水产养殖环境中抗生素抗性基因 (ARGs) 研究进展

李丹怡, 王许诺, 张广桔, 王增焕, 黄珂

李丹怡, 王许诺, 张广桔, 王增焕, 黄珂. 水产养殖环境中抗生素抗性基因 (ARGs) 研究进展[J]. 南方水产科学, 2022, 18(5): 166-176. DOI: 10.12131/20210207
引用本文: 李丹怡, 王许诺, 张广桔, 王增焕, 黄珂. 水产养殖环境中抗生素抗性基因 (ARGs) 研究进展[J]. 南方水产科学, 2022, 18(5): 166-176. DOI: 10.12131/20210207
LI Danyi, WANG Xunuo, ZHANG Guangju, WANG Zenghuan, HUANG Ke. Advances on antibiotic resistance genes (ARGs) in aquaculture environment[J]. South China Fisheries Science, 2022, 18(5): 166-176. DOI: 10.12131/20210207
Citation: LI Danyi, WANG Xunuo, ZHANG Guangju, WANG Zenghuan, HUANG Ke. Advances on antibiotic resistance genes (ARGs) in aquaculture environment[J]. South China Fisheries Science, 2022, 18(5): 166-176. DOI: 10.12131/20210207

水产养殖环境中抗生素抗性基因 (ARGs) 研究进展

基金项目: 海南省自然科学基金青年基金 (321QN0944);广东省渔业生态环境重点实验室开放基金(FEEL-2017-14)
详细信息
    作者简介:

    李丹怡  (1994—),女,研究实习员,硕士,从事渔业环境及水产品的监测与风险评估研究。E-mail: lidy27@mail2.sysu.edu.cn

    通讯作者:

    王许诺 (1983—),女,副研究员,硕士,从事渔业环境及水产品的监测与风险评估研究。E-mail: sanqianli-1983@163.com

  • 中图分类号: S 949

Advances on antibiotic resistance genes (ARGs) in aquaculture environment

  • 摘要: 抗生素对水产养殖业中水生生物疾病防治、生产线增产等发挥着重要作用,但长期滥用抗生素很可能会诱导水生生物体内产生携带抗生素抗性基因 (Antibiotic resistant genes, ARGs) 的耐药菌 (Antibiotic resistant bacteria, ARB)。ARGs在水产养殖环境中的持久性残留、迁移和传播,会埋下基因污染隐患,导致生态失衡并危害人类安全,如何遏制抗生素抗性的传播已引起全球重点关注。就水产养殖环境中ARGs的研究进展,系统总结了ARGs的污染现状及其在水产养殖环境中的来源、迁移传播和影响因素,并简述了ARGs与抗生素、微生物群落和环境因素之间的关联特性,以及抗生素、ARGs和ARB对生态环境与人类健康的影响。基于此,概述了ARGs的控制策略与去除技术,并提出了今后的研究方向,以期为水产养殖环境中ARGs污染机理的解析和抗生素抗性传播风险的控制提供科学参考。
    Abstract: Antibiotics play a significant role in the disease control of aquatic organisms and output increase of aquatic products. However, long-term abuse of antibiotics can result in the occurrence of antibiotic resistant bacteria (ARB) which harbor antibiotics resistance genes (ARGs) in aquatic organisms. The persistent existence, migration and spread of ARGs in aquaculture environment will potentially cause genetic pollution, destroy the ecological balance, and pose risks to human health. Therefore, how to constrain the spread of antibiotic resistance has attracted global attention. In terms of the research advancement of ARGs in aquaculture environment, this review systematically summarizes the status of ARGs pollution coupled with the source, migration and spread behavior of ARGs and their influencing factors, illustrates the correlations between ARGs and antibiotics, microbial communities and environmental factors, as well as discusses the effect of antibiotics, ARGs and ARB on ecological environment and human health. Thus, the paper reviews the management strategies and removal technologies of ARGs, and proposes the future research directions regarding ARGs, so as to provide references for revealing the pollution mechanism of ARGs and reducing the transmission risk of antibiotic resistance.
  • 近年来,在南太平洋海域进行商业捕捞作业的船队主要为中国大陆和中国台湾船队[1]。有研究表明环境变化对于长鳍金枪鱼 (Thunnus alalunga) 的分布和洄游有显著影响[2-3],由于长鳍金枪鱼的高度洄游性使商业捕捞作业船队对其渔场的寻找成为一项高成本生产活动。因此,了解长鳍金枪鱼的资源分布与环境因子的关系对提高该渔业的生产效率具有重要意义。南太平洋长鳍金枪鱼作为延绳钓渔船的主要捕捞对象,一般栖息在200~300 m水层[4]。而目前国内外学者多选择表层环境因子作为研究对象[5-7],较少采用不同水层的环境数据。广义可加模型 (Generalized additive model, GAM) 作为定量分析渔获率与环境要素之间关系的方法,目前已得到广泛应用。而这些研究在变量选择中多加入了时空因子[8-10],忽略了时空因子与环境因子之间和不同环境因子之间的多重共线性[11]。此外,由于缺少该鱼种完整的渔捞日志数据,研究多采用5°方格数据[7,10],但这种较低空间分辨率的环境数据往往会弱化数值分析的效果。

