Effects of primary productivity and ecosystem size on food-chain length in Raohe River, China

Xiao, Xiewen; Wang, Yuyu; Zhang, Huan; Yu, Xiubo

doi:10.1016/j.chnaes.2015.04.003

Cited by 13 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The X1 station and L1 to L4 stations in Group 1 are located at the upstream of the Xin River and the Lean River, respectively, which, due to low population density and the absence of industrial and commercial activity, are far from major point and non-point pollution sources. However, L1-L4 stations are located in the Dexing district, which is one of the largest copper and gold producing districts in China and metal pollution and associated mineral pollution are always a problem [39,40]. Despite relatively high Cu, S, and F concentrations were observed at the L4 Station in this study, Group 1 should be considered as moderate or low pollution.…”

Section: Temporal/spatial Similarity and Groupingmentioning

confidence: 96%

Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

Duan

Nover³

et al. 2016

Sustainability

190

View full text Add to dashboard Cite

Multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA) and component analysis/factor analysis (PCA/FA), were applied to explore the surface water quality datasets including 14 parameters at 28 sites of the Eastern Poyang Lake Basin, Jiangxi Province of China, from January 2012 to April 2015, characterize spatiotemporal variation in pollution and identify potential pollution sources. The 28 sampling stations were divided into two periods (wet season and dry season) and two regions (low pollution and high pollution), respectively, using hierarchical CA method. Four parameters (temperature, pH, ammonia-nitrogen (NH 4 -N), and total nitrogen (TN)) were identified using DA to distinguish temporal groups with close to 97.86% correct assignations. Again using DA, five parameters (pH, chemical oxygen demand (COD), TN, Fluoride (F), and Sulphide (S)) led to 93.75% correct assignations for distinguishing spatial groups. Five potential pollution sources including nutrients pollution, oxygen consuming organic pollution, fluorine chemical pollution, heavy metals pollution and natural pollution, were identified using PCA/FA techniques for both the low pollution region and the high pollution region. Heavy metals (Cuprum (Cu), chromium (Cr) and Zinc (Zn)), fluoride and sulfide are of particular concern in the study region because of many open-pit copper mines such as Dexing Copper Mine. Results obtained from this study offer a reasonable classification scheme for low-cost monitoring networks. The results also inform understanding of spatio-temporal variation in water quality as these topics relate to water resources management.

show abstract

Section: Temporal/spatial Similarity and Groupingmentioning

confidence: 96%

Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

Duan

Nover³

et al. 2016

Sustainability

190

View full text Add to dashboard Cite

show abstract

“…All photosynthetic cells contain one or several chlorophyll pigments, such as green, brown, red, or violet (Wetzel 2001;Kirk 2011). Chlorophyll a concentration is the main indicator used to estimate primary productivity and an important variable in photosynthetic process (Lee et al 2014;Xiao et al 2015;Chen et al 2017). Algae have an efficient photosynthetic mechanism and high biomass production (Moreira and Pires 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Microalgae adsorbs CO2 and stores carbon in form of biomass (Bhakta et al 2015). The presence of phytoplankton in aquaculture production systems contribute to carbon cycle and aquatic food chain (Wetzel 2001;Lee et al 2014;Ma et al 2014;Xiao et al 2015;Mercado-santana et al 2017), because of their photosynthetic activities (Mitra and Zaman 2015) and assimilates or adsorbs CO2 (Geider and Osborne 1992;Baker 2004). Furthermore, in aquatic ecosystems, phytoplankton or microalgae are the major contributors to primary productivity (Nontji 1984;Iriarte and Purdie 1994;Chen et al 2017;Reeder 2017;Vallina et al 2017) while the rest are plants and macroalgae (Silva et al 2009).…”

Section: Introductionmentioning

confidence: 99%

The contribution of phytoplankton in the carbon adsorption and stock during shrimp culture in brackishwater ponds

Widigdo

Rifqi

Nazar³

et al. 2020

Biodiversitas

View full text Add to dashboard Cite

Abstract. Widigdo B, Rifqi M, Mashar A, Nazar F, Wardiatno Y. 2020. The contribution of phytoplankton in the carbon adsorption and stock during shrimp culture in brackishwater ponds. Biodiversitas 21: 5170-5177. In environmental point of view, it is important to determine the potential of adsorption and stock of blue carbon during shrimp farming in ponds with three cultivation technologies namely: extensive/traditional, semi-intensive, and intensive. The aquatic primary productivity approach is used to measure the potential of carbon adsorption in shrimp ponds, while the carbon conversion of chlorophyll-a content is used to measure its stock. The primary productivity and chlorophyll-a content were measured using three ponds for each cultivation technology every ten days from stocking (DOC 0) until the harvest period. The sampling location is in the BLUPPB shrimp pond area of Karawang, West Java - Indonesia. The study concluded that the adsorption and stock of blue carbon varied during shrimp farming in ponds. The primary productivity and chlorophyll-a content of the three cultivation technologies were the significant difference as the results of ANOVA Single Factor. Carbon adsorption average during extensive/traditional, semi-intensive, and intensive shrimp farming in ponds was 1.912832 g C m-3 day-1, 7.097145 g C m-3 day-1 and 8.250195 g C m-3 day-1, respectively. While, the average carbon stock during shrimp farming in extensive/traditional, semi-intensive, and intensive ponds were 0.64896 g m-3, 14.97473 g m-3, and 25.11102 g m-3, respectively.

show abstract

“…In addition, with the calibration of primary consumers as the stable isotope baseline, the comparison of FCL among multiple ecosystems becomes feasible [9][10][11]. As a result, research on FCL is not limited to the determination of FCL in a single ecosystem but can also be applied to multiple ecosystems in order to make comparisons [12,13] or explore the influence of environmental factors on FCL [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Food Chain Length Associated with Environmental Factors Affected by Large Dam along the Yangtze River

Chun

Deng

et al. 2020

Water

View full text Add to dashboard Cite

Food chain length (FCL) is a critical measure of food web complexity that influences the community structure and ecosystem function. The FCL of large subtropical rivers affected by dams and the decisive factors are far beyond clear. In this study, we used stable isotope technology to estimate the FCL of fish in different reaches of the main stream in the Yangtze River and explored the key factors that determined the FCL. The results showed that FCL varied widely among the studied areas with a mean of 4.09 (ranging from 3.69 to 4.31). The variation of FCL among river sections in the upstream of the dam was greater than that in the downstream. Regression analysis and model selection results revealed that the FCL had a significant positive correlation with ecosystem size as well as resource availability, and FCL variation was largely explained by ecosystem size, which represented 72% of the model weight. In summary, our results suggested that ecosystem size plays a key role in determining the FCL in large subtropical rivers and large ecosystems tend to have a longer food chain. Additionally, the construction of the Three Gorges Dam has been speculated to increase the FCL in the impoundment river sections.

show abstract

Effects of primary productivity and ecosystem size on food-chain length in Raohe River, China

Cited by 13 publications

References 21 publications

Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

The contribution of phytoplankton in the carbon adsorption and stock during shrimp culture in brackishwater ponds

Food Chain Length Associated with Environmental Factors Affected by Large Dam along the Yangtze River

Contact Info

Product

Resources

About