Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus Pedro Flombauma,b, José L. Gallegosa, Rodolfo A. Gordilloa, José Rincóna, Lina L. Zabalab, Nianzhi Jiaoc, David M. Karld,1, William K. W. Lie, Michael W. Lomasf, Daniele Venezianog, Carolina S. Verab, Jasper A. Vrugta,h, and Adam C. Martinya,i,1 Departments of aEarth System Science, hCivil Engineering, and iEcology and Evolutionary Biology, University of California, Irvine, CA 92697; bCentro de Investigaciones del Mar y la Atmósfera, Departamento de Ciencias de la Atmósfera y los Océanos, and Instituto Franco Argentino sobre Estudios del Clima y sus Impactos, Consejo Nacional de Investigaciones Científica y Tecnológicas and Universidad de Buenos Aires, 1428 Buenos Aires, Argentina; cInstitute of Microbes and Ecosphere, State Key Lab for Marine Environmental Sciences, Xiamen University, Xiamen 361005, People’s Republic of China; dCenter for Microbial Oceanography: Research and Education (C-MORE), University of Hawaii, Honolulu, HI 96822; eFisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, NS, Canada B2Y 4A2; fBigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544; and gDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 Contributed by David M. Karl, April 25, 2013 (sent for review January 22, 2013) The Cyanobacteria Prochlorococcus and Synechococcus account for outcompeted by other phytoplankton in high-nutrient waters (12, a substantial fraction of marine primary production. Here, we pres- 13). Synechococcus does not extend as deep in the water column as ent quantitative niche models for these lineages that assess present Prochlorococcus, but it has a wider geographical distribution that and future global abundances and distributions. These niche models covers both polar and high-nutrient waters (14). Mortality induced are the result of neural network, nonparametric, and parametric by grazers and viruses balance growth and is, in part, responsible analyses, and they rely on >35,000 discrete observations from all for a marked diel cycle (15, 16); however, the mortality effect on major ocean regions. The models assess cell abundance based on the geographical distribution is unclear. Whereas much knowledge temperature and photosynthetically active radiation, but the indi- is available about the ecology of Prochlorococcus and Synecho- vidual responses to these environmental variables differ for each coccus, quantitative niche models have not been developed. Such lineage. The models estimate global biogeographic patterns and models are essential for projecting the distribution of Pro- seasonal variability of cell abundance, with maxima in the warm chlorococcus and Synechococcus in response to future global en- oligotrophic gyres of the Indian and the western PacificOceans vironmental changes (17). To identify the present and future and minima at higher latitudes. The annual mean global abundances ocean distributions, we compiled a global dataset describing the of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 1027 and 7.0 ± abundance of Prochlorococcus and Synechococcus and associated 0.3 × 1026 cells, respectively. Using projections of sea surface tem- environmental variables. This dataset was then used to build perature as a result of increased concentration of greenhouse gases a realized niche model for each lineage and address the following at the end of the 21st century, our niche models projected increases questions: (i) what are the global quantitative distributions of in cell numbers of 29% and 14% for Prochlorococcus and Synecho- Prochlorococcus and Synechococcus,(ii) how do environmental coccus, respectively. The changes are geographically uneven but factors influence their abundances, and (iii) what are the future include an increase in area. Thus, our global niche models suggest global distributions as a result of climate change? that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high Results abundances and contributions to primary production of Prochloro- Global Distributions of Cyanobacteria. To predict the global dis- coccus and Synechococcus, these changes may have large impacts on tributions of Prochlorococcus and Synechococcus,wefirst com- ocean ecosystems and biogeochemical cycles. piled a global dataset. The dataset consisted of 35,129 and 37,699 observations of abundance, respectively, and contained data from climate change | marine biogeochemistry | microbial biogeography a total of 103 cruises and time series with samples from all major ocean regions (Fig. 1A and Table S1). We also compiled addi- arine phytoplankton contribute roughly one-half of the tional data on habitat temperature, light [described as