Accepted: 15 July 2017 DOI: 10.1111/maec.12454
ORIGINAL ARTICLE
Size scaling patterns of species richness and carbon biomass for marine phytoplankton functional groups Lydia Ignatiades Institute of Biology, National Centre of Scientific Research “Demokritos”, Athens, Greece Correspondence Lydia Ignatiades, Institute of Biology, National Centre of Scientific Research “Demokritos”, Athens, Greece. Emails:
[email protected];
[email protected] Funding information project STRATEGY from the EU
Abstract Phytoplankton assemblages were clustered into associations according to functional taxonomic (diatoms, dinoflagellates and coccolithophores) and “ataxonomic” unimodal (nanoplankton, microplankton and macroplankton) size-based criteria. Scaling relations of species richness-cell size were performed in terms of histogram and log- transformed data analyses for both taxonomic and ataxonomic groups. Frequency distribution histograms were fitted to a negative power function, which was strongly unimodal and right skewed and invariant across taxonomic and ataxonomic units. Regression analyses of the log-transformed data were fitted to negative linear curves, which had common patterns and they were independent of taxonomic or ataxonomic affiliation. Species carbon biomass–cell size spectra produced by log transformation of the relevant data yielded positive slopes for both taxonomic and ataxonomic groups. In contrast, comparisons of the relative cell abundance, cell volume and carbon biomass levels showed large differences among these variables across taxonomic and ataxonomic groups. This work demonstrates that phytoplankton taxonomic and ataxonomic functional group relationships should be considered when developing future models of phytoplankton community structure. KEYWORDS
carbon biomass, cell size scaling, functional groups, phytoplankton, species richness
1 | INTRODUCTION
criteria based on coherent morphological, physiological and ecological properties (traits), has a higher discriminatory power to detect the
One of the most recent phytoplankton research interests is the appli-
degree of relationship between congruent traits of phytoplankton spe-
cation of functional group analysis to understand which factors govern
cies and has fostered a functional, trait-based modeling perspective
community assembly and dynamics in aquatic ecosystems (Litchman
(Acevedo-Trejos, Brandt, Bruggeman, & Merico, 2015; Kruk, Mazzeo,
& Klausmeier, 2008). The number of functional groups (groups of
Lacerot, & Reynolds, 2002).
species with similar traits) is very large (Salmaso, Naselli-Flores, &
Phytoplankton key functional traits scale with cell size (size spectra)
Padisák, 2015) but phytoplankton assemblages are traditionally clus-
as predicted by fundamental scaling relations (Litchman, Klausmeier,
tered either as taxonomic groups on the basis of taxonomic traits, e.g.
Schofield, & Falkowski, 2007). An ataxonomic species richness-cell size
diatoms, dinoflagellates and coccolithophores (Barton et al., 2013;
spectrum (i.e. frequency distributions of species numbers arranged
Follows & Dutkiewicz, 2011; Sabetta et al., 2005) or as “ataxonomic”
by individual cell volumes) provides an important contribution to the
size classes, e.g. nanoplankton, microplankton and macroplankton
search for major changes in trophic structure and species food chain
(Le Quéré et al., 2005; Ward, Dutkiewicz, Jahn, & Follows, 2012).
length (Marañón, 2015). Nano- micro- macrophytoplankton popu-
Phytoplankton organization into associations, according to functional
lations can be strongly influenced by environmental perturbations
Marine Ecology. 2017;38:e12454. https://doi.org/10.1111/maec.12454
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(Sabetta, Basset, & Spezie, 2008) and if environmental conditions
were especially appropriate because they contained a broad
change and species of certain sizes are lost, size-range limits in the
spectrum of cell sizes with taxonomic differences between size
relevant size-richness spectra become altered, thus representing a
classes. The results may be useful for modeling phytoplankton
valuable ecosystem indicator of climate change (Gaston & He, 2002;
community size structure as ocean ecosystem models requiring
Polovina & Woodworth, 2012).
the presentation of size-d ependent functional traits of phyto-
Important progress will be achieved if size-based ataxonomic species richness spectra are related with ones constructed from
plankton often use size scaling parameters obtained from the literature (Hirata et al., 2011).
