2018 3rd International Conference on New Energy and Renewable Resources (ICNERR 2018) ISBN: 978-1-60595-608-4

The Ecological Risk Assessment of Bullacta

Exarata Invasion in the Yellow River Delta

Yan Zou, Aihuan Song, Yingjun Wang, Tong Liu, Yingying Wu, Tianwen Zhang* and Wen Guo

ABSTRACT

In recent years, the rapid economic development as well as the increase in people entering and exiting help to promote the introduction of alien . Risk evaluation of alien species is the basis of developing risk management of alien species and one the most effective method to prevent the intrusion of alien species. Ecological risk of alien species Bullacta exarata in Yellow River Delta was evaluated by this article. The result shows that Bullacta exarata has certain effects on ecological environment in Yellow River Delta, but these effects are acceptable. However, we need to be cautious in development and also prevent significant effects of mass reproduction of mud on the local ecological environment.1

INTRODUCTION

Bullacta exarata is mainly reproduced in Ningbo and the Eastport of Liaoning. In 1980s, No Bullacta exarata was found in the investigation of Yellow River Delta. In 2001, the Bullacta exarata was introduced from Jiangsu, Liaoning, etc. and also spread the seeds in intertidal zone of Yellow River Estuary by bottom sowing for culture and proliferation. Until 2004, Bullacta exarata was found in the estuary area 50km away from initial point of planting, which became the advantageous species having a maximum population density up to thousands per square meter. As a foreign species, Bullacta exarata affects the local ecological environment and

1Yan Zou, Aihuan Song, Yingjun Wang, Tong Liu, Yingying Wu, Tianwen Zhang*, Wen Guo. Marine Biology Institute of Shandong Province, Shandong, Qingdao, 266002; Engineering Laboratory for Data Mining and Utilizing Germplasm Resources of Marine Organisms of Qingdao , Shandong ,Qingdao, 266002 *Corresponding Author: Tianwen Zhang (1983-), Doctor, Engaged in Model Specification. E- mail: [email protected] benthonic organisms after it became the advantageous species after mass propagation in natural waters beyond aquiculture area. Risk evaluation of alien species is the basis of developing risk management of alien species and one the most effective method to prevent the intrusion of alien species. It is important to develop risk evaluation of alien species in order to protect biological diversity in China. After taking Development plan of the Yellow River delta efficient economic zone as the national development strategy, opportunities have been given to the marine economic development in this area. However, pressure is also brought on ecological environment and natural species are changed by alien species [1]. Thus, it is necessary to rationally develop and utilize marine biological resources in Yellow River Delta zone, investigate the present status of typical marine alien species in this area and the influences of these species on local fishery resources, as well as reasonably evaluate the intrusion risks of alien species[2]. Taking Dongying Sea as the main research area, this work discusses the influence of introduction and reproduction of alien species on main local fishery resources by studying the distribution characteristics of Bullacta exarata population in Yellow River Estuary. Furthermore, the technical evaluation system is constructed based on PSR model by evaluating intrusion risks of alien species. Analytic hierarchy process is also applied in order to evaluate ecological safety risks of Bullacta exarata in Dongying sea of Yellow River Delta zone and to provide data support to the rational development, protection and optimization of industrial layout for fishery resources in Yellow River Delta.

OUTPUT CONVERSION OF FISHERY RESOURCES

Presently, the main resource which is utilized among marine biological resources is fishery resource. The loss of Bullacta exarata intrusion to fishery resources can be calculated by market valuation method, according to data collected for evaluating the output, output value, cost, etc. of marine products in sea area (main product). The formula is given as follows:

V  S  Y  P  W (1)  i i i  i

V —Value of resources, Yuan per year; S i —The area of i , hm2; Y i —The per unit area of , kg/(hm2·a); P i —The average market prices of i , Yuan per kg; W i —The cost of imputes, Yuan per year. The direct market value of marine biological resources can be estimated by similar evaluation and discounted value of annual value in sustainable output (including catching and cultivation). The specific formula is given as follows:

R V  i (2)

V —The Value of per unit area, Yuan per hm2; R —The Value of maximum sustainable yield, Yuan per hm2; i —Capitalize rate, %.

