Study on Phytoplankton variability of Sundarban Estuarine river using Random Forests Classifier

S. Chakraborti, G. Sen, Joydeep Mekherjee
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引用次数: 1

Abstract

Statistical Classifier Random Forests (RF) is nowadays extensively used by ecologists for accurate classification, determination of variable importance, understanding of complex interactions in ecosystem studies. In the present study, extensive observation data collected from a river of SES, Sundarban Estuarine System (River Jagaddal, situated in the southernmost part of western Sundarbans and in the closed proximity to Northern boundary of Bay of Bengal) has been used to perform rigorous data analysis using Random Forests. The basic objective of the study is to identify variability, importance and associated interactions for phytoplankton as well as chlorophyll concentrations along the river stretch. This study enables us to identify status of productivity pertinent to availability as well as growth of fish production of this region. It may be noted that most of the people residing in these areas have sole dependency of earning from fishing. RF model has a high predictive power. The interpretations of RF model can be visualized by feature importance graphs which evaluate a feature as a whole which means, contribution of all the properties to a certain phenomena can be observed. In this approach, Random Forest (RF) classification algorithm has been used to classify different properties of estuarine water according to their individual roles on the phytoplankton density and chlorophyll-a concentrations. The properties are classified as significant, moderately significant and insignificant on the basis of their roles (as obtained from RF model) in modulating phytoplankton density and chlorophyll-a concentration. The present study reveals that the phytoplankton density is strongly influenced by the distance from the sea. Surface salinity is the other important factor to be considered as per our findings. Surface phosphate and surface nitrate are marked to be the other important dominating factors on phytoplankton density. In fact, nitrogen and phosphorus are the primary factors that control phytoplankton abundance in estuaries. The bottom temperature has little significance on phytoplankton density as per model investigation. But, the roles of the other factors like surface temperature (st), bottom phosphate (bp) and bottom salinity (bs) have found to be literally insignificant. The chlorophyll-a concentrations found in the study area during study period are found to be strongly correlated to phytoplankton density. This is quite expected since chlorophyll-a concentration can be used as a direct measure of phytoplankton density. The surface phosphate and bottom salinity also observed to have significant influences on chlorophyll-a concentration. It can also be concluded from the model output that, surface phosphate is the most important limiting nutrient on the chlorophyll-a concentration in this specific study area. Usually, there is a general consensus that there are seasonal and spatial variations of the limiting nutrients which in turn are the most important factors responsible for variation in phytoplankton density. Phosphorus is known to be the principal limiting nutrient of phytoplankton growth. Phosphorus limitation is often associated with periods of high river runoff whereas, N or N+P limitation was associated with low river runoff, with comparatively greater influence of sea water. The contribution of other parameters like distance from the sea (d), surface temperature, bottom temperature, bottom phosphate, surface nitrate, bottom nitrate and surface salinity are observed to have more or less insignificant effect on chlorophyll-a concentrations.
孙德班河口河流浮游植物变异的随机森林分类研究
统计分类器随机森林(RF)在生态系统研究中被生态学家广泛用于精确分类、确定变量重要性和理解复杂的相互作用。在本研究中,从SES河流孙德尔本河口系统(Jagaddal河,位于孙德尔本西部最南端,靠近孟加拉湾北部边界)收集的大量观测数据被用于使用随机森林进行严格的数据分析。本研究的基本目的是确定沿河段浮游植物和叶绿素浓度的变异性、重要性和相关的相互作用。这项研究使我们能够确定与可用性有关的生产力状况以及该地区鱼类生产的增长。可以指出的是,居住在这些地区的大多数人都只靠捕鱼为生。RF模型具有较高的预测能力。RF模型的解释可以通过特征重要性图来可视化,特征重要性图将特征作为一个整体进行评估,这意味着可以观察到所有属性对某一现象的贡献。该方法采用随机森林(Random Forest, RF)分类算法,根据不同性质对浮游植物密度和叶绿素a浓度的影响,对河口水体的不同性质进行分类。根据其在调节浮游植物密度和叶绿素-a浓度中的作用(由RF模型获得),将这些特性分为显著、中等显著和不显著。本研究表明,浮游植物的密度受离海距离的强烈影响。根据我们的发现,地表盐度是另一个需要考虑的重要因素。表面磷酸盐和表面硝酸盐是影响浮游植物密度的其他重要因素。事实上,氮和磷是控制河口浮游植物丰度的主要因素。模式调查显示,海底温度对浮游植物密度影响不大。但是,其他因素,如表面温度(st),底部磷酸盐(bp)和底部盐度(bs)的作用被发现是微不足道的。研究期间研究区叶绿素-a浓度与浮游植物密度呈显著正相关。这是意料之中的,因为叶绿素-a浓度可以作为浮游植物密度的直接测量。表层磷酸盐和底部盐度对叶绿素-a浓度也有显著影响。从模型输出也可以得出结论,表面磷酸盐是该特定研究区域最重要的叶绿素-a浓度限制养分。通常,人们普遍认为,限制营养物的季节和空间变化是浮游植物密度变化的最重要因素。磷是浮游植物生长的主要限制性营养物。磷限制通常与河流径流量高的时期有关,而N或N+P限制与河流径流量低有关,受海水的影响相对较大。其他参数如离海距离(d)、表面温度、底部温度、底部磷酸盐、表面硝酸盐、底部硝酸盐和表面盐度对叶绿素-a浓度的影响或多或少不显著。
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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