Measuring Gross Primary Productivity (GPP) using Light and Dark Bottle methods: Sample Question

Measuring Gross Primary Productivity (GPP) using Light and Dark Bottle methods: Sample Question

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The following question is taken from my Cliffnotes AP Biology 4th edition practice exam and it deals with the light and dark method for measuring gross primary productivity.

In a classroom investigation, students filled two bottles with pond water containing only photosynthesizing organisms. They used a dissolved oxygen (DO2) sensor to measure the amount of dissolved oxygen in each bottle. One bottle was put under a light. The second bottle was wrapped with aluminum foil to block all light and was put under the same light. AFter 24 hours, pond water DO2 in the two bottles was again measured. Average values for DO2 from all students are provided in the following table.

Calculate the gross primary productivity for the observed sample. Express your answer in mg fixed carbon/L/day to the nearest hundredth.

Okay, so I know that net primary production (NPP) is the difference between gross primary production (from photosynthesis) minus Autotrophic respiration (Ra):

NPP = GPP - Ra

I also understand that the light bottle is measuring NPP, because it allows for both photosynthesis and cellular respiration. The dark bottle measures Ra because it only allows for respiration. Therefore, GPP can be calculated by rearranging the formula:

GPP = NPP + Ra

Calculating the difference in the final and initial values for both bottles, I get the following:

Light: 0.15 mg O2 / L <-- NPP

Dark: -0.08 mg O2 / L <-- Ra

Next, I plugged these into the formula:

GPP = 0.15 - 0.08 = 0.07 mg O2 / L

Using the conversion formulas provided on the College Board's AP Bio Appendix (essentially just multiplying the mg O2 / L value by 0.374128 to get mg Carbon fixed / L), I get the following:

0.07 mg O2 / L * 0.374128 = 0.02618896 mg C fixed / L

But according to my review book's solution, I was supposed to add 0.08, not subtract it. This seems a bit strange to me. Is this a mistake in the book or am I not understanding something?

The dynamics are:

Light bottle: GPP - Ra = 0.15 Dark bottle : Ra = 0.08

Then GPP = 0.15 + 0.08.

The amount of O2 used in the respiration is 0.08 mg O2 / L, not -0.08 mg O2 / L.

If you think about what is going on in the light bottle, this should make more sense. Oxygen is being produced by photosynthesis, but respiration is using up the oxygen being produced. Therefore, the total amount is NPP. So if you want to find out how much oxygen was originally created through photosynthesis (GPP), you need to put those losses due to cellular respiration back into the light bottle (in other words, add the R amount to the light bottle amount).

An autonomous, in situ light-dark bottle device for determining community respiration and net community production

Availability of data and code:: A MATLAB script to read in, process, and estimate rates and uncertainties in dissolved oxygen data from the PHORCYS is provided online at The script can be easily adapted to calculate -based estimates of uncertainty in any dissolved oxygen time series. All PHORCYS and Winkler titration data and other scripts required to reproduce the results and figures in this work are available online in the same location.


Direct measurements of gross primary productivity (GPP) in the water column are essential, but can be spatially and temporally restrictive. Fast repetition rate fluorometry (FRRf) is a bio-optical technique based on chlorophyll a (Chl-a) fluorescence that can estimate the electron transport rate (ETRPSII) at photosystem II (PSII) of phytoplankton in real time. However, the derivation of phytoplankton GPP in carbon units from ETRPSII remains challenging because the electron requirement for carbon fixation (Фe,C), which is mechanistically 4 mol e − mol C −1 or above, can vary depending on multiple factors. In addition, FRRf studies are limited in freshwater lakes where phosphorus limitation and cyanobacterial blooms are common. The goal of the present study is to construct a robust Фe,C model for freshwater ecosystems using simultaneous measurements of ETRPSII by FRRf with multi-excitation wavelengths coupled with a traditional carbon fixation rate by the 13 C method. The study was conducted in oligotrophic and mesotrophic parts of Lake Biwa from July 2018 to May 2019. The combination of excitation light at 444, 512 and 633 nm correctly estimated ETRPSII of cyanobacteria. The apparent range of Фe,C in the phytoplankton community was 1.1–31.0 mol e − mol C −1 during the study period. A generalised linear model showed that the best fit including 12 physicochemical and biological factors explained 67% of the variance in Фe,C. Among all factors, water temperature was the most significant, while photosynthetically active radiation intensity was not. This study quantifies the in situ FRRf method in a freshwater ecosystem, discusses core issues in the methodology to calculate Фe,C, and assesses the applicability of the method for lake GPP prediction.

Citation: Kazama T, Hayakawa K, Kuwahara VS, Shimotori K, Imai A, Komatsu K (2021) Development of photosynthetic carbon fixation model using multi-excitation wavelength fast repetition rate fluorometry in Lake Biwa. PLoS ONE 16(2): e0238013.

Editor: Bruno Jesus, University of Nantes, FRANCE

Received: August 4, 2020 Accepted: January 19, 2021 Published: February 2, 2021

Copyright: © 2021 Kazama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: TK, KH, KS and AI were supported by the Collaborative Research Fund from Shiga Prefecture entitled “Study on water quality and lake-bottom environment for protection of the soundness of water environment” under the Japanese Grant for Regional Revitalization, and the Environment Research and Technology Development Fund (No. 5-1607) of the Ministry of the Environment, Japan.

Competing interests: The authors have declared that no competing interests exist.

Chlorination-induced cellular damage and recovery in marine microalga, Chlorella salina

Power plants employ chlorination for controlling biofouling in the cooling water system. Phytoplankton drawn into the cooling water system could be impacted by chemical stress induced by the oxidizing biocide. It is likely that microalgae, being sensitive to chlorine, could suffer damage to their cellular structure and function. In this study, we present data on the effect of in-use concentrations of chlorine on the unicellular microalga, Chlorella salina. Chlorophyll autofluorescence was measured in terms of mean fluorescence intensity per cell for rapid assessment of toxicity. Viability of the cells exposed to chlorine was determined by fluorescein diacetate staining. Functionality of the photosynthetic machinery was assessed by gross primary productivity. Results from the study, which combined confocal laser scanning microscopy with image analysis, showed a significant dose-dependant reduction in chlorophyll autofluorescence, esterase activity and gross primary productivity in chlorine-treated cells. Interestingly, the cells injured by chlorination could not recover in terms of autofluorescence, esterase activity or productivity even after 18 h incubation in healthy media. Among the test points evaluated, esterase activity appeared to be sensitive for determining the chlorination-induced impact. Our results demonstrate that low-dose chlorination causes significant decrease in chlorophyll autofluorescence, intracellular esterase activity and primary productivity in Chlorella cells.


► Rapid and sensitive assessment of chlorination-induced damage in Chlorella cells. ► Transient exposure to chlorine caused cellular damage in microalgal cells. ► Impacted cells did not recover in terms of autofluorescence and esterase activity. ► Impacted cells did not recover in terms of gross primary productivity.

AP Sample Lab 12 Dissolved Oxygen

Dissolved oxygen levels are an extremely important factor in determining the quality of an aquatic environment. Dissolved oxygen is necessary for the metabolic processes of almost every organism.

Terrestrial environments hold over 95% more oxygen than aquatic environments. Oxygen levels in aquatic environments are very vulnerable to even the slightest change. Oxygen must be constantly be replenished from the atmosphere and from photosynthesis. There are several factors that effect the dissolved oxygen levels in aquatic environments.

Temperature is inversely proportional to the amount of dissolved oxygen in water. As temperature rises, dissolved oxygen levels decrease.

Wind allows oxygen to be mixed into the water at the surface. Windless nights can cause lethal oxygen depletions in aquatic environments.

Turbulence also increases the mixture of oxygen and water at the surface. This turbulence is caused by obstacles, such as rocks, fallen logs, and water falls, and can cause extreme variations in oxygen levels throughout the course of a stream.

The Trophic State is the amount of nutrients in the water. There are two classifications: oligotrophic and eutrophic. Oligotrophic lakes are oxygen rich, but generally nutrient poor. They are clearer and deeper than eutrophic lakes and are younger. Oxygen levels are constant. Eutrophic lakes are more shallow and nutrient rich. The oxygen levels constantly fluctuate from high to low.

Primary production is the energy accumulated by plants since it is the first and basic form of energy storage. The flow of energy through a community begins with photosynthesis. All of the sun’s energy that is used is termed gross primary production. The energy remaining after respiration and stored as organic matter is the net primary production, or growth. The equation for photosynthesis is as follows:

12H2O + 6CO2 → C6H12O6 + 6O2 + 6H2O

There are two ways to measure primary production, the oxygen method and the carbon dioxide method. The oxygen method uses a dark and light bottle to compare the amount of oxygen produced in photosynthesis and used in respiration. Respiration rate is determined by subtracting the dark bottle from the initial bottle. The carbon dioxide method places a transparent plastic bag over one sample and a dark plastic bag over the other. Each bottle is set up so that air is drawn through the enclosure and passes over carbon dioxide-absorbent material. The amount of carbon under the dark bag is respiration, while the amount of carbon under the transparent bag is the amount of photosynthesis minus the amount of respiration.

