Mangrove ecosystems are one of the habitats that host high environmental diversity at the level of physical, geomorphological and biological features in arid regions. In Saudi Arabia, mangrove ecosystems are heavily threatened by both natural hazards and human pressure. The total estimated area of mangroves in Safwa Al Khurais, Saudi Arabia, is approximately 20 000 ha in extent and comprises two species:

Mangroves in Saudi Arabia are found in the form of fragmented populations or thin stands in many tidal areas of the Red Sea and the Arabian Gulf coasts. They consist mainly of two species:

Although the mangroves of Saudi Arabia are not as luxurious as those on other tropical shores, they play similarly significant ecological and environmental roles. These locations are nurseries for numerous profitable fish species and they shelter coral reefs by retaining sediment loads from the periodic influx of rain water. Because they grow in very different environments, mangroves are very sensitive to over-exploitation. The degradation of mangrove ecosystems has occurred in various parts of the region, due to excessive browsing by camels, overcutting, impeding rainwater from draining through valleys, pollution, and coastal construction (Mandura

Human activities such as coastal urbanisation and related wastewater manage ment problems, industrialisation and related emissions, chemical pollutants, fishing activities and aquaculture development, tourism and the consequent increase of pressures on coastal resources are some of the main stresses introduced by humankind on coastal ecosystems (Hegazy

Global Sensitivity Analysis (GSA) methods assign the output inconsistency to the inconsistency of the input parameters when they fluctuate over their entire uncertainty dimension (Petropoulos

Global Sensitivity Analysis is an influential method due to its capability to assimilate the influence of the input parameters over their whole range of inconsistency (Saltelli, Tarantola & Chan

The aim of this study is to analyse the uncertainty of the survival of

Five sites were identified for this study (

The location of the five study sites in the Red Sea (Usfan and Al Mudhaylif) and the Arabian Gulf (Qurum Island, Sehat and Safwa Al Khurais).

Measurements of the population characteristics of ^{2} for seedlings and adult individuals. The number of dead or partially dead trees was counted. Plant cover was measured by the line intercept method. The density of respiratory roots (pneumatophores) was estimated by counting the number of roots per m^{2}.

Sensitivity analysis approaches are categorised according to the outcome of the related sensitivity procedures into local or global methods, and methods that depend on or are independent of the model characteristics (Schwieger

Consistent with Saltelli

The broad practice of sensitivity analysis is shown in

The general procedure for a sensitivity analysis.

Based on the Monte Carlo methods, sensitivity analysis methods include regression and correlation analyses as well as the analysis of the rank of the transformed data. The general procedure to estimate global sensitivity measures is based on the following equations:

Where:

For non-correlated input additive models:

According to Schwieger (2004), this translates to an easy numerical interpretation of the sensitivity indices, because each _{i}_{i}

The terms of higher order are estimated by taking other fixed input quantities into consideration:

The estimation of higher order terms leads to the estimation of the total effects _{Ti}_{i}

The corresponding total effect is computed as:

Consistently, a judgement between _{i}_{Ti}

The results from the sensitivity analysis focus specifically on the decomposition of variance (%) of the mean total variance in emulator output, where input parameters have been assumed to be non-correlated, normally distributed and varying within their whole range.

A sensitivity analysis of mangrove demographic features was carried out on five different mangrove stands: two stands located on the west coast and three located on the east coast. Both of the western mangrove stands showed a lower sensitivity to the demographic feature number of respiratory roots, where this was observed to be 0.05 and 7.5, respectively (see

Population characteristics and sensitivity analysis in the five study sites.

Site | Population characteristics | Variance (%) | Standard deviation | Total effect |
---|---|---|---|---|

