Marine Micropaleontology 63 (2007) 75 – 90
www.elsevier.com/locate/marmicro
Vertical niche separation control of diversity and size
disparity in planktonic foraminifera
Nadia Al-Sabouni a,b,c,⁎, Michal Kucera c , Daniela N. Schmidt d
a
c
Department of Geology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK
b
Natural History Museum, London, SW7 5BB, UK
Eberhard Karls University of Tübingen, Institute of Geosciences, Sigwartstrasse 10, 72076, Tübingen, Germany
d
Department of Earth Sciences, University of Bristol, Wills Memorial Building, Bristol, BS8 1RJ, UK
Received 26 July 2006; received in revised form 25 October 2006; accepted 1 November 2006
Abstract
Species distribution patterns in planktonic foraminiferal assemblages are fundamental to the understanding of the determinants
of their ecology. Until now, data used to identify such distribution patterns was mainly acquired using the standard N 150 μm sieve
size. However, given that assemblage shell size-range in planktonic foraminifera is not constant, this data acquisition practice could
introduce artefacts in the distributional data. Here, we investigated the link between assemblage shell size-range and diversity in
Recent planktonic foraminifera by analysing multiple sieve-size fractions in 12 samples spanning all bioprovinces of the Atlantic
Ocean. Using five diversity indices covering various aspects of community structure, we found that counts from the N 63 μm
fraction in polar oceans and the N 125 μm elsewhere sufficiently approximate maximum diversity in all Recent assemblages.
Diversity values based on counts from the N 150 μm fraction significantly underestimate maximum diversity in the polar — and
surprisingly also in the tropical provinces. Although the new methodology changes the shape of the diversity/sea-surface
temperature (SST) relationship, its strength appears unaffected. Our analysis reveals that increasing diversity in planktonic
foraminiferal assemblages is coupled with a progressive addition of larger species that have distinct, offset shell-size distributions.
Thus, the previously documented increase in overall assemblage shell size-range towards lower latitudes is linked to an expanding
shell-size disparity between species from the same locality. This observation supports the idea that diversity and shell size-range
disparity in foraminiferal assemblages are the result of niche separation. Increasing SST leads to enhanced surface water
stratification and results in vertical niche separation, which permits ecological specialisation. Specific deviations from the overall
diversity and shell-size disparity latitudinal pattern are seen in regions of surface-water instability, indicating that coupled shell-size
and diversity measurements could be used to reconstruct water column structures of past oceans.
© 2006 Elsevier B.V. All rights reserved.
Keywords: planktonic foraminifera; Recent; Atlantic; diversity; size
1. Introduction
⁎ Corresponding author. Eberhard Karls University of Tübingen,
Institute of Geosciences, Sigwartstrasse 10, 72076, Tübingen,
Germany. Tel.: +49 70712977548; fax: +49 7071295727.
E-mail addresses: [email protected] (N. Al-Sabouni),
[email protected] (M. Kucera),
[email protected] (D.N. Schmidt).
0377-8398/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.marmicro.2006.11.002
Spatial and temporal patterns in biodiversity provide
important insights into the dynamic relationships
between organisms and their environment. Intuitively:
The more diverse an ecosystem, the greater its ability to
adapt to environmental change. Understanding the
76
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
mechanisms that control diversity patterns, particularly
in groups of organisms with a good fossil record is
essential for the debate on the effects of global change
on present-day ecosystems.
Biodiversity is not evenly distributed across the
Earth. The tropics are consistently found to harbour
more species in both the marine (Crame, 2000) and
terrestrial (Woodward, 1987) realms. This simple yet
pervasive pattern, known as the bLatitudinal Diversity
Gradient" (Hawkins, 2001), has taunted ecologists for
many decades (see Terborgh, 1973). An array of
hypotheses have been presented to explain this pattern,
invoking factors such as geographic area (Terborgh,
1973; Rosenzweig, 1995), energy (Currie, 1991; Allen
et al., 2002), rate of evolution and migration (Brown and
Lomolino, 1998; Gaston and Blackburn, 2000; Cardillo
et al., 2005; Jablonski et al., 2006) and biotic
interactions (Pianka, 1966), but there is at present no
consensus as to what the driving forces actually are
(Lawton, 1996).
Marine zooplankton are no exception. An increase in
diversity towards the low latitudes has been observed in a
number of pelagic groups including foraminifera (Rutherford et al., 1999), tintinnids (Dolan et al., 2006) and
copepods (McGowan and Walker, 1993). The oceanic
environment appears to be more uniform and continuous
than the latitudinal succession of terrestrial ecosystems.
Thus, one would expect it to be easier to isolate the factors
that control the latitudinal diversity pattern in the marine
realm. However, a large number of candidate forcing
mechanisms simultaneously co-vary with latitude (Fig. 1).
Rutherford et al. (1999) investigated the effects of several
physical parameters on the diversity of planktonic
foraminifera. They concluded that sea-surface temperature
was the strongest determinant of diversity, most likely
through the control of vertical niche separation in the water
column. In contrast, Irigoien et al. (2004) found that global
zooplankton diversity is a function of zooplankton biomass. These findings appear contradictory since the regions
with the highest foraminiferal diversity, the subtropical
gyres (Ruddimann, 1969), are characterised by the lowest
population densities (Bé and Tolderlund, 1971).
