Honghua Shi

Application of macrobenthos functional groups to estimate the ecosystem health in a semi-enclosed bay

In this study, the functional group concept was first applied to evaluate the ecosystem health of Bohai Bay. Macrobenthos functional groups were defined according to feeding types and divided into five groups: a carnivorous group (CA), omnivorous group (OM), planktivorous group (PL), herbivorous group (HE), and detritivorous group (DE). Groups CA, DE, OM, and PL were identified, but the HE group was absent from Bohai Bay. Group DE was dominant during the study periods. The ecosystem health was assessed using a functional group evenness index. The functional group evenness values of most sampling stations were less than 0.40, indicating that the ecosystem health was deteriorated in Bohai Bay. Such deterioration could be attributed to land reclamation, industrial and sewage effluents, oil pollution, and hypersaline water discharge. This study demonstrates that the functional group concept can be applied to ecosystem health assessment in a semi-enclosed bay.

Study on the cumulative impact of reclamation activities on ecosystem health in coastal waters

The purpose of this study is to develop feasible tools to investigate the cumulative impact of reclamations on coastal ecosystem health, so that the strategies of ecosystem-based management can be applied in the coastal zone. An indicator system and model were proposed to assess the cumulative impact synthetically. Two coastal water bodies, namely Laizhou Bay (LZB) and Tianjin coastal waters (TCW), in the Bohai Sea of China were studied and compared, each in a different phase of reclamations. Case studies showed that the indicator scores of coastal ecosystem health in LZB and TCW were 0.75 and 0.68 out of 1.0, respectively. It can be concluded that coastal reclamations have a historically cumulative effect on benthic environment, whose degree is larger than that on aquatic environment. The ecosystem-based management of coastal reclamations should emphasize the spatially and industrially intensive layout.

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity. Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns. Five southern islands in the Miaodao Archipelago, North China were studied herein. The spatial distribution of herbaceous plant diversity on these islands was analyzed, and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA. The results reveal 114 herbaceous plant species, belonging to 94 genera from 34 families in the 50 plots sampled. The total species numbers on different islands were significantly positively correlated with island area, and the average α diversity was correlated with human activities, while the β diversity among islands was more affected by island area than mutual distances. Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude, slope, total nitrogen, total carbon, and canopy density, and lower moisture content, pH, total phosphorus, total potassium, and aspect. Among the environmental factors, pH, canopy density, total K, total P, moisture content, altitude, and slope had significant gross effects, but only canopy density exhibited a significant net effect. Terrain affected diversity by restricting plantation, plantation in turn influenced soil properties and the two together affected diversity. Therefore, plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.

Assessment and regulation of ocean health based on ecosystem services: Case study in the Laizhou Bay, China

The ecosystem-based management of nearshore waters requires integrated assessment of ocean health and scientific guidance on artificial regulations to promote sustainable development. Quantitative approaches were developed in this paper to assess present and near-term ocean health based on ecosystem services. Results of the case study in the Laizhou Bay of China showed that the index score of ocean health was 0.785 6 out of 1.0 at present and was expected to range from 0.555 1 to 0.804 1 in the near-term future depending on different intensities of artificial regulation of negative pressures. Specifically, the results of ocean health at present mainly indicated that cultural services and provisioning services performed essentially perfectly while supporting services and regulating services functioned less well. It can be concluded that this nearshore ecosystem would partially lose supporting and regulating services in the near-term future if the increasing pressures were not wellregulated but that all of these categories of ecosystem services could be slightly improved if the negative pressures were fully controlled. Additionally, it is recommended that publicity and education on ecosystem services especially on cultural services and regulating services should be further strengthened. The analytical process and resulting quantification provide flexible tools to guide future development of regulations so as to facilitate ecosystem-based management in the coastal zone.

Simulating spatial distribution of coastal soil carbon content using a comprehensive land surface factor system based on remote sensing

Surface soil carbon content (SCC) in coastal area is affected by complex factors, and revealing the SCC spatial distribution is considerably significant for judging the quantity of stored carbon and identifying the driving factors of SCC variation. A comprehensive land surface factor system (CLSFS) was established; it utilized the ecological significances of remote sensing data and included four-class factors, namely, spectrum information, ecological indices, spatial location, and land cover. Different simulation algorithms, including single-factor regression (SFR), multiple-factor regression (MFR), partial least squares regression (PLSR), and back propagation neural network (BPNN), were adopted to conduct the surface (0-30cm) SCC mapping in the Yellow River Delta in China, and a 10-fold cross validation approach was used to validate the uncertainty and accuracy of the algorithms. The results indicated that the mean simulated standard deviations were all <0.5g/kg and thus showed a low uncertainty; the mean root mean squared errors based on the simulated and measured SCC were 3.88g/kg (SFR), 3.85g/kg (PLSR), 3.67g/kg (MFR), and 2.78g/kg (BPNN) with the BPNN exhibiting a high accuracy compared to similar studies. The mean SCC was 17.40g/kg in the Yellow River Delta with distinct spatial heterogeneity; in general, the SCC in the alongshore regions, except for estuaries, was low, and that in the west of the study area was high. The mean SCCs in farmland (18.31g/kg) and wetland vegetation (17.98g/kg) were higher than those in water area (16.07g/kg), saltern (15.61g/kg), and bare land (14.71g/kg). Land-sea interaction and human activity jointly affected the SCC spatial distribution. The CLSFS was proven to have good applicability, and can be widely used in simulating the SCC spatial distribution in coastal areas.

