Research Project: 改善前瞻研究領航計畫【非線性分析探討在氣候變遷下如何改善全球漁業資源評估與管理】
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摘要：近來學界逐漸了解傳統單群資源評估(single species stock assessment)的不足後，為使管理更加完備，將生態系知識納入管理考量。然而，從概念轉化為實際管理策略，仍有一段差距。例如：我們到底需要測量哪些生態系參數？本研究提出一套新的概念。
本研究利用現存之科學文獻建立一數據資料庫，將資料與環境因子連結後，使用非線性分析方法，估算系統複雜度（需要幾個生態系參數）、檢驗系統的非線性程度，以及系統的可預測性。最後再以多變數嵌入 (multivariate embedding) 研究影響物種族群動態的機制（找出重要生態系參數），並預測族群動態。本研究方法更能用來探討未來環境及漁獲努力量的改變將如何影響魚群變動。這些資訊將可作為決策者制定管理方針之科學根據。<br> Abstract: Incorporation of ecosystem knowledge into the management of ocean resources comes from a growing recognition that management based on classical single-stock assessments is insufficient. However, how to operationally implement ecosystem-based management remain elusive. Our objectives are 1) to develop new methods to provide fishery science with fundamental predictive understanding of ecosystem dynamics and, 2) to provide tools to identify objectively “which main variables to measure” in order to achieve a reasonable forecasting skill and thus achieve ecosystem-based management.
First, we develop a global database by compiling data, including catch, CPUE, spawning biomass, recruitment, age (size) structure, and fishing mortality from existing stock assessment reports and literatures. Secondly, we link these data to environmental variables. Thirdly, using nonlinear methods, we determine dimensionality (system complexity), nonlinearity (vulnerability to regime shift driven by climate change), and predictivity (uncertainty) of each time series. Finally, we use multivariate embedding to obtain mechanistic understandings and forecasting dynamics of exploited fishes. Such information allows us to inversely construct a dynamics model based on data and investigate the system behavior. In addition, our prescriptive approach allows one to investigate potential future effects of fishing efforts and climate changes. This predictive and fundamental organizational information combined with subsequent scenario exploration models will serve as a basis for scientific advice for policymakers.
Goals:
Identify objectively key variables (including biological and environmental variables) that determine the dynamics of target species and provide mechanistic prediction for stock fluctuation that is critical in management.
Activities:
1. Construct a global fisheries database, including catch, CPUE, spawning biomass, recruitment, age (size) structure, and fishing mortality from existing stock assessment reports and literatures, and link these data to environmental variables.
2. Investigate the dimensionality (system complexity) and nonlinearity (vulnerability to regime shift) of the target species.
3. Use multivariate embedding to identify objectively key variables that determine the dynamics of target species.
4. Provide prescriptive short-term forecasts as a function of fishing effort and environmental changes.
Outcomes:
1. We provide a protocol to identify objectively “which main variables to measure” in order to achieve a reasonable forecasting skill, and thus achieve ecosystem-based management.
2. Our prescriptive approach allows one to investigate the potential future effects of fishing efforts and climate changes.