This measure of diversity within planktonic foraminifera is further complicated by the fact that shell size
within assemblages also correlates with latitude (Fig. 1;
Schmidt et al., 2004a). The planktonic foraminiferal
diversity data used by Rutherford et al. (1999) was based
on census counts in surface sediment sample residues with
grain sizes larger than 150 μm. The practice of using such
a standard sieve size for the counting of planktonic
foraminiferal assemblages has a long history. The species
composition of a faunal assemblage varies with the size
Fig. 1. A schematic diagram illustrating major latitudinal physical and
biological gradients in the world oceans.
fraction analysed (Berger, 1971; Vincent, 1972, 1976). In
order to eliminate this potential source of bias, a standard
procedure had to be adopted. It was introduced when the
close relationship between planktonic foraminiferal
faunas and surface water hydrography was first utilised
for quantitative palaeoceanographic reconstructions
(Imbrie et al., 1973). Since smaller-sized planktonic
foraminifera were considered difficult to identify and
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
time-consuming to count, the CLIMAP project members
(CLIMAP, 1976) ultimately recommended that the
N 150 μm size fraction should be adopted as a standard
for use in palaeoceanographic and palaeoecolgical
studies. Large quantities of data continued to be generated
using this standard procedure, leading to the database
employed by Rutherford et al. (1999).
Many planktonic foraminiferal shells are smaller than
150 μm and diversity values obtained from this fraction
may not be representative of the entire assemblage.
Although a standardised procedure is essential for
transfer-function applications, it has never been tested
whether it is appropriate for diversity studies. For example,
it has long been known that the shell size distribution of
polar planktonic foraminifera is skewed towards the
smaller sizes and that the standard 150 μm sieve fails to
capture the actual diversity in polar samples (Niebler and
Gersonde, 1998; Kandiano and Bauch, 2002; Smart,
2002). Here we attempt to determine the effect that
changing assemblage shell size-range has on various
diversity metrics. We then discuss the implications of our
data on the understanding of the latitudinal diversity
gradient in planktonic foraminifera.
2. Diversity metrics
The simplest measure of diversity is species richness
(number of species). This index is easy to understand but
it is sensitive to the addition of rare species (Ottens and
Nederbragt, 1992). Therefore, numerous diversity
metrics that compensate for this effect have been
developed. Margalef's richness index (Margalef, 1958)
is defined as MR = (S − 1) / ln(n), where S is the number
of species and n is the total number of individuals. By
calculating the natural logarithm of the total number of
individuals as opposed to the actual total number of
individuals, the ratio between the S and the n is properly
scaled, making this index less vulnerable to the effects of
increasing sample size and the resultant addition of rare
species. This index assumes a logarithmic distribution of
species abundances. However, communities differ in
their proportions of common and rare species, and many
communities may follow logarithmic-normal distribution (bell-shaped curve) (Krebs, 1989). Based on the
assumption that the assemblage follows a log-normal
distribution, Fisher et al. (1943) proposed an alpha index
which is defined by the equation S = α ⁎ ln(1 + n / α). The
problem with these indices is that they only correctly
estimate the diversity in an assemblage when that
assemblage follows a particular distribution model.
The Shannon–Wiener Index takes both species
richness and evenness into account and does not make
77
any assumptions about the distribution of species
abundance within an assemblage (Margurran, 1998).
This index is based on Shannon's entropy (Shannon,
1948), which describes the level of uncertainty that a
random event will take place. In the case of diversity, the
index measures how difficult it is to correctly predict the
species of the next individual picked (Harper, 1999).
The Shannon–Wiener Index is defined here as H =
− Σ(ni / n) ⁎ ln(ni / n), where ni is the number of individuals of a particular species and n is the total number of
counted individuals. If there is only one species in an
assemblage, there is no uncertainty as to what the next
individual picked will be and H = 0. However, at the
opposite end of the spectrum, if the species richness is
high and those species are evenly distributed it is
difficult to accurately predict the species of the next
individual and H will approach the maximum value of 5
(Hammer et al., 2004). Although an index that incorporates both richness and evenness is intuitively preferable,
it may sometimes be useful to isolate evenness in order to
better understand patterns in diversity. This can be
achieved with the Equitability Index, which is defined
as J = H / Hmax, and ranges from 0 when the assemblage
is dominated by one species to 1 when all species are
evenly distributed throughout the assemblage (Harper,
1999). Here we use all five indices to compare their
stability and suitability for diversity analysis in planktonic
foraminifera and to facilitate direct comparisons with
earlier studies (e.g. Rutherford et al., 1999).
3. Materials and methods
A total of 12 core-top samples representing all
planktonic foraminiferal biogeographic zones (Bé and
Tolderlund, 1971) within the Atlantic Ocean were
studied (Table 1; Fig. 2). The samples (washed residues
N 63 μm) were selected from the collections of the
Universities in Bremen, Kiel and Tübingen, based on
their position and preservation. They were taken from
water depths between 940–3946 m and exhibited no
signs of extensive fragmentation and therefore dissolution. The majority of the material is derived from box
cores and multicorer samples which recover undisturbed
sediment surface samples. The ODP piston core sample
is of Holocene age (Curry and Cullen, 1997) as is one of
the two South Atlantic gravity core samples (PS 2498,
Mackensen et al., 2001). The age of the sediment
surface recovered in gravity core GeoB5142 has not
been constrained. Modern annual mean SST values
were assigned to all samples using the data in the World
Ocean Atlas version 2 (WOA, 1998) at 10 m water
depth following the MARGO criteria (Kucera et al.,
78
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
Table 1
Samples used in this study
Sample
1
2
3
4
5
6
7
8
9
10
11
12
PS 1293
PS 1901
L0-09-23 LBC
MC 575
GIK 10737
M 35005
ODP 926A
GeoB 3915
GeoB 5142
GeoB 1206
GeoB 1726
PS 2498
dbsf (cm)
Coring method
Latitude (deg)
Longitude (deg)
Depth (m)
Zone
SST (°C)
0–1
0–1
0–3
0–0.5
0–1
0–1
2–4
0–1
0–1
0–1
0–1
0–1
Box
Box
Box
Multiple
Box
Multiple
Piston
Multiple
Gravity
Box
Box
Gravity
78.0 N
75.9 N
59.0 N
47.1 N
30.2 N
15.5 N
3.4 N
2.3 S
19.1 S
24.7 S
30.3 S
44.2 S
6.7 E
3.7 W
31.1 W
19.3 W
28.3 W
61.2 W
42.5 W
38.0 W
17.1 W
6.5 E
3.3 E
14.2 W
2467
3569
1422
4577
1940
2289
3598
3127
3946
940
1007
3783
1
1
2
3
4
5
5
5
4
3
3
2
1.93
− 0.45
8.03
14.37
21.76
27.42
27.48
27.34
23.97
19.63
19.40
9.18
Average annual sea surface temperatures were assigned using the World Ocean Atlas v. 2 (WOA, 1998). Zones (1 = polar, 2 = subpolar, 3 = temperate,
4 = subtropical, 5 = tropical).