    本文根据中国大陆地区所有渔船2015—2017年在南太平洋的渔捞日志,整理出了空间分辨率为1°×1°的月渔获数据,用GAM模型逐步回归分析各环境因子与长鳍金枪鱼资源的关系,以探讨环境因子的变化对长鳍金枪鱼资源分布的影响,以期为南太平洋长鳍金枪鱼的合理开发利用和资源养护管理提供参考。

    来自中国大陆地区2015—2017年所有渔船渔捞日志,选取在南太平洋海域 (140°E—130°W,0°N—50°S,图1) 且捕捞对象为长鳍金枪鱼的渔捞日志数据 (具体数据总量见表1),包括日期、作业位置、投钩数、各鱼种渔获量尾数和千克数等。

    图  1  2015—2017年南太平洋长鳍金枪鱼单位捕捞努力渔获量的平均分布
    Fig. 1  Average distribution of CPUE of T. alalunga in South Pacific during 2015–2017
    表  1  南太平洋长鳍金枪鱼延绳钓渔业作业天数情况
    Table  1  Fishing days of longline fishery of T. alalunga in South Pacific
    年份
    Year
    总渔船数
    Total vessels
    累计作业天数
    Total fishing days
    2015 107 18 070
    2016 115 20 591
    2017 136 26 291
    下载: 导出CSV 
    | 显示表格

    包括海表温度 (Sea surface temperature,SST) 及不同深度水温、海表盐度 (Sea surface salinity,SSS) 及不同水层盐度、叶绿素a浓度 (Chlorophyll a concentration,Chla)、海表风场 (Sea surface wind,SSW)、海表面高度 (Sea surface height,SSH)、混合层深度 (Mixed layer depth,MLD) 的月数据。其中海表温度及不同水深温度、海表盐度及不同水层盐度、混合层深度来自中国Argo实时资料中心提供的《全球海洋Argo网格数据集 (BOA_Argo) 》,空间分辨率为1°。叶绿素a浓度来自美国国家海洋和大气管理局 (National Oceanic and Atmospheric Administration, NOAA) 的Ocean Watch网站,空间分辨率为0.05°。海表风场来自美国宇航局物理海洋学数据分发存档中心 (PO.DAAC) 提供的CCMP风速,空间分辨率为0.25°,海表面高度来自德国汉堡大学的综合气候数据中心提供的ORA-S4资料,空间分辨率为1°。

    1) 单位捕捞努力量渔获量 (Catch per unit effort,CPUE) 可以作为代表长鳍金枪鱼渔业资源状态的指标。其计算公式为:

    $$ {\rm{CPUE}} = \dfrac{{{U_{{\rm{catch}}}}}}{{{f_{{\rm{hooks}}}}}} $$ (1)

