Constructing end-to-end models using ECOPATH data

被引:44
|
作者
Steele, John H. [1 ]
Ruzicka, James J. [2 ]
机构
[1] Woods Hole Oceanog Inst, Woods Hole, MA 02543 USA
[2] Oregon State Univ, Newport, OR 97365 USA
基金
美国国家科学基金会;
关键词
Marine ecosystems; End-to-end; California Current; Scenarios; BIOLOGICAL PRODUCTIVITY; ECOSYSTEM-MODEL; COASTAL; MANAGEMENT; STRATEGIES; RESOLUTION; REGIONS; OREGON; ECOSIM; SEAS;
D O I
10.1016/j.jmarsys.2011.04.005
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The wide availability of ECOPATH data sets provides a valuable resource for the comparative analysis of marine ecosystems. We show how to derive a bottom-up transform from the top-down ECOPATH: couple this to a simple microbial web with physical forcing; and use the end-to-end model (E2E) for scenario construction. This steady state format also provides a framework and initial conditions for different dynamic simulations. This model can be applied to shelf ecosystems with a wide range of physical forcing, coupled benthic/pelagic food webs, and nutrient recycling. We illustrate the general application and the specific problems by transforming an ECOPATH model for the Northern California Current (NCC). We adapt results on the upwelling regime to provide estimates of physical fluxes and use these to show the consequences of different upwelling rates combined with variable retention mechanism for plankton, for the productivity of fish and other top predators; and for the resilience of the ecosystem. Finally we show how the effects of inter-annual to decadal variations in upwelling on fishery yields can be studied using dynamic simulations with different prey-predator relations. The general conclusion is that the nature of the physical regimes for shelf ecosystems cannot be ignored in comparing end-to-end representations of these food webs. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:227 / 238
页数:12
相关论文
共 50 条
  • [11] An end-to-end vehicle classification pipeline using vibrometry data
    Smith, Ashley
    Mendoza-Schrock, Olga
    Kangas, Scott
    Dierking, Matthew
    Shaw, Arnab
    [J]. GROUND/AIR MULTISENSOR INTEROPERABILITY, INTEGRATION, AND NETWORKING FOR PERSISTENT ISR V, 2014, 9079
  • [12] Data Augmentation Using CycleGAN for End-to-End Children ASR
    Singh, Dipesh K.
    Amin, Preet P.
    Sailor, Hardik B.
    Patil, Hemant A.
    [J]. 29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021), 2021, : 511 - 515
  • [13] Using end-to-end data to infer sensor network topology
    Zhao, Tao
    Cai, Wangdong
    Li, Yongjun
    [J]. 2007 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, VOLS 1-3, 2007, : 99 - 103
  • [14] END-TO-END CHROMOSOME KARYOTYPING WITH DATA AUGMENTATION USING GAN
    Wu, Yirui
    Yue, Yisheng
    Tan, Xiao
    Wang, Wei
    Lu, Tong
    [J]. 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2018, : 2456 - 2460
  • [15] End-to-end data transport using SSCOP in ATM networks
    Lin, WK
    Chen, YC
    [J]. EIGHTH IEEE WORKSHOP ON FUTURE TRENDS OF DISTRIBUTED COMPUTING SYSTEMS, PROCEEDINGS, 2001, : 112 - 118
  • [16] Data Cyberinfrastructure for End-to-End Science
    Rodero, Ivan
    Parashar, Manish
    [J]. COMPUTING IN SCIENCE & ENGINEERING, 2020, 22 (05) : 60 - 70
  • [17] Using disk throughput data in predictions of end-to-end grid data transfers
    Vazhkudai, S
    Schopf, JM
    [J]. GRID COMPUTING - GRID 2002, 2002, 2536 : 291 - 304
  • [18] Parameter inference of queueing models for IT systems using end-to-end measurements
    Liu, Z
    Wynter, L
    Xia, CH
    Zhang, F
    [J]. PERFORMANCE EVALUATION, 2006, 63 (01) : 36 - 60
  • [19] Learning Driving Models From Parallel End-to-End Driving Data Set
    Chen, Long
    Wang, Qing
    Lu, Xiankai
    Cao, Dongpu
    Wang, Fei-Yue
    [J]. PROCEEDINGS OF THE IEEE, 2020, 108 (02) : 262 - 273
  • [20] Stock assessment and end-to-end ecosystem models alter dynamics of fisheries data
    Storch, Laura S.
    Glaser, Sarah M.
    Ye, Hao
    Rosenberg, Andrew A.
    [J]. PLOS ONE, 2017, 12 (02):