There are three main gases dissolved in aquatic environments: nitrogen, oxygen, and carbon dioxide. Most gases obey Henry’s law, which says that at a constant temperature, the amount of gas absorbed by a given volume of liquid is proportional to the pressure in the atmosphere that the gas exerts.

c = Concentration of the gas that is absorbed

p = Partial pressure of the gas

Altitude may affect the p value of the equation. Higher altitudes decrease the solubility of gases in water. Temperature also has an affect, as temperature rises, solubility decreases. Salinity, the occurrence of various minerals in solution, also lowers the solubility of gases in water.

The method used to determine the amount of dissolved oxygen in the water is the Winkler titrametric method. It involves a series of chemical reactions which ends with a quantity of free iodine equal to the amount of oxygen in the sample. The iodine is then titrated with thiosulfate to find this quantity.

The temperature and amount of light an aquatic environment receives greatly affects the dissolved oxygen levels, along with the amount of primary aquatic productivity.

Measurement of Dissolved Oxygen

This part of the lab required a sample bottle of water from a natural source, a BOD bottle, thermometer, mangonous sulfate, alkaline iodide, thiosulfate, a 2-mL pipette, sulfuric acid, a 20-mL sample cup, a white piece of paper, starch solution, and a nomograph.

Measurement of Primary Productivity

Part B required a sample bottle of water from a natural source, 7 BOD bottles, aluminum foil, 17 cloth screens, rubber bands, a light, thermometer, concavity slides, light microscope, mangonous sulfate, alkaline iodide, thiosulfate, a 2-mL pipette, sulfuric acid, a 20-mL sample cup, a white piece of paper, starch solution, and a nomograph.

Productivity Simulation

This section required pencil, paper, calculator, and graph paper.

Measurement of Dissolved Oxygen

The sample bottle was filled completely so that there were no air bubbles in the bottle. The sample bottle was left in the refrigerator until it reached 5° C. A BOD bottle was filled with the sample water until it contained no air bubbles.

Eight drops of mangonous sulfate were added to the bottle. Next, eight drops of alkaline iodide was added and the precipitate manganous hydroxide was formed. The bottle was inverted several times and then allowed to settle until the precipitate was below the shoulders of the bottle. While the solution was settling, a 2mL pipette was filled with thiosulfate. A scoop of sulfuric acid was added, and the bottle was inverted until all of the precipitate dissolved. The sample turned a clear yellow.

20mL of the sample were poured into the sample cup. The cup was placed on a white sheet of paper so that the color changes could be observed. 8 drops of starch solution were added to the sample, making it turn purple. The sample was then titrated with the thiosulfate. One drop of the titrant was added at a time until the color changed to a pale yellow color.

A nomograph was used to determine the percent saturation of dissolved oxygen in the sample.

Measurement of Primary Productivity

A second sample bottle was filled from a natural source making sure there were no air bubbles. Seven BOD bottles were filled completely with the sample with no air bubbles. The first bottle was labeled #1-Initial. The second bottle served as the dark bottle and was labeled #2-Dark. The other five bottles were labeled according to the light intensity: #3-100%, #4-65%, #5-25%, #6-10%, and #7-2%.

Bottle #2 was wrapped completely in aluminum foil so that it received no light. The other five bottles were wrapped in screens to produce the desired light intensity. Bottle #3 had no screens, bottle #4 had 1 screen, bottle #5 had 3 screens, bottle #6 had 5 screens, and bottle #7 had 8 screens. The screens were held in place with rubber bands. Bottles #2-7 were placed under a light source and left overnight.

Bottle #1 was fixed by following the Winkler method. Eight drops of mangonous sulfate were added to the bottle. Next, eight drops of alkaline iodide was added and the precipitate manganous hydroxide was formed. The bottle was inverted several times and then allowed to settle until the precipitate was below the shoulders of the bottle. A scoop of sulfuric acid was added, and the bottle was inverted until all of the precipitate dissolved. The sample turned a clear yellow. It was left at room temperature until the other samples were processed.

A wet mount was observed under a light source, so that the different organisms present could be identified.

The next day, bottles #2-7 were fixed by following the same method used on Bottle #1. The dissolved oxygen levels were determined in each of the seven bottles by titrating. 20mL of the sample were poured into the sample cup. The cup was placed on a white sheet of paper so that the color changes could be observed. 8 drops of starch solution were added to the sample, making it turn purple. The sample was then titrated with the thiosulfate. One drop of the titrant was added at a time until the color changed to a pale yellow color.

Productivity Simulation

The respiration data from Part B was converted to carbon productivity. The data was graphed with comparison to water depths.


We have reviewed the background of leaf photosynthesis and the associated terminology and showed that two differing definitions of gross photosynthesis are used in the literature. The first definition equates gross photosynthesis with the carboxylation rate, which historically has been referred to as ‘true photosynthesis’, while the second definition subsumes carboxylation with photorespiration, which historically has been referred to as ‘apparent photosynthesis’.

We further show that the commonly applied EC CO2 flux partitioning (Reichstein et al. 2005 Lasslop et al. 2010b ) yields estimates of GPP which conceptually correspond to the definition of the ‘apparent photosynthesis’ due to the fact that the nighttime ecosystem respiration on which estimated daytime ecosystem respiration is based does not contain any information on photorespiration. The major new finding of this study is that despite being conceptually not compatible with the definition of ‘true photosynthesis’, GPP inferred by flux partitioning is quantitatively actually closer to the ‘true’ than the ‘apparent’ photosynthesis. This is due to an overestimation of daytime mitochondrial respiration with the flux partitioning approach. The actual degree of overestimation is shown to be the result of a complex interplay between biotic and abiotic influence factors and thus varies seasonally (Fig. 3b) and, although not tested here, very much likely between study sites. While GPP estimated with the flux partitioning approach was still somewhat overestimated in the investigated mountain grassland over the annual cycle, underestimation occurred during certain times as well (Fig. 3b) and might dominate under certain conditions.

A key uncertainty (Fig. 3c), and at the same time highly sensitive parameter (Wohlfahrt et al. 2005 ), is the actual degree to which leaf mitochondrial respiration is reduced in the light relative to darkness, which according to Niinemets ( 2014 ) and Heskel et al. ( 2013 ) varies between 20 and 130%. Due to technical challenges in reliably estimating Rday, available Rday/Rdark ratios should be viewed with caution and it is presently not clear whether this large degree of variation is reflective of biological variability or experimental artefacts. In addition, Pinelli & Loreto ( 2003 ) suggested that the reduction of Rday in the light might actually be an apparent one, the CO2 released by mitochondrial respiration being re-fixed by photosynthesis. In the latter case or more generally if Rday/Rdark ≈ 1, the flux partitioning approach would result in unbiased estimates of the ‘apparent photosynthesis’ (Vc − 0.5Vo) and would underestimate the ‘true photosynthesis’ (Vc) by the flux of photorespiration.

Finally, a word of caution is in order: The present study exclusively focused on issues of photosynthesis terminology in context with the approach of EC flux partitioning and did not quantify other uncertainties with this approach. The uncertainty of nighttime EC CO2 flux measurements is one of these issues (Aubinet 2008 ). Another one is that the extrapolation of nighttime CO2 flux measurements based on a simple temperature-dependent model to daytime conditions ignores other drivers of diurnal variations in respiration rates, as it has been shown that temperature-independent biotic and abiotic processes play a major role in modulating diurnal variations in ecosystem respiration components (e.g. Bahn et al. 2009 Vargas et al. 2011 ). Other authors (Vickers et al. 2009 ) have criticized that flux partitioning creates a spurious correlation between nighttime ecosystem respiration and apparent photosynthesis (but see reply by Lasslop et al. 2010a ). Given the complexity of the processes involved and the associated theoretical and experimental uncertainties, it may be worthwhile to question how meaningful EC CO2 flux partitioning is and to seek other ways of exploiting the strong contrast between night and daytime net ecosystem CO2 exchange.

These measurement and methodological uncertainties call for strict use of photosynthetic terminologies so that communications among researchers and across disciplines can be facilitated. For all the purposes of what gross primary production (GPP) has been used for, this GPP has been and has to continue to be calculated as an integration of ‘apparent photosynthesis’ as knowing true photosynthesis without simultaneously knowing photorespiration is practically useless. Meanwhile, the carbon cycle research community should pay attention to the misuse of the concepts of gross photosynthesis and to some extent, net photosynthesis and stick to the historical use of these terms as outlined in the end of the Introduction section. To avoid confusion with GPP, we suggest that true photosynthesis is used in place of gross photosynthesis.

