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Patterns and Ecological Implications of Historical Marine Phytoplankton Change

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Patterns and Ecological Implications of Historical Marine Phytoplankton Change Vol. 534: 251–272, 2015 MARINE ECOLOGY PROGRESS SERIES Published August 27 doi: 10.3354/meps11411 Mar Ecol Prog Ser REVIEW Patterns and ecological implications of historical marine phytoplankton change Daniel G. Boyce1,2,*, Boris Worm3 1Department of Biology, Queen’s University, Kingston, ON K7L 3N6, Canada 2Ocean Sciences Division, Bedford Institute of Oceanography, PO Box 1006, Dartmouth, NS B2Y 4A2, Canada 3Biology Department, Dalhousie University, Halifax, NS B3H 4R2, Canada ABSTRACT: There is growing evidence that average global phytoplankton concentrations have been changing over the past century, yet published trajectories of change are highly divergent. Here, we review and analyze 115 published phytoplankton trend estimates originating from a wide variety of sampling instruments to explore the underlying patterns and ecological implica- tions of phytoplankton change over the period of oceanographic measurement (1889 to 2010). We found that published estimates of phytoplankton change were much less variable when estimated over longer time series and consistent spatial scales and from the same sampling instruments. Average phytoplankton concentrations tended to increase over time in near-shore waters and over more recent time periods and declined in the open oceans and over longer time periods. Most published evidence suggests changes in temperature and nutrient supply rates as leading causes of these phytoplankton trends. In near-shore waters, altered coastal runoff and increased nutrient flux from land may primarily explain widespread increases in phytoplankton there. Conversely, in the open oceans, increasing surface temperatures are strengthening water column stratification, reducing nutrient flux from deeper waters and negatively influencing phytoplankton. Phytoplank- ton change is further affected by biological processes, such as changes in grazing regimes and nutrient cycling, but these effects are less well studied at large scales. The possible ecosystem consequences of observed phytoplankton changes include altered species composition and abun- dance across multiple trophic levels, effects on fisheries yield, and changing patterns of export production. We conclude that there is evidence for substantial changes in phytoplankton concen- tration over the past century, but the magnitude of these changes remains uncertain at a global scale; standardized long-term measurements of phytoplankton abundance over time can substan- tially reduce this uncertainty. KEY WORDS: Phytoplankton · Marine · Trend · Drivers · Consequences · Global · Change · Ecological Resale or republication not permitted without written consent of the publisher INTRODUCTION et al. 1972, Margalef 1978, Falkowski et al. 2004) and are globally distributed. Although marine phyto- Marine phytoplankton are a diverse group of pe- plankton account for only 0.2% of global photo - lagic photosynthetic microbes that provide over 90% synthetic carbon biomass, they generate 46.2% of of marine primary production (Charpy-Roubaud & the primary production (Field et al. 1998). To achieve Sournia 1990). Individual cells range over 4 orders of this, the global standing stock of phytoplankton turns magnitude in size (~0.2 to 1000 µm; Fig. 1A; Sheldon over every 2 to 6 d on average (Behrenfeld & Fal - *Corresponding author: [email protected] © Inter-Research 2015 · www.int-res.com 252 Mar Ecol Prog Ser 534: 251–272, 2015 104 kowski 1997). Due to this rapid turnover Decades Whales (Fig. 1A), phytoplankton growth often A Seals Years Seabirds depletes available nutrient resources. 102 Fish Over a century of scientific research Months Zooplankton Weeks Protozoa has shown that marine phytoplankton 0 play an important role in determining Days 10 Phytoplankton the structure and functioning of marine Hours ecosystems (Chavez et al. 2003, Richard- 10–2 Doubling time (d) son & Schoeman 2004) and can have Bacteria large effects on fisheries yields (Ryther Minutes Viruses 10–4 1969, Chavez et al. 2003, Ware & Thom- –10 –8 –6 –4 –2 0 2 4 son 2005, Chassot et al. 2007, 2010), bio- 10 10 10 10 10 10 10 10 Organism size (m) geochemical cycles (Redfield 1958, Falkowski et al. 1998), climate regulation (Charlson et al. 1987, 0.2 B Fish (0.006) Murtu gudde et al. 2002), and weather patterns Phytoplankton (0.004) (Gnanadesikan et al. 2010). Reflecting this scien- Mammals (0.002) 0.15 tific interest, the proportion of peer-reviewed sci- Zooplankton (0.001) Birds (0.001) entific studies of marine phytoplankton has increased markedly over time (Fig. 1B). 