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Heterogeneity in nitrogen sources enhances productivity and nutrient use efficiency in algal polycultures SHOVON MANDAL, Jonathan B. Shurin, Rebecca Efroymson, and Teresa Mathews Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05318 • Publication Date (Web): 21 Feb 2018 Downloaded from http://pubs.acs.org on February 22, 2018
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Environmental Science & Technology
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Heterogeneity in nitrogen sources enhances
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productivity and nutrient use efficiency in algal
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polycultures
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Shovon Mandal†, Jonathan B. Shurin‡, Rebecca A. Efroymson†, Teresa J. Mathews†*
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† Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
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‡ University of California at San Diego, La Jolla, CA 92093
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*Corresponding author tel.: (865) 241-9405; fax (865) 576-9938; e-mail:
[email protected] 15
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KEY WORDS: Algae, biofuel, diversity, polyculture, productivity
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ABSTRACT: Algae hold much promise as a potential feedstock for biofuels and other products,
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but scaling up biomass production remains challenging. We hypothesized that multispecies
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assemblages, or polycultures, could improve crop yield when grown in media with mixed
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nitrogen sources, as found in wastewater. We grew mono- and poly- cultures of algae in four
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distinct growth media that differed in the form (i.e. nitrate, ammonium, urea, plus a mixture of
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all three), but not the concentration of nitrogen. We found that mean biomass productivity was
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positively correlated with algal species richness, and that this relationship was strongest in mixed
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nitrogen media (on average 88% greater biomass production in 5-species polycultures than in
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monocultures in mixed nitrogen treatment). We also found that the relationship between nutrient
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use efficiency and species richness was positive across nitrogen treatments, but greatest in mixed
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nitrogen media. While polycultures outperformed the most productive monoculture only 0-14%
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of the time in this experiment, they outperformed the average monoculture 26-52% of the time.
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Our results suggest that algal polycultures have the potential to be highly productive, and can be
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effective in recycling nutrients and treating wastewater, offering a sustainable and cost-effective
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solution for biofuel production.
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Abstract Art: Algal species:
Mean biomass production (mg/L)
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Monoculture
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N sources in media: Nitrate
Polyculture Ammonium
Urea
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Algae hold much promise as a feedstock for biofuels because of their rapid growth rates and
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higher potential capacity for oil yields per unit land area than conventional terrestrial feedstocks.
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However, to be competitive with petroleum- (or other biomass-) based fuels, algal biomass
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production need to take place on a much larger scale and at lower cost [1, 2]. A number of
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technical and economic challenges must be overcome for successful scale up of algal biofuel
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production, but there are also ecological and environmental challenges unique to algal biomass
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production. For example, cultivation in outdoor ponds may be the most cost effective method to
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produce algae on the scales needed to meet national biomass targets, but outdoor algal crops are
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extremely susceptible to invaders and pests, making crop protection strategies critical to the
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success of industrial algal biomass production [3]. Further, scaling up algal biomass production
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will put increasing pressure on already strained freshwater and nutrient (nitrogen, phosphorus)
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resources unless new approaches are developed to increase the efficiency of resource utilization
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[4, 5].
INTRODUCTION
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The use of non-potable water for algal production can help relax resource constraints while
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making productive use of underutilized water resources. While algae can grow in brackish and
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saline waters where these resources are available, wastewater is of particular interest because it is
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geographically distributed and projected to increase commensurately with increases in
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population, urbanization, and economic development [6, 7]. Further, using algae to treat
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wastewater can be a sustainable, low cost strategy to produce algal biomass since cultivation and
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harvest costs are included in the wastewater treatment operation [8, 9]. Because of the
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variability in wastewater volume and treatment strategies both regionally and globally, there is a
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need to optimize production scenarios to move wastewater production of algae from a niche
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opportunity to scaled production.
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The algal biofuel industry has largely focused on identifying ‘super-species’ of algae that can
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be genetically modified and/or grown under conditions that maximize the yield of energy-rich
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compounds. While these strains can be highly productive under controlled conditions,
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maintaining stable algal monocultures in outdoor ponds may not be possible because of biosafety
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concerns (in the case of genetically modified strains), and because of the dispersive nature of
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invading strains of algae and other aquatic microorganisms [10-12]. Whether intentional or not,
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outdoor algal crops are likely to become multispecies assemblages (i.e. polycultures) through
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colonization by wild strains, rather than axenic crops. For effective crop management, it will be
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critical to understand and control the interactions among cultivated algae, their wild competitors,
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microbes, and grazers. These interactions have the potential to provide crop stability as well as
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crop protection [13, 14].