Algae Lab

. Green algae have many similarities to land plants. It has many variety body types and the multicellular forms do not have cells separated into tissues, which is what divides green algae from land plants. Green algae are a very diverse group of freshwater algae. Many green algae form long filaments. The cells stay attached after they divide. Spirogyra can become so numerous they form dense mats of growth in surfaces of ponds, which is called pond scum. This pond scum is interesting to see through a microscope. The chloroplasts from squeezed green algae have many distinct shapes. In Spirogyra the chloroplast runs through the cell like a helix. Most green algae have flagellate cells during the life cell cycle, which a few of them are non-motile. The first organization for motility in green algae is unicellular. Unicellular green algae can be either motile or non-motile. Motile green algae usually reproduce asexually by mitosis and cell division. Unicellular non-motile green algae usually produce zoospores. The second type of organization is colonial. In this organization the cells join together in colonies by attaching to one another with cytoplasmic threads. Colonial green algae can also reproduce either sexually or asexually. The third and final type of organization is filamentous. Green algae.

Control Dynamics Of An Algae-Brine Shrimp Ecosystem Lab Report Essay

. Investigating the Control Dynamics of an Algae-Brine Shrimp Ecosystem 113437768 Introduction The purpose of this lab is to investigate whether an ecosystem consisting of marine algae and brine shrimp are controlled by top-down or bottom-up mechanisms. The terms top-down and bottom-up in the context of ecology describe which trophic levels are enforcing population pressures on the others. Thus, the top-down mechanism of control holds that consumers are responsible for determining the abundance of a producer, while the bottom-up view holds that producers control the abundance of the consumers in higher trophic levels (Power, 1992). In this simulated ecosystem, we will be working with the marine alga Platymonas sp. and the crustacean Artemia salina, commonly referred to as brine shrimp. The former will serve as the producer and the latter as the consumer. Our hypothesis is that if this ecosystem is controlled in a top-down fashion, then manipulating the number of brine shrimp would lead to decreased concentrations of algae in jars with more brine shrimp. Methods and Materials To perform this experiment, one will need a container of seawater, algae beakers with known concentrations of 25,000 cell/mL, six 100 mL jars and lids for each jar, transfer pipettes, a micropipette and micropipette tips, a petri dish, a graduated cylinder, tape and a marker, pH strips, a microscope, cuvettes, and a.

Essay on Thalgo: The Health Benefits Of Algae

. Thalgo was taken over in the year 1999 the company was founded in the south of France. Using the benefits of algae and active marine ingredients in Cosmetics. The sea contains all traces and micro- nutrients all essential to the human metabolism. Algae has the extra ordinary ability to concentrate all the riches of marine life. Algae can contain up to 1000 times more iodine, 100 times more calcium, and 10 times more magnesium than a land plant. Algae is also rich in vitamins and once dried with the micronisation technique they can retain all its vitamins and minerals. The starting point for the company was to develop a patent for micronized marine algae. Thalgo research and development laboratories with universities and reputable phycologists. Researches into algae are ongoing where natural sunscreen is found in the algae. Today Thalgo is the number one leading brand in marine cosmetics which offers a global approach within the spa/beauty industry, Thalgo distribution is said to be based on three pillars the first is the medical use of water more commonly known as thalassotherapy treatment, their distribution to spas and, Thalgo is also highly appreciated in approximately 15,000 high-end beauty salons in France and abroad. Thalgo provides an all-round coverage of beauty ranging from treatment procedures, homecare to suite each client’s needs, these treatments range from.

Essay on Salinity Lab Report

. Effect Salinity Has on Primary Productivity Research Question: How does the amount of salinity in the water affect gross primary productivity? Hypothesis: The higher the salinity content in the water the lower the gross primary productivity values. Variables: Control- The group with zero salt added. Dependent- Dissolved oxygen reading. Independent- The salt concentration in the water. Introduction: On the surface waters of lakes and oceans, plants are mainly unicellular algae, and most consumers are microscopic crustaceans and protozoans. Both the producers and consumers are very small, and they are easily contained in a liter of water. If you put these organisms in a bottle and turn on the lights, you get photosynthesis. If you turn off the lights, you turn off the primary production. Darkness has no effect on respiration. This is because cellular respiration is actually the reverse process of photosynthesis. Oxygen is a necessity of life requirements for basically all living organisms.* In this lab we are testing how different levels of salinity in the water indirectly affects the gross primary productivity in aquatic plants. To measure this you would use the light and dark bottle method. Only respiration (R) can occur in the bottle stored in the dark. The decrease in dissolved oxygen in the dark bottle over time is a measure of the rate of respiration. Both photosynthesis and respiration can occur in the bottle exposed to light.

Algae Growth Condition Research Paper

. removing iron from the medium will have drastic negative effect on the cell growth. (Meng Chen, 2011)3. The efficiency of carbon fixation highly depends on the carbon supply, for microalgae to growth at an optimal rate, the CO2 need to be at least 2%. Carbon supply needs to be pure and if source of carbon is from the flue gas, it might have contamination resulting in growth inhibition (C. A. Santos, 2011)4. Furthermore, limited carbon supply will have negative impact on the cell growth. With a richer carbon supply, specific growth rate will increase and thus resulting in a high cell concentration. This will enhance productivity of cell cultures. Physical conditions Types of culturing (e.g. closed system photobioreactor and open system algae ponds) affect the productivity. This is because closed system has an advantage of better control of certain key parameters (e.g. temperature, incident light intensity) compared to an open system. However, open system allows larger volume of culture whereas closed system will the limitation of culture volume. Another physical condition is the depth of culture. Depth of culture is important when mixing of culture is minimized, this limit the gas and light transfer thus affecting the efficiency of carbon fixation (Alemayehu Kasahun Gebremariam, 2012)2. Light intensity plays an important factor in the enhancement of carbon fixation. Lipid (for biodiesel) content is maximized at an optimal light intensity. Different.

Essay about Stomata Lab Report

. life beginning in the ocean where scientists have traced back to an ancestral bryophyte (also known as freshwater green algae) that has, over time, developed a key process of making food by using the energy from sunlight to convert carbon dioxide and water into by glucose and oxygen. The plant life history also involves the "alternations of generations" that allows a plant to undergo meiotic/mitotic phases between the sporophyte(diploid) and gametophyte(haploid) generation. In leaves, gas exchange occurs through little pores called the stomata which are present in the sporophyte generation. These small openings are light sensitive, so they are most commonly located on the bottom of leaves to prevent dehydration. The stomata usually open in the morning, and close in the night in C3 and C4 plants. Although, many plants species are different when you compare their stomata orientation. One of the two major groups of flowering plants(angiosperms) are monocots, which include palms. These trees have adapted to harsh, dry environments. The deep roots of a palm tree allow it to reach far below the soil and obtain the necessary water and minerals stored at the bottom, and to help it grow. Its giant sized leaves allows for maximum sunlight exposure and its waxy surface cuticle of its leaves prevent the large loss of water from leaf due to transpiration. For the lab, I observed six leaves collected from six different plants growing sporadically around.

The Competition Between the Two Green Algae: Essay

. hypothesis that the pure algae populations of Ankistrodesmus and Chorella will show growth in the number of organisms, and the mixed population will establish a dominant species. Each population should also reach its carrying capacity. All of the populations had the same resources available. The sampling of 0.5ml of each population was used to count the number of organisms. A Neubauer slide and microscope will be needed to do this. From this count, determine the number of organisms in 1ml. After doing this procedure for three weeks the organisms in the pure samples should have grown in size and reached a carrying capacity. The mixed population of the two algae species should show that one species is dominant over the other. The results of the experiment support this hypothesis. Both the pure samples of Ankistrodesmus and Chorella increased dramatically in size and then reached their carrying capacity. The mixed population showed that Chorella is the dominant species over the Ankistrodesmus. Introduction This experiment was conducted by using the green algae: Ankistrodesmus and Chorella. Most species of green algae live in shallow freshwater environments. The can live on moist rocks, trees, and soil some can inhabit shallow ocean waters. Green algae usually occur as single cells or as multi-cellular, threadlike filaments, hollow balls, or flat sheets (Postlethwait and Hopson).

Experiment 1 Lab Report Essay

. Through Photosynthesis Date Performed: September 2, 2014 I. Introduction a. Background Cyanobacteria or blue green algae are renowned for their tolerability and susceptibility even in wide range of environmental conditions, a characteristic of many primitive organisms. CNB are believed to be the agents of autotrophic origin of life thus they probably represent the survivors of the earliest photosynthetic plants, along with photosynthetic and chemosynthetic bacteria. CNB fossilized remains have been found in middle Precambrian rocks, and they may have appeared much earlier (Humm and Wicks 1980). b. Objectives This experiment aims to find out the oxygen production of cyanobacteria in selected sample areas. The experiment will also introduce the use of dissolved oxygen meter in determining oxygen production. II. Materials and Methods Water Samples Fresh or Tap H2O (BOD) bottle Dissolved Oxygen (DO) Meter Micropipette 1. All the needed materials were prepared. 2. Freshwater or tap water was added into the BOD bottle. 3. The initial dissolved oxygen was measured using a DO meter. 4. With the use of a micropipette, a predefined volume of the water samples acquired in different locations was added into four different BOD bottle with H2O. Water samples were obtained from different areas (Pond, Britanico Bridge Stream, Bentoy’s Place Canal and Hatchery Canal) before the lab. 5. The containers were kept in a well-lighted place. 6. The dissolved.