    式中Ucatch表示1°×1°单位渔区内的累计渔获量 (kg),fhooks是1°×1°单位渔区内的累计投钩数 (千钩),时间尺度为月。

    2) 用Matlab读取海表温度、叶绿素a浓度、海表风场数据,并与单位渔区进行数据匹配。受云层遮挡等因素影响,环境数据尤其是叶绿素a浓度数据存在缺失情况。本文采用计算周围变量均值的方法对缺失数据进行处理。

    利用Pearson相关系数计算环境因子之间的相关系数[12],其公式为:

    $$ r = \frac{{\displaystyle\sum\nolimits_{i = 1}^n {\left( {{x_i} - \bar x} \right)\left( {{y_i} - \bar y} \right)} }}{{\sqrt {\displaystyle\sum\nolimits_{i = 1}^n {{{\left( {{x_i} - \bar x} \right)}^2}{{\displaystyle\sum\nolimits_{i = 1}^n {\left( {{y_i} - \bar y} \right)} }^2}} } }} $$ (2)

    式中r表示相关系数,xi表示环境因子x的第i个观测值,$\bar x $表示环境因子的均值,yi表示环境因子y的第i个观测值,${\bar y}_i $表示环境因子y的均值。

    GAM模型能够较好地处理响应变量和一组解释变量之间高度非线性和非单调关系的能力,被广泛用于渔获率与环境关系研究[13-14]。其一般表达式为:

    $$ g\left( {\rm{\mu}} \right) = {\beta _0} + \sum\nolimits_{i = 1}^k {{f_i}} \left( {{x_i}} \right) + \varepsilon \left[ {{\rm{\mu}} = E\left( {Y/X} \right)} \right] $$ (3)

    式中g(μ) 表示联系函数,β0表示常数截距项,fi(xi) 表示用来描述g(μ) 与第i个解释变量关系的平滑函数。

    GAM模型和逐步回归计算在R-3.5.0中实现。

    本研究在不同水深环境数据中选择了60、120、180、240、300 m深度水温和盐度与其他表层环境因子进行了相关性分析。计算表明各层之间温度和盐度相关性较大 (由于篇幅所限,结果未列出),最后选取了相关性较小的三层 (表层、120 m300 m) 参与最后的要素分组中。通常认为如果相关系数大于0.75为强相关[15],介于0.5~0.75为中等程度相关,小于0.5为弱相关。SST和t120t300s300,SST与SSH,t120与SSH为高度相关因子;SST与s120,SST与MLD,SST与Uwndt120s120t120Uwnd均中度相关;SSS、Chla、Vwnd与其他环境因子之间的相关性均较小 (表2)。

    表  2  各环境变量相关系数矩阵
    Table  2  Correlation coefficients matrix of environmental factors
    变量
    Variable
    t120t300SSSs120s300MLDUwndVwndChlaSSH
    SST 0.916 0.005 −0.359 0.545 −0.091 −0.560 −0.541 −0.146 −0.316 0.753
    t120 −0.002 −0.239 0.582 −0.098 −0.307 −0.542 −0.137 −0.221 0.757
    t300 0.176 0.165 0.925 −0.152 −0.277 0.428 −0.068 0.440
    SSS 0.408 0.097 0.396 −0.056 −0.175 0.128 −0.432
    s120 −0.008 −0.124 −0.504 −0.242 −0.150 0.258
    s300 −0.117 −0.127 0.451 0.114 0.407
    MLD 0.255 0.004 0.326 −0.451
    Uwnd −0.308 0.178 −0.493
    Vwnd 0.137 0.212
    Chla −0.238
    注:SST. 海表温度;t120. 120 m水深温度;t300. 300 m水深温度;SSS. 海表盐度;s120.120 m水深盐度;s300. 300 m水深盐度;MLD. 混合层深度;Chla. 海表面叶绿素a浓度;Uwnd. 海表风场东西分量,即纬向风,以东为正;Vwnd. 海表风场南北分量,即经向风,以北为正;SSH. 海表面高度;下同 Note: SST. Sea surface temperature; t120. Sea temperature at depth of 120 m; t300. Sea temperature at depth of 300 m; SSS. Sea surface salinity; s120. Sea salinity at depth of 120 m; s300. Sea salinity at depth of 300 m; MLD. Mixed layer depth; Chla. Sea surface chlorophyll a concentration; Uwnd. Eastward Sea surface wind; Vwnd. Northward Sea surface wind; SSH. Sea surface height. The same case in the following table
    下载: 导出CSV 
    | 显示表格

