Progress in study and application of fish bioenergetics models
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摘要:
鱼类生物能量学在渔业资源管理和可持续利用方面至关重要。文章综述了近年来该领域的研究进展，重点关注生物能量学模型的发展，探讨了水温和水流等环境因子对鱼类能量收支的影响。在生产实践中，鱼类生物能量学模型可用于预测自然鱼类资源的动态变化以及养殖鱼类污染物的排放等，为渔业管理提供科学依据，但在应对复杂的环境变化时，模型的适应性和精确性仍有待提升。此外，现有模型常忽视了影响因子之间的交互作用。未来的研究应专注于多学科数据融合、先进技术的应用以及个体化模型的创新，从而促进鱼类生物能量学模型的不断完善，朝着精确化、实用化和可持续管理的方向发展。
Abstract:Fish bioenergetics plays an important role in the management and sustainable utilization of fisheries resources. This paper provides an overview of recent advance in this field, focusing on the development of bioenergetics models. It also delves into the intricate interplay of environmental factors such as water temperature and flow on fish energy budgets. In practical scenarios, fish bioenergetics models are used to predict the dynamics of natural fish resources and the emission of pollutants from aquaculture, providing scientific support for fisheries management. However, the adaptability and accuracy of these models in dealing with complex environmental changes still need to be improved. Additionally, existed models often overlook the complexity of interactions among influencing factors. Future research should focus on multidisciplinary data fusion, application of advanced technologies, and innovation of individualised models, so as to facilitate the continuous improvement of fish bioenergetics models and promote their development towards precision, practicality and sustainable management.
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Keywords:
- Fish bioenergetics /
- Bioenergetics modeling /
- Energy balance /
- Key factors
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准确高效评估鱼类资源量是渔业保护和科学增殖的基础工作之一[1]。渔业资源调查一般可分为管理性调查和开发性调查两类。前者是针对已开发的渔场，旨在合理利用资源以取得最大持续产量；后者是针对未开发的水域，旨在探明新的捕捞对象和相应的开发手段。目前渔业资源调查的主要对象是前者[2-8]。体长结构的世代分析也可称为体长股分析(Length based Cohort Analysis，LCA)，是鱼类资源量评估高效易行的方法之一[9-12]。该方法是JONES[9-11]提出来的，所以又可称为JONES的体长世代分析。JONES的LCA是在POPE的年龄结构世代分析模型基础上的发展，即考察资源数量变动时，以体长来代替年龄，从而有效地利用渔获体长测定的数据资料[13-15]。
目前，国内已有许多科研人员在淡水水体中采用了体长股分析法[3, 6-8]。传统的体长股通过人工计算效率相对较低，且不能保证精度。吴金明等[3]首次在国内淡水水体中使用了专业渔业资源评估软件FiSAT II，其主要运用了实际种群分析(Virtual Population Analysis，VPA) 模块，估算了赤水河3种鱼的资源量。但迄今为止，国内尚无关于体长股法与FiSAT II关系的专门报道。笔者结合工作实践，对FiSAT II下的体长股分析方法进行了探讨，以期为体长股法在河流鱼类资源评估中的高效广泛应用提供基础资料。
1. 体长股分析法分组组距的选取原则
中华人民共和国国家标准《海洋调查规范——海洋生物调查》规定“鱼类长度分组的组距一般为10 mm，如10~20 mm、…100~110 mm。中值为15 mm、… 105 mm。对鱼体长度过长或过短的种类也可将组距定为20 mm或5 mm”[16]。陈国宝等[17]通过比较研究指出，应当结合体长全距、体长标准差和样品数量等影响因素来共同确定某种鱼类的体长分组组距，从而提高估算结果的可信度。