1 Introduction

The tropical and subtropical marine regions include the five large oligotrophic gyres, which represent about 40% of the Earth's surface. These oligotrophic gyres rank among the most unproductive marine environments in the planet [Field et al., 1998 ] and have been increasing in extent from 0.8 to 4.3%/yr [McClain et al., 2004 Polovina et al., 2008 Irwin and Oliver, 2009 ] in the last decade. Moreover, the oligotrophic gyres are forecasted to continue to expand in a warmer ocean by 4% in the Northern Hemisphere and by 9.4% in the Southern Hemisphere [Sarmiento et al., 2004 ], and with further oligotrophication, net primary production in the ocean is expected to decline [Signorini et al., 2015 ]. The expansion and oligotrophication of the gyres are believed to result from reduced vertical diffusive nutrient fluxes across a steeper thermocline as the ocean warms [Sarmiento et al., 2004 Signorini et al., 2015 ]. However, warming also affects metabolic processes directly (e.g., metabolic theory of ecology, or MTE) [Gillooly et al., 2001 Brown et al., 2004 ] and is expected to affect respiration rates more strongly than primary production rates [Harris et al., 2006 López-Urrutia et al., 2006 Garcia-Corral et al., 2014 ], thereby affecting the metabolic balance of plankton communities [Regaudie-de-Gioux and Duarte, 2012 ].

Net community production (NCP = GPP − CR) is the difference between gross primary production (GPP) and community respiration (CR). NCP distinguishes the role of plankton communities as autotrophic or sinks of CO2 from the atmosphere where productivity is the dominant process (NCP > 0, GPP > CR), from heterotrophic communities, which act as sources of CO2 to the atmosphere [Del Giorgio and Duarte, 2002 ] where respiration is the predominant metabolic process oxidizing organic matter (NCP < 0, GPP < CR). Sustained heterotrophic community metabolism requires, however, allochtonous inputs of organic carbon [Del Giorgio and Duarte, 2002 Duarte et al., 2013 ], which may be derived, in magnitudes sufficient to affect plankton metabolism in the open ocean, from coastal [e.g., Barrón and Duarte, 2015 ] and atmospheric inputs [e.g., Dachs et al., 2005 Jurado et al., 2008 Gonzalez-Gaya et al., 2016 ]. As a consequence of the steeper response of CR to warming compared with GPP [Harris et al., 2006 López-Urrutia et al., 2006 Regaudie-de-Gioux and Duarte, 2012 Garcia-Corral et al., 2014 ], heterotrophic communities are expected to increase in a warmer ocean [Duarte et al., 2013 ]. However, most assessments of the temperature dependence of plankton metabolism are still dominated by data from relatively productive regions of the oceans, such as equatorial and coastal permanent upwelling regions [Regaudie-de-Gioux and Duarte, 2012 ]. Reports of the temperature dependence of plankton metabolism in subtropical regions are mostly derived from the Atlantic Ocean [López-Urrutia et al., 2006 Regaudie-de-Gioux and Duarte, 2012 Garcia-Corral et al., 2014 ], and reports of plankton metabolism from other oligotrophic areas of the ocean, particularly from the Indian Ocean, are lacking [Regaudie-de-Gioux and Duarte, 2012 Duarte et al., 2013 ].

In this study, we compile planktonic metabolic rates measured in the epipelagic ocean during the Malaspina 2010 Circumnavigation Expedition, a global survey that sampled the subtropical and tropical Atlantic, Pacific, and Indian oceans (Figure 1) [Duarte, 2015 ]. Here we assess the temperature dependence of the metabolic rates (GPP, CR, and the ratio GPP/CR) of plankton communities in the subtropical and tropical oceans for the first time using a consistent sampling approach along 7 months.

Water quality, primary productivity and carbon capture potential of microalgae in two urban manmade lakes, Selangor, Malaysia.

Climate change is in progress. Evidence are surfacing almost daily on the impact of climate change to the environment, human health, economic resources and others. Many problems could result from global warming such as changes in weather patterns [23,53,30,76]. Many areas of the world are experiencing increased floods, and other unusual weather. In addition, biodiversity loss can be caused by alteration of ecosystems and loss of the habitats [22,34, 35, 11,12], and because of climate change, heating of the earth's surface affects biodiversity because it endangers all the species that adapted to the colder weathers. Water quality degradation and Scarcity of fresh water are directly caused by changes in land use and urbanization [67]. Land use can disrupt the surface water balance and the splitting of precipitation into evapotranspiration, runoff, and groundwater flow. Surface runoff and river discharge generally increase when natural vegetation is cleared because of agricultural expansion. Water demands associated with land-use practices, especially irrigation, directly affect freshwater supplies through water withdrawals and diversions. Excessive inputs of nutrients, particularly nitrogen and phosphorus also can contribute to aquatic ecosystems degradation, this may lead to hypoxia and biodiversity loss as a result of eutrophication [16, 91,47,72].

Most investigations on effect of climate change on freshwater ecosystems has concentrated on responses of individual's physiological characters under warming [86,80,39]. The productivity of freshwater bodies also was meaningfully altered by increases in water temperatures. Warmer waters are commonly more productive [48], but sometimes they are not desirable for particular species and on other hand other tolerant or even harmful species can flourish [60, 8, 90]. Dominance of cyanophyta was considered as indicator of global climate change [56, 57, 13, 24, 29]. The physical, chemical, and biological reactions of freshwater bodies to climate provide a variant of evidence of their regulation of climate change [83]. [55] Stated by using carbon isotope in sediments of tropical freshwater lake that lake productivity decreased historically by effect of global warming.

Many strategies were considered to mitigate emissions of carbon dioxide and adaptation to climate change [54, 89, 15, 4, 49, 79], however, no much known on using lakes to mitigate climate change through C[O.sub.2] absorption. So using freshwater lakes to uptake C[O.sub.2] from atmosphere by photosynthetic organisms like phytoplankton can be a solution to mitigate climate change by no cost for adaptation [63, 68,1,42, 26,51]. Globally, the amount of fresh water in lakes and rivers forms only 0.2% of the total area of earth, and 2.5% of water resource [20, 21], however, they can contribute in carbon sequestration. Phytoplankton can produce a huge amount of their biomass in a small area in comparison with other productive plants [25,9,74, 19, 84,6]. Productivity of water bodies depends on the availability of solar energy and raw materials as nutrients and minerals within the ecosystem [66, 73]. Phytoplankton as a biotic component in a lake, use the solar energy to grow by Photosynthesis. Light conditions have a direct impact on the growth performance and photosynthesis of phytoplankton in term of duration and intensity [64].

Changes in the chemical, hydrological, biological and Physical variables lakes were observed by many studies. Precipitation decline, evaporation increase were the most factors influenced the lakes characters [77, 87, 17, 36]. Temporal variability in phytoplankton production is a result of the interactions between physical, chemical and biological variables [43]. The physicochemical features greatly influence the primary productivity in an aquatic ecosystem [70]. Weather conditions variability have an important effect on water properties in a hydrologic ecosystem. Most of studies done in lakes were focused on zooplankton and phytoplankton diversity, Lake Limnology, primary productivity and nutrient dynamics. Sampling were done monthly or bimonthly but very rare weekly.

Past studies on primary production in Malaysia were more focussed on the relation of phytoplankton productivity, their diversity and the trophic status [52,66]. The main objective of current study to observe how much two urban manmade lakes can contribute to fix carbon dioxide by phytoplankton in relation to physical and chemical parameters of lakes in different weather conditions, and different trophic status.

The sampling were done in two manmade lakes, Faculty of engineering (UPM) (3.005885 latitude, 101.720271 longitude) as oligotrophic lake, and Seri Serdang (3.004689 latitude, 101.714071 longitude) as mesotrophic lake (Fig. 1). Engineering lake is located in the middle of the academic building of Engineering Faculty (UPM) with total surface area is about (24,140 m2). The Seri Serdang Lake is located near the housing estate of Seri Serdang at (3.004689 latitude, 101.714071 longitude). The total surface area is about 18,000 m with mean of depth is about 5 meter. The study was done from September 2014 to January 2015 and July 2015.

