Coastal SEES (Track 1): Novel Approaches to Understanding Human Use Patterns and Mobility for Coastal Natural Resources Management

沿海 SEES（轨道 1）：了解人类利用模式和沿海自然资源管理流动性的新方法

• 批准号: 1325452
• 负责人: Steven Murawski
• 金额: $ 55.06万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1325452&HistoricalAwards=false
• 关键词: Coastal; SEES; Track; Novel; Approaches

This project will address a critical gap in fisheries science and management by developing better models of fisher location choice in response to management measures such as closed areas and individual fishing quotas. Coastal and marine spatial planning and management is increasingly viewed as the basis with which to allocate access to resources and reduce negative interactions among sectors that are not compatible with long term ecosystem sustainability. Yet, ecological-behavioral models that might assist managers in addressing these needs are rudimentary. This project will evaluate the utility of new classes of behavioral models and entropy statistics, originally developed from terrestrial studies of human activity patterns enabled by advanced technologies for tracking human movements. Assessing traditional methods of predicting human use patterns, introducing and testing new methods, and empirically testing for changes in use patterns due to regulations and ecological conditions are critical for formulating new predictive modeling tools supporting coastal sustainability. While this project focuses on fisheries, the methods will have broader applicability for coastal sustainability, e.g. shipping, coastal wind power, military preparedness, oil production, and other sectors. The project will build upon collaborative networks and partnerships among scientists, academia, government agencies, and others involved in coastal resource management.A unique dataset that includes millions of observations of individual choices of where, when, and what to fish for under a number of regulatory regimes, will be used. Past approaches of modeling and predicting human use patterns (random utility and logit modeling) have, for the most part, utilized data recorded at coarse spatial resolutions (e.g., National Marine Fisheries Service statistical areas) and timespans. In addition to assessing the implications of underlying key assumptions of the past approaches (e.g., spatial aggregation), new methods (entropy models) will be tested for modeling and predicting human-use patterns that have been developed in other research fields that also have high frequency location data (e.g., cellphone usage). Vessel monitoring system (VMS) data that are updated every 60 minutes by satellite provide high fidelity observations of the actual locations of fishing across years. Location choices are determined by ecological conditions (e.g., fish aggregations) and by the incentives created by the regulatory institutions. For example, fishermen engaging in a race to catch fish will likely behave differently than those that have more secure rights to the fish at the beginning of the fishing season as is the case in catch share fisheries. Due to the long time series of VMS data and the changing regulatory institutions in the management of the Gulf of Mexico fisheries during the period of analysis, observational research will estimate how spatial decision making has changed in response to changing regulations.This project is supported under NSF's Coastal SEES (Science, Engineering and Education for Sustainability) program.

该项目将根据封闭区域和个人捕捞配额等管理措施，制定更好的渔民地点选择模式，解决渔业科学和管理方面的一个重大差距。沿海和海洋空间规划和管理日益被视为分配资源和减少与长期生态系统可持续性不相容的部门之间消极相互作用的基础。然而，可能帮助管理者解决这些需求的生态行为模型还处于初级阶段。该项目将评估新型行为模型和熵统计的效用，这些模型和熵统计最初是通过跟踪人类运动的先进技术，从对人类活动模式的陆地研究中发展出来的。评估预测人类使用模式的传统方法，引入和测试新方法，以及对法规和生态条件导致的使用模式变化进行经验测试，对于制定支持沿海可持续性的新预测建模工具至关重要。虽然本项目侧重于渔业，但这些方法将更广泛地适用于沿海的可持续性，例如航运、沿海风力发电、军事准备、石油生产和其他部门。该项目将建立在科学家、学术界、政府机构和其他参与沿海资源管理的人员之间的合作网络和伙伴关系的基础上。将使用一个独特的数据集，其中包括在许多监管制度下对个人选择的地点、时间和捕捞内容的数百万次观察。过去模拟和预测人类使用模式的方法（随机效用和logit模型）在很大程度上利用了以粗空间分辨率（例如，国家海洋渔业局统计区域）和时间跨度记录的数据。除了评估过去方法（例如，空间聚集）的潜在关键假设的影响外，还将测试新方法（熵模型），以模拟和预测在其他研究领域开发的人类使用模式，这些研究领域也具有高频位置数据（例如，手机使用）。船只监测系统（VMS）数据每60分钟由卫星更新一次，提供多年来实际捕鱼地点的高保真观测。地点的选择取决于生态条件（例如鱼群聚集）和管理机构创造的激励措施。例如，参与捕鱼竞赛的渔民的行为可能与那些在捕鱼季节开始时对鱼类拥有更安全权利的渔民的行为不同，就像捕捞份额渔业的情况一样。由于VMS数据的时间序列较长，并且在分析期间墨西哥湾渔业管理的监管机构不断变化，因此观测研究将估计空间决策如何随着法规的变化而变化。该项目得到了美国国家科学基金会沿海可持续发展科学、工程和教育项目的支持。

项目成果

Resilience of a commercial fishing fleet following emergency closures in the Gulf of Mexico

墨西哥湾紧急关闭后商业捕鱼船队的恢复能力

• DOI: 10.1016/j.fishres.2019.04.017
• 发表时间: 2019
• 期刊: Fisheries Research
• 影响因子: 2.4
• 作者: Cockrell, Marcy L.;O’Farrell, Shay;Sanchirico, James;Murawski, Steven A.;Perruso, Larry;Strelcheck, Andrew
• 通讯作者: Strelcheck, Andrew

Scale-dependency in discrete choice models: A fishery application

离散选择模型中的尺度依赖性：渔业应用

• DOI: 10.1016/j.jeem.2020.102388
• 发表时间: 2021
• 期刊: Journal of Environmental Economics and Management
• 影响因子: 4.6
• 作者: Dépalle, Maxime;Sanchirico, James N.;Thébaud, Olivier;O’Farrell, Shay;Haynie, Alan C.;Perruso, Larry
• 通讯作者: Perruso, Larry