2005). Abundances of 31 species of planktonic
foraminifera were determined following the taxonomy
of Kennett and Srinivassan (1983) and Hemleben et al.
(1989). Using PAST v. 1.20 (Hammer et al., 2004), the
five diversity measures described above were calculated
for each census count.
Determining the minimum number of specimens that
are sufficient to represent the diversity in a sample is a
general problem when conducting diversity studies
(Revets, 2004). Phleger (1960) applied the results by
Dryden (1931) and proposed the now commonly used 300
specimens (CLIMAP, 1976) as a sufficient and practical
number with which to determine relative abundances of
planktonic foraminiferal species. However, it is not known
whether this number is sufficient to characterise the
diversity in a sample and if it is, with what precision.
Therefore, as a first step in our analyses, we determined the
minimum number of individuals that provide a representative and stable estimate for each of the diversity indices.
The determination of minimum sample size was based
on a tropical sample (Fig. 2, sample 6) because of its high
diversity. The sample was split ten times using a
microsplitter into representative aliquots of different
sample sizes, ranging from 26–1100 individuals and the
abundance of planktonic foraminifera was determined in
each split as described above. Assuming that the aliquot
with the highest number of individuals is most representative of the actual assemblage, we have expressed the
variability of the diversity indices in each of the smaller
aliquots as the percentage deviation from the diversity
index value of the largest aliquot. This procedure was
conducted on both the N 125 μm and N150 μm fractions
given that these are the most frequently used fractions for
use in palaeoceanographic studies (e.g. Niebler and
Gersonde, 1998; Rutherford et al., 1999) (Fig. 3). Our
results indicate that census counts of 300–600 individuals
are likely to deviate by less than 8% from the diversity in
the largest aliquot for all diversity indices with the
exception of Species richness and Fisher's alpha, where
Fig. 2. Sample locations. Biogeographic zones are based on Bé and
Tolderlund (1971) and Hemleben et al. (1989).
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
Fig. 3. Diversity indices (species richness, Shannon–Wiener, Margalef, Equitability and Fisher's alpha) calculated from repeated census
counts of 26–1100 individuals from a tropical sample 6, in two size
fractions (N125 μm and N150 μm). Nmax denotes the number of
individuals counted in the largest aliquot.
79
determined, because these include a high proportion of
juveniles. Peeters et al. (1999) showed that in warm-water
assemblages the abundance of juvenile shells increases
above 20% in sieve size fractions smaller than 125 μm.
Therefore, we have not attempted to generate species
abundance counts in warm-water samples from the
N 63 μm fraction. Each sieve fraction was then split into
aliquots of 300–600 individuals, the species abundances
were counted and diversity indices calculated (Table 2).
Some of the high-latitude samples did not yield enough
individuals in the N 500 μm fraction and both polar
samples only yielded enough individuals in the fractions
b 250 μm (Table 2). The data was visualised by
constructing contour maps in Surfer (v.8) using the
Kriging gridding method.
The biogeographic relationships among our samples
were analysed by means of Detrended Correspondence
Analysis (DCA) as implemented in PAST v. 1.20
(Hammer et al., 2004). Correspondence analysis (CA) is
a multidimensional form of ranking and its results often
plot along an arched curve. DCA is a subsequent
procedure to remove this tendency to enable identification of unidirectional trends in the multivariate distribution of data points (Davis, 1986). In order to compare
the effects of varying sieve size on latitudinal diversity
patterns, our data was compared with the MARGO
Atlantic planktonic foraminifera data set, which was
counted from the N 150 μm size fraction (Kucera et al.,
2005). To allow for direct comparison of the two data
sets, all counts containing b 300 individuals in the
MARGO data set were excluded, leaving a total of 1024
samples.
4. Results
4.1. The effect of sieve size on diversity estimates
the deviation can be as much as 16%. Given that diversity
estimates obtained from counts based on close to 300
individuals do not greatly deviate from those based on 600
or more, and yet it takes twice as long to conduct the larger
count, it seems counter-productive to greatly exceed the
300 individual minimum sample-size, and we therefore
use a sample-size of N 300 individuals for the remainder
of this study. In order to establish the influence of varying
assemblage shell size-range on diversity, each sample
(Table 1) was sieved through the N125 μm; N150 μm;
N 212 μm; N 250 μm; N 355 μm and N 500 μm size
fractions, with the exception of sub-polar and polar
samples where the N63 μm size fraction was additionally
used to better represent the small foraminiferal shell sizes
in high latitudes (e.g. Smart, 2002). Small specimens in
tropical assemblages cannot be always taxonomically
The influence of sieve size fraction analysed on
diversity in faunal assemblages of planktonic foraminifera was compared with SST (Fig. 4). The overriding
trend is a strong unimodal diversity increase with
increasing SST peaking at ∼ 25 °C for all diversity
indices measured. Furthermore, the diversity is consistently greater in the smaller size fractions. The highest
diversity values within each sample were found in the
N 63 μm fraction in the polar samples and in the
N 125 μm fraction elsewhere (Fig. 4). We note that the
diversity in both subpolar samples peaks at N 125 μm
indicating that the diversity in smaller fractions in
warm-water samples is unlikely to exceed that of the
N 125 μm fraction. The offset between the maximum
diversity and diversity for the standard N 150 μm
80
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
Table 2
Sample counts and diversity indices calculated for this study
Sample Sieve
B.