2. FiSAT II下的体长股分析法的主要优势
2.1 生长方程中相关生长参数的推算
体长股分析法要求推算Von Bertalanffy生长方程中的渐近体长(L∞)和生长系数(k)。FiSAT II软件提供了3种方式推算Von Bertalanffy生长方程，包括ELEFANⅠ法、Shepherd氏法和Powell-Wetherall图解法[18]。这些方法中有多种不同的选择，以尝试错误的方式对生长方程式中的参数进行求解[19]。但是在实际运用体长股法估算鱼类资源量时，人们一般习惯引用前人已有研究成果，当与前人的结果有差异时通常取其平均值[3, 8]。这样在满足研究要求的同时，又提高了工作效率。
2.2 死亡系数的估算
死亡系数可分为总死亡系数(Z)、捕捞死亡系数(F)及自然死亡系数(M)，三者之间的关系为$Z=F+M$[20-21]。在推算得到生长方程中的L∞和k的基础上，FiSAT II软件提供了4种方式，以连续样本的体长频度分布为基本数据，估算总死亡系数，分别为体长变换渔获曲线法、JONSE和Van ZALINGE图解法、平均体长法、HOENING模型法[18]。其中常用的是以体长变换渔获曲线的方法通过回归来实现相应的估算[21]。
由于补充、捕捞死亡率、生长等参数的影响，鱼类的M值成为最难估算的参数之一[22]。FiSAT II软件采用以下2个公式估算M值。RIKHTER和EFANOV(1976)认为M值和种群成熟度达50%时的年龄t(mass)之间有很紧密的关系，经研究得出其关系为$M=1.52 /\left[t(\text { mass })^{0.72}\right]-0.16$，此即RIKHTER和EFANOV公式法[18]。另外软件中还提供了Pauly的经验公式法，即$\lg M=-0.006\;6-0.279 \lg L_{\infty}+0.6543 \lg k+0.4634 \lg T$，公式中T为鱼类生活水体年平均水温[23]。
在体长股研究中，除了F、Z和M之外，通常还使用开发率(E)的概念，所谓开发率是指捕捞死亡占总死亡的比例：$E=F / Z$。传统的体长股法，各体长组的F值是根据最大体长组的开发率估算的(最大体长组E一般取0.5)，即首先根据最大体长组资源量N=渔获尾数C/E，求出最大体长组N，再推算各体长组N，最后依据E = CL/(NL+NL+△L)，先求出各体长组E，再结合E、M和F三者关系求出F[21]。这里常见的错误是依据N= C/E反推各体长组E，进而求取各体长组的F。传统方法求取F时直接使用最大体长组E的估算值，不存在迭代过程，其准确性依赖于E的估算[20]。现在一般是通过FiSAT II软件求取捕捞死亡系数，具体操作如下：1)分别将特定对象按照体长分组录入VPA模型中，并输入L∞、k和M，最大体长组的F初始值一般取0.5，如果需要估算生物量，还需要输入体长与体质量关系式中的2个参数，即条件系数a和指数系数b。2)通过FISATⅡ软件运行，得出各体长组的捕捞死亡系数Fi和各体长组的资源量Ni，计算总体的捕捞死亡系数F[公式为∑(Fi×Ni)/∑Ni], 再以总体的捕捞死亡系数F作为最大体长组的捕捞死亡系数，放入软件中运算，如此反复3~4次，计算出的总体捕捞死亡系数F与前一次相差甚微，表明迭代运算可以结束[3]。
3. 结果与讨论
3.1 传统与FiSAT II下的体长股法
施秀帖[24]根据南海北部连续4年各围网渔汛的渔获统计资料，在国内首次利用LCA法分别对各渔汛主要鱼种的资源量进行了估算。现以其中的粤东春汛鲐鱼的基本数据为基础，即L∞(mm)=380，k=0.30，M =0.49，E=0.5。分别采用传统LCA在计算机辅助下重新进行了计算，同时运行FiSAT II，估算其N和F，分别记为传统法下NT和FT以及FiSAT II下NF 和FF，结果见表 1。体长股法估算F和VPA估算的F值存在较大差异，理论上后者利用了反复迭代，使得其精度得到提高。同时，对传统与FiSAT II下各体长组N的结果进行比较，发现NF/ NT比值总体趋势表现为随体长组增大，其比值也随之增大，但中间体长组两者差异更小。一般认为体长股法的优点在于随着体长组的逆推，其与真实值的差异会逐渐缩小[21]，而通过上述比较，表明中间体长组N的稳定性更好，小体长组和大体长组N波动较大。因此，在运用FiSAT II软件进行体长股的相关研究时应当采取审慎的态度，严格保证其数据要求，FiSAT II中的体长及尾数等数据应当是研究对象的完整样本，不应把全年个别时间段的数据随意合并。样本中的鱼体最大体长与L∞不能差距太大。据POPE的研究结论，在一个时间段内，M值大于0.3、F值大于1.2时使用传统股分析法估算的资源量结果与VPA会产生较大的差异。在国内外，尤其是日本在资源评估的实践中，VPA以及各种基于传统VPA的改进型已逐步成为资源评估的主流。
