Biodiv Sci ›› 2023, Vol. 31 ›› Issue (3): 22478. DOI: 10.17520/biods.2022478
• Conservation and Governance • Previous Articles Next Articles
Jingjing Zhao1,*(), Haibin Jia2, Tien Ming Lee1,3,*()
Received:
2022-08-21
Accepted:
2022-09-27
Online:
2023-03-20
Published:
2023-01-18
Contact:
Jingjing Zhao,Tien Ming Lee
Jingjing Zhao, Haibin Jia, Tien Ming Lee. Market status and the sustainable utilization strategy of wild earthworm (earth dragon) for medicinal use[J]. Biodiv Sci, 2023, 31(3): 22478.
Fig. 1 Time series analysis of earth dragons (earthworms) market price between 1998 and 2022. (A) Decomposition of the market price time series of earth dragons (earthworms); (B) The seasonal fluctuation of earth dragons price after detrending analysis.
Fig. 2 The characteristics of earth dragons (earthworms) price, supply and demand changes and analysis of supply and demand curves from 2004 to 2021. (A) The characteristics of earth dragons (earthworms) supply and demand changes during 2004?2021; (B?H) Supply-demand curves during different year duration. In Figures B?H, the points are the equilibrium price, which is the intersection of the supply curve and the demand curve.
Fig. 3 The effect of using “electric shocking” methods to harvest earthworm on supply quantity and market price. (A) The effect of harvest on supply (A) after controlling the demand variable; The effect of harvest on market price (B) after controlling for demand and supply and (C) after controlling for demand; (D) The supply changes of earth dragons across various main provinces from 2004 to 2021; (E) The proportion changes of supply of earth dragons across various main provinces from 2004 to 2021. For Figures A, B, and C: In the top panel row, the solid line represents the original value, while the dotted line represents the predicted value based on the original results for the counterfactual prediction (i.e., if the “electric shocking” methods was not popularized), and the shaded area represents the 95% confidence interval. The middle panel row is the difference value between the original value and predicted value in the upper panel. The bottom panel row is the sum of the values of the middle panel, reflecting a plot of the cumulative effect of the “electric shocking” methods.
模型名称 Model name | 模型1 Model 1 | 模型2 Model 2 | 模型3 Model 3 | |||
---|---|---|---|---|---|---|
响应变量 Response variable | 供应 Supply quantity (t) | 价格 Price (yuan/kg) | 价格 Price (yuan/kg) | |||
控制变量 Control variable | 需求量 Demand quantity | 供应量与需求量 Supply & demand quantity | 需求量 Demand quantity | |||
平均 Average | 累计 Cumulative | 平均 Average | 累计Cumulative | 平均 Average | 累计Cumulative | |
平均实际值 Actual | 1,205 | 9,643 | 119 | 836 | 119 | 836 |
平均预测值(标准差) Prediction (SD) | 858 (203) | 6,866 (1,621) | 183 (42) | 1,281 (294) | 214 (42) | 1,496 (292) |
95%置信区间 CI | [458, 1,270] | [3,662, 10,158] | [99, 265] | [691, 1,856] | [132, 299] | [921, 2,091] |
绝对效应(标准差) Absolute effect (SD) | 347 (203) | 2,778 (1,621) | ?63 (42) | ?444 (294) | ?94 (42) | ?659 (292) |
95%置信区间 CI | [?64, 748] | [?515, 5,981] | [?146, 21] | [?1,019, 145] | [?179, ?12] | [?1,255, ?85] |
相对效应(标准差) Relative effect (SD) | 40% (24%) | 40% (24%) | ?35% (23%) | ?35% (23%) | ?44% (20%) | ?44% (20%) |
95%置信区间 CI | [?7.5%, 87%] | [?7.5%, 87%] | [?80%, 11%] | [?80%, 11%] | [?84%, ?5.7%] | [?84%, ?5.7%] |
后验尾区概率 P Posterior tail-area probability P | 0.04 | 0.07 | 0.01 | |||
因果影响的后验概率 Posterior probability of a causal effect | 95.6% | 93.0% | 99.7% |
Table 1 Posterior inference of impacts on supply quantity and market price
模型名称 Model name | 模型1 Model 1 | 模型2 Model 2 | 模型3 Model 3 | |||
---|---|---|---|---|---|---|
响应变量 Response variable | 供应 Supply quantity (t) | 价格 Price (yuan/kg) | 价格 Price (yuan/kg) | |||
控制变量 Control variable | 需求量 Demand quantity | 供应量与需求量 Supply & demand quantity | 需求量 Demand quantity | |||
平均 Average | 累计 Cumulative | 平均 Average | 累计Cumulative | 平均 Average | 累计Cumulative | |
平均实际值 Actual | 1,205 | 9,643 | 119 | 836 | 119 | 836 |
平均预测值(标准差) Prediction (SD) | 858 (203) | 6,866 (1,621) | 183 (42) | 1,281 (294) | 214 (42) | 1,496 (292) |
95%置信区间 CI | [458, 1,270] | [3,662, 10,158] | [99, 265] | [691, 1,856] | [132, 299] | [921, 2,091] |
绝对效应(标准差) Absolute effect (SD) | 347 (203) | 2,778 (1,621) | ?63 (42) | ?444 (294) | ?94 (42) | ?659 (292) |
95%置信区间 CI | [?64, 748] | [?515, 5,981] | [?146, 21] | [?1,019, 145] | [?179, ?12] | [?1,255, ?85] |
相对效应(标准差) Relative effect (SD) | 40% (24%) | 40% (24%) | ?35% (23%) | ?35% (23%) | ?44% (20%) | ?44% (20%) |
95%置信区间 CI | [?7.5%, 87%] | [?7.5%, 87%] | [?80%, 11%] | [?80%, 11%] | [?84%, ?5.7%] | [?84%, ?5.7%] |
后验尾区概率 P Posterior tail-area probability P | 0.04 | 0.07 | 0.01 | |||
因果影响的后验概率 Posterior probability of a causal effect | 95.6% | 93.0% | 99.7% |
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