    本研究使用GAM模型分析长鳍金枪鱼CPUE与各环境因子之间的关系,其中CPUE作为响应变量,SST、t120t300、SSS、s120s300、Chla、UwndVwnd、SSH、MLD作为解释变量,根据相关性分析结果,将环境因子分为4组分别建立GAM模型,表达式为:

    $$ \begin{array}{c} {\rm{log}}\left( {{\rm{CPUE}} + 1} \right)\sim s\left( {{\rm{SST}}} \right) + s\left( {{t_{300}}} \right) + s({\rm{SSS}}) + {\rm{ }}s\left( {{V_{{\rm{wnd}}}}} \right) + \\s\left( {{\rm{Chla}}} \right) + \varepsilon \\ {\rm{log}}\left( {{\rm{CPUE}} + 1} \right)\sim s({t_{120}}) + s\left( {{\rm{SSS}}} \right) + s\left( {{s_{300}}} \right) + s\left( {{\rm{MLD}}} \right) + \\s\left( {{V_{{\rm{wnd}}}}} \right) + s\left( {{\rm{Chla}}} \right) + \varepsilon \\ {\rm{log}}\left( {{\rm{CPUE}} + 1} \right)\sim s({t_{300}}) + s\left( {{\rm{SSS}}} \right) + s\left( {{s_{120}}} \right) + s({\rm{MLD}}) +\\ s\left( {{V_{{\rm{wnd}}}}} \right) + s\left( {{\rm{SSH}}} \right) + s\left( {{\rm{Chla}}} \right) + \varepsilon \\ {\rm{log}}\left( {{\rm{CPUE}} + 1} \right)\sim s\left( {{t_{300}}} \right) + s\left( {{\rm{SSS}}} \right) + s\left( {{\rm{MLD}}} \right) + s\left( {{U_{{\rm{wnd}}}}} \right) + \\s\left( {{V_{{\rm{wnd}}}}} \right) + s\left( {{\rm{SSH}}} \right) + s\left( {{\rm{Chla}}} \right) + \varepsilon \end{array} $$

    式中为了防止零值出现,采用CPUE加上1再进行对数化处理,s为自然立方样条平滑 (Natural cube spline smoother),ε为误差项。

    结果显示,各模型的总偏差解释率介于30%~40% (表3)。各环境因子的平均可解释偏差大小依次为t120 (19.7%)、SST (18.5%)、t300 (12.5%)、t120 (12.3%)、SSH (7.0%)、s300 (4.9%)、SSS (3.3%)、MLD (2.4%)、Vwnd (2.4%)、Uwnd (1.8%)、Chla (0.8%)。

