دانلود مقاله ISI انگلیسی شماره 161822
ترجمه فارسی عنوان مقاله

عملکرد موقت و بلند مدت روش های استاتیک و تطبیقی

عنوان انگلیسی
Interim and long-term performance of static and adaptive management procedures
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
161822 2017 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Fisheries Research, Volume 190, June 2017, Pages 84-94

ترجمه کلمات کلیدی
ارزیابی استراتژی مدیریت، استراتژی برداشت، شبیه سازی، مدیریت شیلات، اطلاعات فقیر، محدودیت داده
کلمات کلیدی انگلیسی
Management strategy evaluation; Harvest strategy; Simulation; Fisheries management; Data-poor; Data-limited;
پیش نمایش مقاله
پیش نمایش مقاله  عملکرد موقت و بلند مدت روش های استاتیک و تطبیقی

چکیده انگلیسی

Static and adaptive management procedures (MPs) based on contemporary data-poor approaches were tested by management strategy evaluation to reveal short- and long-term performance trade-offs. Short-term (10 year) and long-term (60 year) performance was evaluated for MPs such as Depletion-Corrected Average Catch, Depletion-Based Stock Reduction Analysis, and catch-MSY, and also for historical catch scalars. Static MPs established a catch limit at the outset of a projection time period and were not modified thereafter, which reflected their use as interim measures while data collection is improved. Conversely, adaptive MPs recursively adjusted catch limits, which reflected their longer-term use where data-poor circumstances are unlikely to improve. On average, adaptive MPs provided comparable performance over the short term, but had superior performance over the long-term and when stocks were considered moderately to heavily depleted. Our results highlight depletion as a critical quantity in establishing sustainable catches from historical catch observations. We reconfirm that depletion-based MPs are improvements over catch scalar methods that are widely used in US data-poor circumstances and we examine the value in obtaining updated depletion estimates to improve catch sustainability in data-poor circumstances.