C.
G.
G.
G.
G.
G.
G.
G.
G. ruber G. ruber G.
G.
G.
G.
G.
G.
G.
G.
G.
No.
size (cm) digitata nitida bulloides falconensis calida siphonifera glutinata uvula conglobatus (pink) (white) sacculifer trilobus crassaformis hirsuta inflata menardii scitula truncatulinoides tumida
1
2
3
4
5
6
7
8
9
10
11
12
N 63
N 125
N 150
N 212
N 250
N 63
N 125
N 150
N 212
N 250
N 63
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 125
N 150
N 212
N 250
N 355
N 500
N 63
N 125
N 150
N 212
N 250
N 355
N 500
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
7
11
5
0
0
0
0
0
0
0
0
4
0
1
0
0
0
0
0
0
1
0
0
0
9
6
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
7
4
2
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
5
8
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
2
1
0
29
63
124
264
269
442
1
68
184
107
97
24
4
45
75
45
4
0
0
0
0
0
0
0
0
13
3
3
0
0
0
0
0
0
0
0
0
5
5
3
0
0
0
70
81
46
23
7
1
20
32
31
16
0
0
71
97
229
204
292
246
411
0
0
0
0
0
0
0
0
0
0
3
12
4
0
0
0
0
0
0
0
0
0
0
10
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
59
1
0
0
0
0
0
0
0
0
0
0
32
40
18
22
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
3
0
2
0
0
20
14
2
15
0
0
0
0
0
0
0
0
2
3
3
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
12
22
10
25
3
3
15
24
56
57
9
18
16
17
15
6
0
11
34
35
18
4
0
32
37
26
29
4
1
16
29
27
33
38
12
18
26
16
30
11
1
8
7
5
10
41
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14
61
50
36
0
0
0
88
86
21
4
0
0
68
58
20
9
0
0
50
16
5
0
0
0
82
48
9
2
0
0
70
33
1
0
0
0
62
38
7
1
0
0
13
19
2
0
0
0
24
16
0
0
0
0
28
23
14
8
1
1
0
15
0
0
0
0
13
1
0
0
0
199
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
52
7
9
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
4
11
45
22
8
2
6
5
8
32
1
0
5
3
10
8
0
2
1
3
4
8
2
9
5
8
22
29
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
5
1
4
0
0
64
50
68
68
69
7
23
14
25
28
12
0
12
8
8
7
6
0
14
20
14
28
16
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
6
16
11
6
3
0
112
164
141
79
8
0
182
117
103
49
13
0
170
167
153
102
2
0
219
263
216
123
10
3
105
211
114
94
26
0
58
87
66
53
6
2
39
41
47
41
6
0
0
4
4
12
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
5
0
0
1
0
1
10
9
15
30
1
29
20
47
43
52
47
35
43
91
87
130
70
42
48
69
92
196
189
29
38
39
56
94
80
1
4
7
9
11
11
5
20
2
7
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
22
16
17
26
13
0
62
56
69
51
41
25
42
47
124
139
113
39
73
75
76
94
45
45
27
66
72
69
70
21
32
30
22
9
9
3
6
8
14
4
4
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
2
2
3
6
5
1
4
2
5
13
0
0
2
2
5
1
30
5
11
5
2
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
2
12
13
17
46
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
14
10
11
27
4
8
7
8
9
31
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
11
11
8
0
41
79
83
79
125
20
6
23
26
32
75
1
0
0
1
2
0
0
0
0
0
0
0
0
1
1
1
6
0
0
11
29
23
29
14
3
115
203
204
165
106
13
80
108
93
120
72
1
13
48
46
247
123
139
77
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
3
4
13
32
52
0
0
0
0
0
0
2
6
15
3
19
44
14
22
22
24
66
74
12
19
46
38
116
139
7
3
4
11
24
18
7
11
10
2
17
76
0
0
0
0
6
2
0
0
0
0
0
0
0
2
0
0
1
1
3
0
0
0
0
3
1
0
0
0
0
0
18
28
0
0
0
0
4
8
0
0
0
0
7
3
0
0
0
0
6
2
0
0
0
0
2
0
0
0
0
0
3
2
0
0
0
0
1
9
0
0
0
0
6
7
2
2
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
22
46
38
145
138
16
33
45
81
239
90
3
0
2
1
7
16
1
1
2
1
3
0
0
0
0
0
0
0
13
20
30
30
73
39
47
68
91
89
156
170
47
42
75
83
138
15
8
14
9
70
41
31
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
3
0
3
0
0
0
1
0
0
0
4
1
0
3
3
14
6
4
21
26
52
37
11
24
30
28
45
25
3
10
11
8
27
8
3
6
1
4
8
10
0
0
0
0
0
0
0
0
0
0
0
0
0
G. ungulata G. rubescens G. rubescens G. tenella N. dutertrei N. pachyderma N. pachyderma O. universa P. obliquiloculata S. dehiscens T. quinqueloba Species No. of
Shannon– Margalef Equitability Fisher's
(red)
(white)
DEX
SIN
richness individuals Wiener
alpha
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
81
Table 2 (continued)
