Pesticides Alter Ecosystem Respiration via Phytoplankton Abundance and Community Structure: Effects on the Carbon Cycle?

2021 ◽  
Author(s):  
Samantha L. Rumschlag ◽  
Dale A. Casamatta ◽  
Michael B. Mahon ◽  
Jason T. Hoverman ◽  
Thomas R. Raffel ◽  
...  
Keyword(s):  
Community Structure ◽  
Carbon Cycle ◽  
Ecosystem Respiration ◽  
Phytoplankton Abundance

scholarly journals Pesticide-induced Alterations to Phytoplankton Abundance and Community Structure Alter Ecosystem Respiration: Implications for the Carbon Cycle?

2020 ◽  
Author(s):  
Samantha L. Rumschlag ◽  
Dale A. Casamatta ◽  
Michael B. Mahon ◽  
Jason T. Hoverman ◽  
Thomas R. Raffel ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Phytoplankton Community ◽  
Ecosystem Respiration ◽  
Freshwater Ecosystems ◽  
Phytoplankton Abundance ◽  
Chemical Contaminants ◽  
Synthetic Chemicals ◽  
Taxonomic Groups ◽  
Global Carbon ◽  
Sheer Number

AbstractCurrent predictions of the effects of synthetic chemicals on freshwater ecosystems are hampered by the sheer number of chemical contaminants entering aquatic systems, the diversity of organisms inhabiting these systems, and uncertainties about how contaminants alter ecosystem metabolism. We conducted a mesocosm experiment that elucidated the responses of ponds composed of phytoplankton and zooplankton to standardized concentrations of 12 pesticides, nested within four pesticide classes and two pesticide types. We show that the effects of the pesticides on algae were consistent within herbicides and insecticides and responses of over 70 phytoplankton species and genera were consistent within broad taxonomic groups. Insecticides generated top-down effects on phytoplankton community composition and abundance, which were associated with persistent increases in ecosystem respiration. Herbicides reduced phytoplankton abundance, which was associated with decreases in primary productivity and ecosystem respiration. These results suggest that widespread pesticide use could have underexplored implications for the global carbon cycle. While these effects on ecosystem respiration were mediated through complex effects on communities, taxonomic groups of organisms responded similarly to pesticide types, suggesting opportunities to simplify ecological risk assessment.

scholarly journals The Connectivity of Nutrient Ratios on The Abundance of Phytoplankton Population In Jakarta Bay

2020 ◽  
Vol 147 ◽  
pp. 02012 ◽  
Author(s):  
Tumpak Sidabutar ◽  
Endang S. Srimariana
Keyword(s):  
Community Structure ◽  
Strong Correlation ◽  
Rainy Season ◽  
Mesh Size ◽  
Nutrient Ratios ◽  
Phytoplankton Abundance ◽  
Phytoplankton Population ◽  
Very High

The frequency of algal bloom’s event has been increased in Jakarta Bay, recently. Most of the bloom events were tend to be reoccurred after the rainy season. The research was conducted from 2008 until 2015 to study the linkage of nutrients and the ratios on the growth of the phytoplankton population. Collecting samples were conducted using a canonical plankton net of 20 µm mesh size, 125 cm length and 30 cm diameter of the opening mouth. The results of the study showed that the concentration of phosphate in the waters ranged from 0.01-12.5 µg/l (average 4.58 µg/l) and nitrate ranged from 0.01-15.89 µg/l (average 0.72 µg/l). The N/P ratio during the study ranging from 0.2 up to 45.4. High ratios of nutrients were mostly recorded in 2010 where the overall abundance of phytoplankton is very high. There is a strong correlation of N/P ratio with the community structure or composition of the phytoplankton population. The variability of phytoplankton abundance appears to be related to nutrient ratios of nitrate and phosphate.