Weekly sampling from three sampling points for three 500 ml water samples were done for alkalinity, nutrient analysis and phytoplankton biomass, from the surface of both lakes at 0.5m depth. Three sets of 4 BOD (300ml) bottles were placed at the sampling depth (0.5m) in both of lakes, two light bottles and two dark bottles to measure biochemical oxygen demand and primary productivity. The sites and the time of sampling were fixed throughout sampling period. Light intensity ([[micro]mol. [m.sup.-2] [s.sup.-1]) was determined by using light meter Licor model (L1-250). Physical water parameter such as dissolve oxygen (DO), pH, and total dissolve solid (TDS), conductivity and water temperature were taken by Yellow Spring Instrument multi parameter probe model (YSI-556 MPS) from surface water. Water transparency was measured by Secchi disk and rain rainfall using rain gauge.

Alkalinity was measured by titration of 100 ml of sample with 0.02 N sulphuric acid using few drops of mixed reagent (methyl red and bromocresol green) as an indicator to determine the end point of the titration, the colour change from blue to colourless [71].

Nitrate-nitrogen analyses were determined following Kitamura method [44]. Ammonium concentration was analysed based on phenol hypochlorite method [82]. Phosphate-phosphorus (P[O.sub.4]-P) was determined according to the ascorbic acid method [32].

Water samples were measured immediately by Hitachi UC-1900 UV visible spectrophotometer at 620 nm three times for each samples and the average was taken to determine optical density. To measure dry weight, 50 ml of water sample was filtered by predried Sartorius glass filter paper, and oven dried at 60[degrees]c for 24h [10]. To measure Chlorophyll a, 30 ml of water sample from each bottle was filtered by MS[R] cellulose acetate membrane filter (0.45 [micro]m). Chlorophyll-a was extracted with 5ml (90%) acetone overnight at 4C[degrees]. The extraction was homogenized by driller. After centrifugation, the absorbance of the supernatant was measured by spectrophotometer (Hitachi UC-1900) [38]. Chlorophyll a was calculated by the equation of Jeffrey and Humphrey [41]:

[Chl. a] extract = 11.85A664-1.54A647-0.08A630

For biological oxygen demand (BO[D.sub.5]), dissolved oxygen (DO) was measured initially with transparent bottle and dark bottle after five days of incubation. For primary productivity, dissolved oxygen (pairs light bottles and one dark bottle) was measured after two hours of incubation at the sampling depth in the lake. Dissolved oxygen was measured using the Azide modification method by titration [2]. Gross primary productivity, net primary productivity and fixed carbon were calculated using next equations [3]:

Community respiration (R) = initial - dark

Gross primary productivity (GPP) = Light - dark

Net primary productivity (NPP) = GPP-R

Since (0.375) is the factor comes from differences in atomic mass (12/32) Trophic status index (TSI) was calculated based on chlorophyll- a concentration as next equation [14]:

Weather recording and classification:

Weather were recorded 3 times daily: morning 8-9 am, noon 12.00-2.00 pm and afternoon at 4-5 pm. To determine rainfall, rain gauge was constructed from 5L size bottles with fixed funnel at the top and placed beside each lake and the volume of water in the gauge was measured after each raining time [31]. Calculation formula:

Rain fall gauge (mm) = (Collected water volume) / (funnel surface area/bottle base area). Based on light intensity, temperature and rainfall data scores in table (1&2), and the weather conditions classified into three categories. Mix weather conditions, wet weather conditions and dry weather conditions

One-way ANOVA statistical analysis from SPSS version 21 was used to indicate the significant of variance in physical environmental parameters and primary productivity among different sampling periods. Principal component analysis was analysed using XLSTAT Version 2014.5.03. PCA is a variable reduction technique it is designed to reduce the original variables into new, uncorrelated variables or axes, called the principal components which are linear combinations of the original variables.

Light intensity and rain fall were more important to classify the weather conditions than air temperature. Mix weather conditions characterized by dense cloud cover, mix cloudy sky, heavy haze, light rains one or twice a week. Wet weather conditions characterized by heavy rains many times a day or a week, and cloud cover. Dry weather conditions characterized by sunny sky, no rains or light rain once a week.

Physical and chemical parameters:

A significant temporal variation was recorded in surface water temperature, salinity, alkalinity in both lakes (Table 3). Total dissolved solids and conductivity were significantly differed in Seri Serdang lake with rang of (0.161 - 313 mg/L) (0.25- 0.54 mS [cm.sup.-1]) respectively. The nitrate concentration was higher in Engineering lake (0.003 - 0.48mg/L) than in Seri Serdang lake, while Ammonium and phosphate concentration were higher in Seri Serdang lake with range of (0.30 - 1.20 mg [L.sup.-1]), (0.04 - 0.75 mg [L.sup.-1]) respectively. Nitrate (N[O.sup.-.sub.3]), Total nitrogen, TN: TP concentration were differed significantly in Engineering lake than Seri Serdang Lake with range of (0.003- 0.84 mg[L.sup.-1]), (0.016 - 0.28 mg[L.sup.-1]) and (0.36 - 21.1mg[L.sup.-1]) respectively.

Primary production and biomass:

There were temporal fluctuations of primary production (Table 4), the highest values of gross primary productivity (GPP) rates were recorded in dry weather conditions in both lakes, while the lowest values were during mix weather conditions in both lakes, and net primary productivity. Peaks of fixed C[O.sub.2] (0.96 mg/C/[m.sup.2]/h) (3.71mg/C/[m.sup.2]/h) were in dry weather condition in both lakes

The Chlorophyll a concentration was significantly fluctuated (Table 4) in Engineering lake with range of (0.12-1.0 [micro]g [L.sup.-1]), while that of Seri Serdang lake was not significantly differed with range of (0.46- 6.9 [micro]g [L.sup.-1]).

Principal component analysis:

Two components were extracted by PCA from 24 original environmental variables for both study locations. Principle components (PCs) extraction was based on choosing the Eigenvalues > 1). In Faculty of Engineering Lake, they explain 82.5% of total variance. After Varimax rotation, PC1 was defined by (TN: TP, trophic status index, chlorophyll a, dry weight, salinity, and alkalinity) and explained (22.50%) of variance, PC2 was defined by (community respiration, gross production, Net production, C[O.sub.2] fixed, dissolved oxygen and secchi depth) and explained (15.56%) of variance.

In Seri Serdang Lake PCs explain 88.87% of total variance. After Varimax rotation, PC1 was defined by physical and chemical parameters (Phosphate, total nitrogen, conductivity, total dissolved solids, salinity, alkalinity, and trophic status index) and explained (28.95%) of variance, PC2 was defined by biological parameters (gross production, Net production, C[O.sub.2] fixed, optical density, chlorophyll-a) and explained (18.14%) of variance.

Assumptions for both lakes primary production:

The surface area of Seri Serdang Lake is (18000 [m.sup.2]), the sampling depth is (0.5 m) so there is (9000 L) of productive phytoplankton. In 6 hours period during day time, these amount of productive phytoplankton could fix (5.49) mg C [m.sup.-2][h.sup.-2], while the surface area of in Engineering lake is (24,140 [m.sup.2]), the sampling depth is (0.5 m) so there is (12,070 L) of productive phytoplankton. In 6 hours period during day time, these amount of productive phytoplankton could fix (1.62) mg C [m.sup.-2][h.sup.-2]

Weather conditions have profound effect on the aquatic ecosystem. Malaysia weather conditions have no distinct pattern like in the temperate regions [85]. Typical Malaysian weather can be clear sunny sky, cloudy or rain which can happen at any moment during the day, weeks, monthly or yearly. Rain and cloud covers were disrupting the important light for photosynthesis [33], alter the temperature and dilute the water chemistry [81].

Different weather conditions seem to be the major factor behind fluctuation of water quality parameters and primary production in both lakes. PCA biplots in Figures (3&4) indicates larger differences in environmental variables between the three weather categories and type of lakes.

Light intensity and then photosynthesis process were significantly affected by dense cloud cover during mix weather conditions resulting in decreased of dissolved oxygen in surface water lake. The average dissolved oxygen was (4.61 mg [L.sup.-1]) and (4.73 mg [L.sup.-1]) in Engineering lake and Seri Serdang lake respectively. pH and nitrate nitrogen (N[O.sub.3.sup.-]) marginally increased during wet weather conditions probably due to acidic rain and reduced temperature. In addition, water temperature, total dissolved solids, electric conductivity, salinity, alkalinity, trophic status index also decreased in both lakes during wet weather conditions due to the dilution factor impact of rainfall. During dry weather conditions, water temperature increased in tandem with high solar radiation, resulting in increased evaporation of water thus lowering water level, concentrating more materials and suspended solids which may contributed to higher conductivity levels. Electric conductivity associate negatively with DO [28], since there were negative correlations of DO observed by PCA with water temperature, salinity and total dissolved solids.