Archipelago bird habitat suitability evaluation based on a model of form-structure-function-disturbance

Archipelagos are always the key nodes of bird migration routes, but they exhibit ecological vulnerability and heterogeneity due to their unique natural conditions and various human disturbances. An evaluation of archipelago bird habitat suitability that focuses on archipelago features is essential for island ecosystem conservation under human disturbances and for optimisation of bird migration routes. A form-structure-function-disturbance (FSFD) model for archipelago bird habitat suitability was established, and the spatial distributions of habitat suitability were analysed at the island and grid scales on Miaodao Archipelago, which are important islands in North China. Results indicated that form values were high in islands with large areas and centre position; structure values were high in areas with simple terrain condition and landscape pattern; function values were lower in traffic, building and bare lands than those in plantation and grassland; and disturbance values were closely related to human activities, which were lower in inhabited islands than those in uninhabited islands, and in traffic and building lands than those in other areas. The archipelago bird habitat suitability showed evident spatial heterogeneity. At the island scale, five islands were in status of good suitability, and 27 islands were in status of ordinary suitability; the different suitability levels at the grid scale were ordinary suitability, good suitability, poor suitability and best suitability in the descending order of areas. FSFD were determined by natural and anthropogenic factors, and human disturbance was the major factor that influences the spatial distribution of archipelago bird habitat suitability.

Spatiotemporal heterogeneity of phytoplankton diversity and its relation to water environmental factors in the southern waters of Miaodao Archipelago, China

To study the water quality influenced by the anthropogenic activities and its impact on the phytoplankton diversity in the surface waters of Miaodao Archipelago, the spatiotemporal variations in phytoplankton communities and the environmental properties of the surface waters surrounding the Five Southern Islands of Miaodao Archipelago were investigated, based on seasonal field survey conducted from November 2012 to August 2013. During the survey, a total of 109 phytoplankton species from 3 groups were identified in the southern waters of Miaodao Archipelago, of which 77 were diatoms, 29 were dinoflagellates, and 3 were chrysophytes. Species number was higher in winter (73), moderate in autumn (70), but lower in summer (31) and spring (27). The species richness index in autumn (5.92) and winter (4.28) was higher than that in summer (2.83) and spring (1.41). The Shannon-Wiener diversity index was high in autumn (2.82), followed by winter (1.99) and summer (1.92), and low in spring (0.07). The species evenness index in autumn (0.46) and summer (0.39) was higher than that in winter (0.32) and spring (0.02). On the basis of principal component analysis (PCA) and redundancy analysis (RDA), we found that dissolved inorganic nitrogen (DIN) and chemical oxygen demand (COD) in spring, COD in summer, pH in autumn, and salinity and oil pollutant in winter, respectively, showed the strongest association with the distribution of phytoplankton diversity. The spatial heterogeneity of the southern waters of Miaodao Archipelago was quite obvious, and three zones, i.e., northeastern, southwestern and inter-island water area, were identified by cluster analysis (CA) based on key environmental variables.

Evaluation on island ecological vulnerability and its spatial heterogeneity

The evaluation on island ecological vulnerability (IEV) can help reveal the comprehensive characteristics of the island ecosystem and provide reference for controlling human activities on islands. An IEV evaluation model which reflects the land-sea dual features, natural and anthropogenic attributes, and spatial heterogeneity of the island ecosystem was established, and the southern islands of Miaodao Archipelago in North China were taken as the study area. The IEV, its spatial heterogeneity, and its sensitivities to the evaluation elements were analyzed. Results indicated that the IEV was in status of mild vulnerability in the archipelago scale, and population pressure, ecosystem productivity, environmental quality, landscape pattern, and economic development were the sensitive elements. The IEV showed significant spatial heterogeneities both in land and surrounding waters sub-ecosystems. Construction scale control, optimization of development allocation, improvement of exploitation methods, and reasonable ecological construction are important measures to control the IEV.

Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models, to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay, Fujian, China, were developed using a back propagation (BP) network. The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train, test and build a three-layer BP artificial neural network with multi-input and single-output. Ten water quality parameters were used to forecast phytoplankton biomass (measured as chlorophyll-a concentration). Correlation coefficient between biomass values predicted by the model and those observed was 0.964, whilst the average relative error of the network was −3.46% and average absolute error was 10.53%. The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass. A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass. Indicators were classified according to the sensitivity of response and its risk degree. The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH, sea surface temperature, sea surface salinity, chemical oxygen demand and ammonium.