RELIABILITY ANALYSIS

Reliability refers to the consistency or stability of results obtained by different testing tools, which can reflect the reality indicator of characteristics to be detected. Generally, the more consistent the two results in the test, the less the error and the more the reliability will be. Kendall W coefficient, also known as Kendall concordance coefficient, is a method that shows the relevance of variables in multiple columns and levels. It can be used for variables having two or more levels. Kendall concordance coefficient：

2 (∑ R2) ∑ R2− i i N W = 1 (3) K2(N3−N) 12 W—Kendall concordance; K—Number of experts; N—Number of evaluation indicators; Ri—Index score of i.

Different experts may have different ratings of the same investigation form. The error is mainly from the difference between scoring experts. In evaluation, it is important to determine the reliability of evaluation results. In statistics, the indicator showing the scoring consistency is Kendall concordance coefficient. By testing, the significance and reliability of scoring consistence can be determined.

WEIGHT DETERMINATION FOR EVALUATION INDICATOR SYSTEM

TABLE I. WEIGHT DETERMINATION FOR EVALUATION INDICATOR SYSTEM. Weight The type of Weight Evaluation Indicator System Determinatio Evaluation index Determination n Introduction of Bullacta exarata 0.065 The invasion of 0.264 Biomass of Bullacta exarata 0.078 Bullacta exarata Status Density of Bullacta exarata 0.121

Primary productivity 0.053 Ecological condition 0.128 Diversity of benthos 0.075 Lusoria resource 0.091

Mactra veneriformis 0.082 Benthic fishery 0.441 Cyclina sinensis resources 0.093 Rubromuscula 0.097 Tellinidae resource 0.078 Manual elimination of Bullacta 0.075 Environmental exarata 0.167 protection Enhancement and releasing native 0.092 species

Weight determination for evaluation indicators is one of the key links in evaluation process[3]. The appropriateness of weight (also called Ai) for evaluation indicators is directly related to influence evaluation. There are several methods to determine Ai, but common methods include expert investigation and judgment matrix analytical method [4-6]. When constructing PSR model for intrusion risk evaluation of Bullacta exarata, indicator weight should be determined by both AHP and expert judgment. Subsequently, the model can be constructed by multi-level fuzzy comprehensive evaluation and its corresponding evaluation system be established[7-8]. According to the principles for determining the weight of evaluation indicators and corresponding formulas, weight of indicators in intrusion risk system of Bullacta exarata can be determined as shown in Table I.

STANDARDIZATION OF EVALUATION INDICATORS

To make evaluation indicators comparable, the original data of every evaluation indicator should be normalized. All evaluation indicators are converted between 0 - 1 by extreme standardization to make the indicators non-dimensional. According to the influential effects on marine resources, all indicators can be divided into efficiency indicators and cost indicators. Efficiency indicators are those that benefit from the utilization of marine resources. The larger the indicator, the better it will be. Cost indicators are those that are adverse to the utilization of marine resources. The smaller the indicator, the better it will be. Different standardization methods will be used for these two kinds of indicators[9-11]. Evaluation indicators in pressure subsystem positively reflect the pressure on fishery resources. The larger the value, the stronger the pressure will be. All these evaluation indicators are known as cost indicator. Evaluation indicators in state subsystem and response subsystem are known as efficiency indicators. Table II shows the standardized form of evaluation indicators.