Nitrate concentration was higher in Engineering Lake (oligotrophic) than Seri Serdang Lake (mesotrophic), since oligotrophic waters remain aerobic and there is more chance for ammonium oxidations to nitrate and also there is no assimilation by producers as in Seri Serdang Lake. Ammonium ([H.sub.4]) and orthophosphate (PO4) were higher in Seri Serdang Lake. Typically, phosphorus is the best chemical indicator of the trophic status of a water body [18]. Phytoplankton need amounts of phosphorus around (0.02mg [l.sup.-1]) to form blooms [58], and the averages concentration of total phosphorus (TP) during the whole study period were ranged from (0.01 to 0.07mg [l.sup.-1]) in Engineering lake and from (0.07 to 0.09 mg [l.sup.-1]) in Seri Serdang lake, the overall total phosphorus concentration was (0.03 mg [l.sup.-1]) in Engineering lake and (0.08 mg [l.sup.-1]) in Seri Serdang lake was because of anthropogenic factors and waste waters input.

The best ratio of TN: TP for aquatic plant growth is 10:1, ratios higher than 10 indicates a phosphorus-limited system. The ratios that are less than 10:1 represent nitrogen-limited systems. This finding suggests that Engineering Lake was phosphorus limited during dry weather conditions (18.2:1) while Seri Serdang Lake was phosphorus limited during wet weather conditions (22.5:1).

Alkalinity is an important component of water quality and was strongly correlated with TSI ([r.sup.2]= 1.00). The negative correlation between alkalinity & TP was observed in Seri Serdang Lake where the alkalinity was relatively higher. It is speculated that Calcium may have a role to play in precipitating phosphorus, making it less bioavailable [50]. Increasing alkalinity is often associated with increasing of phytoplankton productivity. Water transparency (Secchi disk reading) in Seri Serdang Lake was low ranging from (0.175 to 0.254 m) with dense green colour suggesting it is more productive than Engineering Lake where the transparency ranged from (0.291 to 0.423m).

Chlorophyll-a is a direct measurement of algal biomass. The nutrient limitation based on TN: TP ratio in both lakes is directly related with the biomass concentration of the water [88]. Phytoplankton biomass and productivity in both lakes are also closely depending on light availability and intensity. Primary productivity generally increases in conditions where the combination of available light and high nutrient concentrations are optimum. Therefore, it is most probable that the higher values of primary productivity observed during dry weather conditions may be due to combination of high concentration of nutrients, higher temperature, better light availability and higher photosynthesis.

The seasonal decline in light intensity during the mix weather conditions and wet weather conditions is likely to be a controlling factor in depressing of primary production at these conditions. In comparison between mix and dry weather conditions, the water temperature was not much affected due to the vast volume of water in both lake and high specific heat capacity of water. There were four water inlets for Seri Serdang Lake with only one outlet for water outflow. In this configuration, volume of water in the lake is relatively stable partly contributing to the stability of water temperature in Seri Serdang Lake. In dry weather conditions, low cloud cover, better light availability and intensity together with low rainfall reduces the dilution factor resulting better phytoplankton productivity. It is possible that evaporation reduced certain volume of the water in lakes, concentrating the nutrient and improving primary productivity.

The decline of primary production in the wet season may partly cause by the dilution of essential ions [52]. Rainfalls flushed nutrients from urban residential areas or from any other main sources into the lakes. The proportions of nutrient inside the lake from anthropogenic sources might fluctuate every time after rainfalls. In recent times the pH of rain water in Malaysia especially the urban areas have been below normal level which is acidic. Acid rains can lower water pH in fresh water bodies and has been known to affect primary productivity in many freshwater lakes around the world [46].

Higher temperatures may also increase the metabolic rate of organisms, resulting in increased consumption of oxygen. Therefore, water temperature can have a significant influence on DO levels, particularly in tropical regions [40], therefore decrease of dissolved oxygen during mix and dry weather conditions can be also due to microbial respiration, slight depression of photosynthesis or both.

According to observation of PCA biplot of engineering Lake (Figure 3), there is negative correlations between phytoplankton biomass with nutrients (N[O.sup.3], [H.sub.4], TN, and TP). In biplot of Seri Serdang Lake, Secchi depth was negatively correlated with biological parameters, because as productivity increase, water transparency decreased. Majority of biomass and primary production parameters during dry weather conditions were distributed in the positive space of PC1 in both PCA biplots, suggesting the dependence of phytoplankton growth and productivity on trophic status index, TN: TP and alkalinity.

Although both lake have small surface area ([m.sup.2]) are but they have a significant contribution in carbon dioxide capturing as shown in table (5). In Malaysia there are 90 lakes [69] but small manmade lakes are not included. The finding of this study showed that many small lakes in urban and suburban area can do a vital role in carbon fixation to solve climate change problems.

The present study provides better understanding about the effect of weather conditions on primary productivity in general and the role of small urban lake in carbon capture and providing oxygen. In the past there are not many studies about the effect of weather conditions and the study mostly emphasized on larger lake. According to the PCA, the lakes showed biological and limnological differences among the different weather conditions and weather is the main factor in variability of phytoplankton biomass and primary production. Water lake characters impacted principally by weather conditions, consequently, biological process of phytoplankton have changed during different weather conditions.

This work has been done with support of Biology Department, Faculty of Science, Universiti Putra Malaysia.

[1] Abdel-Fattah, S., and G. Krantzberg, 2014. "Commentary: Climate change adaptive management in the Great Lakes", J Great Lakes Res., 40(3): 587-580

[2] Ademoroti, C.M.A., 1996. "Standard method for water and effluent analysis", March prints and Consultancy, Foludex press Ltd. Ibadan, p: 182.

[3] Agrawal, A., and K. Gopal, 2013. "Measurement of Primary Productivity in Relation to Food Chain: Biomonitoring of Water and Waste Water", (p. 1-13), Springer India.

[4] Alhorr, Y., E. Eliskandarani and E. Elsarrag, 2014. "Approaches to reducing carbon dioxide emissions in the built environment: Low carbon cities". International Journal of Sustainable Built Environment, 3(2): 167-178.

[5] Balogun, K.J., A.K. Adedeji and I.A. Ladigbolu, 2014. "Primary Production estimation in the euphotic zone of a Tropical Harbour Ecosystem, Nigeria", IJSRP: 106.

[6] Bergamino, N., S. Horion, S. Stenuite, Y. Cornet, S. Loiselle, P.D. Plisnier and J.P. Descy, 2010. "Spatiotemporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series". Remote Sens. Environ., 114(4): 772-780.

[7] Bernini, E.L.A.I.N.E. and E. Rezende, 2010. "Litter fall in a mangrove in Southeast Brazil", PanamJAS, 5(4): 508-519.

[8] Bittar, T.B., A. Stubbins, A.A. Vieira and K. Mopper, 2015. "Characterization and photo degradation of dissolved organic matter (DOM) from a tropical lake and its dominant primary producer, the cyanobacteria Microcystis aeruginosa", Mar Chem., 177: 205-217.

[9] Blindow, I., A. Hargeby, J. Meyercordt and H. Schubert, 2006. "Primary production in two shallow lakes with contrasting plant form dominance: A paradox of enrichment?", Limnol. Oceanogr., 51(6): 2711-2721.

[10] Borowitzka, M.A., J.M. Huisman and A. Osborn, 1991. "Culture of the astaxanthin-producing green algae Haematococcus pluvialis 1. Effects of nutrients on growth and cell type", J. Appl. Phycol., 3(4): 295-304.

[11] Butchart, S.H., M. Walpole, B. Collen, A. Van Strien, J.P. Scharlemann, R.E. Almond and K.E. Carpenter, 2010. "Global biodiversity: indicators of recent declines", Science, 328(5982): 1164-1168.

[12] Cardinale, B.J., J.E. Duffy, A. Gonzalez, D.U. Hooper, C. Perrings, P. Venail and A.P. Kinzig, 2012. "Biodiversity loss and its impact on humanity", Nature, 486(7401): 59-67.

[13] Carey, C.C., B.W. Ibelings, E.P. Hoffmann, D.P. Hamilton and J.D. Brookes, 2012. "Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate", Water Res., 46(5): 1394-1407.

[14] Carlson, R.E., 1977. "A trophic state index for lakes1", Limnol Oceanogr., 22(2): 361-369.

[15] Chang, S.J., 2013. "Solving the problem of carbon dioxide emissions", Forest Policy Econ, (35): 92-97.

[16] Chislock, M.F., E. Doster, R.A. Zitomer and A.E. Wilson, 2013. "Eutrophication: causes, consequences, and controls in aquatic ecosystems", Nature Education Knowledge, 4(4): 10.

[17] Cohen, T.J., G.C. Nanson, J.D. Jansen, B.G. Jones, Z. Jacobs, J.R. Larsen and A.M. Smith, 2012. "Late Quaternary mega-lakes fed by the northern and southern river systems of central Australia: varying moisture sources and increased continental aridity", Palaeogeogr. Palaeoclimatol. Palaeoecol., 356: 89-108.