表 1 传统与FiSATⅡ下的体长股法结果比较Table 1. Comparison of traditional LCA results with FiSAT II software supported LCA data体长组[24] body length group CL/105尾 NT/105尾 FT NF/105尾 FF NF/NT 1 91~100 0.063 849.52 0.001 1 074.56 0.001 1.26 2 101~110 0.803 806.68 0.009 1 014.54 0.007 1.26 3 111~120 4.034 763.83 0.048 955.14 0.035 1.25 4 121~130 9.08 718.61 0.111 894.03 0.081 1.24 5 131~140 5.37 669.45 0.068 829.66 0.049 1.24 6 141~150 5.72 625.17 0.074 770.84 0.054 1.23 7 151~160 5.51 581.64 0.074 713.45 0.054 1.23 8 161~170 5.66 539.44 0.078 658.07 0.058 1.22 9 171~180 3.59 498.23 0.051 604.36 0.038 1.21 10 181~190 2.79 460.21 0.041 554.51 0.031 1.20 11 191~200 1.25 424.01 0.019 507.16 0.014 1.20 12 201~210 1.48 390.33 0.023 462.99 0.018 1.19 13 211~220 5.13 357.37 0.083 420.20 0.064 1.18 14 221~230 7.71 321.91 0.131 375.59 0.101 1.17 15 231~240 16.88 285.33 0.309 330.59 0.240 1.16 16 241~250 29.80 241.68 0.623 279.31 0.484 1.16 17 251~260 28.92 188.44 0.733 219.36 0.565 1.16 18 261~270 22.09 140.18 0.697 165.36 0.530 1.18 19 271~280 12.84 102.57 0.500 122.85 0.375 1.20 20 281~290 10.50 77.13 0.498 93.22 0.370 1.21 21 291~300 8.89 56.30 0.528 68.81 0.387 1.22 22 301~310 7.27 39.15 0.562 48.66 0.405 1.24 23 311~320 7.14 25.55 0.787 32.59 0.547 1.28 24 321~330 4.84 13.96 0.880 19.05 0.562 1.36 25 331~340 2.20 6.43 0.730 9.99 0.402 1.55 26 341~350 0.80 2.75 0.487 5.11 0.223 1.86 27 351~360 0.44 1.15 0.531 2.55 0.193 2.22 28 361~370 0.15 0.30 0.490 0.99 0.087 3.30 ∑ 210.95 9 187.34 11 233.55 3.2 自我控制体长组缺失试验
通过自我控制体长组缺失实验，即分别选择缺失体长组1、7、14、21和28，对其N进行了估算，结果见表 2。通过比较发现，在运用体长股法估算鱼类资源量时，最小体长组缺失引起最大偏离，达到10.24%，表明运用体长股法估算鱼类资源量时，小体长组的数据准确性和完整性尤为重要，这与体长股法采取从最大体长组逐级逆推，不断接近真实值是相吻合的[21]。但是笔者注意到，中间体长组缺失，即N14 和N21缺失，分别产生了2.04%和6.79%的偏离，高于最大体长组的缺失时产生的偏离(1.69%)，与上述研究结果，即中间体长组N的稳定性更好存在一定的相似性，其内在规律有待深入研究。此外，笔者还发现，体长组缺失产生偏离的大小与其CL大小没有明显关系，C1、C7和C14分别为0.063、5.51和7.71，但其缺失产生的偏离分别为10.24、0.18和2.04，显示出体长组缺失带来的资源量估算偏离大小与体长组别可能存在一定关系。
表 2 体长组缺失时N估算结果比较Table 2. Resources estimation results of different groups in the loss of body length体长组[24] body length group N1/105尾 N7/105尾 N14/105尾 N21/105尾 N28/105尾 N/105尾 1 91~100 - 1 068.72 1 050.70 1 005.77 1 091.17 1 074.56 2 101~110 1 007.69 1 009.02 992.01 949.59 1 030.22 1 014.54 3 111~120 948.69 949.95 933.92 893.95 969.92 955.14 4 121~130 887.96 889.15 874.08 836.50 907.92 894.03 5 131~140 823.97 825.08 810.95 775.70 842.69 829.66 6 141~150 765.52 766.56 753.34 720.38 783.03 770.84 7 151~160 708.49 709.46 697.13 666.38 724.82 713.45 8 161~170 653.45 659.66 642.89 614.30 668.64 658.07 9 171~180 600.09 605.84 590.30 563.80 614.16 604.36 10 181~190 550.56 555.88 541.53 517.07 563.55 554.51 11 191~200 503.53 508.42 495.23 472.74 515.48 507.16 12 201~210 459.67 464.14 