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Ecosystem science has shown that productivity is positively related to species diversity [15]
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and that diverse communities are able to produce more biomass by utilizing available resources
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(e.g. nutrients) more efficiently [16]. However, experimental studies that manipulated terrestrial
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plant species richness to examine effects on biomass productivity have shown mixed results,
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with only a small fraction of polycultures out-producing monocultures (i.e. overyielding) [17,
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18]. We hypothesize that the lack of relationship between species diversity and productivity in
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laboratory experiments may be due to the controlled and homogeneous experimental
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environment which gives little opportunity for species to express niche differentiation [19-23]. In
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the present study, we created a heterogeneous environment by providing algal assemblages with
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equimolar concentrations of the different forms of nitrogen found in municipal wastewater (i.e.
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nitrate, ammonium, and urea) [8, 24]. We compared the productivity of five industrial algal
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strains when grown in monoculture and in polyculture in homogeneous and heterogeneous
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nitrogen culture media. We measured the productivity and stability of these cultures, and
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calculated the nutrient use efficiency (NUE) in all experimental treatments. Our results are
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relevant to the broader understanding of algal ecology as well as the economic and sustainable
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production of algal biomass for the biofuel industry.
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MATERIALS AND METHODS Species selection. The fresh water algae used in our experiment included four species of
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Chlorophyceae (Tetraselmis sp. UTEX LB 2767, Raphidocelis subcapitata UTEX 1648,
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Chlamydomonas reinhardtii UTEX 2243, Scenedesmus obliquus UTEX 393) and one
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Bacillariophyceae (Navicula sp.). The species were obtained from algae culture collections at the
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University of Texas at Austin except for Navicula sp. (isolated species, courtesy Shurin Lab, UC
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San Diego). These species met a number of criteria that were essential for our experimental
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design. All are high yielding strains targeted for biofuel production [25], and in preliminary
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screening experiments showed different performance when exposed to the different chemical
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forms of nitrogen used in this study (ammonium, nitrate and urea) (Fig. S1; Table S1).
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Experimental design. This study was designed to compare the interactive effect of species
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richness and various types of nitrogen in culture media on biomass production, stability, and
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nutrient use efficiency. To manipulate species richness we grew each of the five species alone as
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monoculture, in ten pairwise combinations of two-species polycultures and in one five-species
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polyculture (16 different “culture” treatments) (Fig. S2). Cultures were grown in standard WC
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culture medium [26], except different nitrogen treatments were created by altering the form of
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nitrogen within the media. While standard WC medium calls for 1.00 x 10-3 M nitrogen as
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NaNO3- (nitrate treatment in this study), we replaced nitrate with: -0.03 g L-1 urea to create a
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urea treatment, with 0.053 g L-1 NH4Cl to create an ammonia treatment, and with equimolar
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amounts of nitrate, ammonium, and urea to create a simulated wastewater treatment (which we
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call mixed nitrogen treatment for the remainder of this manuscript). Nitrogen concentration was
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kept constant at 1.00 x 10-3 M throughout all four media treatments (pH and other nutrients kept
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constant across all media types). The experiment was carried out using a 16 × 4 factorial design
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with sixteen types of algal species compositions and four nitrogen media (nitrate, ammonium,
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urea and mixed nitrogen) (Fig. S2). Each of the 48 treatments was replicated three times for a
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total of 192 microcosms.
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The experiment was performed in 250-mL Erlenmeyer flasks with a volume of 100 mL
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medium and flasks were placed in a temperature controlled incubator at 25° C with a 14:10 hour
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(light : dark) photoperiod of 100 µmol m-2 s-1 photosynthetically active radiation (PAR). Culture
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media and flasks were sterilized in an autoclave at 15 lb inch-2 pressure and 121 °C for 15 min.