species identity that are more focused on taxonomic size spectra of diatoms, dinoflagellates and coccolithophores (Petchey & Belgrano, 2010). Here, there is a clear inter-group functional trait differentiation, as diatoms are exclusively photoautotrophic, having an absolute requirement for silica to construct their cell wall
2 | MATERIAL AND METHODS 2.1 | Species identification and cell size classification
(Bradbury, 2004), dinoflagellates exhibit photoautotrophy, mixotro-
The phytoplankton species data set used in this analysis was obtained
phy and heterotrophy (Ignatiades, 2012) as well as dimethylsulfide
from the MedOBIS database (http://lifewww-00.her.hcmr.gr:8080/
(DMS) production in the ocean (Caruana, 2010), and coccolitho-
medobis/resource.do?r=strategy). The data were collected from
phores are sensitive to carbonate chemistry and ocean acidifica-
four gulfs (22 stations, 224 samples) along the coastline of the North
tion (Beaufort et al., 2011). Research conducted by the European
Aegean Sea during the period 2001–2003 (Ignatiades, Gotsis-Skretas,
Science Foundation Research Network (SIZEMIC) suggests that
& Metaxatos, 2007).
organismal size, supported by taxonomy, provides the most prob-
A data set of 401 species from three major groups of eukary-
able route to universal indicators of ecological status (Petchey &
otic taxa (diatoms, dinoflagellates, coccolithophores) was used
Belgrano, 2010). However, research on species richness-cell size
for cell size classification. Quantitative and qualitative analysis of
is scanty (Cermeño & Figueiras, 2008) and limited to ataxonomic
phytoplankton to species level was performed under an inverted
spectral analysis.
(Zeiss IM) microscope according to Utermöhl (1958) and the mag-
Algal cell carbon content is of major concern as chlorophyll con-
nification used was ×400. Species identification was accompanied
tent and fluorescence responses cannot be used to gauge phyto-
by measurements of their linear dimensions using an ocular micro
plankton biomass, nutrient status or growth rate reliably (Kruskopf
meter. The cell volume (μm3) was calculated by fitting the cellular
& Flynn, 2005). It has been demonstrated that carbon biomass
dimensions in formulae for solid geometric shapes most closely
may represent key phytoplankton functional groups in ocean car-
matching the shapes of the cells (Hillebrand, Duerselen, Kirschtel,
bon cycle models, e.g. coccolithophores, which mediate fluxes of
Pollingher, & Zohary, 1999; Sun & Liu, 2003). Average cell volume
carbon between the ocean, the atmosphere, and the lithosphere
was calculated from estimates of the cell volumes of 20 individ-
(Iglesias-Rodríguez et al., 2002), and to be closely scaled to cell size
uals of each species per sample. Cell size-class thresholds were
(Cermeño, Marañón, Rodríguez, & Fernández, 2005; Ruiz-Halpern,
defined (Ignatiades, 2016) according to cell volume as nanoplank-
Echeveste, Agustí, & Duarte, 2014). Ocean acidification and carbon-
ton (10–103 μm3), microplankton (103–106 μm3) and macroplank-
ation, driven by anthropogenic emissions of CO2, have been shown
ton (106–109 μm3).
to affect phytoplankton physiology and are likely to have various and potentially adverse effects on phytoplankton cell size, ecosystem functioning (Reul et al., 2014; Schulz et al., 2013) and photosynthetic activity, as well as the biogeochemical cycling of carbon in the sea (Finkel et al., 2010). It has also been reported that different
2.2 | Data analyses 2.2.1 | Species richness-cell size spectra
taxonomic groups of plankton play different roles in the cycles of
All statistical tests were carried out using SPSS version 20 software.
carbon influencing climate change (Denman, 2003). Size spectrum
Spectral analysis was applied to un-normalized values of cell volume
analysis of species richness and carbon biomass, from both taxo-
size classes applied in taxonomic and ataxonomic classification as
nomic and ataxonomic perspectives, increases the resolution for
original data without previous normalization provide a better fit and
assessing variation in phytoplankton community structure (Kamenir
also yield a direct estimate of the examined allometric relationships
& Dubinsky, 2012) but to date there is no relevant research in the
(Vidondo, Prairie, Blanco, & Duarte, 1997).