G. ungulata G. rubescens G. rubescens G. tenella N. dutertrei N. pachyderma N. pachyderma O. universa P. obliquiloculata S. dehiscens T. quinqueloba Species No. of
Shannon– Margalef Equitability Fisher's
(red)
(white)
DEX
SIN
richness individuals Wiener
alpha
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
6
2
4
1
2
3
11
8
6
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
19
12
0
0
0
0
43
5
1
0
0
0
11
2
0
0
0
0
2
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
39
32
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
1
0
0
0
16
32
1
0
0
0
11
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
4
2
0
0
0
3
0
0
2
1
0
0
0
0
0
0
25
26
39
43
76
59
3
0
5
11
17
4
10
10
6
2
10
4
1
0
1
2
1
1
0
7
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
4
2
0
0
22
13
10
6
3
15
91
110
82
5
0
0
120
230
126
45
0
1
1
0
2
1
0
0
0
0
0
0
0
0
3
5
5
5
3
1
5
8
13
8
0
0
6
9
1
3
0
0
110
140
30
13
0
0
44
57
26
5
0
0
20
54
60
3
1
0
0
313
341
328
325
67
285
454
347
623
230
9
24
42
3
23
0
0
4
8
4
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
4
1
1
0
0
7
5
1
0
0
0
0
0
0
0
0
0
104
112
114
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
3
2
5
5
24
22
118
211
1
3
6
12
41
43
9
7
13
26
32
94
7
7
14
16
61
74
3
5
6
8
29
44
8
10
19
20
77
154
8
13
21
19
67
122
4
4
13
19
57
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
8
0
2
2
2
5
7
11
4
3
10
18
18
12
8
14
14
18
31
24
0
2
5
1
5
2
1
4
1
4
10
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
5
1
0
0
1
6
5
0
0
0
1
1
2
0
0
2
0
1
4
0
0
0
0
3
13
0
0
0
0
0
3
0
0
0
0
0
0
0
7
17
15
3
1
55
23
26
1
6
83
74
54
2
0
0
0
30
26
5
5
0
0
34
8
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
17
0
1
0
0
0
10
0
0
0
0
0
16
12
4
0
0
0
15
10
1
1
0
0
145
86
57
0
0
0
0
5
4
3
4
5
5
4
5
8
8
9
8
6
4
13
15
12
11
11
9
18
17
18
15
12
16
16
14
15
14
12
23
19
19
17
15
11
18
16
17
16
12
11
23
23
23
20
17
16
19
19
18
14
14
14
15
15
14
14
11
10
10
10
9
8
5
4
342
363
345
331
70
378
492
385
632
239
355
330
390
403
311
457
3
391
712
456
320
479
431
369
480
368
353
565
180
486
344
391
318
337
357
509
431
530
500
498
326
536
560
523
464
497
484
413
554
392
404
500
385
531
740
538
433
443
444
341
400
337
341
367
38
473
450
553
554
464
419
497
0.39
0.27
0.21
0.11
0.69
0.51
0.34
0.52
0.24
0.19
0.20
0.08
0.83
0.63
0.45
0.64
0.81
0.34
0.40
0.09
0.67
0.65
0.50
0.62
0.51
0.21
0.29
0.06
0.81
0.77
0.62
0.74
1.30
1.70
1.63
1.04
0.55
0.18
1.19
1.21
1.34
1.17
0.87
0.49
0.63
0.82
0.74
0.50
0.31
0.13
1.45
1.48
1.64
1.41
1.05
0.60
1.88
1.93
1.94
1.87
1.66
1.23
2.09
2.20
2.09
2.17
1.85
2.01
2.13
1.80
1.73
1.62
1.32
2.88
2.59
2.88
2.39
1.74
0.73
0.71
0.78
0.78
0.69
0.56
0.72
0.78
0.72
0.80
0.75
2.59
2.69
2.26
2.21
2.01
1.61
3.96
3.44
3.96
3.18
2.15
2.04
2.09
2.06
2.13
2.14
2.13
2.30
2.11
2.07
2.15
2.04
1.88
2.03
1.89
1.92
2.07
1.78
1.64
2.48
2.27
2.32
2.32
2.34
1.90
2.29
2.24
2.01
1.92
1.92
1.69
2.31
2.26
2.06
1.92
1.76
2.43
2.57
2.18
2.43
2.23
1.87
3.53
2.97
2.87
2.58
2.25
1.73
2.71
2.37
2.56
2.44
1.77
1.62
3.65
3.48
3.68
3.17
2.58
2.52
2.87
2.73
2.70
2.14
2.13
2.13
2.40
2.34
2.23
2.23
1.69
0.74
0.76
0.78
0.79
0.81
0.86
0.73
0.72
0.70
0.76
0.75
0.79
0.70
0.68
0.68
0.75
0.72
0.68
0.79
0.73
0.74
0.77
0.83
0.69
0.78
0.76
0.70
0.73
0.73
0.64
0.85
0.83
0.78
0.73
0.73
3.18
3.47
2.84
3.27
2.95
2.40
4.96
4.07
3.85
3.40
2.91
2.20
3.59
3.07
3.37
3.21
2.22
2.00
5.26
4.84
5.34
4.42
3.40
3.37
3.85
3.56
3.59
2.77
2.75
2.75
3.21
3.08
2.95
2.94
2.14
1.89
1.90
1.71
1.25
0.95
0.91
0.53
1.46
1.47
1.43
1.27
1.14
0.66
0.48
0.82
0.83
0.74
0.57
0.46
0.57
0.38
1.79
1.81
1.73
1.53
1.37
0.80
0.59
82
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
fraction is not constant (Fig. 5). It appears that diversity
values based on the N 150 μm fraction underestimate the
maximum diversity mainly at the extremes of the SST
range. The deviations between the maximum diversity
and the diversity in the N150 μm fraction in polar and
tropical assemblages exceed the variability expected due
Fig. 4. Three dimensional plots showing the effects of sieve size on
diversity estimates using the Kriging gridding method in surfer version 8.