    表  3  GAM模型拟合结果的偏差分析
    Table  3  Analysis of deviance for generalized additive models (GAM)
    分组
    Group
    累加影响因子
    Cumulative of influencing factors
    P决定系数
    R2
    累计解释偏差
    Cumulative of deviance explained
    可解释偏差
    Deviance explained
    AIC值
    AIC value
    第一组 Group 1 +SST <2×10−16*** 0.185 18.5% 18.5% 24 376.49
    +t300 <2×10−16*** 0.297 29.9% 11.4% 23 222.20
    +SSS <2×10−16*** 0.306 30.8% 0.9% 23 136.20
    +Vwnd <2×10−16*** 0.326 32.9% 2.1% 22 908.96
    +Chla <2×10−16*** 0.341 34.3% 1.4% 22 746.25
    第二组 Group 2 +t120 <2×10−16*** 0.196 19.7% 19.7% 24 265.62
    +SSS <2×10−16*** 0.215 21.6% 1.9% 24 087.74
    +s300 <2×10−16*** 0.263 26.5% 4.9% 23 599.79
    +MLD <2×10−16*** 0.282 28.5% 2.0% 23 410.19
    +Vwnd <2×10−16*** 0.295 29.8% 1.3% 23 276.47
    +Chla <2×10−16*** 0.307 31% 1.2% 23 147.81
    第三组 Group 3 +t300 <2×10−16*** 0.13 13.1% 13.1% 24 881.85
    +SSS <2×10−16*** 0.18 18.2% 5.1% 24 428.97
    +s120 <2×10−16*** 0.303 30.5% 12.3% 23 165.18
    +MLD <2×10−16*** 0.319 32.1% 1.6% 23 000.88
    +Vwnd <2×10−16*** 0.337 34% 1.9% 22 795.21
    +SSH <2×10−16*** 0.393 39.6% 5.6% 22 110.88
    +Chla 0.002 75** 0.394 39.8% 0.2% 22 100.98
    第四组 Group 4 +t300 <2×10−16*** 0.13 13.1% 13.1% 24 881.85
    +SSS 4.89×10-8*** 0.18 18.2% 5.1% 24 428.97
    +MLD <2×10−16*** 0.214 21.7% 3.5% 24 105.96
    +Uwnd 0.030 1* 0.231 23.4% 1.7% 23 942.02
    +Vwnd <2×10−16*** 0.267 27.1% 3.7% 23 574.18
    +SSH <2×10−16*** 0.352 35.5% 8.4% 22 626.40
    +Chla 2.51×10−15*** 0.356 36% 0.5% 22 571.78
    下载: 导出CSV 
    | 显示表格

    GAM模型分析发现,SST、t120t300s120是对CPUE影响较大的环境变量 (表3)。SST与CPUE介于15~30 ℃整体呈现负相关,其中介于25~28 ℃显示正相关 (图2-a)。t120与SST的趋势一致,呈负效应关系,其中介于18~23 ℃保持平稳状态 (图2-b)。t300在10 ℃之前置信区间较大且数据量较小故不予讨论,在10 ℃之后呈现出明显的正相关 (图2-c)。s120与CPUE以35.5为中心总体呈开口向上的抛物线,在36.4之后呈负相关 (图2-e)。

    图  2  不同环境因子对南太平洋延绳钓渔业长鳍金枪鱼单位捕捞努力渔获量的影响
    Fig. 2  Effects of different environmental factors on CPUE of longline fishery of T. alalunga in South Pacific

    对CPUE影响比较小的几个环境因子分别为SSS、s300、MLD、UwndVwnd、SSH、Chla。各模型得出的SSS对CPUE的影响略有差异,总体与CPUE保持平稳状态 (图2-d)。s300与CPUE以34.8为中心呈开口向上的抛物线 (图2-f),在35.0之后基本保持平稳。MLD与CPUE总体保持稳定 (图2-g),呈现轻微的正效应关系。Uwnd与CPUE基本保持平稳 (图2-h)。Vwnd与CPUE总体上呈现正效应关系 (图2-i),其中在−1~3 m·s−1保持平稳。Chla与CPUE整体上呈现轻微的负相关 (图2-j)。SSH与CPUE总体上呈负效应关系 (图2-k),在0.5 m之后这种负效应明显放缓。

    在参与2个及以上模型的环境因子中,MLD、Vwnd、Chla在较小的置信区间内总体趋势相似,与CPUE基本保持平稳状态,t300、SSH对CPUE的影响较大,但依然表现出了较高的拟合度。SSS在对CPUE的影响略有差异,介于35.2~35.0拟合较好。

    目前通过GAM模型分析环境因子与渔场的关系时通常会加入时空因子。然而时空因子的加入可能会影响环境因子与渔场关系的判断,如海表温度分布与纬度存在相关性,海表温度从低纬度向高纬度递减,纬度因素的加入会影响海表温度与渔场真实关系的推断。因此时空因子的加入对渔场与环境因子的关系分析可能会造成误判。

    除了时空因子与环境因子的相互影响外,环境因子之间也存在不可避免的自相关和多重共线性问题[16]。长鳍金枪鱼的渔场分布与各环境因子显著相关,是各个环境因子综合作用的结果,而环境因子间的高度相关性又会掩盖单个环境因子与渔场分布的真实关系。本研究发现,海表温度除了与混合层深度、海表风场、海表面高度等海表面环境因素有较大的相关性外,与各水层的温度、盐度也具有较大的相关性,因此在构建模型探讨海表温度与长鳍金枪鱼渔场的关系前,需要进行环境因子的相关性分析,通过排除相关系数较大的环境因子,降低这种多重共线性的负面影响[17]