photosyn- Mglobal net primary production and play a key role in regu- thetically active radiation (PAR)], and nutrients (i.e., nitrate and lating global biogeochemical cycles (1). Marine phytoplankton are phosphate concentration). This extensive dataset covered nearly very diverse (2), including phylogenetic, biochemical, metabolic, the full environmental range of these variables (Fig. 1B) as well as and ecological variability (3–6). Thus, understanding the contri- their combinations (Fig. S1). We then applied a three-step ap- bution of different phytoplankton groups to ecosystem functioning proach to formulate quantitative niche models (Fig. S2). First, we is central to predicting the biogeochemical impact of future en- identified the environmental factors that explained most of the vironmental changes (7). However, our limited quantitative un- variance using artificial neural network (ANN) models. Tem- derstanding of the global distribution and abundance of most perature and PAR were found to be important for predicting Prochlorococcus Synechococcus R2 = phytoplankton groups constrains our ability to incorporate phy- both and (ANN, Pro 0.66 R2 = toplankton diversity into ocean biogeochemical models. and Syn 0.35), whereas nutrients explained little additional The marine Cyanobacteria Prochlorococcus and Synechococcus are abundant in many ocean regions. The known geographical distributions of the two lineages are based primarily on individual Author contributions: P.F., J.A.V., and A.C.M. designed research; P.F., D.V., J.A.V., and A.C.M. performed research; N.J., D.M.K., W.K.W.L., M.W.L., D.V., and C.S.V. contributed new re- cruises or time series observations and secondarily on macro- agents/analytic tools; P.F., J.L.G., R.A.G., J.R., L.L.Z., D.V., and A.C.M. analyzed data; and P.F., ecological descriptions, indicating central tendencies and bound- N.J., D.M.K., W.K.W.L., M.W.L., D.V., J.A.V., and A.C.M. wrote the paper. ary constraints related to light, temperature, nutrients, and chlo- The authors declare no conflict of interest. a – Prochlorococcus rophyll concentration (8 10). is present from Freely available online through the PNAS open access option. ∼ the surface to a depth of 150 m in the open ocean between 40°N 1To whom correspondence may be addressed. E-mail: [email protected] or amartiny@uci. and 40°S. The population size declines beyond these latitudinal edu. Prochlorococcus limits, and is thought to be absent at temperatures This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. below 15 °C (11). Furthermore, the lineage is believed to be 1073/pnas.1307701110/-/DCSupplemental. 9824–9829 | PNAS | June 11, 2013 | vol. 110 | no. 24 www.pnas.org/cgi/doi/10.1073/pnas.1307701110 Downloaded by guest on October 2, 2021 variance (Table S2). Second, we predicted cell abundance for each combination of temperature and PAR using nonpara- metric local regression models and found that the performances 2 2 were similar to the ANN models (R Pro = 0.68 and R Syn = 0.42). Third, we closely reproduced the nonparametric predictions using more robust parametric regression models (Fig. S3A). The parametric models for Prochlorococcus and Synechococcus explained a high fraction of the total variances based on tem- 2 2 perature and PAR (R Pro = 0.67 and R Syn = 0.34) (Table S3). Thus, the variances of the parametric models were comparable with both the ANN and nonparametric models. A subsequent sensitivity analysis using split sampling showed that the model 2 parameters, R , and predicted abundance were robust relative to the selection of input data and cruises (Fig. S4 A–C). No regional bias was detected for either lineage (Fig. S4D). Using the parametric models, we then estimated the global quantitative distributions of Prochlorococcus and Synechococcus. On average, Prochlorococcus was most abundant in the warm ol- igotrophic waters, especially the Indian and western Pacific Ocean − subtropical gyres (maximum = 2.5 × 105 and 2.1 × 105 cells mL 1, respectively) (Fig. 2A), and the highest monthly average value − estimated was 2.8 × 105 cells mL 1. The mean abundance de- − clined from 4.4 × 104 to 2.8 × 103 cells mL 1 from lower latitude waters (30°N to 30°S) to the latitudinal band 30° to 40° (either N or S) and was low at latitudes above 40°. The analysis also suggested lower abundances in areas dominated by cold surface currents and strong upwelling. The median abundance of Prochlorococcus in − these areas was 1.0 × 104 cells mL 1, about one order of magnitude lower than in the Indian and western Pacific oligotrophic waters. The niche model predicted that Synechococcus was also most abundant in the Indian and western