it is found that the ecosystem does not meet the stability conditions [17].

Construction of the evaluation index system
PSR model

For environmental problems, the PSR framework model has a very clear causal relationship, i.e. human activities exert a certain amount of pressure on the environment. Resultantly, the state of the environment has changed to a certain extent, and human society should respond to the changes to restore environmental quality and prevent environmental degradation [12, 13]. These three links exactly constitute the entire process of decision-making and formulating countermeasures. Figure 2 shows the PSR model logic diagram.

Fig. 2

PSR model logic diagram.

Pressure subsystem

This paper divides the ecosystem pressure of the mangrove wetland into natural pressure and anthropic pressure. Based on the actual natural conditions and current environmental characteristics of the wetland in the Futian Mangrove Nature Reserve, several indexes from both natural and anthropic factors will be selected as evaluation elements. The specific selected indexes are shown in Table 1.

Evaluation index selection of the pressure subsystem.

Criterion level Factor level Index level
Pressure subsystem Anthropic pressure Density of population
Per capita GDP
Urbanisation rate
Noise pollution
Light pollution
Natural pressure Mangrove pest
Extreme temperature weather per year
State subsystem

Ecosystem refers to a unified whole system formed by the continuous process of material circulation and energy flow between all creatures living together in a certain space and their environment. Ecosystem health assessments take into account changes in composition and structure and pattern under the influence of anthropic and natural factors. In this paper, the state subsystem is divided into three aspects, which are assessed individually: environment quality, biology and ecology and biological productivity. Specific indexes are selected as shown in Table 2.

Evaluation index selection of the state subsystem.

Criterion level Factor level Index level
State subsystem Biology and ecology Diversity of species
Environmental quality Water quality
Biological productivity Conversion between nutrient levels
Total energy conversion
Response subsystem

Response criterion level should cover two factor levels: natural response and social response. The natural response of the ecosystem mainly reflects the changes and trends of various environmental and biological indexes under pressure. However, in the process of the state subsystem evaluation, this paper will analyse and explain the current situation of various indexes by comparing historical data. Therefore, the response subsystems are no longer repeatedly discussed. Only the response activities related to social factors are evaluated. Specific indexes are selected as shown in Table 3.

Evaluation index selection of response subsystem.

Criterion level Factor level Index level
Response subsystem Social response Proportion of the tertiary industry
Education level of the population
Investment in environmental pollution
Planning regulations and policies
Degree of public participation
Analytic hierarchy process (AHP) model

The ecosystem of the Futian mangrove wetland is a comprehensive system composed of several subsystems. The index system for comprehensive evaluation is a collection of indexes from various aspects based on the components of the ecosystem of the Futian mangrove wetland, which have functions and characteristics such as time, space, level and quantity. Therefore, in order to analyse the structural mechanism of complex ecosystem, it is necessary to carry out hierarchical analysis on the index system. Based on the screening principle of indexes, this paper uses the thought of system analysis to decompose the complex problems of the ecosystem into a number of interrelated and different order levels and then combines the actual situation to decompose the system into four levels: objective level, criterion level, factor level and index level [14, 15]. Figure 3 provides further details.

Fig. 3

Evaluation system block diagram based on AHP.

Calculation of relative membership degree

The factors affecting the Futian mangrove wetland ecosystem can be quantifiable, i.e. its fuzziness can be transformed into the membership degree relative to the stability level. According to the actual situation of the evaluation system studied, the evaluation index system of fuzzy comprehensive evaluation is established from the perspective of representativeness, systematicness and applicability. The fuzzy evaluation matrix of relative membership of a single evaluation index is established from the sample data of each evaluation index.

It is assumed that n evaluation indicators were set to constitute the sample data of evaluation indicators {x(i, j)|i = 1 ∼ n, j = 1 ∼ m} for all m schemes. Each index value x(i, j) is non-negative. In order to determine the fuzzy evaluation matrix of the relative membership degree of a single evaluation index, we eliminate the dimensional effect of each evaluation index and make a universal model; it is necessary to standardise the sample data set x(i, j).