Data-based machine learning unveils ecosystem metabolic regimes at the scale of entire stream networks

2020 ◽  
Author(s):  
Pier Luigi Segatto ◽  
Tom J. Battin ◽  
Enrico Bertuzzo
Keyword(s):  
Machine Learning ◽  
Carbon Cycle ◽  
Ecosystem Respiration ◽  
Spatial Scales ◽  
Gross Primary Production ◽  
Data Availability ◽  
Ecosystem Metabolism ◽  
Sensor Technology ◽  
Stream Network ◽  
Metabolic Fluxes

<p>Inland waters are major contributors to the global carbon cycle. Nowadays, new sensor technology has changed the way we study ecosystem metabolism in streams. We are able to produce long-term time series of gross primary production (GPP) and ecosystem respiration (ER) to infer drivers of the stream ecosystem metabolic regime and its seasonal timing. Despite big data availability, most studies are limited to individual stream reaches and do not allow the appreciation of metabolic regimes at the scale of entire networks, which, however, would be fundamental to properly assess the relevance of metabolic fluxes within streams and rivers for carbon cycling at the regional and global scale. Machine learning (ML) has great potential in this direction. Firstly, ML could be used to extrapolate both in time and space heterogeneous forcings (e.g., streamwater temperature (T) and photosynthetic active radiation (PAR)) required to run a process-based model for reach-scale metabolism to the scale of an entire stream network. Secondly, the same procedure could be applied to reach-scale estimates of ecosystem metabolism to check whether available data contain enough information to explain the network scale variability. In this study, we used Random Forest to predict patterns of environmental forcings (T and PAR) and stream metabolism (GPP and ER) at the scale of an entire stream network. We used available high-frequency measurements of T and PAR, estimates of ecosystem metabolism and major proximal controls (e.g., incident light, discharge, stream-bed slope, drainage area, water level,  air temperature) from twelve reaches within the Ybbs River network (Austria) and explicitly trained our Random Forests by integrating distal factors, namely:  vegetation type, canopy cover, hydro-geomorphic properties, light,  precipitation, and other climatic variables. We designed two different training setups to assess spatial and temporal predicting model capabilities, respectively. This approach allowed us to reliably infer the target variables (T, PAR, GPP, and ER) on annual basis across a stream network, to filter the most important predictors, to assess the relative contribution of the metabolic fluxes from small to large streams, to estimate annual metabolic budgets at different spatial scales and to provide empirical evidence for long-standing theory predicting shifts of ecosystem metabolism along the stream continuum. Finally, we estimated autochthonous and allochthonous respiration for the entire stream network, which is crucial to integrate the role of ecosystem processes for the carbon cycle.</p>

scholarly journals Annual dynamics of phytoplankton abundance and community structure (2010) in Lake Caizi, Anhui Province

2012 ◽  
Vol 24 (5) ◽  
pp. 771-779 ◽  
Author(s):  
LIU Xuehua ◽  
◽  
ZHAO Xiuxia ◽  
GAO Pan ◽  
HAN Feiyuan ◽  
...  
Keyword(s):  
Community Structure ◽  
Anhui Province ◽  
Phytoplankton Abundance

Carbon cycle response to temperature overshoot beyond 2 °C – an analysis of CMIP6 models

2021 ◽  
Author(s):  
Irina Melnikova ◽  
Olivier Boucher ◽  
Patricia Cadule ◽  
Philippe Ciais ◽  
Thomas Gasser ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Climate Models ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Coupled Model ◽  
Paris Agreement ◽  
Climate Feedback ◽  
Concentration Peak ◽  
Early Peak ◽  
Response To Temperature

<p><span>There is a substantial gap between the current emissions of greenhouse gases and levels required for achieving the 2 and 1.5 °C temperature targets of the Paris Agreement. Understanding the implications of a temperature overshoot is thus an increasingly relevant research topic. We carry out a study as part of the “Achieving the Paris Agreement Temperature Targets after Overshoot (PRATO)” project of the MOPGA programme on the 2 °C overshoot of the Paris Agreement temperature target. We explore the carbon cycle feedbacks over land and ocean in the SSP5-3.4-OS overshoot scenario by using an ensemble of Coupled Model Intercomparison Project 6 Earth system models. Models show that after the CO<sub>2</sub> concentration and air temperature peaks, land and ocean are decreasing carbon sinks from the 2040s and become sources for a limited time in the 22<sup>nd</sup> century. The decrease in the carbon uptake precedes the CO<sub>2</sub> concentration peak. The early peak of the ocean uptake stems from its dependency on the atmospheric CO<sub>2</sub> growth rate. The early peak of the land uptake occurs due to a larger increase in ecosystem respiration than the increase in gross primary production, as well as due to a concomitant increase in land-use change emissions primarily attributed to the wide implementation of biofuel croplands. The carbon cycle feedback parameters amplify after the CO<sub>2</sub> concentration and temperature peaks, so that land and ocean absorb more carbon per unit change in the atmospheric CO<sub>2</sub> change (stronger negative feedback) and lose more carbon per unit temperature change (stronger positive feedback) compared to if the feedbacks stayed unchanged. The increased negative CO<sub>2</sub> feedback outperforms the increased positive climate feedback. This feature should be investigated under other scenarios and reflected in simple climate models.</span></p>

scholarly journals Development and evaluation of CNRM Earth system model – CNRM-ESM1