    在建模过程中为避免同一模型内环境因子之间的多重共线性,同时又要确保放入尽量多的环境因子,本研究将环境因子分为4组分别建立GAM模型。由于每组模型选择的环境因子不同,得出的总解释偏差也有所差异。而在参与多个模型的环境因子中,t300、MLD、Vwnd、Chla、SSH的总体趋势相似 (图2),验证了这种分组建模方式分析环境因子与渔场分布具有较高的可信度。海表面的盐度略有差异,可能是由于其与120 m水深盐度、混合层深度、海表面高度均呈中等程度相关,不同的环境因子的选择影响了海表盐度与CPUE的关系。

    作为影响海洋鱼类活动最重要的环境因子之一,温度的变化对鱼类的分布、洄游和集群等会造成直接或者间接的影响[18],在进行长鳍金枪鱼时空分布与主要环境因子的关系研究时,一般都会选用SST作为环境因子[19-20]。海表温度对南太平洋长鳍金枪鱼的分布有显著影响,由GAM模型结果可以发现,长鳍金枪鱼的主要作业渔区出现在SST介于20~30 ℃的海域 (图2-a),这与樊伟等[21]的南太平洋长鳍金枪鱼产量高密度区呈双峰型,出现在20 ℃和29 ℃海域的研究结果一致。同时发现,SST与CPUE总体上呈现负相关,在25 ℃之前随温度的升高CPUE逐渐降低,在25 ℃之后略有回升。说明相对低温海域的作业频率较低但CPUE较高,可以探寻相对低温海域的长鳍金枪鱼渔区并进行开发。

    延绳钓渔船通常以长鳍金枪鱼成鱼为目标鱼种,主要在0~400 m深度设钩。水温垂直结构在长鳍金枪鱼渔场的形成过程中有直接影响[22]。研究结果显示,120~300 m水层,温度显著影响长鳍金枪鱼的产量,这可能是因为该水层是水温急剧下降的温跃层,温度和密度变化大,溶解氧含量高,饵料资源丰富。Hoyle等[23]发现,东南太平洋长鳍金枪鱼主要栖息于170~220 m水层,中西太平洋为150~300 m,与本文研究结果相似。Williams等[24]在汤加附近海域发现,长鳍金枪鱼高渔获率水深一般为200~300 m,与本文研究结果有一定差异,这可能是缘于研究海域不同。

    海表面盐度对于长鳍金枪鱼渔获量的影响较小,这与范永超等[25]、蒋汉凌[26]的研究结果一致。本研究还发现,s120对长鳍金枪鱼渔获量有显著影响。这说明海表面盐度影响较小可能是因为其主要通过影响溶解气体、海流等其他海表面环境因素从而对长鳍金枪鱼CPUE间接造成影响,而各水深盐度是影响长鳍金枪鱼CPUE的一个重要因素,在以后的研究中不可忽略。120~300 m温度和盐度显著影响长鳍金枪鱼CPUE的分布,这也验证了本研究长鳍金枪鱼主要栖息于120~300 m水层的结果。

    海面高度主要与水团、水系、海流、潮汐、中尺度涡等海洋动力信息有关。随着海面高度的增加,表层水团进行辐散或汇合[27],底层水上升进行补充,海水底层营养盐上升对表层营养盐进行补充,使海水表层初级生产力增加,浮游生物密度增加,长鳍金枪鱼资源量增加。本研究中,SSH与长鳍金枪鱼CPUE呈现负效应,可能是由于处于上升流的中心区域,营养盐浓度高,初级生产力高,浮游生物密度高,导致水体中氧气的大量消耗。

    混合层深度会对栖息于混合层的长鳍金枪鱼造成垂直分布的限制[28]。本研究中混合层深度与CPUE呈现出轻微的正效应。以往的研究中,可能由于存在与混合层深度有较大相关性的环境因素,导致混合层深度与CPUE的关系并不明显,这与本文结果略有不同,未来可作进一步探究。