The standardised treatment formula of the index ‘the larger the better’ can be written as follows: r(i,j)=x(i,j)/[xmax(i)+xmin(i)] r(i,j) = x(i,j)/[{x_{\max }}(i) + {x_{\min }}(i)]

The standardised treatment formula of the index ‘the smaller the better’ can be written as follows: r(i,j)=[xmax(i)+xmin(i)x(i,j)]/[xmax(i)+xmin(i)] r(i,j) = [{x_{\max }}(i) + {x_{\min }}(i) - x(i,j)]/[{x_{\max }}(i) + {x_{\min }}(i)]

The standardised treatment formula of the index ‘the more medium the better’ can be written as follows: r(i,j)={x(i,j)/[xmid(i)+xmin(i)]xmin(i,j)x(i,j)<xmid(i)[xmax(i)+xmid(i)x(i,j)]/{xmax(i)+xmid(i)}xmid(i)x(i,j)<xmax(i,j) r(i,j) = \left\{ {\matrix{{x(i,j)/[{x_{{\rm{mid}}}}(i) + {x_{\min }}(i)]} \hfill & {{x_{\min }}(i,j) \le x(i,j) < {x_{{\rm{mid}}}}(i)} \hfill \cr {[{x_{\max }}(i) + {x_{{\rm{mid}}}}(i) - x(i,j)]/\{ {x_{\max }}(i) + {x_{{\rm{mid}}}}(i)\} } \hfill & {{x_{{\rm{mid}}}}(i) \le x(i,j) < {x_{\max }}(i,j)} \hfill \cr } } \right. where xmin, xmax and xmid(i) are the minimum, maximum and median optimum values of the indicator i in the scheme, respectively, and r(i, j) is the standardised evaluation index value, which is also the relative membership value of the evaluation index i of the j scheme that is subordinate to the optimal one. i = 1 ∼ n, j = 1 ∼ m.

Fuzzy evaluation matrix R = (r(i, j))n×m of single evaluation index can be constituted by taking these r(i, j) values as elements.

The fuzzy evaluation matrix R = (r(i, j))n×m is used to construct the judgement matrix B = (bij)m×n to determine the weight of each evaluation index. Since fuzzy comprehensive evaluation is essentially an optimisation process, from the comprehensive perspective to make the results more accurate, the sample standard deviation s(i)=[Σj=1m(r(i,j)ri¯)2/m]0.5 s(i) = [\sum\nolimits_{j = 1}^m {(r(i,j) - \overline {{r_i}} )^2}/m{]^{0.5}} of each evaluation index can be used to reflect the impact degree of each evaluation index on the comprehensive evaluation, and the judgement matrix B can be constructed as well. r¯i=Σj=1mr(i,j)/m {\overline r _i} = \sum\nolimits_{j = 1}^m r(i,j)/m is the mean value of the sample series under each evaluation index. i = 1 ∼ n. Thus, according to Eq. (4), the judgement matrix of judgement scales of grades 1–9 can be obtained, as indicated below: bij={s(i)s(j)smaxsmin(bm1)+1s(i)s(j)1/[s(j)s(i)smaxsmin(bm1)+1]s(i)<s(j) {b_{ij}} = \left\{ {\matrix{ {{{s(i) - s(j)} \over {{s_{\max }} - {s_{\min }}}}({b_m} - 1) + 1} & {s(i) \ge s(j)} \cr {1/\left[ {{{s(j) - s(i)} \over {{s_{\max }} - {s_{\min }}}}({b_m} - 1) + 1} \right]} & {s(i) < s(j)} \cr } } \right. where smin and smax are the minimum and maximum values of {s(i)|i = 1 ∼ n}, respectively. The relative importance parameter is bm = min{9,[smax/smin + 0.5]}. Min and [] are the minimum function and the integral function, respectively.

Calculation of index weight

The hierarchical structure model is constructed to pre-process the data of the index level, and the relatively reliable membership degree is obtained through calculation, analysis and comparison. The membership degree is taken as the index level. According to the practical problems, the factors in the indicator level are denoted as follows: c1, c2, c3,…, cn. (The value of c1, c2, c3,…, cn is determined according to the factors that affect the inherent property of road traffic that is caused by the opening of the community.) ci : cjaij, aij = 1/aji, the matrix is written as follows: A=[a11a12a1na21a22a2nan1an2ann] A = \left[ {\matrix{ {{a_{11}}} & {{a_{12}}} & \ldots & {{a_{1n}}} \cr {{a_{21}}} & {{a_{22}}} & \ldots & {{a_{2n}}} \cr \ldots & \ldots & \ldots & \ldots \cr {{a_{n1}}} & {{a_{n2}}} & \ldots & {{a_{nn}}} \cr } } \right]