2016 ◽  
Vol 9 (4) ◽  
pp. 1423-1453 ◽  
Author(s):  
Roland Séférian ◽  
Christine Delire ◽  
Bertrand Decharme ◽  
Aurore Voldoire ◽  
David Salas y Melia ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Sea Ice ◽  
Ecosystem Respiration ◽  
World Ocean ◽  
Earth System Model ◽  
System Model ◽  
Shortwave Radiation ◽  
Climate Simulation ◽  
Co2 Uptake ◽  
Earth System

Abstract. We document the first version of the Centre National de Recherches Météorologiques Earth system model (CNRM-ESM1). This model is based on the physical core of the CNRM climate model version 5 (CNRM-CM5) model and employs the Interactions between Soil, Biosphere and Atmosphere (ISBA) and the Pelagic Interaction Scheme for Carbon and Ecosystem Studies (PISCES) as terrestrial and oceanic components of the global carbon cycle. We describe a preindustrial and 20th century climate simulation following the CMIP5 protocol. We detail how the various carbon reservoirs were initialized and analyze the behavior of the carbon cycle and its prominent physical drivers. Over the 1986–2005 period, CNRM-ESM1 reproduces satisfactorily several aspects of the modern carbon cycle. On land, the model captures the carbon cycling through vegetation and soil, resulting in a net terrestrial carbon sink of 2.2 Pg C year−1. In the ocean, the large-scale distribution of hydrodynamical and biogeochemical tracers agrees with a modern climatology from the World Ocean Atlas. The combination of biological and physical processes induces a net CO2 uptake of 1.7 Pg C year−1 that falls within the range of recent estimates. Our analysis shows that the atmospheric climate of CNRM-ESM1 compares well with that of CNRM-CM5. Biases in precipitation and shortwave radiation over the tropics generate errors in gross primary productivity and ecosystem respiration. Compared to CNRM-CM5, the revised ocean–sea ice coupling has modified the sea-ice cover and ocean ventilation, unrealistically strengthening the flow of North Atlantic deep water (26.1 ± 2 Sv). It results in an accumulation of anthropogenic carbon in the deep ocean.

Effects of Grazer Community Composition and Fish on Algal Dynamics

1992 ◽  
Vol 49 (9) ◽  
pp. 1908-1915 ◽  
Author(s):  
Andrew M. Turner ◽  
Gary G. Mittelbach
Keyword(s):  
Community Structure ◽  
Factorial Design ◽  
Chlorophyll A ◽  
Community Composition ◽  
Standing Crop ◽  
Zooplankton Community ◽  
The Other ◽  
Light Extinction ◽  
Phytoplankton Abundance ◽  
Zooplankton Community Structure

We examined the effects of grazer community composition and fish on phytoplankton abundance by manipulating zooplankton community structure and the intensity of planktivory in a factorial experiment. Enclosures (1700-L bags) were treated with fish (present/absent) and two grazer communities (one a large-bodied community dominated by Daphnia and the other a small-bodied community dominated by Ceriodaphnia) in a 2 × 2 factorial design. We sampled zooplankton and algae every 4–8 d during the 5-wk experiment. Algal biovolume, chlorophyll a, total particulates, and light extinction were all significantly higher in the presence of fish. Further, the effect of fish on algal standing crop did not depend on which grazer assemblage was initially present. Fish enhanced algal standing crop to the same degree in both Daphnia and Ceriodaphnia treatments. We discuss these results in light of patterns reported in the literature, and the nature of size-structured interactions among fish, zooplankton, and algae.

scholarly journals Nutrient ratios driven by vertical stratification regulate phytoplankton community structure in the oligotrophic western Pacific Ocean

