转野生荠菜凝集素基因棉花对赤子爱胜蚓的影响

在实验室条件下分别给赤子爱胜蚓(Eisenia foetida)饲喂在自然状况下可能接受的棉叶最高添加量和2倍最高添加量的转野生荠菜凝集素(WSA)基因棉花(Gossypium hirsutum)叶片,并以其亲本棉花叶片为对照,以牛粪为阴性对照,以农药为阳性对照,研究种植转WSA基因棉花对赤子爱胜蚓死亡率、生长及繁殖的影响。结果表明,在49 d的试验周期内,与取食亲本棉花叶片的赤子爱胜蚓相比,取食转WSA基因棉花叶片的赤子爱胜蚓的死亡率,体质量,GST、SOD、纤维素酶活性以及蚓茧数和小蚯蚓数均无显著差异,未发现转WSA基因棉花对赤子爱胜蚓有不利影响。     

Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems (VMS). Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives (a true fishing set detected as a fishing set); conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. This spatially explicit information on effort, provided with error estimation, should greatly reduce misleading interpretations of catch per unit effort and thus significantly improve the adaptive management of fisheries. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.     

Risks to large marine protected areas posed by drifting fish aggregation devices

Mapping and predicting the potential risk of fishing activities to large marine protected areas (MPAs), where management capacity is low but fish biomass may be globally important, is vital to prioritizing enforcement and maximizing conservation benefits. Drifting fish aggregating devices (dFADs) are a highly effective fishing method employed in purse seine fisheries that attract and accumulate biomass fish, making fish easier to catch. However, dFADs are associated with several negative impacts, including high bycatch rates and lost or abandoned dFADs becoming beached on sensitive coastal areas (e.g., coral reefs). Using Lagrangian particle modeling, we determined the potential transit of dFADs in a large MPA around the Chagos Archipelago in the central Indian Ocean. We then quantified the risk of dFADs beaching on the archipelago's reefs and atolls and determined the potential for dFADs to pass through the MPA, accumulate biomass while within, and export it into areas where it can be legally fished (i.e., transit). Over one-third (37.51%) of dFADs posed a risk of either beaching or transiting the MPA for >14 days, 17.70% posed a risk of beaching or transiting the MPA for >30 days, and 13.11% posed a risk of beaching or transiting the MPA for >40 days. Modeled dFADs deployed on the east and west of the perimeter were more likely to beach and have long transiting times (i.e., posed the highest risk). The Great Chagos Bank, the largest atoll in the archipelago, was the most likely site to be affected by dFADs beaching. Overall, understanding the interactions between static MPAs and drifting fishing gears is vital to developing suitable management plans to support enforcement of MPA boundaries and the functioning and sustainability of their associated biomass.     

水葫芦控制性种养技术研究

利用水葫芦强大的无性繁殖能力和对水体氮磷的吸附能力,可以有效治理水体内源污染。大规模的水葫芦圈养必须解决水葫芦逃逸问题,在满足安全生长的前提下实施经济效益和生态效益高的圈养方式,是水葫芦产业得以推广的必要保证。