EVALUATION SCORING AND CRITERIA

The ideal score of intrusion risk evaluation for Bullacta exarata is within 0.67 - 1, which shows that the effects of Bullacta exarata is minimal on local ecological environment. If the score is between 0.34 and 0.66, it means that Bullacta exarata has had effects on local ecological environment, but these effects are not severe. If the score is between 0.1 and 0.33, it means that Bullacta exarata has had severe effects on local ecological environment, so corresponding measures should be taken on time in order to reduce hazard. According to the research by Zhang Zongshu and Wu Dun, et al. [12-15], the evaluation criteria for intrusion risks of Bullacta exarata can be divided into three different classes as shown in Table III.

TABLE II. THE STANDARDIZED FORM OF EVALUATION INDICATORS. The The type of Evaluation Indicator System standardized Evaluation index form Introduction of Bullacta exarata 0 The invasion of Bullacta Biomass of Bullacta exarata 0 exarata Status Density of Bullacta exarata 0 Primary productivity 0.438 Ecological condition Diversity of benthos 0.453 Lusoria resource 0.735 Mactra veneriformis 0.536 Benthic fishery resources Cyclina sinensis 0.589 Rubromuscula 0.187 Tellinidae resource 0.386 Manual elimination of 1 Environmental protection Bullacta exarata Enhancement and releasing native species 1 TABLE III. EVALUATION CRITERIA FOR INVASION RISK OF BULLACTA EXARATA. I 0.1-0.33 0.34-0.66 0.67-1

It has severely effects on It has certain effects on It has small effects on Impact ecological environment ecological environment ecological environment

The score of evaluation parameter I is 0.438, which shows that Bullacta exarata has certain effects on ecological environment in Yellow River Delta, but these effects are acceptable. However, we need to be cautious in development and also prevent significant effects of mass reproduction of Bullacta exarata on the local ecological environment.

PREVENTION AND CONTROL MEASURES OF ALIEN SPECIES

Improve Legislation and Form Management Policies

Presently, there are no special laws or regulations for alien species, so laws for preventing alien intrusion and alien species should be formulated rapidly in order to pay more attention to biological intrusion in legislation. Biosecurity Act should be issued as soon as possible to specifically regulate the risk evaluation, pre-warning, introduction, elimination, control, biological recovery as well as the compensation responsibility of alien species[16-17].

Enhancement of Risk Evaluation for Alien Species

Further investigation and evaluation are conducted to foreign species, so as to establish the system for pre-warning mechanism, monitoring and rapid response system. Relevant units, scientific and research personnel should be organized in time to investigate the species, distribution, quantity and hazard of foreign aliens. When all these are performed, the evaluation system of intrusion risks can be established and also enhanced.

Improvement of Public Awareness

Strict measures are required to prevent the intrusion of alien species. Therefore, public positivity should be fully mobilized and social prevention should be improved in order to ensure that the public can also prevent biological intrusion.

Full Resource Utilization

After verification by scientific cost-income analysis, intrusive biological resources are effectively utilized and intrusive biology can be strictly controlled. During this process, new artificial spread shall also be prevented.

Control and Policy Recovery

For the introduced intrusive species with hazard, control measures will be taken in time, including developing artificial prevention, mechanical elimination, biological elimination, alternative control, chemical elimination as well as other comprehensive preventive measures. After eliminating intrusive species, regular examination will be conducted in occurrence scope after eliminating intrusive species in order to eliminate problems in time and avoid re-intrusion. Meanwhile, local community biological system will be recovered by artificial fecundation, in order to restore the local ecological system to its natural state.

ACKNOWLEDGEMENTS

This work was financially supported by Key research and development projects of Shandong province (2016GSF115007), Evaluation and utilization of database establishment for aquaculture germplasm resources, China CNOOC Charity Foundation Project (2016-2020), Major projects of China association of marine affairs and The earmarked fund for Modern Agro-industry Technology Research System (SDAIT-14).