(White boxes denote no available data).
Fig. 5. The offset between recorded maximum diversity and a) diversity
in the N150 μm size fraction and b) diversity obtained using the 63,125protocol. Grey regions denote deviation from maximum diversity and
demonstrate uneven distribution across the latitudes. Circled points
denote counts which significantly deviate from maximum diversity and
are restricted to the N150 μm fraction.
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
83
Fig. 6. The relationship between SST and diversity indices using the standardised N150 μm fraction and the 63,125-protocol, overlain onto values for
the MARGO Atlantic dataset (Kucera et al., 2005). The N150 μm data underestimates diversity at the extremes of the SST range, but the strength of
the relationship is only slightly reduced.
to sample size (Fig. 3). Therefore, diversity values
determined from the N 150 μm fraction cannot be used to
represent diversity differences in samples where the
shell size-range of foraminiferal shells varies. Although
the actual highest diversity was not always found in the
same size fraction (Table 2), the values from N63 μm
fraction in the polar-regions and N 125 μm fraction
elsewhere, henceforth denoted the ‘63,125-protocol'
always fall within 16% (for species richness and
Fisher's alpha) or 8% (for other diversity indices) of
the maximum diversity value recorded. This protocol is
simple and reproducible and we consider it a more
appropriate method for determination of diversity in
Modern planktonic foraminifera.
4.2. Diversity and sea surface temperature
In order to test the effect of varying shell size-ranges
within assemblages on the strength and shape of the
diversity-SST trend, diversity values based on counts
from the N 150 μm size fraction and the 63,125-protocol
were plotted for all diversity indices and overlain onto
the MARGO dataset (Kucera et al., 2005) (Fig. 6),
which was generated using counts from the N150 μm
fraction. The diversity values (N 150 μm size fraction)
for the samples used in this study follow the same
pattern as the MARGO dataset, suggesting that our
samples are representative (Fig. 6). Rutherford et al.
(1999) found that a third-order polynomial was most
84
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
Fig. 7. Detrended Correspondence Analysis of species abundances in all samples and size fractions representing all the major biogeographic zones.
Numbers correspond to sample numbers in Fig. 1. Samples are separated by the combined effects of SST (axis 1) and shell size distribution (axis 2).
The lines connect counts from successive sieve sizes of the same sample. The difference between counts from small and large size fractions increases
towards warmer samples.
Fig. 8. Detrended Correspondence Analysis of species abundances in all samples and size fractions representing all the major biogeographic zones.
Species are distributed according to the combined effects of SST (axis 1) and shell size distribution (axis 2). An orthogonal axis has been overlain to
match the SST and size trends.
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
appropriate to describe the relationship between the
diversity of planktonic foraminifera (Margalef index)
and SST. We found that a third-order polynomial model
was also most effective for the MARGO data and for our
samples, explaining more than 86% of the variation for
all diversity indices. Equitability shows a different
relationship with SST. Following a steep rise between 0
and 10 °C, the index levels off indicating that temperate
to tropical assemblages have approximately equally
evenly distributed species. The regression lines for
diversity values based on the counts from the N150 μm
size fraction and on the 63,125-protocol are similar
overall, but there are clear differences, particularly at the
extreme ends of the SST range (Fig. 6). Statistically, the
differences are not significant (F-test, 1 and 20 df,
p N 0.05), but the explanatory power of the test is limited
by the small sample size of our dataset. Faunal counts
based on the N150 μm fraction underestimate the
diversity in polar and tropical provinces and make the
SST-diversity relationship appear stronger. However,
the differences are small and the strength of the diversity
trend is maintained when the 63,125-protocol is used.
4.3. The effect of temperature and assemblage shell sizerange on the composition of foraminiferal assemblages
The combined effects of SST and assemblage shell
size-range on assemblage composition were investigated using DCA in a pooled analysis of assemblage counts
from all samples and sieve size fractions (Figs. 7 and 8).
The first two DCA axes describe 85% of the overall
variation in the data. Based on the distribution of species
scores (Fig. 8), the first axis (65% of variation) appears
to represent the effect of SST. The effect of size fraction
can be best seen in Fig. 7 where counts from different
sieve size fractions of the same sample are connected by
lines. Assemblage counts made in increasingly larger
sieve size fractions of the same sample appear
“warmer”, i.e. they contain a higher proportion of
warmer-water species. This trend is largely unidirectional and shows a similar slope in all provinces, except
for the polar one where little variation is observed. This
slope can be used to separate the effects of temperature
and size, as shown by the two orthogonal axes in Fig. 8.
The order of species scores along the “Size” axis agrees
remarkably well with the size ranking of Schmidt et al.
(2004a,b), except for O. universa and G. ruber. The
“Temperature” axis separates samples perfectly according to bioprovince as defined by Bé and Tolderlund
(1971), which indicates that this model is robust.