0.1 Despite these increased research ef forts, one of Proportion the most fundamental questions in phytoplankton 0.05 re search remains poorly resolved: How are aver- age marine phytoplankton biomass concentra- 0 tions chan ging over the long term? Answering 1985 1990 19952000 2005 2010 this seemingly simple question is complicated by Year the fact that phytoplankton concentrations are highly variable in space and time and are difficult Fig. 1. Phytoplankton in the scientific literature. (A) Dominant to distinguish from other marine microbes and space and time scales at which major groups of marine organ- isms operate. The average size range (x-axis) is plotted as a particles, making it difficult to obtain direct function of the average doubling time (y-axis) for various marine measurements of their carbon biomass. As a con- groups. Phytoplankton are represented in green. Figure was sequence, the total concentration of the light-har- adapted after Murphy et al. (1988). (B) Time trends in the scaled vesting pigment chlorophyll, which is present in proportion of peer-reviewed studies reporting on major marine species groups (1985−2010; see Supplement at www.int- all phytoplankton cells, has been used as a first- res.com/articles/suppl/m534p251_supp.pdf for details). Taxo- order proxy of abundance and biomass. Despite nomic groups are represented as colors, with the linear rates of documented variability in the phytoplankton change over time reported in brackets chlorophyll:carbon ratio (Geider 1987), chloro- phyll continues to be the most practical and extensively used proxy of phytoplankton carbon bio- MATERIALS AND METHODS mass over large spatial scales (Huot et al. 2007, Hen- son et al. 2010). This review deals with changes in Phytoplankton trends phytoplankton concentrations as measured via ocean color and chlorophyll assessed over the era of We systematically searched scientific databases to oceanographic measurement, 1889 to 2010, and at identify peer-reviewed studies of temporal marine regional to global scales. We did not attempt to phytoplankton change. Our literature search covered include the literature on phytoplankton cell counts or a minimum of ~22 million articles from over 16 500 species composition and make only limited infer- peer-reviewed journals. We limited our search to ences on changes in primary production. Following publications estimating phytoplankton change from this, we review the physical and biological drivers of chlorophyll concentrations or ocean color collected long-term marine phytoplankton change. We con- from the upper ocean at multi-year scales (>5 yr). clude by summarizing some potential ecosystem con- Studies conducted in fresh or brackish waters were sequences of phytoplankton change both across eco- not included. We extracted 115 phytoplankton time systems and globally. series and estimates of temporal phytoplankton change from 25 publications (Table 1). Boyce & Worm: Marine phytoplankton change 253 To standardize measurements that were reported trend variability as a function of several covariates in different units, we extracted the estimated total within a generalized least-squares model as: percentage change in phytoplankton over the log [σ ] = β + β Predictor + ε (1) available time span as reported by the authors. In 10 i 0 1 i i σ some cases, data extraction software was used to ex - where i is the standard deviation of the trends in tract and calculate these rates (www.getdata-graph- celli, which was log transformed to ensure normality; digitizer. com). Where the time series were extracted β0 is the model intercept; β1 is the rate of response from the publication, we fitted linear time series change as a function of the predictor in question; and ε models to the observations and calculated the total i is the model error, specified as: percentage change as the difference between the ε ~ N(0,δ) (2) average concentration at the start and end of the fitted time series referenced to the initial value. The where 0 is the mean, and δ is the error covariance percent change was then divided by the length of the matrix. To account for spatial autocorrelation, the time series to yield the standardized percent change covariance parameters of δ were assumed to follow a per year, relative to the initial phytoplankton concen- spatially dependent process, whereby the correlation tration. To spatially standardize the rates of change, between them decreases exponentially with increas- we binned all estimates into 5° × 5° cells. This resolu- ing spatial separation (Cressie 1993). Using this ap - tion was selected because the majority of published proach, we quantitatively estimated the influence of phytoplankton time series were estimated over spa- several predictors on the variability of phytoplankton tial domains equal to or greater than 5°. The extrac - time trends. Predictor variables tested include the ted trends were also referenced according to the number and type of sampling instrument, range and sampling
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