Then, the maximum eigen root of the judgement matrix and its corresponding eigenvectors are solved. We calculate the product of each row of the judgement matrix, as follows: Mi=j=1naiji=1,2,3,,n {M_i} = \mathop {\prod }\limits_{j = 1}^n {a_{ij}}i = 1,2,3, \ldots ,n

Then, we calculate the NTH root of Mi, as follows: Wi=Mini=1,2,3,,n {W_i} = \root n \of {{M_i}} i = 1,2,3, \ldots ,n

Next, we normalise vector W = [W1,W2,…, Wn]T as follows: Wi=Wi/n=1nWii=1,2,3,,n {W_i} = {W_i}/\sum\limits_{n = 1}^n {W_i}i = 1,2,3, \ldots ,n where W = [W1,W2,…, Wn]T is the eigenvector. According to the judgement matrix table established above, the reordering method is used to screen the index factors. Then, according to the relative importance of the index factors in the road traffic after consultation, the corresponding values are given by pairwise comparison, and the judgement matrix is constructed for weight calculation.

It is assumed that aij=wiwj {a_{ij}} = {{{w_i}} \over {{w_j}}} , and the reconstruction judgement matrix B is shown in Eq. (9). When the vectors in the matrix satisfy this condition, aij · ajk = aik, and matrix B is the uniform distance. B=[w1w1w1w2w1wnw2w1w2w2w1wnwiwjwnw1wnw2wnwn] B = \left[ {\matrix{ {{{{w_1}} \over {{w_1}}}} & {{{{w_1}} \over {{w_2}}}} & \ldots & {{{{w_1}} \over {{w_n}}}} \cr {{{{w_2}} \over {{w_1}}}} & {{{{w_2}} \over {{w_2}}}} & \ldots & {{{{w_1}} \over {{w_n}}}} \cr \ldots & \ldots & {{{{w_i}} \over {{w_j}}}} & \ldots \cr {{{{w_n}} \over {{w_1}}}} & {{{{w_n}} \over {{w_2}}}} & \ldots & {{{{w_n}} \over {{w_n}}}} \cr } } \right]

Consistency check of the judgement matrix

To test the consistency of the judgement matrix, its consistency should be calculated as follows: CI=λmaxnn1 CI = {{{\lambda _{\max }} - n} \over {n - 1}} where λ is the eigenvalue of the matrix.

In order to test whether the judgement matrix has satisfactory consistency, the consistency index CI should be compared with the mean random consistency index RI, which is also the test coefficient CR. When CR satisfies this condition, the following equation is obtained: CR=CIRI<0.10 CR = {{CI} \over {RI}} < 0.10

The judgement matrix has satisfactory consistency. Otherwise, the judgement matrix needs to be adjusted until it is satisfied.

Comprehensive index model

The calculation method of the Futian mangrove wetland ecosystem evaluation index is as follows: DI=i=1nLiWi DI = \sum\limits_{i = 1}^n {L_i}{W_i} where DI represents the comprehensive evaluation index of mangrove wetland health assessment and early warning; Li is the evaluation value of index I; Wi is the weight of the index; and n is the number of indexes. The value of the comprehensive index is between (0, 1). The smaller the value, the worse the health of the ecosystem. Since the criterion level of the system consists of pressure subsystem, state subsystem and response subsystem, the final evaluation result of the system will be appropriate for Eq. (13), as follows: DIcomprehensiveevaluationindex=DIpressure'×Wistate+DIresponse×Wiresponse D{I_{\matrix{ {comprehensive} \hfill \cr {evaluation\;index} \hfill \cr } }} = DI_{pressure}^\prime \times {W_{i\;state}} + D{I_{response}} \times {W_{i\;response}}

In the formula, DIpressure, DIstate and DIresponse represent the scores of pressure subsystem, state subsystem and response subsystem, respectively. Pressure subsystem scores for positive change, that is, DIpressure'=1DIpressure DI_{pressure}^\prime = 1 - D{I_{pressure}} .

Evaluation criterion

The determination of environmental standards in this paper mainly refers to the following terms: (1) national, industrial and local standards, including environmental quality standards for surface water (GB3838-2002) and classification standards for nutritional state of lakes (reservoirs); secondary water quality standards of marine water quality standards (GB3097-1997); soil environmental quality standards (GB15618-1995) etc. (2) grading criteria for wetland ecosystem evaluation provided by Sun Longqi [16].