literature. The objectives of this work were (i) to describe the cell size
The spectral analysis of the species richness-cell size relationship was performed (White, Enquist, & Green, 2008) by applying two
spectra of phytoplankton species richness and carbon biomass
methods:
as well as their characteristic ranges and endpoints in both tax-
1. Frequency distribution analysis (Pareto distribution) displayed
onomic and ataxonomic assemblages, and (ii) to quantify how
the relationship of species richness-cell volume size classes
much the levels of the community variables (i.e. relative cell
values as a histogram expressed by the negative allometric
abundance, cell volume and carbon biomass) may change as tax-
scaling power law equation: Y = αΧ−b, where α is a group-de-
onomic and/or ataxonomic resolution changes. The data used
pendent co-efficient and −b is the negative power exponent
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(negative exponential distribution), describing how parameter (Y), the “number of species,” scales over different values of cell volume size classes (X), created by doubling the cell volume values. 2. Log transformation analysis of these parameters: log Y = log a + b log X, producing the linear regression model.
3 | RESULTS 3.1 | Taxonomic and ataxonomic cell volume–species number scaling relationships Numerical data (species number, cell volume, cell abundance, carbon biomass) for taxonomic (diatoms, dinoflagellates, coccolithophores) and ataxonomic (nano-, micro-and macrophytoplankton)
2.2.2 | Carbon biomass–cell size spectra
cell-size classification are presented in Table 1. The species rich-
Individual cell carbon (C) biomass was estimated from cell volume (V) according to empirical volume-to-carbon conversion factors as follows: For diatoms: log10C = 0.811 log10V − 0.541 (Menden-Deuer & Lessard, 2000). For dinoflagellates: log10C = 0.819 log10V − 0.119 (Menden-Deuer & Lessard, 2000). For coccolithophores: log10C = −0.34 log10V − 0.06 (Llewellyn & Gibb, 2000). The application of these equations showed that carbon content per cell is proportional to cell volume. Total carbon biomass for each size class and each taxonomic unit was calculated by multiplying the cell carbon (derived from cell volume) of each species by cell concentration and then summing up the results for each group (Cermeño et al., 2005). Taxonomic (diatoms, dinoflagellates and coccolithophores) and ataxonomic (nano-, micro-, macrophytoplankton) carbon biomass–cell size spectra as well as taxonomic and ataxonomic carbon biomass–cell abundance–cell volume relationships were examined.
ness–cell volume relative frequency distributions of the three taxa are indicated (Figure 1a) by the heights of the vertical bars (histograms), and have similar patterns, e.g. species richness decreases consistently with increase in cell size, whereas the exponent values of the spectra, ranging from −1.25 to −1.64, comply with the negative power distribution. However, the taxonomic analysis illustrates (Table 1) inter-specific differences among taxa as regards cell volume and species number levels. Dinoflagellates dominated in total cell volume (1.3 × 108 μm3/cell) and species number (177), followed by diatoms (1.3 × 107 μm3/cell; species number: 158) and coccolithophores (2.3 × 105 μm3/cell; species number: 66). The taxonomic log-transformed data (Figure 1b) are in conformity with the negative linear regression without significant slope differences among groups, i.e. diatoms: −0.41; dinoflagellates: −0.50; coccolithophores: −0.46. Ataxonomic cell volume–species richness spectra were also defined, by histogram (Figure 2a) and log transformation (Figure 2b) data analyses. They exhibited common patterns, with the relevant taxonomic histogram and log-log spectra both dictated by a common