Usfan | Density (100 m^{2}) |
8.13 | 0.20 | 8.17 |

Cover (%) | 10.60 | 0.19 | 10.64 | |

Tree height (m) | 18.34 | 0.25 | 18.4 | |

Crown diameter (m^{2}) |
20.10 | 0.38 | 20.04 | |

Number of dead trees (100 m^{2}) |
8.18 | 0.38 | 8.21 | |

Number of seedlings (100 m^{2}) |
1.43 | 0.12 | 1.47 | |

Number of respiratory roots (100 m^{2}) |
||||

Average height of respiratory roots (m) | 33.15 | 0.45 | 33.18 | |

Al Mudhaylif | Density (100 m^{2}) |
7.52 | 0.30 | 7.54 |

Cover (%) | 20.25 | 0.31 | 20.29 | |

Tree height (m) | 12.41 | 0.32 | 12.45 | |

Crown diameter (m^{2}) |
9.14 | 0.19 | 9.16 | |

Number of dead trees (100 m^{2}) |
16.79 | 0.34 | 16.83 | |

Number of seedlings (100 m^{2}) |
10.24 | 0.20 | 1.29 | |

Number of respiratory roots (100 m^{2}) |
||||

Average height of respiratory roots (m) | 16.04 | 0.22 | 16.07 | |

Sehat | Density (100 m^{2}) |
9.56 | 0.18 | 9.56 |

Cover (%) | 15.80 | 0.18 | 15.82 | |

Tree height (m) | 16.81 | 0.23 | 16.82 | |

Crown diameter (m^{2}) |
8.62 | 0.15 | 8.63 | |

Number of dead trees (100 m^{2}) |
||||

Number of seedlings (100 m^{2}) |
28.64 | 0.21 | 28.65 | |

Number of respiratory roots (100 m^{2}) |
14.80 | 0.28 | 14.82 | |

Average height of respiratory roots (m) | 3.07 | 0.09 | 3.08 | |

Safwa Al Khurais | Density (100 m^{2}) |
6.57 | 0.27 | 6.61 |

Cover (%) | 14.16 | 0.96 | 14.19 | |

Tree height (m) | 3.46 | 0.49 | 3.50 | |

Crown diameter (m^{2}) |
9.95 | 0.36 | 9.99 | |

Number of dead trees (100 m^{2}) |
17.13 | 0.68 | 17.16 | |

Number of seedlings (100 m^{2}) |
37.46 | 0.74 | 37.50 | |

Number of respiratory roots (100 m^{2}) |
7.68 | 0.31 | 7.73 | |

Average height of respiratory roots (m) | ||||

Qurum Island | Density (100 m^{2}) |
23.91 | 1.46 | 23.96 |

Cover (%) | 5.97 | 0.44 | 6.01 | |

Tree height (m) | 5.06 | 0.21 | 5.25 | |

Crown diameter (m^{2}) |
8.76 | 0.24 | 8.96 | |

Number of dead trees (100 m^{2}) |
38.45 | 1.41 | 38.51 | |

Number of seedlings (100 m^{2}) |
16.34 | 1.28 | 16.38 | |

Number of respiratory roots (100 m^{2}) |
||||

Average height of respiratory roots (m) | 0.99 | 0.34 | 1.05 |

Bold, affects the outputs significantly.

Histogram chart representations of the total effect of the sensitivity analysis demonstrated inter- and intra comparability differences, shown collaterally in

The sensitivity analysis total effect (%).

In the Usfan stand, the results indicated that the most sensitive demographic feature was the average height of respiratory roots, which accounted for 33%. The average height of respiratory roots of mangroves alone accounts for one third of the total effect of the sensitivity analysis. The higher total effect of the average height of respiratory roots explains the lower stability of such demographic features (Elhag

The total variance of the sensitivity analysis of mangrove seedlings was calculated to be 0.9%. Such a minor value indicated a robust stability of the mangrove demographic feature in the study area. The next stand on the western side of Saudi Arabia, located at Al Mudhaylif, showed a relative stability compared to the stand located in Usfan (according to the demographic features used in the current study). Both the number of respiratory roots and the number of dead trees (8% for both) were considered to be the least sensitive demographic features according to Petropoulos

On the eastern side of Saudi Arabia, three different stands of mangroves were considered for sensitivity analysis. Sehatstand is a moderately stable stand, and both the number of dead trees and average height of respiratory roots accounted for 3% of the total variance. The low total variance percentages represent more stable demographic features, which correspond to environmental variability (Holvoet

Within the same geographical region, on the eastern side of the current study, both mangrove stands of Safwa Al Khurais and Qurum Island were found to be extremely sensitive to environmental impacts. The number of seedlings (38%) and tree density of mangroves (39%) were found to be the most unstable demographic features in Safwa Al Khurais and on Qurum Island, respectively. The average height and number of respiratory roots were the most stable demographic features in Safwa Al Khurais and on Qurum Island, respectively.