Analysis of calculation result

According to the biodiversity monitoring report data of the Futian Mangrove Reserve for the past 3 years, the calculation is as follows:

Calculation of index weight

According to the above analytic hierarchy process, the characteristic vector of the judgement matrix is written as follows: W=[0.0381,0.1293,0.0800,0.5150,0.0800,0.0701,0.0515,0.0096,0.1293,0.0800,0.0606,0.0606,0.0606,0.0515,0.0800,0.0381]T W = {\left[ {\matrix{ {0.0381,0.1293,0.0800,0.5150,0.0800,0.0701,0.0515,0.0096,} \cr {0.1293,0.0800,0.0606,0.0606,0.0606,0.0515,0.0800,0.0381} \cr } } \right]^T}

Calculation of combination weight vector and performance of combination consistency test

According to consistency check, CR = CI/RI = 0.0790 ≤ 0.10, and it is concluded that the judgement matrix has a satisfactory one-off. The weight values of each index are calculated by MATLAB, and the calculated results are shown in Table 9.

From the comparison of the weights of the pressure subsystems in Figure 4, it can be seen that the weighted score of natural pressure is 0.01988, whereas the anthropic weighted score is 0.28706. The lower the score, the less stress the system is under and the more stable the ecosystem is. Therefore, the score indicates that anthropic pressure in the Futian mangrove wetland in Shenzhen is higher than that of natural pressure. With the development of society and economy, the exploitation and utilisation of resources by humans has exerted a great influence on the ecological environment of the sea area, and the influence of humans has exceeded the influence of natural ecological disasters on the environment.

Fig. 4

Comparison diagram of weighted score of each index of the pressure subsystem.

As for natural pressure, in terms of the weighted score of third-level indexes, the annual extreme temperature weather score is the lowest, amounting to 0.00100. This indicates that the Futian mangrove wetland in Shenzhen has a lower pressure effect on the ecosystem than other pressure indexes. The study shows that annual extreme temperature weather in the sea area does not cause stress. However, the impact of mangrove pests on the whole ecosystem is much greater than that of annual extreme temperature weather. For anthropic pressure, the weighted scores of population density, per capita GDP, urbanisation rate, light pollution and noise pollution are 0.02630, 0.09873, 0.06322, 0.06322 and 0.03559, respectively. Socio-economic indexes indirectly reflect the frequency of human socio-economic activities. According to the weighted scores of these five indexes, the population and economy of Futian, Shenzhen, have a great influence on the coastal ecosystem. Furthermore, the influence of human activities on the marine ecological environment is on the increase.

In order to reflect the comprehensive situation of all factors more intuitively, Figure 5 presents a comparison diagram of the weighted scores of each indicator of the state subsystem, and the figure indicates that the weighted scores of species diversity and water quality between total energy conversion and nutrient level are 0.05531, 0.04446, 0.00100 and 0.03559, respectively. In addition, energy conversion and infringement score lowest, which indicates that the ecosystem of mangrove wetland in Shenzhen is damaged due to human activities, and in particular to the discharge of domestic sewage. The structure of the intertidal community is unstable, and the energy flow and material circulation of the ecosystem are broken, both of which lead to the reduction of species diversity and serious damage to the ecosystem.

Fig. 5

Comparison diagram of weighted score of each index in the state subsystem.

According to the weighted score of the response subsystem in Figure 6, the highest weighted score of the tertiary industry is 0.09873, which shows that the country attaches great importance to economic development. However, the education level of the population, environmental pollution investment and public participation index scores are significantly low, which indicates that there is still room for promotion and improvement in the local social response. The good state of the ecosystem is closely related to the importance of the state, the efforts of local government departments in policies and management, the educational level of the population and the importance of people to the environment. Therefore, the establishment of a sound system of laws and regulations, lax management measures, and strict and effective administrative law enforcement can play a decisive role in mangrove wetland protection.

Fig. 6

Comparison diagram of weighted scores of each index in the response subsystem.

Using the health index formula, the health index and comprehensive health index of the ecosystem stress, state and response in the protected area were calculated. (Table 4 indicates the corresponding health level.) The results are shown in Table 10.