452.06 431.48 470.60 462.99 13 211~220 417.17 421.25 410.25 391.50 427.13 420.20 14 221~230 372.85 376.54 366.58 349.61 381.86 375.59 15 231~240 328.12 331.44 329.79 307.22 336.23 330.59 16 241~250 277.11 280.07 278.60 258.43 284.35 279.31 17 251~260 217.41 220.04 218.73 200.88 223.83 219.36 18 261~270 163.65 165.95 164.81 149.15 169.27 165.36 19 271~280 121.37 123.36 122.37 108.80 126.24 122.85 20 281~290 91.95 93.66 92.81 81.21 96.12 93.22 21 291~300 67.74 69.18 68.46 58.71 71.25 68.81 22 301~310 47.79 48.97 48.38 48.38 50.67 48.66 23 311~320 31.89 32.83 32.36 32.36 34.20 32.59 24 321~330 18.51 19.24 18.88 18.88 20.30 19.05 25 331~340 9.59 10.13 9.86 9.86 10.91 9.99 26 341~350 4.83 5.21 5.02 5.02 5.75 5.11 27 351~360 2.38 2.61 2.50 2.50 2.95 2.55 28 361~370 0.91 1.03 0.97 0.97 - 0.99 ∑ 10 082.89 11 213.38 11 004.51 10 471.13 11 423.25 11 233.55 偏离/% deviation 10.24 0.18 2.04 6.79 1.69 - 值得特别注意的是，VPA是一种很好的推算F和同一年级群进入补充时数量的方法，但人们往往以为可以直接用来推算多年级群群量。对于鱼群有世代重迭时，必须用年龄结构区分为独立年级群，进行个别推算，或者使用所谓年级群的分析工具，但类似工具还很不成熟。而且这样推算出来的仅是该年鱼群的生物量而不是平衡鱼群量持续产量的长期估值[18]。
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图 1 鱼类能量流概念图
注：参照崔奕波[4]。摄食能为从食物获取的能量；粪能为粪便中损失的能量；消化能为饲料可消化养分所含的能量；排泄能为氮排泄物中损失的能量；代谢能为建设自身或维持生命活动的能量；净能为代谢能减去体增热 (热增耗) 能后的能量；呼吸能为呼吸耗能；生长能为鱼体贮存能量；特殊动力作用为与食物消化有关的摄食热增能；标准代谢能为在饥饿、静止状态下的能量消耗；运动代谢能为与游泳等活动有关的能量消耗；Apelin为下丘脑释放调控鱼类食欲的激素。
Figure 1. Concept map of fish energy flow
Note: Referenced to Cui[4]. Consumption energy (C) is the energy obtained from food; fecal energy (F) is the energy lost through feces; digestible energy (DE) is the energy contained in the digestible nutrients of the feed; urine energy (U) is the energy lost through nitrogenous waste; metabolic energy (ME) is the energy used for growth and maintaining vital activities; net energy (NE) is the energy remaining after subtracting the heat increment of feeding (Heat increment) from the metabolic energy; respiratory energy (R) is the energy consumed for respiration; growth energy (G) is the energy stored in the fish body; specific dynamic action (SDA) is the heat increment of feeding related to the digestion of food; standard metabolism energy (Rs) is the energy consumption in a state of hunger and rest; activity metabolism energy (Ra) is the energy consumption related to activities such as swimming; Apelin is a hormone released by the hypothalamus that regulates the appetite of fish.
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