Another interesting observation is that the variability
among assemblage counts from different size fractions
85
Fig. 9. D212–125 values (Euclidian distances between counts from
212 μm and 125 μm sieve size fraction DCA scores (Fig. 7)), overlain
onto overall assemblage shell size-range measurements from Schmidt
et al. (2004a,b) (in grey). Sample numbers same as Fig. 2, Table 1.
of the same sample increases towards the tropics
(Fig. 7). This pattern is mirrored in the plot of species
loadings (Fig. 8), which shows a higher separation
between species in warm-water samples. To quantify
this pattern, Euclidian distances between the scores in
the space of the two DCA axes in the largest (N 212 μm)
and smallest (N 125 μm) size fractions common to all
samples were calculated for each sample and denoted as
D212–125. This variable is reflecting the shell size
disparity among species: samples containing species
with different shell size distributions covering a large
shell size-range will display high values of D212–125,
whereas the index will be zero if all species in a sample
show identical shell size distributions. The D212–125
distance was indeed found to increase towards the
tropics (Fig. 9), although a pronounced decrease was
observed for samples 6, 8, 10 and 11. This appears to be
the result of an increase in the scores of the smaller sieve
size fractions rather than a reduction of the scores in the
larger sieve size fractions (Fig. 7), indicating that some
of the species usually dominating the larger sieve size
fractions are more abundant in the smaller sieve size
fractions in these samples.
5. Discussion
The remarkably strong relationship between planktonic foraminiferal biodiversity and SST demands a
mechanism responsible for this biogeographic pattern. It
is clearly not an artefact of the use of counts from the
standard sieve size fraction. Although the diversity
86
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
values at the extremes of the SST range are underestimated by the use of the N150 μm fraction (Figs. 5
and 6), this seems to affect mainly the shape of the
diversity-SST relationship, not its strength.
Irigoien et al. (2004) argued that zooplankton diversity
in the ocean is a unimodal function of zooplankton
biomass, which, in turn is a function of primary
productivity. Interestingly, our data does not show any
clear correlation between diversity and primary productivity (Fig. 10a), even so, the pattern is strongly affected by the
two polar samples, if these are removed, the diversity
shows very little variation with productivity. Maximum
diversity appears to occur between 50 and 150 g C m− 2
yr− 1 (Fig. 10a). Thus, the model of Irigoien et al. (2004)
does not seem to apply to planktonic foraminifera, which
show highest diversities in the oligotrophic subtropical
gyres. Planktonic foraminiferal assemblages from upwelling (high productivity) regions are dominated by fewer
species (e.g. Vincent and Berger, 1981) and the assemblage
shell size-ranges are comparatively smaller (Schmidt et al.,
2004a) than those from the same latitudes in the open
ocean. This is reflected in the negative correlation between
D212–125 and primary productivity (Fig. 10b), which
indicates that foraminiferal assemblages from higher
productivity regions tend to be composed of species with
similar (small) shell sizes.
Another explanation for diversity patterns is the socalled “species-area hypothesis”, which predicts that larger
biotic provinces will contain more species (Arrhenius,
1921; Rosenzweig, 1995) because larger areas stimulate
speciation and inhibit extinction (Rosenzweig, 1992; Losos
and Schulter, 2000). In the modern ocean, planktonic
foraminifera are distributed in distinct provinces, which are
closely related to hydrographical features of oceanic water
masses (Bé and Tolderlund, 1971). Vincent and Berger
(1981) estimated the area of the five main provinces as 30%
of ocean area for the tropical, 32% for the subtropical, 17%
for the temperate, 8.5% for the subpolar and 12.5% for the
polar provinces (Fig. 2). If the area of planktonic
foraminiferal bioprovinces alone should exert a primary
control on species richness, planktonic foraminiferal
diversity should thus be approximately equal in the tropics
and subtropics. Although the area of bioprovinces does
decrease with increasing latitude, the diversity is markedly
higher in the subtropics than in the tropics (Fig. 6),
suggesting that bioprovince area alone cannot be the main
driving force behind the foraminiferal latitudinal diversity
gradient. In addition, the species area concept has been
developed for terrestrial ecosystems where life is concentrated on the narrow interface between the lithosphere and
the atmosphere. In contrast, the distribution of planktonic
foraminiferal species in surface sediments is a result of a
Fig. 10. The relationship between primary productivity (mean annual
values, Behrenfeld and Falkowski (1997a,b) and Stocks et al. (2000))
and a) Shannon–Wiener diversity index values using the 63,125protocol and b) Shell size disparity (D212–125) values.
multi-dimensional niche space (including vertical position
and time), compressed onto a two-dimensional template.
Therefore, data from surface sediments are not really
adequate to test the species-area hypothesis.
Since neither resource availability nor bioprovince area
are likely to be the primary drivers of the latitudinal
diversity gradient in planktonic foraminifera, physical
oceanographic factors should be considered as the potential
driving force. Rutherford et al. (1999) speculated that the
strong correlation between SST and foraminiferal diversity
could be the result of the relationship between SST and
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
thermocline configuration. In the high latitudes, where
diversity and SST are lowest, the thermocline is nearly
absent and there is thus very little vertical niche separation.
The middle latitudes are characterised by higher SST, a
thick permanent thermocline and greatest diversity. In the
tropics, diversity decreases because this region has a very
sharp thermocline with a shallow base as a result of
equatorial upwelling. An increase in vertical niche
separation could influence diversity in two ways. Firstly,
the greater vertical heterogeneity (water column stratification) permits a larger number of niches to coexist over a
specific surface area (Whittaker et al., 2001). Secondly, the
spatial separation will promote depth-parapatric speciation
thus increasing the number of species over time. Planktontow studies of copepods (McGowan and Walker, 1979) and
phytoplankton (Venrick, 1982) support the idea that groups
of species occur as vertically segregated communities.
Vertical niche separation towards the tropics results in
an increased potential for ecological specialisation. This
could also account for the increase in overall shell size in
planktonic foraminifera (Hecht, 1976; Schmidt et al.,
87
2004a). The D212–125 values plotted against SST show a
remarkably similar pattern when compared with the assemblage shell size-range data of Schmidt et al. (2004a,b).
