Modelling the presence/absence of Samii's riffle minnow (Alburnoides samiii) in river

IF 2.7 4区 环境科学与生态学 Q2 ECOLOGY
Rahmat Zarkami , Hananeh Seyyed Mohamadpour Kohgasht , Hamed Mousavi-Sabet , Roghayeh Sadeghi Pasvisheh
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Abstract

Determining the most important environmental variables influencing the habitat of fish species is a very important issue to achieve an effective management of river ecosystem. The present research aimed to implement a data-driven model (classification tree; CT) to predict the presence and absence of Samii's spirlin (Alburnoides samiii) in a river basin system. 96 real observations together with various water quality and physical-structural variables were monitored monthly for one-year study period (2017–2018) in the Sefidroud River, northern Iran. The fish's presence/absence was successfully predicted by the CT model leading to a high model reliability (CCI> 75 % and Cohen Kappa> 0.60). The outcomes of the CT model at high levels of tree pruning (PCFs = 0.01 and 0.10, and with five-time randomization effort) confirmed that an increase in the water turbidity, biological oxygen demand, electric conductivity, river depth and orthophosphate may restrict the fish's presence, while the probability of fish's presence in the river may show an increase at higher slope and flow velocity. A significant difference (Kruskal-Wallis's test) was found between all predictors decided by the CT model and the samples with fish and without fish (p < 0.05 for the selected variables). The fish's abundance significantly differed with the sampling seasons/sites (Pearson Chi-Square= 40.6; p < 0.01). The generalized linear model (GLM) also indicated that the probability of the fish's occurrence may be diminished with increasing water depth and nutrient pollution (e.g., orthophosphate) in the river. The outcomes of the CT model, thus, can be applied for predicting the presence/absence of A. samiii and similar fish species in other ecoregions.
模拟河中萨米米诺鱼(Alburnoides samiii)的存在/缺失
确定影响鱼类生境的最重要的环境变量是实现河流生态系统有效管理的一个重要问题。本研究旨在实现一个数据驱动模型(分类树;CT)来预测Samii螺旋藻(Alburnoides samiii)在流域系统中的存在和缺失。在为期一年的研究期间(2017-2018年),对伊朗北部塞菲德鲁德河的96个实际观测结果以及各种水质和物理结构变量进行了每月监测。CT模型成功地预测了鱼的存在/不存在,从而获得了较高的模型可靠性(CCI>;75%和Cohen Kappa>;0.60)。CT模型在高水平的树木修剪(pcf = 0.01和0.10,并进行了五次随机化努力)的结果证实,水浊度、生物需氧量、电导率、河流深度和正磷酸盐的增加可能会限制鱼类的存在,而鱼类在河流中的存在概率可能在较高的坡度和流速下增加。CT模型决定的所有预测因子与有鱼和没有鱼的样本之间存在显著差异(Kruskal-Wallis检验)(p <;所选变量为0.05)。鱼的丰度随采样季节/地点的不同而显著差异(Pearson Chi-Square= 40.6;p & lt;0.01)。广义线性模型(GLM)还表明,随着水深的增加和河流中营养物污染(如正磷酸盐)的增加,鱼类出现的概率可能会降低。因此,CT模型的结果可用于预测其他生态区域中samiii和类似鱼类的存在/缺失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ecohydrology & Hydrobiology
Ecohydrology & Hydrobiology Agricultural and Biological Sciences-Aquatic Science
CiteScore
5.40
自引率
3.80%
发文量
51
期刊介绍: Ecohydrology & Hydrobiology is an international journal that aims to advance ecohydrology as the study of the interplay between ecological and hydrological processes from molecular to river basin scales, and to promote its implementation as an integrative management tool to harmonize societal needs with biosphere potential.
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