Mean random consistency index.

n 1 2 3 4 5 6 7 8 9
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45

Classification criteria and definitions of Futian mangrove wetland ecosystem evaluation.

Grade DI value State System feature
I ≥0.8 A The ecosystem has stable structure and function, and the system has adequate strength to recover and regenerate.
II 0.6–0.8 B The ecosystem function is relatively perfect, and can recover after being commonly disturbed.
III 0.4–0.6 C Although damaged to a certain extent, the ecosystem can still maintain its basic functions.
IV 0.2–0.4 D The structure and function of the ecosystem are degraded, biodiversity declines and environmental problems are serious.
V ≤0.2 E The structure and function of the ecosystem have almost collapsed, and the ecological environment has become seriously damaged.

Noise evaluation criteria of mangrove reserve (day).

Grade Evaluation standard value (dB(A)) Degree of membership
0 50 0
1 55 0.2
2 60 0.5
3 65 0.7
4 70 1.0

Classification of pressure evaluation.

Range of value Pressure classification
0–0.2 Mild stress
0.2–0.4 Small pressure
0.4–0.6 Medium pressure
0.6–0.8 High pressure
0.8–1.0 Extreme pressure

Classification of state evaluation.

Evaluation value State Definitions
0.8–1.0 A The ecosystem has stable structure and function, and the system possesses adequate strength to recover and regenerate.
0.6–0.8 B The ecosystem function is relatively perfect, and can recover after being commonly disturbed.
0.4–0.6 C Although damaged to a certain extent, the ecosystem can still maintain its basic functions.
0.2–0.4 D The structure and function of the ecosystem are degraded, biodiversity declines and environmental problems are serious.
0–0.2 E The structure and function of the ecosystem have almost collapsed, and the ecological environment has become seriously damaged.

Weight of each index.

Criterion level Factor level Index level Degree of membership Weight Weighted rating
Pressure subsystem 0.41173 Anthropic pressure 0.36391 Density of population 0.55 0.04782 0.02630
Per capita GDP 1 0.09873 0.09873
Urbanisation rate 0.8 0.07902 0.06322
Noise pollution 0.6 0.05932 0.03559
Light pollution 0.8 0.07902 0.06322
Natural pressure 0.04782 Mangrove pest 0.5 0.03776 0.01888
Extreme temperature weather per year 0.1 0.01006 0.00100
State subsystem 0.20301 Biology and ecology 0.07902 Diversity of species 0.7 0.07902 0.05531
Environmental quality 0.07410 Water quality 0.6 0.07410 0.04446
Biological productivity 0.06938 Conversion between nutrient levels 0.6 0.05932 0.03559
Total energy conversion 0.1 0.01006 0.00100
Response subsystem 0.38526 Social response 0.38526 Proportion of the tertiary industry 1 0.09873 0.09873
Education level of the population 0.8 0.07902 0.06322
Investment in environmental pollution 0.7 0.06917 0.04842
Planning regulations and policies 0.7 0.06917 0.04842
Degree of public participation 0.7 0.06917 0.04842

Ecosystem health index and level of the reserve.

Item Pressure index State index Response index General health
Health index 0.256962 0.140323 0.307203 0.25262
Health level IV V IV IV

It is calculated that the mangrove wetland ecosystem is in a poor state (level IV). Its ecosystem structure and function are degraded. There has been a radical decline in biodiversity, and environmental problems are serious.

Conclusion

Based on the PSR model, the ecological health evaluation system of Futian Mangrove Reserve in Shenzhen was established. The AHP model was used to evaluate the health state of the reserve. The results showed that the proportion of pressure, state and response in the system was 41.17%, 20.3% and 38.53%, respectively. The health index was 0.256962, 0.140323 and 0.307203, respectively. The comprehensive health index of the reserve is 0.25262, which indicates health grade IV (poor state). The main reasons for the above results are as follows: (1) in the past 30 years, urbanisation has led to the loss of a significant portion of the ecological functions of the reserve ecosystem and the continuous decline of the health level; (2) water pollution, artificial introduction of plants and pests, and so on have inflicted great external pressure on the protection area; and (3) construction and other human activities affect the restoration of the natural state of the reserve.

Fig. 1

Map of Futian Reserve.
Map of Futian Reserve.

Fig. 2

PSR model logic diagram.
PSR model logic diagram.

Fig. 3

Evaluation system block diagram based on AHP.
Evaluation system block diagram based on AHP.

