Complex dynamics may limit prediction in marine fisheries
Complex dynamics may limit prediction in marine fisheries
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DOI：
10.1111/faf.12037
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发表时间：
2014-12-01
影响因子：
6.7
通讯作者：
Sugihara, George
中科院分区：
文献类型：
--
作者：
Glaser, Sarah M.;Fogarty, Michael J.;Sugihara, George
海洋渔业中复杂的非线性动力学给预测和管理带来了挑战，但它们在渔业中发生的程度尚不清楚。使用非线性预测模型，我们分析了200多个时间序列的调查丰度和降落从两个不同的生态系统的动态复杂性（维数和非线性动力学）和可预测性的模式。系统维度和非线性动力学的差异与反映人类通过捕捞活动进行干预的时间序列有关，这意味着人类和自然系统之间的耦合产生的动力学与仅在自然资源子系统中检测到的动力学不同。上岸量的估计维数最高，未捕捞鱼种的丰度指数高于捕捞鱼种。已捕捞鱼种比未捕捞鱼种更有可能表现出非线性动态，上岸量比丰度指数更难预测。结果对生活史特征的变化具有鲁棒性。在70%的时间序列中，动态在1年的时间范围内是可预测的，但可预测性在5年的时间范围内呈指数下降。因此，在渔业系统中进行预测的能力极为有限。据我们所知，这是第一次跨系统比较研究，也是第一次在单个物种的规模上，对渔业数据中观察到的动态复杂性进行经验分析，并广泛量化可预测性。我们概述了一个应用程序的短期预测，以预防性的渔业管理方法，可以提高不确定性和预测误差纳入评估，通过渔获量限制缓冲。
Complex nonlinear dynamics in marine fisheries create challenges for prediction and management, yet the extent to which they occur in fisheries is not well known. Using nonlinear forecasting models, we analysed over 200 time series of survey abundance and landings from two distinct ecosystems for patterns of dynamic complexity (dimensionality and nonlinear dynamics) and predictability. Differences in system dimensionality and nonlinear dynamics were associated with time series that reflected human intervention via fishing effort, implying the coupling between human and natural systems generated dynamics distinct from those detected in the natural resource subsystem alone. Estimated dimensionality was highest for landings and higher in abundance indices of unfished species than fished species. Fished species were more likely to display nonlinear dynamics than unfished species, and landings were significantly less predictable than abundance indices. Results were robust to variation in life history characteristics. Dynamics were predictable over a 1-year time horizon in seventy percent of time series, but predictability declined exponentially over a 5-year horizon. The ability to make predictions in fisheries systems is therefore extremely limited. To our knowledge, this is the first cross-system comparative study, and the first at the scale of individual species, to analyse empirically the dynamic complexity observed in fisheries data and to quantify predictability broadly. We outline one application of shortterm forecasts to a precautionary approach to fisheries management that could improve how uncertainty and forecast error are incorporated into assessment through catch limit buffers.