The uncertainty analysis of mangrove demographic features of the Usfan area is shown in

Usfan demographic features sensitivity analysis. (a) Density (100 m^{2}); (b) Cover (%); (c) Tree height (m); (d) Crown diameter (m); (e) Number of dead trees (100 m^{2}); (f) Number of seedlings (100 m^{2}); (g) Number of respiratory roots (100 m^{2}) and (h) Average height of respiratory roots (m).

The values of mangrove demographic features were proportionally related to the corresponding uncertainty as the main effect, except for the number of seedlings, which was inversely proportionate to its uncertainty value. The number of respiratory roots of mangroves in the Usfan area displayed a steady behaviour of uncertainty levels, with different values of the total number of respiratory roots.

Al Mudhaylif demographic features sensitivity analysis: (a) Density (100 m^{2}); (b) Cover (%); (c) Tree height (m); (d) Crown diameter (m); (e) Number of dead trees (100 m^{2}); (f) Number of seedlings (100 m^{2}); (g) Number of respiratory roots (100 m^{2}) and (h) Average height of respiratory roots (m).

A GSA was also applied to the eastern mangrove stands. Sehat, Safwa Al Khurais and Qurum Island showed that Sehatis was the most stable mangrove stand in the area of investigation (east and west). The mangrove stand at Sehatstand showed good resilience to environmental impacts (

Sehat demographic features sensitivity analysis: (a) Density (100 m^{2}); (b) Cover (%); (c) Tree height (m); (d) Crown diameter (m); (e) Number of dead trees (100 m^{2}); (f) Number of seedlings (100 m^{2}); (g) Number of respiratory roots (100 m^{2}) and (h) Average height of respiratory roots (m).

Safwa Al Khurais demographic features sensitivity analysis: (a) Density (100 m^{2}); (b) Cover (%); (c) Tree height (m); (d) Crown diameter (m); (e) Number of dead trees (100 m^{2}); (f) Number of seedlings (100 m^{2}); (g) Number of respiratory roots (100 m^{2}) and (h) Average height of respiratory roots (m).

Qurum Island demographic features sensitivity analysis (a) Density (100 m^{2}); (b) Cover (%); (c) Tree height (m); (d) Crown diameter (m); (e) Number of dead trees (100 m^{2}); (f) Number of seedlings (100 m^{2}); (g) Number of respiratory roots (100 m^{2}) and (h) Average height of respiratory roots (m).

The GSA of the mangroves were measured against eight different demographic features: density, cover, tree height, crown diameter, number of dead trees, number of seedlings, number of respiratory roots and average height of respiratory roots. The GSA delivered quantitative and model-independent sensitivity measures to each of the input factors, and to the input factors collectively, in response to the simulated outputs under consideration.

The results of this study show the model concept to be sufficiently sensitive to represent the behaviour of the natural systems. The sensitivity analysis confirms that demographic features were alternately sensitive to different locations of mangrove stands and that input parameters related directly to the estimated variables derived from the uncertainty analysis. The purpose of implementing a GSA approach was to recognise variance associated with different input measures.

The GSA model is independent of the features of the investigated model. Conducted findings shall mainly be used for better model performance, for example, comparing different demographic features from different locations. Immediate remediation and restoration techniques need to be applied urgently in order to conserve the mangrove stands, especially in the eastern section of the study area.

This work is funded by the project number 11-ENV1756-02, NPST-KACST, Saudi Arabia, entitled ‘Conservation and utilisation of mangrove ecosystems in Saudi Arabia: from knowledge to development’.

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

A.K.H. (King Saud University) proposed the project idea, participated in all the field work, data analysis and writing of the manuscript. M.E. (King Abdulaziz University) and J.A.B. (King Abdulaziz University) were responsible for the data analysis, and wrote most of the manuscript. M.E.B. (Port Said University) participated in the field work and data collection. A.A-G. (King Abdulaziz University) made conceptual contributions in the writing of the manuscript. A.A.A. (King Saud University) and M.F. (King Saud University) performed some of the experiments.