T A B L E 1 Species number, cell volume, cell abundance and carbon biomass ranges and total values of taxonomic (diatoms, dinoflagellates, coccolithophores) and ataxonomic (nanoplankton, microplankton, macroplankton) classification groups Taxonomic classification Variable
Diatoms
Dinoflagellates
Coccolithophores
Species number
158
177
66
Cell volume (μm3/cell)
6.5 × 10–1.6 × 106
9.2 × 10–1.8 × 107
1.6 × 10–1.8 × 104
Total (μm3/cell)
1.3 × 107
1.3 × 108
2.3 × 105
Cell abundance (cells/L)
8 × 10–2.4 × 107
8 × 10–2.8 × 106
8 × 10–2.1 × 105
7
7
Total (cells/L)
6.9 × 10
1.1 × 10
4.8 × 105
Carbon biomass (μgC/L)
2.4 × 103–3.7 × 1010
2.1 × 104–2.7 × 1011
6.7 × 102–9.7 × 107
Total (μgC/L)
10
11
9.5 × 10
2.3 × 108
2.9 × 10
Ataxonomic classification Nanoplankton
Microplankton
Macroplankton
Species number
245
148
8
Cell volume (μm3/cell)
3.3 × 10–9.8 × 103
1.0 × 104–8.4 × 105
1.3 × 106–1.1 × 108
Total (μm3/cell)
5.7 × 105
1.5 × 107
1.3 × 108
7
Cell abundance (cells/L)
8.0 × 10–2.4 × 10
Total (cells/L)
7.4 × 107 2
6
8.0 × 10–2.6 × 10 5.6 × 106 9
5
8.0 × 10–9.6 × 103 1.9 × 104
10
Carbon biomass (μgC/L)
6.7 × 10 –5.6 × 10
2.1 × 10 –3.7 × 10
2.6 × 107–2.7 × 1011
Total (μgC/L)
2.4 × 1010
8.4 × 1010
2.8 × 1011
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Diatoms
40%
y = 0.42x–1.25 R² = 0.90
Diatoms
(b)
2.0
20% 10% 0%
1.2 0.8 0.4 0.0
Dinoflagellates 40%
3.0
5.0
2.0
6.0
7.0
y = –0.50x + 3.54 R² = 0.95
1.6
30% 20% 10% 0%
1.2 0.8 0.4 0.0 3.5
Coccolithophores 60%
y = 0.49x–1.57 R² = 0.75
50% 40% 30% 20% 10% 0%
5.5
6.5
7.5
y = –0.46x + 2.45 R² = 0.96
1.2 0.8 0.4 0
Cell volume (μm3) classes
4.5
Coccolithophores
1.6
Log species No
Species No relative frequency
4.0
Dinoflagellates
y = 0.62x–1.64 R² = 0.78
Log species No
Species No relative frequency
y = –0.41x + 2.87 R² = 0.91
1.6
30%
Log species No
Species No relative frequency
(a)
2.5
3.5
4.5
Log cell volume (μm3)
5.5
F I G U R E 1 Taxonomic species richness-cell size spectra. (a): Histograms produced by frequency distribution analysis of species richness-cell volume size classes for diatoms, dinoglagellates and coccolithophopres. (b): Fitting linear regression functions to log-tranformed values of these variables allometric scaling relationship. Comparisons based on species richness–cell volume data showed (Table 1) that nanoplankton dominated in species richness (245 species), followed by microplankton (species
3.2 | Taxonomic and ataxonomic cell volume–carbon biomass scaling relationships
richness: 148) and macroplankton (species richness: 7). However, in
Carbon biomass–cell size spectra were constructed from log-
terms of total cell volume size, macroplankton dominated by one order
transformed data (Figure 3) and followed the allometric linear rela-
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3
of magnitude (total: 1.3 × 10 μm /cell) compared to microplankton
tionship of these parameters in both taxonomic and ataxonomic
(total: 1.5 × 107 μm3/cell) and by three orders of magnitude compared
assemblages. The results demonstrate that in both taxonomic and
to nanoplankton (total: 5.7 × 105 μm3/cell).
ataxonomic classifications carbon biomass increases with increasing
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Species No relave frequency
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(total: 2.3 × 108 μgC/L; 1%) was recorded. Ataxonomic cell abun-
(a)
y = 0.61x –1.7 R² = 0.95
80% 60%
dance relationships identified the dominance of nanoplankton (total: 7.4 × 107 cells/L; 91%) compared to microplankton (total: 5.6 × 106 cells/L; 7%) and macroplankton (total: 1.9 × 104 cells/L;
40%
2%), but those based on carbon biomass showed the dominance of
20%
plankton (total: 8.4 × 1010 μgC/L; 22%) and nanoplankton (total:
macroplankton (total: 2.8 × 1011 μgC/L; 72%) in relation to micro2.4 × 1010 μgC/L; 6%).