    从食物链的角度看,叶绿素a浓度通常表征以浮游植物为食的浮游动物量,间接影响渔场分布[29]。以往的研究中,叶绿素a浓度通常是影响南太平洋长鳍金枪鱼CPUE的重要原因,而本研究中仅呈现出轻微的负相关,这可能是由于在叶绿素a浓度升高、浮游植物生物量增大、长鳍金枪鱼聚集增加的过程中存在时间延迟现象[30]

    国内外学者在长鳍金枪鱼CPUE与环境因子关系的研究中多使用海表面温度、海表面高度、叶绿素a浓度等,除此之外,海面风场也是影响鱼类活动的一个重要因子。

    本研究表明,Uwnd与CPUE总体保持稳定,Vwnd与CPUE呈现正效应,其中在−2~4 m·s−1保持平稳状态。海面风场对长鳍金枪鱼CPUE的影响显著,总体呈正相关关系,可能是由于较大的风速导致海水的湍流混合加大、水柱混合加深以及海水浑浊度增加[31-32],使海域营养盐增加、生产力提高,因此形成了良好的渔场,也有可能是这种混合造成海域适宜的水温对长鳍金枪鱼资源有促进作用。

    由于本文仅有3年 (2015—2017年)的渔捞日志数据,对环境关系的研究存在一些制约,未来将选择更长时间尺度的渔业数据并结合溶解气体、水系和海流、潮汐和潮流、气象因素等其他影响鱼类行为的环境因子作进一步研究。

  • 图  1   水产环境中ARGs的来源、迁移与传播

    Figure  1.   Source, migration and spread behavior of ARGs in aquaculture environment

    图  2   水产养殖环境中ARGs与抗生素、微生物群落和环境因素之间的关联特性

    Figure  2.   Correlations between ARGs and antibiotics, microbial communities and environmental factors in aquaculture environment

    表  1   现有技术对ARGs的去除效果

    Table  1   Reduction efficiency of ARGs by existing technologies

    去除技术
    Removal technology
    去除原理
    Removal principle
    去除效果
    Reduction efficiency
    参考文献
    Reference
    添加大孔吸附树脂
    Adding macroporous adsorption resin (MAR)
    MAR是一种多孔交联聚合物,能够降低ARGs和微生物群落的丰度,并且通过吸附重金属以降低其对ARGs的协同效应和选择压力。 ARGs (14.14%~99.44%)和MGEs (47.83%~99.48%)的丰度显著降低。 [101]
    UV/氯消毒
    UV/chlorine
    UV/氯协同作用可以有效灭活ARB、打破ARGs结构并抑制其水平转移。 UV (320 mJ·cm−2)/氯(2 mg·L−1)协同作用下,ARGs的去除率增强了1~1.5 log。 [102]
    臭氧后处理
    Ozone post-treatment
    臭氧具有高氧化电位 (2.07 V),可以有效去除ARGs和ARB。 胞内ARGs (iARGs)的去除率达到89%。 [103]
    高铁酸盐
    Ferrate
    高铁酸盐作为一种高价铁基氧化剂,其强氧化电位能够直接去除ARGs,且具备较强的杀菌效能,能够灭活携带ARGs的细菌,从而抑制其垂直转移。 高铁酸盐的剂量为10 mg-Fe·L−1时,ARGs的去除率达到1.10~4.37 log。 [104]
    生物过滤
    Biofiltration
    水体中的微生物会附着在过滤介质 (石英砂、颗粒活性炭和无烟煤等) 表面并形成生物膜。 ARGs平均丰度降低了0.97 log。 [105]
    污泥处理湿地
    Sludge treatment wetlands (STWs)
    STWs法是传统沙干化床和垂直流人工湿地的联合技术,剩余污泥进入湿地后会形成不同污泥层,而植物在其中生长,有利于稳定污泥、减少污泥体积并去除ARGs等污染物。 磺胺类ARGs的丰度降低了21%。 [106]
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  • 收稿日期:  2021-07-23
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