Fig. 4

Comparison diagram of weighted score of each index of the pressure subsystem.
Comparison diagram of weighted score of each index of the pressure subsystem.

Fig. 5

Comparison diagram of weighted score of each index in the state subsystem.
Comparison diagram of weighted score of each index in the state subsystem.

Fig. 6

Comparison diagram of weighted scores of each index in the response subsystem.
Comparison diagram of weighted scores of each index in the response subsystem.

Mean random consistency index.

n 1 2 3 4 5 6 7 8 9
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45

Ecosystem health index and level of the reserve.

Item Pressure index State index Response index General health
Health index 0.256962 0.140323 0.307203 0.25262
Health level IV V IV IV

Classification of pressure evaluation.

Range of value Pressure classification
0–0.2 Mild stress
0.2–0.4 Small pressure
0.4–0.6 Medium pressure
0.6–0.8 High pressure
0.8–1.0 Extreme pressure

Classification of state evaluation.

Evaluation value State Definitions
0.8–1.0 A The ecosystem has stable structure and function, and the system possesses adequate strength to recover and regenerate.
0.6–0.8 B The ecosystem function is relatively perfect, and can recover after being commonly disturbed.
0.4–0.6 C Although damaged to a certain extent, the ecosystem can still maintain its basic functions.
0.2–0.4 D The structure and function of the ecosystem are degraded, biodiversity declines and environmental problems are serious.
0–0.2 E The structure and function of the ecosystem have almost collapsed, and the ecological environment has become seriously damaged.

Evaluation index selection of the state subsystem.

Criterion level Factor level Index level
State subsystem Biology and ecology Diversity of species
Environmental quality Water quality
Biological productivity Conversion between nutrient levels
Total energy conversion

Classification criteria and definitions of Futian mangrove wetland ecosystem evaluation.

Grade DI value State System feature
I ≥0.8 A The ecosystem has stable structure and function, and the system has adequate strength to recover and regenerate.
II 0.6–0.8 B The ecosystem function is relatively perfect, and can recover after being commonly disturbed.
III 0.4–0.6 C Although damaged to a certain extent, the ecosystem can still maintain its basic functions.
IV 0.2–0.4 D The structure and function of the ecosystem are degraded, biodiversity declines and environmental problems are serious.
V ≤0.2 E The structure and function of the ecosystem have almost collapsed, and the ecological environment has become seriously damaged.

Evaluation index selection of the pressure subsystem.

Criterion level Factor level Index level
Pressure subsystem Anthropic pressure Density of population
Per capita GDP
Urbanisation rate
Noise pollution
Light pollution
Natural pressure Mangrove pest
Extreme temperature weather per year

Evaluation index selection of response subsystem.

Criterion level Factor level Index level
Response subsystem Social response Proportion of the tertiary industry
Education level of the population
Investment in environmental pollution
Planning regulations and policies
Degree of public participation

Noise evaluation criteria of mangrove reserve (day).

Grade Evaluation standard value (dB(A)) Degree of membership
0 50 0
1 55 0.2
2 60 0.5
3 65 0.7
4 70 1.0

Weight of each index.

Criterion level Factor level Index level Degree of membership Weight Weighted rating
Pressure subsystem 0.41173 Anthropic pressure 0.36391 Density of population 0.55 0.04782 0.02630
Per capita GDP 1 0.09873 0.09873
Urbanisation rate 0.8 0.07902 0.06322
Noise pollution 0.6 0.05932 0.03559
Light pollution 0.8 0.07902 0.06322
Natural pressure 0.04782 Mangrove pest 0.5 0.03776 0.01888
Extreme temperature weather per year 0.1 0.01006 0.00100
State subsystem 0.20301 Biology and ecology 0.07902 Diversity of species 0.7 0.07902 0.05531
Environmental quality 0.07410 Water quality 0.6 0.07410 0.04446
Biological productivity 0.06938 Conversion between nutrient levels 0.6 0.05932 0.03559
Total energy conversion 0.1 0.01006 0.00100
Response subsystem 0.38526 Social response 0.38526 Proportion of the tertiary industry 1 0.09873 0.09873
Education level of the population 0.8 0.07902 0.06322
Investment in environmental pollution 0.7 0.06917 0.04842
Planning regulations and policies 0.7 0.06917 0.04842
Degree of public participation 0.7 0.06917 0.04842

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