Ocean Science ◽  
2021 ◽  
Vol 17 (6) ◽  
pp. 1775-1789
Author(s):  
Zhuo Chen ◽  
Jun Sun ◽  
Ting Gu ◽  
Guicheng Zhang ◽  
Yuqiu Wei
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Pacific Ocean ◽  
Phytoplankton Community ◽  
Western Pacific ◽  
Vertical Stratification ◽  
Nutrient Ratios ◽  
Western Pacific Ocean ◽  
Phytoplankton Community Structure ◽  
Phytoplankton Abundance

Abstract. The stratification of the upper oligotrophic ocean has a direct impact on biogeochemistry by regulating the components of the upper-ocean environment that are critical to biological productivity, such as light availability for photosynthesis and nutrient supply from the deep ocean. We investigated the spatial distribution pattern and diversity of phytoplankton communities in the western Pacific Ocean (WPO) in the autumn of 2016, 2017, and 2018. Our results showed the phytoplankton community structure mainly consisted of cyanobacteria, diatoms, and dinoflagellates, while the abundance of Chrysophyceae was negligible. Phytoplankton abundance was high from the equatorial region to 10∘ N and decreased with increasing latitude in spatial distribution. Phytoplankton also showed a strong variation in the vertical distribution. The potential influences of physicochemical parameters on phytoplankton abundance were analyzed by a structural equation model (SEM) to determine nutrient ratios driven by vertical stratification to regulate phytoplankton community structure in the typical oligotrophic ocean. Regions with strong vertical stratification were more favorable for cyanobacteria, whereas weak vertical stratification was more conducive to diatoms and dinoflagellates. Our study shows that stratification is a major determinant of phytoplankton community structure and highlights that physical processes in the ocean control phytoplankton community structure by driving the balance of chemical elements, providing a database to better predict models of changes in phytoplankton community structure under future ocean scenarios.

scholarly journals Community Structure And Phytoplankton Abundance In Different Estuary In Coast Waters Lekok Pasuruan East Java

2021 ◽  
Vol 695 (1) ◽  
pp. 012001
Author(s):  
E. Y. Herawati ◽  
A. Darmawan ◽  
M. Azkiya
Keyword(s):  
Community Structure ◽  
Phytoplankton Abundance

scholarly journals Comprehensive description of the carbon cycle of an ancient temperate broadleaved woodland

2010 ◽  
Vol 7 (3) ◽  
pp. 3735-3763 ◽  
Author(s):  
K. Fenn ◽  
Y. Malhi ◽  
M. Morecroft ◽  
C. Lloyd ◽  
M. Thomas
Keyword(s):  
Carbon Cycle ◽  
Eddy Covariance ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Ecosystem Respiration ◽  
Stem Respiration ◽  
Ecosystem Carbon ◽  
Deciduous Woodland ◽  
Below Ground ◽  
Eddy Covariance Measurements

Abstract. There exist very few comprehensive descriptions of the productivity and carbon cycling of forest ecosystems. Here we present a description of the components of annual Net Primary Productivity (NPP), Gross Primary Productivity (GPP), autotrophic and heterotrophic respiration, and ecosystem respiration (RECO) for a temperate mixed deciduous woodland at Wytham Woods in southern Britain, calculated using "bottom-up" biometric and chamber measurements (leaf and wood production and soil and stem respiration). These are compared with estimates of these parameters from eddy-covariance measurements made at the same site. NPP was estimated as 7.0±0.8 Mg C ha−1 yr−1, and GPP as 20.3+1.0 Mg C ha−1 yr−1, a value which closely matched to eddy covariance-derived GPP value of 21.1 Mg C ha−1 yr−1. Annual RECO was calculated as 18.9±1.7 Mg C ha−1 yr−1, close to the eddy covariance value of 19.8 Mg C ha−1 yr−1; the seasonal cycle of biometric and eddy covariance RECO estimates also closely matched. The consistency between eddy covariance and biometric measurements substantially strengthens the confidence we attach to each as alternative indicators of site carbon dynamics, and permits an integrated perspective of the ecosystem carbon cycle. 37% of NPP was allocated below ground, and the ecosystem carbon use efficiency (CUE, = NPP/GPP) calculated to be 0.35±0.05, lower than reported for many temperate broadleaved sites.

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