Predicting the distribution of Coilia nasus abundance in the Yangtze River estuary: From interpolation to extrapolation

IF 2.6 3区 地球科学 Q1 MARINE & FRESHWATER BIOLOGY
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Abstract

Coilia nasus was once an economically important fish in the Yangtze River estuary, but overfishing and other anthropogenic factors have severely depleted its population. To conserve and restore C. nasus, there is an urgent need to determine its precise spatiotemporal distribution. However, as a typical anadromous species, C. nasus seasonally uses estuarine habitats, resulting in a very high proportion of nulls in some seasons and posing a great challenge to predicting abundance. This study compared three commonly used tree methods (gradient boosting machine (GBM), random forest (RF), and conditional random forest (CRF)) to predict the abundance of C. nasus in the Yangtze River estuary using trawl resource monitoring survey data from 2013 to 2018. Based on the survey data, 16 explanatory variables, including temperature, salinity, pH, and chemical oxygen demand, were used as predictors, and the coefficient of determination (R2), root mean square error (RMSE), and root mean square logarithmic error (RMSLE) were used to evaluate the performance of the three tree methods. Three metrics were used to assess the performance difference between interpolation and extrapolation for the three tree methods when modeling by season and combining seasons. The results showed that (1) compared with combined modeling, seasonal modeling could accurately determine the high- and low-abundance regions in interpolation, and the quarterly model greatly improved the extrapolation prediction accuracy. (2) Almost all metrics indicated that the interpolation RF model had the best performance, while CRF and GBM were significantly worse than other methods for some indicators, and the RF model had better robustness and could be applied to the abundance of all seasons. (3) The model performance in extrapolation was significantly lower than that of interpolation, RF was also the best method, and RF could still identify high-abundance areas when the amount of data was much smaller than that used for interpolation. The findings of our study can be generalized to species distribution modeling of other migratory species in the Yangtze River estuary or estuarine ecosystems in the Northwest Pacific Ocean.

预测长江口鲚鱼丰度分布:从内插法到外推法
鲚(Coilia nasus)曾是长江口重要的经济鱼类,但过度捕捞和其他人为因素导致其种群数量严重减少。为了保护和恢复鲚鱼,迫切需要确定其精确的时空分布。然而,作为一种典型的溯河物种,C. nasus会季节性地使用河口栖息地,这就导致在某些季节,C. nasus的空游比例非常高,给丰度预测带来了巨大挑战。本研究利用2013年至2018年的拖网资源监测调查数据,比较了三种常用的树方法(梯度提升机(GBM)、随机森林(RF)和条件随机森林(CRF))来预测长江口鲚鱼的丰度。基于调查数据,采用温度、盐度、pH值、化学需氧量等16个解释变量作为预测因子,利用判定系数(R2)、均方根误差(RMSE)和均方根对数误差(RMSLE)来评价三种树方法的性能。在按季节建模和结合季节建模时,使用了三个指标来评估三种树方法的内插法和外推法的性能差异。结果表明:(1)与组合建模相比,季节建模能在内插法中准确确定高丰度区和低丰度区,而季度建模则大大提高了外推法的预测精度。(2)几乎所有指标都表明,内插 RF 模型的性能最好,而 CRF 和 GBM 在某些指标上明显差于其他方法,RF 模型具有更好的鲁棒性,可适用于所有季节的丰度。(3)外推法的模型性能明显低于内插法,RF 也是最好的方法,当数据量远小于内插法时,RF 仍能识别高丰度区。我们的研究结果可推广到长江口其他洄游物种或西北太平洋河口生态系统的物种分布建模中。
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来源期刊
CiteScore
5.60
自引率
7.10%
发文量
374
审稿时长
9 months
期刊介绍: Estuarine, Coastal and Shelf Science is an international multidisciplinary journal devoted to the analysis of saline water phenomena ranging from the outer edge of the continental shelf to the upper limits of the tidal zone. The journal provides a unique forum, unifying the multidisciplinary approaches to the study of the oceanography of estuaries, coastal zones, and continental shelf seas. It features original research papers, review papers and short communications treating such disciplines as zoology, botany, geology, sedimentology, physical oceanography.
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