耦合动态能源预算和种群动态模型,为渔业管理中的种群增加提供信息

IF 5.6 1区 农林科学 Q1 FISHERIES Fish and Fisheries Pub Date : 2023-07-14 DOI:10.1111/faf.12776
Yiwen Liu, Chongliang Zhang, Xiujin Wei, Binduo Xu, Ying Xue, Yiping Ren, Yong Chen
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引用次数: 0

摘要

渔业种群增强在世界范围内的广泛应用引起了人们对其负面影响的广泛关注,这就迫切需要对增强战略进行基于科学的评估和规划。然而,对增强的种群动态,特别是密度依赖过程缺乏机械理解,导致模型开发中的妥协,并限制了预测增强效应的能力。在这里,我们基于动态能量预算理论和全生命史过程开发了一个基于个体的模型,以了解个体水平过程中出现的种群动力学中的密度依赖机制。我们通过将该模型框架应用于一个广泛增强的物种——中国对虾(中国对虾,对虾科),证明了该模型框架的实用性。该模型可以对反映观察到的种群生物量和产量轨迹的产量进行预测。该模型还描绘了密度依赖性对生命生长率、繁殖力和饥饿死亡率的关键影响。关于种群增加的多重影响,我们证明了种群生物量和产量随着增加的幅度而减弱,以及生态和经济目标之间的权衡,即追求高效益可能会在没有适当管理的情况下损害野生种群。此外,我们还说明了在保持渔业产量的同时,将种群增加和捕捞管制结合起来促进种群恢复的可能性。我们强调了所提出的模型在理解增强计划中的密度依赖性以及设计综合管理战略方面的潜力。本文开发的方法可以作为评估库存增强中的种群动态并为增强管理提供信息的通用方法。
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Coupling dynamic energy budget and population dynamic models to inform stock enhancement in fisheries management

Extensive applications of fishery stock enhancement worldwide bring up broad concerns about its negative effects, creating a pivotal need for science-based assessment and planning of enhancement strategies. However, the lack of mechanistic understanding of enhanced population dynamics, particularly the density-dependent processes, leads to compromise in model development and limits the capacity in predicting enhancement effects. Here, we developed an individual-based model based on dynamic energy budget theory and full life-history processes, to understand the mechanism of density dependence in population dynamics that emerge from individual-level processes. We demonstrated the utility of the model framework by applying it to an extensively enhanced species, Chinese prawn (Fenneropenaeus chinensis, Penaeidae). The model could yield projections reflecting the observed trajectory of population biomass and yields. The model also delineated the key effects of density dependence on the vital rates of growth, fecundity and starvation mortality. Regarding the manifold effects of stock enhancement, we demonstrated a dampened shape in population biomass and yields with increasing magnitude of enhancement, and trade-offs between the ecological and economic objectives, that is, pursuing high benefit might compromise the wild population without proper management. Furthermore, we illustrated the possibility of combining stock enhancement and harvest regulation in promoting population recovery while maintaining fisheries yields. We highlight the potential of the proposed model for understanding density dependence in enhancement programme, and for designing integrated management strategies. The approach developed herein may serve as a general approach to assess the population dynamics in stock enhancement and inform enhancement management.

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来源期刊
Fish and Fisheries
Fish and Fisheries 农林科学-渔业
CiteScore
12.80
自引率
6.00%
发文量
83
期刊介绍: Fish and Fisheries adopts a broad, interdisciplinary approach to the subject of fish biology and fisheries. It draws contributions in the form of major synoptic papers and syntheses or meta-analyses that lay out new approaches, re-examine existing findings, methods or theory, and discuss papers and commentaries from diverse areas. Focal areas include fish palaeontology, molecular biology and ecology, genetics, biochemistry, physiology, ecology, behaviour, evolutionary studies, conservation, assessment, population dynamics, mathematical modelling, ecosystem analysis and the social, economic and policy aspects of fisheries where they are grounded in a scientific approach. A paper in Fish and Fisheries must draw upon all key elements of the existing literature on a topic, normally have a broad geographic and/or taxonomic scope, and provide general points which make it compelling to a wide range of readers whatever their geographical location. So, in short, we aim to publish articles that make syntheses of old or synoptic, long-term or spatially widespread data, introduce or consolidate fresh concepts or theory, or, in the Ghoti section, briefly justify preliminary, new synoptic ideas. Please note that authors of submissions not meeting this mandate will be directed to the appropriate primary literature.
期刊最新文献
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