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Algae were inoculated under sterile conditions into the flasks and were grown in incubator as
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described above. Each algal species was centrifuged separately at 5000 rpm for 10 minutes and
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washed with sterile nitrogen deplete WC medium five times before inoculation in to the
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treatment flasks. Algal cells were measured under the microscope (Leica DM 2500) and volumes
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were calculated following geometric formulas in Hillebrand et al. (1999) [27]. Cell densities
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were obtained by counting algal samples using a Benchtop FlowCAM (Fluid Imaging
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Technologies Inc., Yarmouth, ME, USA). Cell densities were multiplied by measured cell
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volume to estimate the biovolume for algal inoculation. The algae were inoculated into flasks at
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a biovolume of 1.74×109 µm3 mL-1. For polyculture treatments, algae were inoculated with
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equal biovolumes to create a total biovolume of 1.74×109 µm3 mL-1. The cultures were shaken
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daily to keep algae suspended. We sampled 20% of the volume (20 mL) twice a week for
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biomass measurements and replaced sampled volume with freshly prepared sterile medium.
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Measurements. We initially measured the optical density at 750 nm [28] twice a week as a
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proxy for algal growth on a Multiskan FC Microplate Photometer (Thermo Scientific, Waltham,
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MA, USA). By day 15 of the experiment, the majority of algal cultures reached steady state
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based on visual inspection of the optical density curves. Algal biomass was then measured twice
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a week (while media were exchanged as described above) from day 15 to 36 by filtering 20 mL
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from each flask onto a pre-weighed Whatman GF/F filter and re-weighing filters with algae after
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they were dried at 80°C until constant weight was reached. To measure the carbon and nitrogen
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content in biomass, 15-mL samples were collected in 50 mL Falcon tubes at the end of
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experiment, centrifuged and washed two times with nitrogen free medium. The algal pellet was
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dried in a freeze drier (Labconco Corporation, Kansas City, MO) at -50°C, 0.08 mbar pressure
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for 48 hr. The dried samples were then weighed and packed into combustible tin capsules and
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sent to the University of California at Davis Stable Isotope facility for C and N analysis using an
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Elementar Vario EL Cube or Micro Cube elemental analyzer (Elementar Analysensysteme
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GmbH) interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd.). At the
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end of the experiment, we calculated the NUE for each treatment as the ratio between algal
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biomass and total nitrogen content of biomass.
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Statistical analyses. We calculated mean and standard deviation of biomass production across
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the 7 sampling dates for each experimental replicate. The effect of species richness and nitrogen
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treatment on mean biomass production, stability and NUE was assessed using general linear
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model with lm() function in R [29]. Species richness was treated as a continuous variable and
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nitrogen treatment as categorical variable. First, we tested for significance in slopes based on
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nitrogen treatment using “homogeneity of slope model”. We then applied ANCOVA with
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nitrogen treatment as the factor and algal species richness as the covariate. Data were fitted to a
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linear model independently for each nitrogen treatment and estimated coefficients were used to
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compare effect of species richness on each dependent variable in each treatment.
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We used two indices to explore the biodiversity effect on algal biomass yield. First, we
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measured the difference between the biomass yield of each polyculture (Yo) and the mean of the
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biomass of each component species in the polyculture (when grown as monocultures; expected
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yield, YE), called net biodiversity effect (NBE) [30].
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NBE= Yo - YE
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Secondly, we defined the overyielding (OY) by calculating the deviation of biomass in
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(1)
polyculture from their maximum component monocultures [22, 31, 32].
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OY= Yo-Ymax
(2)
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where Ymax is the yield of the most productive strain present in the polyculture (when grown as
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a monoculture). Two-tailed t-tests were applied to test whether NBE and OY were significantly
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different from zero. Calculating mean of biomass across all species compositions in the same
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nitrogen treatment, we tested the effect of nitrogen treatment and time on OY or NBE using
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analysis of variance (ANOVA).
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Our experimental design permits us to quantify the temporal stability of biomass production.
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Stability of biomass production was estimated using the coefficient of variation (CV) that
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measures the percentage of a variation around the mean, so greater CVs suggests lower stability,
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and vice versa [33, 34]. Therefore, stability was expressed as inverse of CV as follows:
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Stability=µ/SD
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where µ is the mean biomass production across 7 sampling dates and SD is standard deviation
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(3)
of biomass production.
RESULTS AND DISCUSSION
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The mean biomass of the algal cultures increased with species richness across all nitrogen
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treatments (Pearson r = 0.2801, t= 2.642, P