REFERENCES

1. Hongwei Liu, et al. Sustainable Utilization of Marine Fishery Resources of [J]. Journal of Anhui Agri.Sci, 2010, 38(26): 14579-14581. 2. P. Baake,and A. Boom, Vertical Product Differentiation, Network Externalities, and Compatibility Decisions[J].International Journal of Industrial Organization 2001, 19:267-284. 3. Yanying Guo, Yunfeng Deng, Jun Ren. Application of Analytic Hierarchy Process (AHP) Method in Comprehensive Assessment of Surface Water Quality [J]. Journal of Lanzhou Jiaotong University, 2006, 25(3): 70-76. 4. Xiansen Li, Mingxiang Niu, Fangqun Dai. The Characteristics of Structure and Distribution of Spawning Stock of Fishery Species in the Bohai Sea[J]. Marine Fisheries Research, 2008, 29(4):15-21. 5. Yanci Xiang, Shaolin Peng, Hai Ren. Management and Ecological Risk Assessment of Exotic Plants. [J]. Chinese Journal of Ecology.2002, 21(5):40-48. 6. Dingyong Yu, Changhai Wang, Hongchao Liu. Study on Reclamation Impact on Marine Resources Based on PSR Model[J]. Periodical Of University Of China, 2011, 41(7/8):170- 175. 7. Shaozhi Mai, Songjun Xu, Yingjun Pan. Application of the PSR Model to the Evaluation of Wetland Eco System Health [J]. Tropical Geography, 2005, 25(4):317-321. 8. Bingzhong Zhou, Hao Yang, Haosheng Bao, et al. PSR Model and its Application in the Evalution of Sustainable Land-Use in the Region [J]. Journal of Natural Resources, 2002, 17(5):541-548. 9. Xianghui Li, Ke-ning Da. Tactics of Sustainable Development of Little-Size Town Based on the Conceptual Frame of PSR[J]. Journal of Shenyang Jian Zhu University.(Social Science)， 2005,7(1):48-51. 10. Serveiss V B, Bowen J L, Dow D. Using Ecological Risk Assessment to Identify the Major Anthropogenic Stressor in the Waquoit Bay Watershed, Cape Cod, Massachusetts [R]. Washington, D.C. U.S. Environmental Protection Agency,2004. 11. George M B, Steffani C N. Can We Predict the Effects of Alien Species? A Case-history of the Invasion of South Africa by Mytilus Gallo Provincialis (Lamarck) [J].Journal of Experimental Marine Biology and Ecology.2004, 300:189-215. 12. Yongming Shen, Yongmei Liu, Quanzhan Chen. Analysis of the Expanding Process of the Spartina Alterniflora Loisel Salt Marsh on Jiangsu Province Coast by Remote Sensing[J]. Journal of Plant Resources and Environment.2002, 11(2):33-38. 13. Zhongyi Chen, Bo Li, Jiakuan Chen. Ecological Consequenees and Management of Spartina Spp. Invasions in Coastal Ecosystems[J]. Biodiversity Science, 2004, 12(2):280-289. 14. Jianiun Wang, Zongguo Huang, Chengxing Zheng, et al. Population Dynamies and Structure of Aliens Peeies Mytilo Psis Sallei in Fujian, China[J]. Journal of Oceanography in Taiwan Strait. 1999.18(4):372-377. 15. Tianwen Zhang, Aihuan Song, Qiaochu Li, Meili Xin, Zhaoxing Qiu, Yongyun Zheng. Research Method in the Influence of Reclamation on Fishery Resources. Advanced Materials Research. Vols. 726-731(2013):1022-1026. 16. Huawei Zheng,Youzhao Liu. Spatial Difference Analysis for Cultivated Land Intensive Utilization Based on the PSR Model—a Case Study of Sichuan Province [J]. System Sciences and Comprehensive Studies in Agriculture. 2001, 27(3),257-262. 17. Yanling Xiao, Xiaojing Liu, Jianbo Liu. The Method of Giving Weight for Performance Indicator Based on Entropy Method[J]. Journal of Daqing Institute.2005,2 9(1): 107- 109.