[18] Da Costa Lobato, T., R.A. Hauser-Davis, T.F. de Oliveira, M.C. Maciel, M.R.M. Tavares, A.M. da Silveira, and A.C.F. Saraiva, 2015. "Categorization of the trophic status of a hydroelectric power plant reservoir in the Brazilian Amazon by statistical analyses and fuzzy approaches", Sci. Total Environ., 506: 613-620.

[19] Darchambeau, F., H. Sarmento and J.P. Descy, 2014. "Primary production in a tropical large lake: The role of phytoplankton composition", Sci. Total Environ., 473: 178-188.

[20] De Vooys, C.G.N., 1979. "Primary production in aquatic environments". The global carbon cycle, 13: 259292.

[21] Diop, S., P. M'mayi, D. Lisbjerg and R. Johnstone, 2002. "Vital water graphics: an overview of the state of the world's fresh and marine waters" (1). UNEP, Earth print.

[22] Dudgeon, D., A.H. Arthington, M.O. Gessner, Z.I. Kawabata, D.J. Knowler, C. Leveque and C.A. Sullivan, 2006. "Freshwater biodiversity: importance, threats, status and conservation challenges", Boil. Rev., 81(02): 163-182.

[23] Dutzik, A. and N. Willcox, 2010. "Global warming and extreme weather: the science, the forecast, and the impacts on America", Environment Colorado Research & Policy Centre, Denver, CO.

[24] Elliott, J.A., 2012. "Is the future blue-green? A review of the current model predictions of how climate change could affect pelagic freshwater cyanobacteria". Water Res., 46(5): 1364-1371.

[25] Fahnenstiel, G., S. Pothoven, H. Vanderploeg, D. Klarer, T. Nalepa and D. Scavia, 2010. "Recent changes in primary production and phytoplankton in the offshore region of southeastern Lake Michigan". J. Great Lakes Res., 36: 20-29.

[26] Farrelly, D.J., C.D. Everard, C.C. Fagan and K.P. McDonnell, 2013. "Carbon sequestration and the role of biological carbon mitigation: a review", Renew. Sust. Energ. Rev., 21: 712-727.

[27] Feresin, E.G., M.S. Arcifa, L.H.S.D. Silva and A.L.H. Esguicero, 2010. "Primary productivity of the phytoplankton in a tropical Brazilian shallow lake: experiments in the lake and in mesocosms", Acta. Limnol. Bras., 22(4): 384-396.

[28] Gill, R.T., M.J. Harbottle, J.W.N. Smith and S.F. Thornton, 2014. "Electrokinetic-enhanced bioremediation of organic contaminants: A review of processes and environmental applications", Chemosphere, 107: 3142.

[29] Gkelis, S., T. Papadimitriou, N. Zaoutsos and I. Leonardos, 2014. "Anthropogenic and climate-induced change favors toxic cyanobacteria blooms: Evidence from monitoring a highly eutrophic, urban Mediterranean lake". Harmful Algae, 39: 322-333.

[30] Gleditsch, N.P., 2012. "Whither the weather? Climate change and conflict", J. Peace Res., 49(1): 3-9.

[31] Goldstein, M., 2002. "The complete idiot's guide to weather". Penguin.

[32] Greenburg, A.E., L.S. Clesceri and A.D. Eaton, 1992. "Standard methods for the examination of water and wastewater". Public Health Assoc., Washington, DC.

[33] Guildford, S.J., H.A. Bootsma, W.D. Taylor and R.E. Hecky, 2007. "High variability of phytoplankton photosynthesis in response to environmental forcing in oligotrophic Lake Malawi/Nyasa". J. Great Lakes Res., 33(1): 170-185.

[34] Heller, N.E. and E.S. Zavaleta, 2009. "Biodiversity management in the face of climate change: a review of 22 years of recommendations", Biol. Conserv., 142(1): 14-32.

[35] Hobday, A.J. and J.M. Lough, 2011. "Projected climate change in Australian marine and freshwater environments", Mar. Freshwater Res., 62(9): 1000-1014.

[36] Hoerling, M., J. Eischeid, D. Easterling, T. Peterson and R. Webb, 2010. "Understanding and explaining hydro-climate variations at Devils Lake". ANOAA Climate Assessment. Natl. Oceanic and Atmospheric Admin., Washington, DC.

[37] Hossain, M., S. Othman, J.S. Bujang and M. Kusnan, 2008. "Net primary productivity of Bruguiera parviflora dominated mangrove forest at Kuala Selangor, Malaysia". Forest ecology and management, 255 (1): 179-182.

[38] Hotzel, G., and R. Croome, 1999. "A phytoplankton methods manual for Australian freshwaters" (p. 52). Canberra, Australia, Land and Water Resources Research and Development Corporation.

[39] Hoveka, L.N., B.S. Bezeng, K. Yessoufou, J.S. Boatwright and M. Van der Bank, 2016. "Effects of climate change on the future distributions of the top five freshwater invasive plants in South Africa", South African Journal of Botany, 102: 33-38.

[40] Hunt, R.J. and I.H. Christiansen, 2000. "Understanding dissolved oxygen in streams", Information Kit. Townsville: CRC Sugar Technical Publication (CRC Sustainable Sugar Production).

[41] Jeffrey, S.T., and G.F. Humphrey, 1975. "New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton". Biochem. Physiol. Pflanz BPP.

[42] Kaspersen, B.S., T.V. Jacobsen, M.B. Butts, E. Bogh, H.G. Muller, M. Stutter and T. Kjsr, 2016. "Integrating climate change mitigation into river basin management planning for the Water Framework Directive-A Danish case", Environmental Science & Policy, 55: 141-150.

[43] Kirchman, D. L., 2012. "Microbial primary production and phototrophy". Processes in Microbial Ecology, OXFORD, 74-78.

[44] Kitamura, H., H. Ishitani, Y. Kuge and N. Nakamoto, 1982. "Determination of nitrate in freshwater and seawater by a hydrazine reduction method", J. Water Pollut. Res., 5: 35-42.

[45] Koli, V.K., and M.M. Ranga, 2011. "Physicochemical Status and Primary Productivity of Ana Sagar Lake, Ajmer (Rajasthan), India", Univers. J. Environ. Res. Technol., pp: 1(3).

[46] Kwiatkowski, R.E., and J.C. Roff, 1976. "Effects of acidity on the phytoplankton and primary productivity of selected northern Ontario lakes", Can. J. Bot., 54(22): 2546-2561.

[47] Le, C., Y. Zha, Y. Li, D. Sun, H. Lu and B. Yin, 2010. "Eutrophication of lake waters in China: cost, causes, and control". Environmental Management, 45(4): 662-668.

[48] Lewandowska, A.M., P. Breithaupt, H. Hillebrand, H.G. Hoppe, K. Jurgens and U. Sommer, 2012. "Responses of primary productivity to increased temperature and phytoplankton diversity". J. sea research, 72: 87-93.

[49] Lin, B., and X. Ouyang, 2014. "Analysis of energy-related C[O.sub.2] (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry". Energy, 68: 688-697.

[50] Lukawska-Matuszewska, K. and D. Burska, 2011. "Phosphate exchange across the sediment-water interface under oxic and hypoxic/anoxic conditions in the southern Baltic Sea", Oceanol. Hydrobiol. Stud., 40(2): 5771.

[51] Maity, J.P., J. Bundschuh, C.Y. Chen and P. Bhattacharya, 2014. "Microalgae for third generation biofuel production, mitigation of greenhouse gas emissions and wastewater treatment: Present and future perspectives-A mini review". Energy, 78: 104-113.

[52] MdYusoff, F., M. Mohsin, A. Khair, A. Satar and M. Kamal, 1984. "Phytoplankton composition and productivity of a shallow tropical lake". Pertanika, 7(3): 101-113.

[53] Miller, K., L. Mearns and S. Rhodes, 2000. "Effects of changing climate on weather and human activities" (p. 15). Sausalito, CA: University science books.

[54] Moss, R.H., J.A. Edmonds, K.A. Hibbard, M.R. Manning, S.K. Rose, D.P. Van Vuuren and T.J. Wilbanks, 2010. "The next generation of scenarios for climate change research and assessment", Nature, 463(7282): 747-756.

[55] O'Reilly, C.M., S.R. Alin, P.D. Plisnier, A.S. Cohen and B.A. McKee, 2003. "Climate change decreases aquatic ecosystem productivity of Lake Tanganyika, Africa", Nature, 424(6950): 766-768.

[56] Paerl, H.W. and J. Huisman, 2009. "Climate change: a catalyst for global expansion of harmful cyanobacterial blooms", Environ Microbiol Rep., 1(1): 27-37.

[57] Paerl, H.W. and V.J. Paul, 2012. "Climate change: links to global expansion of harmful cyanobacteria". Water Res., 46(5): 1349-1363.

