Plankton dynamics and their relationship with environmental factors shape fish community structure in fluvial ecosystem: A case study

Chandran, Rejani; Jaiswar, A. K.; Raizada, Sudhir; Mandal, Sangeeta; Mayekar, Trivesh; Bisht, Amit Singh; Singh, Sanjay Kumar; Mohindra, Vindhya; Lal, Kuldeep Kumar; Tyagi, L. K.

doi:10.21077/ijf.2021.68.4.111193-01

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The essential biological elements that provide energy to higher organisms via the food chain are phytoplankton and zooplankton [11], which are trustworthy resources for assessing the levels of water contamination and function as bioindicators [12]. Plankton is, therefore, essential to the health of ecosystems and provides a variety of ecosystem services (ES), such as acting as a sink for CO 2 in order to regulate the climate and forming the foundation of food webs that support the production of higher trophic levels [13].…”

Section: Introductionmentioning

confidence: 99%

Qualitative Model of the Causal Interactions between Phytoplankton, Zooplankton, and Environmental Factors in the Romanian Black Sea

Bișinicu,

Boicenco,

Pantea

et al. 2024

Phycology

View full text Add to dashboard Cite

In order to analyze how environmental factors affect planktonic organisms along the Romanian Black Sea coast, this study created semi-quantitative models of the causal relationships between phytoplankton, zooplankton, and physicochemical parameters by utilizing user-friendly modeling tools. Eleven years of time-series data (March–September 2008–2018) were used to investigate the relationships between phytoplankton, zooplankton, and environmental factors (such as temperature, salinity, and nutrients). Variables such as marine reporting units and phytoplankton species and classes were used to identify developmental patterns, utilizing the Mental Modeler platform to consider interactions between the physicochemical parameters and phytoplankton, phytoplankton and zooplankton, and zooplankton and physicochemical parameters. Although the increase in the overall number of elements and linkages was uncertain in waters with variable salinity compared to marine ones, the semi-quantitative models created for the three marine reporting units along the Romanian Black Sea coast were comparable in terms of complexity. Across the typical and examined types of phytoplankton proliferation (normal, abundant, and blooms), the number of components and connections in the case of phytoplankton blooms substantially decreased as species- and growth-promoting variables increased.

show abstract

Section: Introductionmentioning

confidence: 99%

Qualitative Model of the Causal Interactions between Phytoplankton, Zooplankton, and Environmental Factors in the Romanian Black Sea

Bișinicu,

Boicenco,

Pantea

et al. 2024

Phycology

View full text Add to dashboard Cite

show abstract

Evaluating the influence of environmental variables on the length-weight relationship and prediction modelling in flathead grey mullet, Mugil cephalus Linnaeus, 1758

Chandran

Singh

et al. 2023

PeerJ

View full text Add to dashboard Cite

Fish stocks that are grown under diverse environmental conditions have different biometric relationships and growth patterns. The biometric length-weight relationship (LWR) is an essential fishery assessment tool, as fish growth is continuous and depends on genetic and environmental factors. The present study attempts to understand the LWR of the flathead grey mullet, Mugil cephalus Linnaeus, 1758, from different locations. The study area encompassed its distribution in the wild across freshwater location (one), coastal habitats (eight locations), and estuaries (six locations) in India to determine the relationship between various environmental parameters. Specimens (n = 476) of M. cephalus were collected from commercial catches and the length and weight of individual specimens were recorded. Monthly data from the study locations were extracted for nine environmental variables from the datasets downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the Copernicus Marine Environment Monitoring Service (CMEMS) over 16 years (2002 to 2017) on the Geographical Information System platform. The parameters of the LWR, intercept ‘a’ and slope or regression coefficient ‘b’, varied from 0.005321 to 0.22182 and 2.235 to 3.173, respectively. The condition factor ranged from 0.92 to 1.41. The partial least squares (PLS) score scatter plot matrix indicated differences in the environmental variables between the locations. PLS analysis of the regression coefficient and environment parameters revealed that certain environment variables viz., sea surface temperature, salinity, dissolved oxygen, nitrate, and phosphate, played a positive role. However, chlorophyll, pH, silicate, and iron played a negative role in influencing weight growth across various locations. The results revealed that the M. cephalus specimens from three locations, Mandapam, Karwar, and Ratnagiri, possessed significantly higher fitness to their environment than those from the other six locations. The PLS model can be used to predict weight growth under the various environmental conditions of different ecosystems. The three identified locations are useful sites for the mariculture of this species considering their growth performance, the environmental variables, and their interactions. The results of this study will improve the management and conservation of exploited stocks in regions affected by climate change. Our results will also aid in making environment clearance decisions for coastal development projects and will improve the efficiency of mariculture systems.

show abstract

Plankton dynamics and their relationship with environmental factors shape fish community structure in fluvial ecosystem: A case study

Cited by 2 publications

References 0 publications

Qualitative Model of the Causal Interactions between Phytoplankton, Zooplankton, and Environmental Factors in the Romanian Black Sea

Qualitative Model of the Causal Interactions between Phytoplankton, Zooplankton, and Environmental Factors in the Romanian Black Sea

Evaluating the influence of environmental variables on the length-weight relationship and prediction modelling in flathead grey mullet, Mugil cephalus Linnaeus, 1758

Contact Info

Product

Resources

About