Predicting sustainable shark harvests when stock assessments are lacking

Author:

Bradshaw Corey J A1,Prowse Thomas A A2,Drew Michael3,Gillanders Bronwyn M4,Donnellan Steven C56,Huveneers Charlie7

Affiliation:

1. Global Ecology, College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA, Australia

2. School of Mathematical Sciences, University of Adelaide, Adelaide, SA, Australia

3. South Australian Research and Development Institute—Aquatic Sciences, West Beach, Adelaide, SA, Australia

4. Southern Seas Ecology Laboratories, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia

5. Centre for Evolutionary Biology and Biodiversity, University of Adelaide, Adelaide, SA, Australia

6. South Australian Museum, Adelaide, SA, Australia

7. Southern Shark Ecology Group, College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA, Australia

Abstract

Abstract Effective fisheries management generally requires reliable data describing the target species’ life-history characteristics, the size of its harvested populations, and overall catch estimates, to set sustainable quotas and management regulations. However, stock assessments are often not available for long-lived marine species such as sharks, making predictions of the long-term population impacts of variable catch rates difficult. Fortunately, stage- or age-structured population models can assist if sufficient information exists to estimate survival and fertility rates. Using data collected from the bronze whaler (Carcharhinus brachyurus) fishery in South Australia as a case study, we estimated survival probabilities from life tables of harvested individuals, as well as calculated natural mortalities based on allometric predictions. Fertility data (litter size, proportion mature) from previous studies allowed us to build a fertility vector. Deterministic matrices built using estimates of life-table data or natural mortality (i.e. harvested-augmented and natural mortality) produced instantaneous rates of change of 0.006 and 0.025, respectively. Assuming an incrementing total catch at multiples of current rates, stochastic simulations suggest the relative rate of population decline starts to become precipitous around 25% beyond current harvest rates. This is supported by a sharp increase in weighted mean age of the population around 25% increase on current catches. If the catch is assumed to be proportional (i.e. a constant proportion of the previous year’s population size), the relative r declines approximately linearly with incrementing harvest beyond the current rate. A global sensitivity analysis based on a Latin-hypercube sampling design of seven parameters revealed that variation in the survival estimates derived from the life tables was by far the dominant (boosted-regression tree relative influence score = 91.14%) determinant of model performance (measured as variation in the long-term average rate of population change r). While current harvest rates therefore appear to be sustainable, we recommend that fisheries-management authorities attempt to sample a broader size range of individuals (especially older animals) and pursue stock assessments. Our models provide a framework for assessing the relative susceptibility of long-lived fishes to harvest pressure when detailed stock data are missing.

Funder

Australian Research Council

Department of Primary Industries and Resources of South Australia

Western Australia Department of Fisheries

Nature Foundation of South Australia

South Australian Museum

Marine Fishers Association

Publisher

Oxford University Press (OUP)

Subject

Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

Reference59 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3