[58] Parrish, J., 2014. "The Role of Nitrogen and Phosphorus in the Growth, Toxicity, and Distribution of the Toxic Cyanobacteria, Microcystis aeruginosa", Master's Projects, University of San Francisco, USF Scholarship Repository.

[59] Perera, K.S., M.D. Amarasinghe and S. Somaratna, 2013. "Vegetation Structure and Species Distribution of Mangroves along a Soil Salinity Gradient in a Micro Tidal Estuary on the North-western Coast of Sri Lanka", American Journal of Marine Science, 1(1): 7-15.

[60] Poff, N.L., M.M. Brinson and J.W. Day, 2002. "Aquatic ecosystems and global climate change". Pew Centre on Global Climate Change, Arlington, VA, 44.

[61] Portela, R.C.Q., D.M.S. Matos, L.P.D. Siqueira, M.I.G. Braz, L. Silva-Lima and R.H. Marrs, 2009. "Variation in aboveground biomass and necromass of two invasive species in the Atlantic rainforest, southeast Brazil", Acta Bot. Bras., 23(2): 571-577.

[62] Purvaja, R., and R. Ramesh, 2000. "Human impacts on methane emission from mangrove ecosystems in India", Reg. Environ. Change. J., 1(2): 86-97.

[63] Ramaraj, R., D.D.W. Tsai and P.H. Chen, 2014. "Freshwater microalgae niche of air carbon dioxide mitigation", Ecol. Eng., 68: 47-52.

[64] Raymont, J.E., 2014. "Plankton & Productivity in the Oceans", Phytoplankton, V. (1), Elsevier.

[65] Reef, R., I.C. Feller and C.E. Lovelock, 2010. "Nutrition of mangroves". Tree Physiol., 30(9): 1148-1160.

[66] Rouf, A.A., S.M. Phang and M.A. Ambak, 2010. "Depth distribution and ecological preferences of periphytic algae in Kenyir Lake, the largest tropical reservoir of Malaysia". Chinese J. Oceanol. Limnol., 28(4): 856-867.

[67] Sallam, G.A. and E.A. Elsayed, 2015. "Estimating relations between temperature, relative humidity as independed variables and selected water quality parameters in Lake Manzala, Egypt", Ain Shams Engineering Journal. In Press, Corrected Proof.

[68] Scheraga, J.D. and A.E. Grambsch, 1998. "Risks, opportunities and adaptation to climate change", Climate research, 11(1): 85-95.

[69] Sharip, Z. and S. Zakaria, 2008. "Lakes and Reservoir in Malaysia: Management and Research Challenges", In Proceedings of Taal2007: The 12th World Lake Conference, (1349): 1355

[70] Sivakumar, K. and R. Karuppasamy, 2008. "Factors affecting productivity of phytoplankton in a reservoir of Tamilnadu, India", Magnesium, (2): 2.

[71] Snoeyink, V.L. and D. Jenkins, 1980. "Water chemistry". John Wiley.

[72] Song, H.L., X.N. Li, X.W. Lu and Y. Inamori, 2009. "Investigation of microcystin removal from eutrophic surface water by aquatic vegetable bed", Ecological engineering, 35(11): 1589-1598.

[73] Sontakke, G.K., and S.S. Mokashe, 2014. "Seasonal variation in primary productivity of two freshwater lakes of Aurangabad district, Maharashtra, India", International Journal of Fauna and Biological Studies, 1(6): 07-10.

[74] Sterner, R.W., 2010. "In situ-measured primary production in Lake Superior". Journal of Great Lakes Research, 36(1): 139-149.

[75] Tang Che Ing, A., P.C. Stoy and L. Melling, 2014. "Net ecosystem C[O.sub.2] exchange of a primary tropical peat swamp forest in Sarawak, Malaysia", In AGU Fall Meeting Abstracts, 1(42).

[76] Trenberth, K.E., 2008. "Climate change and extreme weather events. In Process of Catastrophe Modelling Forum: Changing Climatic Dyn. and Catast", Model Projections, P Epstein, Ed., New York, NY, pp: 7.

[77] Tweed, S., M. Grace, M. Leblanc, I. Cartwright and D. Smithyman, 2011. "The individual response of saline lakes to a severe drought", Science of the Total Environment, 409(19): 3919-3933.

[78] Van der Molen, J.S., and R. Perissinotto, 2011. "Microalgae productivity in an estuarine lake during a drought cycle: The St. Lucia Estuary, South Africa", Estuar. Coast. Shelf S., 92(1): 1-9.

[79] VijayaVenkataRaman, S., S. Iniyan and R. Goic, 2012. "A review of climate change, mitigation and adaptation". Renew. Sust. Energ. Rev., 16 (1): 878-897.

[80] Walther, G. R., 2010. "Community and ecosystem responses to recent climate change", Phil. Trans. R. Soc. B 365: 2019-2024

[81] Wasige, J.E., T.A. Groen, E. Smaling and V. Jetten, 2013. "Monitoring basin-scale land cover changes in Kagera Basin of Lake Victoria using ancillary data and remote sensing", Int. J. Appl. Earth. Obs., 21: 3242.

[82] Weatherburn, M.W., 1967. "Phenol-hypochlorite reaction for determination of ammonia", Anal. Chem., 39(8): 971-974.

[83] Williamson, C.E., J.E. Saros, W.F. Vincent and J.P. Smold, 2009. "Lakes and reservoirs as sentinels, integrators, and regulators of climate change", Limnol. Oceanogr., 54(6): 2273-2282.

[84] Wondmagegne, T., A. Wondie and M. Mingist, 2012. "Seasonality in abundance, biomass and production of the phytoplankton of Welala and Shesher wetlands, Lake Tana sub-basin (Ethiopia)", J. Water Resource Prot., 4(10): 877.

[85] Wong, C.L., R. Venneker, S. Uhlenbrook, A.B.M. Jamil and Y. Zhou, 2009. "Variability of rainfall in Peninsular Malaysia", Hydrol. Earth Syst. Sci., 6(4): 5471-5503.

[86] Woodward, G., D.M. Perkins and L.E. Brown, 2010. "Climate change and freshwater ecosystems: impacts across multiple levels of organization", Philos. Trans. R. Soc. Lond. B. Biol. Sci., 365(1549): 2093-2106.

[87] Woszczyk, M., N. Grassineau, W. Tylmann, G. Kowalewski, M. Lutynska and A. Bechtel, 2014. "Stable C and N isotope record of short term changes in water level in lakes of different morphometry: Lake Anastazewo and Lake Skulskie, central Poland", Org. Geochem., 76: 278-287.

[88] Yang, X.E., X. Wu, H.L. Hao and Z.L. He, 2008. "Mechanisms and assessment of water eutrophication", J. Zhejiang Univ. Sci. B., 9(3): 197-209.

[89] Yousuf, I., A.R. Ghumman, H.N. Hashmi and M.A. Kamal, 2014. "Carbon emissions from power sector in Pakistan and opportunities to mitigate those", Renew. Sust. Energ. Rev., 34: 71-77.

[90] Zhang, M., Y. Yu, Z. Yang and F. Kong, 2016. "Deterministic diversity changes in freshwater phytoplankton in the Yunnan-Guizhou Plateau lakes in China", Ecolo. Indic., 63: 273-281.

[91] Zhang, Y., M.A. van Dijk, M. Liu, G. Zhu and B. Qin, 2009. "The contribution of phytoplankton degradation to chromophoric dissolved organic matter (CDOM) in eutrophic shallow lakes: field and experimental evidence". Water Res, 43(18): 4685-4697.

Munay Abdulqadir Omar, Mohammad Noor Amal Azmai, Hishamuddin Omar and Ahmad Ismail

Department of Biology, Faculty of Science, University Putra Malaysia, 43400, UPM Serdang, Selangor Darul Ehsan, Malaysia.

Address for Correspondence:

Ahmad Ismail, Department of Biology, Faculty of Science, University Putra Malaysia, 43400, UPM Serdang, Selangor Darul Ehsan, Malaysia.

Received 12 February 2016 Accepted 28 March 2016 Available online 25 April 2016

Primary Production in River Birupa, India

Seasonal changes of primary production in relation to water temperature, secchi transparency and rainfall were measured from January 2009 to December 2010 by using light-and-dark bottle technique at three different sampling sites along the course of river Birupa between 20°36 ′ 57 ″ north latitude and 86°24 ′ 29 ″ east longitude of Odisha, India. The Gross Primary Production (GPP) and Net Primary Production (NPP) were least during monsoon (August) and attained peak during summer (April) at all stations except GPP during March at station 3. Community Respiration (CR) was minimum from February to April and maximum from October to January among the stations. GPP and NPP were significantly correlated,both temporally and spatially,with water temperature, secchi transparency and rainfall. Annual GPP was maximum at station 2 and minimum at station 3. Annual GPP for both the years was found to be 1.364 gC m −2 day −1 or 497.86 gC m −2 year −1 in river Birupa. This value was compared to some other temperate and tropical rivers.

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