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Time series analysis of tuna and swordfish catches and climate variability in the Indian Ocean (1968-2003)

机译:印度洋金枪鱼和旗鱼渔获量的时间序列分析和气候变化(1968-2003年)

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摘要

We analysed the patterns of variation that characterize 33 catch time series of large pelagic fishes exploited by the Japanese and Taiwanese longline fisheries in the Indian Ocean from 1968 to 2003. We selected four species, the yellowfin (Thunnus albacares), the bigeye (T. obesus), the albacore (T. alalunga), and the swordfish (Xiphias gladius) and aggregated data into five biogeographic provinces of Longhurst (2001). We carried out wavelet analyses, an efficient method to study non-stationary time series, in order to get the time-scale patterns of each signals. We then compared and grouped the different wavelet spectra using a multivariate analysis to identify the factors (species, province or fleet) that may influence their clustering. We also investigated the associations between catch time series and a large-scale climatic index, the Dipole Mode Index (DMI), using cross wavelet analyses. Our results evidenced that the geographical province is more important than the species level when analyzing the 33 catch time series in the tropical Indian Ocean. The DMI further impacted the variability of tuna and swordfish catch time series at several periodic bands and at different temporal locations, and we demonstrated that the geographic locations modulated its impact. We discussed the consistency of time series fluctuations that reflect embedded information and complex interactions between biological processes, fishing strategies and environmental variability at different scales.
机译:我们分析了1968年至2003年日本和台湾延绳钓渔业在印度洋上开采的33种大型远洋鱼类捕捞时间序列的变异模式。我们选择了四种鱼类,即黄鳍金枪鱼(Thunnus albacares),大眼鲷(T. (例如obesus),长鳍金枪鱼(T. alalunga)和箭鱼(Xiphias gladius),并将数据汇总到Longhurst的五个生物地理省(2001年)。我们进行了小波分析,这是研究非平稳时间序列的一种有效方法,以便获得每个信号的时标模式。然后,我们使用多元分析对不同的小波谱进行了比较和分组,以识别可能影响其聚类的因素(物种,省或车队)。我们还使用交叉小波分析研究了捕获时间序列与大规模气候指数(偶极子模式指数(DMI))之间的关联。我们的结果证明,在分析热带印度洋的33个捕获时间序列时,地理省比物种水平更为重要。 DMI进一步影响了金枪鱼和箭鱼捕捞时间序列在几个周期带和不同时间位置的变异性,我们证明了地理位置调节了其影响。我们讨论了时间序列波动的一致性,这些波动反映了嵌入的信息以及生物过程,捕捞策略和不同规模环境变异之间的复杂相互作用。

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