0%
4 | DISCUSSION 4.1 | Taxonomic and ataxonomic species richness- cell size relationships
Cell volume (μm3) classes (b)
y = –0.47x + 3.68 R² = 0.91
Log species numbers
2.50 2.00
Cell size is a “master trait” that places important constraints on many key organismal characteristics (Litchman et al., 2007) affecting numerous other functional traits and crucial physiological and ecological processes (Finkel et al., 2010). However, there is a general lack of reports on taxo-
1.50
nomic analysis of marine phytoplankton cell size–species richness rela-
Nano
tionships as most studies to date have been based on ataxonomic log
1.00
Macro
0.50 0.00
transformation of these parameters, assuming that this approach alone is sufficient to model the dynamics of natural communities (Cermeño &
Micro
Figueiras, 2008; Irwin, Finkel, Schofield, & Falkowski, 2006). Size and species identity are both significant for determining the functional role
3.0
4.0
5.0
6.0
7.0
8.0
Log cell volume (μm3) F I G U R E 2 Ataxonomic species richness-cell size spectra. (a): Histograms produced by frequency distribution analysis of species richness-cell volume size classes for the entire population irrespective of species. (b): Fitting linear regression functions to log- transformed values of these variables and defining the nano-micro- macrophytoplankton size classes
of individuals, because knowing only the species or the size class of a given taxon may not adequately describe its function in a community (Rudolf, Rasmussen, Dibble, & Van Allen, 2014). Phytoplankton size is an indicator of food quantity and quality for grazers, and can be related to habitat conditions and eutrophication (Capriulo et al., 2002). However, the exclusive focus on ataxonomic size classes comes at the cost of neglecting taxonomic or phylogenetic constraints on species’ traits and interactions, as changing climatic conditions can modify environmental factors and alter phytoplankton structure and taxonomic
cell size, despite the fact that large species were relatively rare in
composition (Winder & Sommer, 2012).
terms of frequency of occurrence (Figure 1a) and species number
This work focused on both taxonomic (diatoms, dinoflagellates,
(Table 1, nano-, micro-, macroplankton). Comparisons based on taxo-
coccolithophores) and ataxonomic (nano- , micro- , macroplankton)
nomic assignments (Table 1) showed that the carbon biomass content
species richness–cell size relationships in terms of histogram and log-
11
of dinoflagellates (total: 2.9 × 10 μgC/L) was one order of magni-
transformed data analyses because a comparison of these functions
tude greater than that of diatoms (total: 9.5 × 1010 μgC/L) and three
can form the basis for interpretation of phytoplankton community
orders of magnitude greater than that of coccolithophores (total:
structure (Campbell & Yentsch, 1989; Vidondo et al., 1997; White
2.3 × 108 μgC/L). Size- based ataxonomic variations of this param-
et al., 2008). It is important to consider a community as a frequency
eter showed that macroplankton carbon biomass was one order of
distribution histogram of both taxonomic and ataxonomic values
magnitude (total: 8.2 × 1011 μgC/L) higher than that of microplankton
(Kamenir & Dubinsky, 2012). Some investigators (Schartau, Landry, &
(total: 8.4 × 1010 μgC/L) and one order of magnitude higher than that
Armstrong, 2010) have reported that histograms may produce results
of nanoplankton (2.4 × 1010 μgC/L).
of dubious quality, but according to Litchman and Klausmeier (2008),
Cell abundance and carbon biomass (Table 1, Figure 4) were highly
histogram analysis is a valuable tool because it reveals detailed infor-
variable within and between taxonomic and ataxonomic assemblages.
mation about the status of a phytoplankton community in time and
Taxonomic comparisons in terms of cell abundance indicated the dom-
space while determining its fitness under given environmental and
inance of diatoms (total: 6.9 × 107 cells/L; 85%) in relation to dino-
predatory conditions. Histogram analysis may also provide information
7
flagellates (total: 1.1 × 10 cells/L; 13%) and coccolithophores (total:
on the functional trait ranges among and within communities (McGill,
4.8 × 105 cells/L; 2%), while in terms of carbon biomass, a strong
Enquist, Weiher, & Westoby, 2006). If the cell size spectrum of a com-
dominance of dinoflagellates (total: 2.9 × 1011 μgC/L; 57%) in rela-
munity is altered via some perturbation (seawater temperature eleva-
tion to diatoms (total: 9.5 × 1010 μgC/L; 25%) and coccolithophores
tion, acidification, invasions of new species), this may produce shifts in
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Diatoms Log carbon biomass (μgC · l–1)
12
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P