Both variables show an overall increase towards higher
SST's (Fig. 9). Theoretically this overall assemblage shell
size-range increase found by Schmidt et al. (2004a,b)
could be the result of one of two effects: The gradual
addition of species capable of growing to large sizes, but
whose lower size ranges overlap the size ranges of smaller
species (Fig. 11, scenario A), or the overall assemblage
shell size-range increase could be due to the progressive
addition of larger species with distinct specific shell sizeranges (Fig. 11, scenario B). Given that the overall
assemblage shell size-range increase towards the lower
latitudes is coupled with a D212–125 expansion (Fig. 9), the
latter scenario seems more likely: the shell size increase is
caused by addition of larger species that have progressively distinct shell size distributions.
The latitudinal shell size trend is punctuated in regions
of enhanced surface mixing (Fig. 9). Assemblages that
grow in regions of surface-water instability are
Fig. 11. Two alternative scenarios explaining increasing overall assemblage size with SST, and their hypothesised effects on diversity and disparity
relationships with SST.
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N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
characterised by smaller shell sizes and lower diversities
(Schmidt et al., 2004a). As expected, samples 6 and 8,
which are located in the vicinity of tropical upwelling, show
lower diversity and D212–125 values than their stratified
tropical counterpart sample 7 (Fig. 12). Although, samples
10 and 11 have extremely low D212–125 values they also
demonstrate markedly higher diversities than expected. To
understand these patterns we need to consider the effects of
vertical instability on planktonic foraminiferal assemblages: The frontal region between the subtropical and
temperate provinces (samples 10 and 11) is characterised
by two neighbouring faunas, both of which are growing
outside their ecologically optimum ranges (see also
Schmidt et al., 2003). The resulting assemblage is thus
characterised by many equally abundant species with
overlapping shell size distributions. This results in low
D212–125 values and higher diversity values due to higher
equitability (Fig. 6). The shell size reduction in samples
near equatorial upwelling sites also corresponds to growth
to smaller sizes, but in this case, it is not accompanied by
the mixing of assemblages from neighbouring provinces.
As a result, the D212–125 values are lower, but overall
diversity is reduced.
The iterative radiation of planktonic foraminifera
throughout the Cretaceous and Cenozoic was accompanied by marked changes in assemblage shell sizerange distribution (Schmidt et al., 2004b). After the size
decrease at the Cretaceous/Paleogene boundary, high
latitude assemblages remained consistently small,
whereas the low latitude species underwent three
periods of diversification, each of which resulted in an
Fig. 12. Shell size disparity (D212–125) plotted against Shannon–
Wiener diversity using the 63, 125-protocol. A linear relationship
explains 95% of the variability when samples from regions of surfacewater instability are excluded.
increase in overall shell size (Schmidt et al., 2004b). To
understand these temporal and spatial patterns of
biodiversity in this group, it is essential that the
confounding effect of assemblage shell size-range on
diversity measures be eliminated. The 63,125-protocol
developed herein can provide sample size-independent
diversity estimates in planktonic foraminifera, at least for
the latest diversification interval spanning the Neogene.
Other protocols will have to be devised for periods with
significantly different shell size ranges, such as the early
Paleogene (Schmidt et al., 2004b). Assuming fossil
planktonic foraminiferal assemblages behaved in a similar
way to Recent assemblages, combined shell size and
diversity analyses have the potential to indicate the degree
of stratification in past oceans.
6. Conclusions
• Counts based on at least 300 individuals are
sufficient for biodiversity studies in Recent planktonic foraminifera. Diversity indices calculated from
counts based on 300–600 individuals are likely to
deviate by less than 16% (Species richness and
Fisher's alpha) or 8% (Shannon–Wiener, Margalef
and Equitability) from diversity indices calculated
from counts based on up to 1100 individuals.
• We propose a new method for the determination of
diversity in planktonic foraminifera: The ‘63,125protocol’ (N 63 μm size fraction in polar regions and
N125 μm elsewhere) provides a robust estimate of
maximum diversity, independent of assemblage shell
size-range variations. Diversity values based on the
N150 μm size fraction underestimate maximum
diversity in polar and tropical assemblages. Despite
this, the previously documented relationship between
SST and diversity in planktonic foraminifera is not a
methodological artefact. The use of the 63, 125protocol changes the shape of this relationship, but
not its strength.
• Diversity is not primarily controlled by productivity
or by bioprovince area (species-area hypothesis). The
peak of maximum diversity in planktonic foraminifera is observed at extremely low productivity values
(50–150 g C m− 2 yr− 1). Furthermore, the area of
foraminiferal bioprovinces does not reflect the
diversity differences among them.
• Surface water stratification is linked to SST and
results in vertical niche separation, which provides
the potential for ecological specialisation resulting in
higher diversity. Our data show that the diversity
gradient reflects the addition of species, which are
increasingly larger in size and have different shell
N. Al-Sabouni et al. / Marine Micropaleontology 63 (2007) 75–90
size ranges. This leads to a simultaneous expansion
in shell size disparity with the progressive overall
increase in the assemblage shell size-range towards
the tropics.
• The link between SST and vertical niche separation
breaks down in regions of surface water instability.
Assemblages in such regions (upwelling and frontal
regions) are smaller than their counterpart assemblages in stratified water masses, because species
growing outside their preferred ecological ranges are
unable to reach their full potential shell sizes.
Acknowledgements
We would like to pay special thanks to Mara Weinelt,
Andreas Mackensen, Barbara Donner, Ralf Schiebel, Hans
Thierstein, Hartmut Schulz and the Bremen Core Repository for kindly providing the material for this study. This
research was funded by NERC (Natural Environment
Research Council, UK) and the Natural History Museum in
London. We would also like to thank Helen Coxall and an
anonymous referee for their constructive comments.
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